gluon.nn

Gluon provides a large number of build-in neural network layers in the following two modules:

mxnet.gluon.nn

Neural network layers.

We group all layers in these two modules according to their categories.

Sequential Containers

nn.Sequential

Stacks Blocks sequentially.

nn.HybridSequential

Stacks HybridBlocks sequentially.

Concatenation Containers

nn.Concatenate

Lays Block s concurrently.

nn.HybridConcatenate

Lays HybridBlock s concurrently.

Basic Layers

nn.Dense

Just your regular densely-connected NN layer.

nn.Activation

Applies an activation function to input.

nn.Dropout

Applies Dropout to the input.

nn.Flatten

Flattens the input to two dimensional.

nn.Lambda

Wraps an operator or an expression as a Block object.

nn.HybridLambda

Wraps an operator or an expression as a HybridBlock object.

nn.Identity

Block that passes through the input directly.

Convolutional Layers

nn.Conv1D

1D convolution layer (e.g.

nn.Conv2D

2D convolution layer (e.g.

nn.Conv3D

3D convolution layer (e.g.

nn.Conv1DTranspose

Transposed 1D convolution layer (sometimes called Deconvolution).

nn.Conv2DTranspose

Transposed 2D convolution layer (sometimes called Deconvolution).

nn.Conv3DTranspose

Transposed 3D convolution layer (sometimes called Deconvolution).

nn.DeformableConvolution

2-D Deformable Convolution v_1 (Dai, 2017).

nn.ModulatedDeformableConvolution

2-D Deformable Convolution v2 (Dai, 2018).

Pixel Shuffle Layers

nn.PixelShuffle1D

Pixel-shuffle layer for upsampling in 1 dimension.

nn.PixelShuffle2D

Pixel-shuffle layer for upsampling in 2 dimensions.

nn.PixelShuffle3D

Pixel-shuffle layer for upsampling in 3 dimensions.

Pooling Layers

nn.MaxPool1D

Max pooling operation for one dimensional data.

nn.MaxPool2D

Max pooling operation for two dimensional (spatial) data.

nn.MaxPool3D

Max pooling operation for 3D data (spatial or spatio-temporal).

nn.AvgPool1D

Average pooling operation for temporal data.

nn.AvgPool2D

Average pooling operation for spatial data.

nn.AvgPool3D

Average pooling operation for 3D data (spatial or spatio-temporal).

nn.GlobalMaxPool1D

Gloabl max pooling operation for one dimensional (temporal) data.

nn.GlobalMaxPool2D

Global max pooling operation for two dimensional (spatial) data.

nn.GlobalMaxPool3D

Global max pooling operation for 3D data (spatial or spatio-temporal).

nn.GlobalAvgPool1D

Global average pooling operation for temporal data.

nn.GlobalAvgPool2D

Global average pooling operation for spatial data.

nn.GlobalAvgPool3D

Global average pooling operation for 3D data (spatial or spatio-temporal).

nn.ReflectionPad2D

Pads the input tensor using the reflection of the input boundary.

Normalization Layers

nn.BatchNorm

Batch normalization layer (Ioffe and Szegedy, 2014).

nn.InstanceNorm

Applies instance normalization to the n-dimensional input array.

nn.LayerNorm

Applies layer normalization to the n-dimensional input array.

nn.SyncBatchNorm

Cross-GPU Synchronized Batch normalization (SyncBN)

Embedding Layers

nn.Embedding

Turns non-negative integers (indexes/tokens) into dense vectors of fixed size.

Advanced Activation Layers

nn.LeakyReLU

Leaky version of a Rectified Linear Unit.

nn.PReLU

Parametric leaky version of a Rectified Linear Unit.

nn.ELU

Exponential Linear Unit (ELU)

nn.SELU

Scaled Exponential Linear Unit (SELU)

nn.Swish

Swish Activation function (SiLU with a hyperparameter)

nn.SiLU

Sigmoid Linear Units

nn.GELU

Gaussian Exponential Linear Unit (GELU)

API Reference

Neural network layers.

Classes

Activation(activation, **kwargs)

Applies an activation function to input.

AvgPool1D([pool_size, strides, padding, …])

Average pooling operation for temporal data.

AvgPool2D([pool_size, strides, padding, …])

Average pooling operation for spatial data.

AvgPool3D([pool_size, strides, padding, …])

Average pooling operation for 3D data (spatial or spatio-temporal).

BatchNorm([axis, momentum, epsilon, center, …])

Batch normalization layer (Ioffe and Szegedy, 2014).

Block()

Base class for all neural network layers and models.

Concatenate([axis])

Lays Block s concurrently.

Conv1D(channels, kernel_size[, strides, …])

1D convolution layer (e.g. temporal convolution).

Conv1DTranspose(channels, kernel_size[, …])

Transposed 1D convolution layer (sometimes called Deconvolution).

Conv2D(channels, kernel_size[, strides, …])

2D convolution layer (e.g. spatial convolution over images).

Conv2DTranspose(channels, kernel_size[, …])

Transposed 2D convolution layer (sometimes called Deconvolution).

Conv3D(channels, kernel_size[, strides, …])

3D convolution layer (e.g. spatial convolution over volumes).

Conv3DTranspose(channels, kernel_size[, …])

Transposed 3D convolution layer (sometimes called Deconvolution).

DeformableConvolution(channels[, …])

2-D Deformable Convolution v_1 (Dai, 2017).

Dense(units[, activation, use_bias, …])

Just your regular densely-connected NN layer.

Dropout(rate[, axes])

Applies Dropout to the input.

ELU([alpha])

Exponential Linear Unit (ELU)

Embedding(input_dim, output_dim[, dtype, …])

Turns non-negative integers (indexes/tokens) into dense vectors of fixed size.

Flatten(**kwargs)

Flattens the input to two dimensional.

GELU([approximation])

Gaussian Exponential Linear Unit (GELU)

GlobalAvgPool1D([layout])

Global average pooling operation for temporal data.

GlobalAvgPool2D([layout])

Global average pooling operation for spatial data.

GlobalAvgPool3D([layout])

Global average pooling operation for 3D data (spatial or spatio-temporal).

GlobalMaxPool1D([layout])

Gloabl max pooling operation for one dimensional (temporal) data.

GlobalMaxPool2D([layout])

Global max pooling operation for two dimensional (spatial) data.

GlobalMaxPool3D([layout])

Global max pooling operation for 3D data (spatial or spatio-temporal).

GroupNorm([num_groups, epsilon, center, …])

Applies group normalization to the n-dimensional input array.

HybridBlock()

HybridBlock supports forwarding with both Symbol and NDArray.

HybridConcatenate([axis])

Lays HybridBlock s concurrently.

HybridLambda(function)

Wraps an operator or an expression as a HybridBlock object.

HybridSequential()

Stacks HybridBlocks sequentially.

Identity()

Block that passes through the input directly.

InstanceNorm([axis, epsilon, center, scale, …])

Applies instance normalization to the n-dimensional input array.

Lambda(function)

Wraps an operator or an expression as a Block object.

LayerNorm([axis, epsilon, center, scale, …])

Applies layer normalization to the n-dimensional input array.

LeakyReLU(alpha, **kwargs)

Leaky version of a Rectified Linear Unit.

MaxPool1D([pool_size, strides, padding, …])

Max pooling operation for one dimensional data.

MaxPool2D([pool_size, strides, padding, …])

Max pooling operation for two dimensional (spatial) data.

MaxPool3D([pool_size, strides, padding, …])

Max pooling operation for 3D data (spatial or spatio-temporal).

ModulatedDeformableConvolution(channels[, …])

2-D Deformable Convolution v2 (Dai, 2018).

PReLU([alpha_initializer, in_channels])

Parametric leaky version of a Rectified Linear Unit.

PixelShuffle1D(factor)

Pixel-shuffle layer for upsampling in 1 dimension.

PixelShuffle2D(factor)

Pixel-shuffle layer for upsampling in 2 dimensions.

PixelShuffle3D(factor)

Pixel-shuffle layer for upsampling in 3 dimensions.

ReflectionPad2D([padding])

Pads the input tensor using the reflection of the input boundary.

SELU(**kwargs)

Scaled Exponential Linear Unit (SELU)

Sequential()

Stacks Blocks sequentially.

SiLU(**kwargs)

Sigmoid Linear Units

Swish([beta])

Swish Activation function (SiLU with a hyperparameter)

SymbolBlock(outputs, inputs[, params])

Construct block from symbol.

SyncBatchNorm([in_channels, num_devices, …])

Cross-GPU Synchronized Batch normalization (SyncBN)

class Activation(activation, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Applies an activation function to input.

Parameters

activation (str) – Name of activation function to use. See Activation() for available choices.

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class AvgPool1D(pool_size=2, strides=None, padding=0, layout='NCW', ceil_mode=False, count_include_pad=True, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Average pooling operation for temporal data.

Parameters
  • pool_size (int) – Size of the average pooling windows.

  • strides (int, or None) – Factor by which to downscale. E.g. 2 will halve the input size. If None, it will default to pool_size.

  • padding (int) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

  • layout (str, default 'NCW') – Dimension ordering of data and out (‘NCW’ or ‘NWC’). ‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions respectively. padding is applied on ‘W’ dimension.

  • ceil_mode (bool, default False) – When True, will use ceil instead of floor to compute the output shape.

  • count_include_pad (bool, default True) – When ‘False’, will exclude padding elements when computing the average value.

Inputs:
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 3D output tensor with shape (batch_size, channels, out_width) when layout is NCW. out_width is calculated as:

    out_width = floor((width+2*padding-pool_size)/strides)+1
    

    When ceil_mode is True, ceil will be used instead of floor in this equation.

class AvgPool2D(pool_size=(2, 2), strides=None, padding=0, ceil_mode=False, layout='NCHW', count_include_pad=True, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Average pooling operation for spatial data.

Parameters
  • pool_size (int or list/tuple of 2 ints,) – Size of the average pooling windows.

  • strides (int, list/tuple of 2 ints, or None.) – Factor by which to downscale. E.g. 2 will halve the input size. If None, it will default to pool_size.

  • padding (int or list/tuple of 2 ints,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

  • layout (str, default 'NCHW') – Dimension ordering of data and out (‘NCHW’ or ‘NHWC’). ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.

  • ceil_mode (bool, default False) – When True, will use ceil instead of floor to compute the output shape.

  • count_include_pad (bool, default True) – When ‘False’, will exclude padding elements when computing the average value.

Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

    out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
    out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1
    

    When ceil_mode is True, ceil will be used instead of floor in this equation.

class AvgPool3D(pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', count_include_pad=True, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Average pooling operation for 3D data (spatial or spatio-temporal).

Parameters
  • pool_size (int or list/tuple of 3 ints,) – Size of the average pooling windows.

  • strides (int, list/tuple of 3 ints, or None.) – Factor by which to downscale. E.g. 2 will halve the input size. If None, it will default to pool_size.

  • padding (int or list/tuple of 3 ints,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

  • layout (str, default 'NCDHW') – Dimension ordering of data and out (‘NCDHW’ or ‘NDHWC’). ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’ dimension.

  • ceil_mode (bool, default False) – When True, will use ceil instead of floor to compute the output shape.

  • count_include_pad (bool, default True) – When ‘False’, will exclude padding elements when computing the average value.

Inputs:
  • data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 5D output tensor with shape (batch_size, channels, out_depth, out_height, out_width) when layout is NCDHW. out_depth, out_height and out_width are calculated as:

    out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
    out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
    out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1
    

    When ceil_mode is True, ceil will be used instead of floor in this equation.

class BatchNorm(axis=1, momentum=0.9, epsilon=1e-05, center=True, scale=True, use_global_stats=False, beta_initializer='zeros', gamma_initializer='ones', running_mean_initializer='zeros', running_variance_initializer='ones', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.basic_layers._BatchNorm

Batch normalization layer (Ioffe and Szegedy, 2014). Normalizes the input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

Parameters
  • axis (int, default 1) – The axis that should be normalized. This is typically the channels (C) axis. For instance, after a Conv2D layer with layout=’NCHW’, set axis=1 in BatchNorm. If layout=’NHWC’, then set axis=3.

  • momentum (float, default 0.9) – Momentum for the moving average.

  • epsilon (float, default 1e-5) – Small float added to variance to avoid dividing by zero.

  • center (bool, default True) – If True, add offset of beta to normalized tensor. If False, beta is ignored.

  • scale (bool, default True) – If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.

  • use_global_stats (bool, default False) – If True, use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator. If False, use local batch-norm.

  • beta_initializer (str or Initializer, default ‘zeros’) – Initializer for the beta weight.

  • gamma_initializer (str or Initializer, default ‘ones’) – Initializer for the gamma weight.

  • running_mean_initializer (str or Initializer, default ‘zeros’) – Initializer for the running mean.

  • running_variance_initializer (str or Initializer, default ‘ones’) – Initializer for the running variance.

  • in_channels (int, default 0) – Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

class Block[source]

Bases: object

Base class for all neural network layers and models. Your models should subclass this class.

Block can be nested recursively in a tree structure. You can create and assign child Block as regular attributes:

import mxnet as mx
from mxnet.gluon import Block, nn

class Model(Block):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        self.dense0 = nn.Dense(20)
        self.dense1 = nn.Dense(20)

    def forward(self, x):
        x = mx.npx.relu(self.dense0(x))
        return mx.npx.relu(self.dense1(x))

model = Model()
model.initialize(device=mx.cpu(0))
model(mx.np.zeros((10, 10), device=mx.cpu(0)))

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

cast(dtype)

Cast this Block to use another data type.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

forward(*args)

Overrides to implement forward computation using NDArray.

hybridize([active])

Please refer description of HybridBlock hybridize().

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

register_child(block[, name])

Registers block as a child of self.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install callback monitor.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Child Block assigned this way will be registered and collect_params() will collect their Parameters recursively. You can also manually register child blocks with register_child().

apply(fn)[source]

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

cast(dtype)[source]

Cast this Block to use another data type.

Parameters

dtype (str or numpy.dtype) – The new data type.

collect_params(select=None)[source]

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

forward(*args)[source]

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

Parameters

*args (list of NDArray) – Input tensors.

hybridize(active=True, **kwargs)[source]

Please refer description of HybridBlock hybridize().

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)[source]

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)[source]

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')[source]

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')[source]

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_child(block, name=None)[source]

Registers block as a child of self. Block s assigned to self as attributes will be registered automatically.

register_forward_hook(hook)[source]

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)[source]

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)[source]

Install callback monitor.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)[source]

This function has been deprecated. Please refer to Block.reset_device.

reset_device(device)[source]

Re-assign all Parameters to other devices.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)[source]

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)[source]

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)[source]

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)[source]

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)[source]

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()[source]

Sets all Parameters’ gradient buffer to 0.

class Concatenate(axis=-1)[source]

Bases: mxnet.gluon.nn.basic_layers.Sequential

Lays Block s concurrently.

This block feeds its input to all children blocks, and produce the output by concatenating all the children blocks’ outputs on the specified axis.

Example:

net = Concatenate()
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
net.add(Identity())

Methods

add(*blocks)

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

cast(dtype)

Cast this Block to use another data type.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

forward(x)

Overrides to implement forward computation using NDArray.

hybridize([active])

Activates or deactivates HybridBlock s recursively.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

register_child(block[, name])

Registers block as a child of self.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install callback monitor.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters

axis (int, default -1) – The axis on which to concatenate the outputs.

add(*blocks)

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

cast(dtype)

Cast this Block to use another data type.

Parameters

dtype (str or numpy.dtype) – The new data type.

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

forward(x)[source]

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

Parameters

*args (list of NDArray) – Input tensors.

hybridize(active=True, **kwargs)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • **kwargs (string) – Additional flags for hybridized operator.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_child(block, name=None)

Registers block as a child of self. Block s assigned to self as attributes will be registered automatically.

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install callback monitor.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to Block.reset_device.

reset_device(device)

Re-assign all Parameters to other devices.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Conv1D(channels, kernel_size, strides=1, padding=0, dilation=1, groups=1, layout='NCW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 1 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 1 int,) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 1 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • dilation (int or tuple/list of 1 int) – Specifies the dilation rate to use for dilated convolution.

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCW') – Dimension ordering of data and weight. Only supports ‘NCW’ layout for now. ‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions respectively. Convolution is applied on the ‘W’ dimension.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 3D output tensor with shape (batch_size, channels, out_width) when layout is NCW. out_width is calculated as:

    out_width = floor((width+2*padding-dilation*(kernel_size-1)-1)/stride)+1
    
class Conv1DTranspose(channels, kernel_size, strides=1, padding=0, output_padding=0, dilation=1, groups=1, layout='NCW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

Transposed 1D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 1 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 1 int) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 1 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • output_padding (int or a tuple/list of 1 int) – Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension.

  • dilation (int or tuple/list of 1 int) – Controls the spacing between the kernel points; also known as the a trous algorithm

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCW') – Dimension ordering of data and weight. Only supports ‘NCW’ layout for now. ‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions respectively. Convolution is applied on the ‘W’ dimension.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 3D output tensor with shape (batch_size, channels, out_width) when layout is NCW. out_width is calculated as:

    out_width = (width-1)*strides-2*padding+kernel_size+output_padding
    
class Conv2D(channels, kernel_size, strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, layout='NCHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 2 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 2 int,) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 2 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • dilation (int or tuple/list of 2 int) – Specifies the dilation rate to use for dilated convolution.

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCHW') – Dimension ordering of data and weight. Only supports ‘NCHW’ and ‘NHWC’ layout for now. ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively. Convolution is applied on the ‘H’ and ‘W’ dimensions.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

    out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
    out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
    
class Conv2DTranspose(channels, kernel_size, strides=(1, 1), padding=(0, 0), output_padding=(0, 0), dilation=(1, 1), groups=1, layout='NCHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

Transposed 2D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 2 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 2 int) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 2 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • output_padding (int or a tuple/list of 2 int) – Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension.

  • dilation (int or tuple/list of 2 int) – Controls the spacing between the kernel points; also known as the a trous algorithm

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCHW') – Dimension ordering of data and weight. Only supports ‘NCHW’ and ‘NHWC’ layout for now. ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively. Convolution is applied on the ‘H’ and ‘W’ dimensions.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

    out_height = (height-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
    out_width = (width-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]
    
class Conv3D(channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), groups=1, layout='NCDHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 3 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 3 int,) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 3 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • dilation (int or tuple/list of 3 int) – Specifies the dilation rate to use for dilated convolution.

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCDHW') – Dimension ordering of data and weight. Only supports ‘NCDHW’ and ‘NDHWC’ layout for now. ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. Convolution is applied on the ‘D’, ‘H’ and ‘W’ dimensions.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 5D output tensor with shape (batch_size, channels, out_depth, out_height, out_width) when layout is NCDHW. out_depth, out_height and out_width are calculated as:

    out_depth = floor((depth+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
    out_height = floor((height+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
    out_width = floor((width+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2])+1
    
class Conv3DTranspose(channels, kernel_size, strides=(1, 1, 1), padding=(0, 0, 0), output_padding=(0, 0, 0), dilation=(1, 1, 1), groups=1, layout='NCDHW', activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Conv

Transposed 3D convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

If in_channels is not specified, Parameter initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Parameters
  • channels (int) – The dimensionality of the output space, i.e. the number of output channels (filters) in the convolution.

  • kernel_size (int or tuple/list of 3 int) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 3 int) – Specify the strides of the convolution.

  • padding (int or a tuple/list of 3 int,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points

  • output_padding (int or a tuple/list of 3 int) – Controls the amount of implicit zero-paddings on both sides of the output for output_padding number of points for each dimension.

  • dilation (int or tuple/list of 3 int) – Controls the spacing between the kernel points; also known as the a trous algorithm.

  • groups (int) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • layout (str, default 'NCDHW') – Dimension ordering of data and weight. Only supports ‘NCDHW’ and ‘NDHWC’ layout for now. ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. Convolution is applied on the ‘D’, ‘H’ and ‘W’ dimensions.

  • in_channels (int, default 0) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

  • activation (str) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool) – Whether the layer uses a bias vector.

  • weight_initializer (str or Initializer) – Initializer for the weight weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

Inputs:
  • data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 5D output tensor with shape (batch_size, channels, out_depth, out_height, out_width) when layout is NCDHW. out_depth, out_height and out_width are calculated as:

    out_depth = (depth-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]
    out_height = (height-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]
    out_width = (width-1)*strides[2]-2*padding[2]+kernel_size[2]+output_padding[2]
    
class DeformableConvolution(channels, kernel_size=(1, 1), strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, num_deformable_group=1, layout='NCHW', use_bias=True, in_channels=0, activation=None, weight_initializer=None, bias_initializer='zeros', offset_weight_initializer='zeros', offset_bias_initializer='zeros', offset_use_bias=True, op_name='DeformableConvolution', adj=None)[source]

Bases: mxnet.gluon.block.HybridBlock

2-D Deformable Convolution v_1 (Dai, 2017). Normal Convolution uses sampling points in a regular grid, while the sampling points of Deformablem Convolution can be offset. The offset is learned with a separate convolution layer during the training. Both the convolution layer for generating the output features and the offsets are included in this gluon layer.

Parameters
  • channels (int,) – The dimensionality of the output space i.e. the number of output channels in the convolution.

  • kernel_size (int or tuple/list of 2 ints, (Default value = (1,1))) – Specifies the dimensions of the convolution window.

  • strides (int or tuple/list of 2 ints, (Default value = (1,1))) – Specifies the strides of the convolution.

  • padding (int or tuple/list of 2 ints, (Default value = (0,0))) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

  • dilation (int or tuple/list of 2 ints, (Default value = (1,1))) – Specifies the dilation rate to use for dilated convolution.

  • groups (int, (Default value = 1)) – Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two convolution layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

  • num_deformable_group (int, (Default value = 1)) – Number of deformable group partitions.

  • layout (str, (Default value = NCHW)) – Dimension ordering of data and weight. Can be ‘NCW’, ‘NWC’, ‘NCHW’, ‘NHWC’, ‘NCDHW’, ‘NDHWC’, etc. ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. Convolution is performed over ‘D’, ‘H’, and ‘W’ dimensions.

  • use_bias (bool, (Default value = True)) – Whether the layer for generating the output features uses a bias vector.

  • in_channels (int, (Default value = 0)) – The number of input channels to this layer. If not specified, initialization will be deferred to the first time forward is called and input channels will be inferred from the shape of input data.

  • activation (str, (Default value = None)) – Activation function to use. See activation(). If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • weight_initializer (str or Initializer, (Default value = None)) – Initializer for the weight weights matrix for the convolution layer for generating the output features.

  • bias_initializer (str or Initializer, (Default value = zeros)) – Initializer for the bias vector for the convolution layer for generating the output features.

  • offset_weight_initializer (str or Initializer, (Default value = zeros)) – Initializer for the weight weights matrix for the convolution layer for generating the offset.

  • offset_bias_initializer (str or Initializer, (Default value = zeros),) – Initializer for the bias vector for the convolution layer for generating the offset.

  • offset_use_bias (bool, (Default value = True)) – Whether the layer for generating the offset uses a bias vector.

  • Inputs

    • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

  • Outputs

    • out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

      out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1
      out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1
      

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(x)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

pre_infer_offset_weight()

Pre-infer the shape of offsite weight parameter based on kernel size,

pre_infer_weight()

Pre-infer the shape of weight parameter based on kernel size, group size and channels

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(x)[source]

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

pre_infer_offset_weight()[source]

Pre-infer the shape of offsite weight parameter based on kernel size, group size and offset channels

pre_infer_weight()[source]

Pre-infer the shape of weight parameter based on kernel size, group size and channels

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Dense(units, activation=None, use_bias=True, flatten=True, dtype='float32', weight_initializer=None, bias_initializer='zeros', in_units=0, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Just your regular densely-connected NN layer.

Dense implements the operation: output = activation(dot(input, weight.T) + bias) where activation is the element-wise activation function passed as the activation argument, weight is a weights matrix created by the layer, and bias is a bias vector created by the layer (only applicable if use_bias is True).

Parameters
  • units (int) – Dimensionality of the output space.

  • activation (str) – Activation function to use. See help on Activation layer. If you don’t specify anything, no activation is applied (ie. “linear” activation: a(x) = x).

  • use_bias (bool, default True) – Whether the layer uses a bias vector.

  • flatten (bool, default True) – Whether the input tensor should be flattened. If true, all but the first axis of input data are collapsed together. If false, all but the last axis of input data are kept the same, and the transformation applies on the last axis.

  • dtype (str or np.dtype, default 'float32') – Data type of output embeddings.

  • weight_initializer (str or Initializer) – Initializer for the kernel weights matrix.

  • bias_initializer (str or Initializer) – Initializer for the bias vector.

  • in_units (int, optional) – Size of the input data. If not specified, initialization will be deferred to the first time forward is called and in_units will be inferred from the shape of input data.

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(x, *args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Inputs:
  • data: if flatten is True, data should be a tensor with shape (batch_size, x1, x2, …, xn), where x1 * x2 * … * xn is equal to in_units. If flatten is False, data should have shape (x1, x2, …, xn, in_units).

Outputs:
  • out: if flatten is True, out will be a tensor with shape (batch_size, units). If flatten is False, out will have shape (x1, x2, …, xn, units).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(x, *args)[source]

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Dropout(rate, axes=(), **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Applies Dropout to the input.

Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting.

Parameters
  • rate (float) – Fraction of the input units to drop. Must be a number between 0 and 1.

  • axes (tuple of int, default ()) – The axes on which dropout mask is shared. If empty, regular dropout is applied.

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

References

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class ELU(alpha=1.0, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Exponential Linear Unit (ELU)

“Fast and Accurate Deep Network Learning by Exponential Linear Units”, Clevert et al, 2016 https://arxiv.org/abs/1511.07289 Published as a conference paper at ICLR 2016

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters

alpha (float) – The alpha parameter as described by Clevert et al, 2016

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Embedding(input_dim, output_dim, dtype='float32', weight_initializer=None, sparse_grad=False, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Turns non-negative integers (indexes/tokens) into dense vectors of fixed size. eg. [4, 20] -> [[0.25, 0.1], [0.6, -0.2]]

Note

if sparse_grad is set to True, the gradient w.r.t weight will be sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. By default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: https://mxnet.apache.org/versions/master/api/python/docs/api/optimizer/index.html

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters
  • input_dim (int) – Size of the vocabulary, i.e. maximum integer index + 1.

  • output_dim (int) – Dimension of the dense embedding.

  • dtype (str or np.dtype, default 'float32') – Data type of output embeddings.

  • weight_initializer (Initializer) – Initializer for the embeddings matrix.

  • sparse_grad (bool) – If True, gradient w.r.t. weight will be a ‘row_sparse’ NDArray.

  • Inputs

    • data: (N-1)-D tensor with shape: (x1, x2, …, xN-1).

  • Output

    • out: N-D tensor with shape: (x1, x2, …, xN-1, output_dim).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Flatten(**kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Flattens the input to two dimensional.

Inputs:
  • data: input tensor with arbitrary shape (N, x1, x2, …, xn)

Output:
  • out: 2D tensor with shape: (N, x1 cdot x2 cdot … cdot xn)

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class GELU(approximation='erf', **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Gaussian Exponential Linear Unit (GELU)

“Gaussian Error Linear Units (GELUs)”, Hendrycks et al, 2016 https://arxiv.org/abs/1606.08415

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters
  • approximation (string) – Which approximation of GELU calculation to use (erf or tanh).

  • Inputs

    • data: input tensor with arbitrary shape.

  • Outputs

    • out: output tensor with the same shape as data.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class GlobalAvgPool1D(layout='NCW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global average pooling operation for temporal data.

Parameters

layout (str, default 'NCW') – Dimension ordering of data and out (‘NCW’ or ‘NWC’). ‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions respectively. padding is applied on ‘W’ dimension.

Inputs:
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 3D output tensor with shape (batch_size, channels, 1).

class GlobalAvgPool2D(layout='NCHW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global average pooling operation for spatial data.

Parameters

layout (str, default 'NCHW') – Dimension ordering of data and out (‘NCHW’ or ‘NHWC’). ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively.

Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 4D output tensor with shape (batch_size, channels, 1, 1) when layout is NCHW.

class GlobalAvgPool3D(layout='NCDHW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global average pooling operation for 3D data (spatial or spatio-temporal).

Parameters

layout (str, default 'NCDHW') – Dimension ordering of data and out (‘NCDHW’ or ‘NDHWC’). ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’ dimension.

Inputs:
  • data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 5D output tensor with shape (batch_size, channels, 1, 1, 1) when layout is NCDHW.

class GlobalMaxPool1D(layout='NCW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Gloabl max pooling operation for one dimensional (temporal) data.

Parameters

layout (str, default 'NCW') – Dimension ordering of data and out (‘NCW’ or ‘NWC’). ‘N’, ‘C’, ‘W’ stands for batch, channel, and width (time) dimensions respectively. Pooling is applied on the W dimension.

Inputs:
  • data: 3D input tensor with shape (batch_size, in_channels, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 3D output tensor with shape (batch_size, channels, 1) when layout is NCW.

class GlobalMaxPool2D(layout='NCHW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global max pooling operation for two dimensional (spatial) data.

Parameters

layout (str, default 'NCHW') – Dimension ordering of data and out (‘NCHW’ or ‘NHWC’). ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.

Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 4D output tensor with shape (batch_size, channels, 1, 1) when layout is NCHW.

class GlobalMaxPool3D(layout='NCDHW', **kwargs)[source]

Bases: mxnet.gluon.nn.conv_layers._Pooling

Global max pooling operation for 3D data (spatial or spatio-temporal).

Parameters

layout (str, default 'NCDHW') – Dimension ordering of data and out (‘NCDHW’ or ‘NDHWC’). ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’ dimension.

Inputs:
  • data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCW. For other layouts shape is permuted accordingly.

Outputs:
  • out: 5D output tensor with shape (batch_size, channels, 1, 1, 1) when layout is NCDHW.

class GroupNorm(num_groups=1, epsilon=1e-05, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', in_channels=0)[source]

Bases: mxnet.gluon.block.HybridBlock

Applies group normalization to the n-dimensional input array. This operator takes an n-dimensional input array where the leftmost 2 axis are batch and channel respectively:

\[x = x.reshape((N, num_groups, C // num_groups, ...)) axis = (2, ...) out = \frac{x - mean[x, axis]}{ \sqrt{Var[x, axis] + \epsilon}} * gamma + beta\]

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(data)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(data, *args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters
  • num_groups (int, default 1) – Number of groups to separate the channel axis into.

  • epsilon (float, default 1e-5) – Small float added to variance to avoid dividing by zero.

  • center (bool, default True) – If True, add offset of beta to normalized tensor. If False, beta is ignored.

  • scale (bool, default True) – If True, multiply by gamma. If False, gamma is not used.

  • beta_initializer (str or Initializer, default ‘zeros’) – Initializer for the beta weight.

  • gamma_initializer (str or Initializer, default ‘ones’) – Initializer for the gamma weight.

Inputs:
  • data: input tensor with shape (N, C, …).

Outputs:
  • out: output tensor with the same shape as data.

References

Group Normalization

Examples

>>> # Input of shape (2, 3, 4)
>>> x = mx.np.array([[[ 0,  1,  2,  3],
                      [ 4,  5,  6,  7],
                      [ 8,  9, 10, 11]],
                     [[12, 13, 14, 15],
                      [16, 17, 18, 19],
                      [20, 21, 22, 23]]])
>>> # Group normalization is calculated with the above formula
>>> layer = GroupNorm()
>>> layer.initialize(device=mx.cpu(0))
>>> layer(x)
[[[-1.5932543 -1.3035717 -1.0138891 -0.7242065]
  [-0.4345239 -0.1448413  0.1448413  0.4345239]
  [ 0.7242065  1.0138891  1.3035717  1.5932543]]
 [[-1.5932543 -1.3035717 -1.0138891 -0.7242065]
  [-0.4345239 -0.1448413  0.1448413  0.4345239]
  [ 0.7242065  1.0138891  1.3035717  1.5932543]]]
apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(data)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(data, *args)[source]

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class HybridBlock[source]

Bases: mxnet.gluon.block.Block

HybridBlock supports forwarding with both Symbol and NDArray.

HybridBlock is similar to Block, with a few differences:

import mxnet as mx
from mxnet.gluon import HybridBlock, nn

class Model(HybridBlock):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        self.dense0 = nn.Dense(20)
        self.dense1 = nn.Dense(20)

    def forward(self, x):
        x = mx.npx.relu(self.dense0(x))
        return mx.npx.relu(self.dense1(x))

model = Model()
model.initialize(device=mx.cpu(0))
model.hybridize()
model(mx.np.zeros((10, 10), device=mx.cpu(0)))

Methods

cast(dtype)

Cast this Block to use another data type.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x, *args)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_child(block[, name])

Registers block as a child of self.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

Forward computation in HybridBlock must be static to work with Symbol s, i.e. you cannot call NDArray.asnumpy(), NDArray.shape, NDArray.dtype, NDArray indexing (x[i]) etc on tensors. Also, you cannot use branching or loop logic that bases on non-constant expressions like random numbers or intermediate results, since they change the graph structure for each iteration.

Before activating with hybridize(), HybridBlock works just like normal Block. After activation, HybridBlock will create a symbolic graph representing the forward computation and cache it. On subsequent forwards, the cached graph will be used instead of forward().

Please see references for detailed tutorial.

References

Hybridize - A Hybrid of Imperative and Symbolic Programming

cast(dtype)[source]

Cast this Block to use another data type.

Parameters

dtype (str or numpy.dtype) – The new data type.

export(path, epoch=0, remove_amp_cast=True)[source]

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x, *args)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)[source]

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)[source]

Infers shape of Parameters from inputs.

infer_type(*args)[source]

Infers data type of Parameters from inputs.

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)[source]

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

register_child(block, name=None)[source]

Registers block as a child of self. Block s assigned to self as attributes will be registered automatically.

register_op_hook(callback, monitor_all=False)[source]

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)[source]

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)[source]

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

class HybridConcatenate(axis=-1)[source]

Bases: mxnet.gluon.nn.basic_layers.HybridSequential

Lays HybridBlock s concurrently.

This block feeds its input to all children blocks, and produce the output by concatenating all the children blocks’ outputs on the specified axis.

Example:

net = HybridConcatenate()
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
net.add(Identity())

Methods

add(*blocks)

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters

axis (int, default -1) – The axis on which to concatenate the outputs.

add(*blocks)

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class HybridLambda(function)[source]

Bases: mxnet.gluon.block.HybridBlock

Wraps an operator or an expression as a HybridBlock object.

Parameters
  • function (str or function) –

    Function used in lambda must be one of the following: 1) The name of an operator that is available in both symbol and ndarray. For example:

    block = HybridLambda('tanh')
    
    1. A function that conforms to def function(F, data, *args). For example:

      block = HybridLambda(lambda F, x: F.LeakyReLU(x, slope=0.1))
      

  • Inputs

    • ** args *: one or more input data. First argument must be symbol or ndarray. Their

      shapes depend on the function.

  • Output

    • ** outputs *: one or more output data. Their shapes depend on the function.

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x, *args)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x, *args)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class HybridSequential[source]

Bases: mxnet.gluon.block.HybridBlock

Stacks HybridBlocks sequentially.

Example:

net = nn.HybridSequential()
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
net.hybridize()

Methods

add(*blocks)

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x, *args)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

add(*blocks)[source]

Adds block on top of the stack.

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x, *args)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Identity[source]

Bases: mxnet.gluon.block.HybridBlock

Block that passes through the input directly.

This block can be used in conjunction with HybridConcatenate block for residual connection.

Example:

net = HybridConcatenate()
net.add(nn.Dense(10, activation='relu'))
net.add(nn.Dense(20))
net.add(Identity())

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(*args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class InstanceNorm(axis=1, epsilon=1e-05, center=True, scale=False, beta_initializer='zeros', gamma_initializer='ones', in_channels=0, **kwargs)[source]

Bases: mxnet.gluon.block.HybridBlock

Applies instance normalization to the n-dimensional input array. This operator takes an n-dimensional input array where (n>2) and normalizes the input using the following formula:

\[ \begin{align}\begin{aligned}\bar{C} = \{i \mid i \neq 0, i \neq axis\}\\out = \frac{x - mean[data, \bar{C}]}{ \sqrt{Var[data, \bar{C}]} + \epsilon} * gamma + beta\end{aligned}\end{align} \]

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(x)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(x, *args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters
  • axis (int, default 1) – The axis that will be excluded in the normalization process. This is typically the channels (C) axis. For instance, after a Conv2D layer with layout=’NCHW’, set axis=1 in InstanceNorm. If layout=’NHWC’, then set axis=3. Data will be normalized along axes excluding the first axis and the axis given.

  • epsilon (float, default 1e-5) – Small float added to variance to avoid dividing by zero.

  • center (bool, default True) – If True, add offset of beta to normalized tensor. If False, beta is ignored.

  • scale (bool, default True) – If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.

  • beta_initializer (str or Initializer, default ‘zeros’) – Initializer for the beta weight.

  • gamma_initializer (str or Initializer, default ‘ones’) – Initializer for the gamma weight.

  • in_channels (int, default 0) – Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

References

Instance Normalization: The Missing Ingredient for Fast Stylization

Examples

>>> # Input of shape (2,1,2)
>>> x = mx.np.array([[[ 1.1,  2.2]],
...                 [[ 3.3,  4.4]]])
>>> # Instance normalization is calculated with the above formula
>>> layer = InstanceNorm()
>>> layer.initialize(device=mx.cpu(0))
>>> layer(x)
[[[-0.99998355  0.99998331]]
 [[-0.99998319  0.99998361]]]
apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(x)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(x, *args)[source]

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this Block and its children.

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • device (Device or list of Device) – Keeps a copy of Parameters on one or many device(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

load(prefix)

Load a model saved using the save API

Reconfigures a model using the saved configuration. This function does not regenerate the model architecture. It resets each Block’s parameter UUIDs as they were when saved in order to match the names of the saved parameters.

This function assumes the Blocks in the model were created in the same order they were when the model was saved. This is because each Block is uniquely identified by Block class name and a unique ID in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph (Symbol & inputs) and settings are restored if it had been hybridized before saving.

Parameters

prefix (str) – The prefix to use in filenames for loading this model: <prefix>-model.json and <prefix>-model.params

load_dict(param_dict, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from dict

Parameters
  • param_dict (dict) – Dictionary containing model parameters

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represented in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this dict.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

load_parameters(filename, device=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • device (Device or list of Device, default cpu()) – Device(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

optimize_for(x, *args, backend=None, clear=False, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None, **kwargs)

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass. Modifies the HybridBlock in-place.

Immediately partitions a HybridBlock using the specified backend. Combines the work done in the hybridize API with part of the work done in the forward pass without calling the CachedOp. Can be used in place of hybridize, afterwards export can be called or inference can be run. See README.md in example/extensions/lib_subgraph/README.md for more details.

Examples

# partition and then export to file block.optimize_for(x, backend=’myPart’) block.export(‘partitioned’)

# partition and then run inference block.optimize_for(x, backend=’myPart’) block(x)

Parameters
  • x (NDArray) – first input to model

  • *args (NDArray) – other inputs to model

  • backend (str) – The name of backend, as registered in SubgraphBackendRegistry, default None

  • backend_opts (dict of user-specified options to pass to the backend for partitioning, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

  • clear (bool, default False) – clears any previous optimizations

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

  • **kwargs (The backend options, optional) – Passed on to PrePartition and PostPartition functions of SubgraphProperty

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

register_forward_hook(hook)

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)

Install op hook for block recursively.

Parameters
  • callback (function) – Function called to inspect the values of the intermediate outputs of blocks after hybridization. It takes 3 parameters: name of the tensor being inspected (str) name of the operator producing or consuming that tensor (str) tensor being inspected (NDArray).

  • monitor_all (bool, default False) – If True, monitor both input and output, otherwise monitor output only.

reset_ctx(ctx)

This function has been deprecated. Please refer to HybridBlock.reset_device.

reset_device(device)

Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

save(prefix)

Save the model architecture and parameters to load again later

Saves the model architecture as a nested dictionary where each Block in the model is a dictionary and its children are sub-dictionaries.

Each Block is uniquely identified by Block class name and a unique ID. We save each Block’s parameter UUID to restore later in order to match the saved parameters.

Recursively traverses a Block’s children in order (since its an OrderedDict) and uses the unique ID to denote that specific Block.

Assumes that the model is created in an identical order every time. If the model is not able to be recreated deterministically do not use this set of APIs to save/load your model.

For HybridBlocks, the cached_graph is saved (Symbol & inputs) if it has already been hybridized.

Parameters

prefix (str) – The prefix to use in filenames for saving this model: <prefix>-model.json and <prefix>-model.params

save_parameters(filename, deduplicate=False)

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

setattr(name, value)

Set an attribute to a new value for all Parameters.

For example, set grad_req to null if you don’t need gradient w.r.t a model’s Parameters:

model.setattr('grad_req', 'null')

or change the learning rate multiplier:

model.setattr('lr_mult', 0.5)
Parameters
  • name (str) – Name of the attribute.

  • value (valid type for attribute name) – The new value for the attribute.

share_parameters(shared)

Share parameters recursively inside the model.

For example, if you want dense1 to share dense0’s weights, you can do:

dense0 = nn.Dense(20)
dense1 = nn.Dense(20)
dense1.share_parameters(dense0.collect_params())
which equals to

dense1.weight = dense0.weight dense1.bias = dense0.bias

Note that unlike the load_parameters or load_dict functions, share_parameters results in the Parameter object being shared (or tied) between the models, whereas load_parameters or load_dict only set the value of the data dictionary of a model. If you call load_parameters or load_dict after share_parameters, the loaded value will be reflected in all networks that use the shared (or tied) Parameter object.

Parameters

shared (Dict) – Dict of the shared parameters.

Returns

Return type

this block

summary(*inputs)

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

class Lambda(function)[source]

Bases: mxnet.gluon.block.Block

Wraps an operator or an expression as a Block object.

Parameters
  • function (str or function) –

    Function used in lambda must be one of the following: 1) the name of an operator that is available in ndarray. For example:

    block = Lambda('tanh')
    
    1. a function that conforms to def function(*args). For example:

      block = Lambda(lambda x: npx.leaky_relu(x, slope=0.1))
      

  • Inputs

    • ** args *: one or more input data. Their shapes depend on the function.

  • Output

    • ** outputs *: one or more output data. Their shapes depend on the function.

Methods

forward(*args)

Overrides to implement forward computation using NDArray.

forward(*args)[source]

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

Parameters

*args (list of NDArray) – Input tensors.

class LayerNorm(axis=-1, epsilon=1e-05, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', in_channels=0)[source]

Bases: mxnet.gluon.block.HybridBlock

Applies layer normalization to the n-dimensional input array. This operator takes an n-dimensional input array and normalizes the input using the given axis:

\[out = \frac{x - mean[data, axis]}{ \sqrt{Var[data, axis] + \epsilon}} * gamma + beta\]

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

collect_params([select])

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

export(path[, epoch, remove_amp_cast])

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

forward(data)

Overrides the forward computation.

hybridize([active, partition_if_dynamic, …])

Activates or deactivates HybridBlock s recursively.

infer_shape(data, *args)

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize([init, device, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load(prefix)

Load a model saved using the save API

load_dict(param_dict[, device, …])

Load parameters from dict

load_parameters(filename[, device, …])

Load parameters from file previously saved by save_parameters.

optimize_for(x, *args[, backend, clear, …])

Partitions the current HybridBlock and optimizes it for a given backend without executing a forward pass.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install op hook for block recursively.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign all Parameters to other devices.

save(prefix)

Save the model architecture and parameters to load again later

save_parameters(filename[, deduplicate])

Save parameters to file.

setattr(name, value)

Set an attribute to a new value for all Parameters.

share_parameters(shared)

Share parameters recursively inside the model.

summary(*inputs)

Print the summary of the model’s output and parameters.

zero_grad()

Sets all Parameters’ gradient buffer to 0.

Attributes

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

Parameters
  • axis (int, default -1) – The axis that should be normalized. This is typically the axis of the channels.

  • epsilon (float, default 1e-5) – Small float added to variance to avoid dividing by zero.

  • center (bool, default True) – If True, add offset of beta to normalized tensor. If False, beta is ignored.

  • scale (bool, default True) – If True, multiply by gamma. If False, gamma is not used.

  • beta_initializer (str or Initializer, default ‘zeros’) – Initializer for the beta weight.

  • gamma_initializer (str or Initializer, default ‘ones’) – Initializer for the gamma weight.

  • in_channels (int, default 0) – Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Inputs:
  • data: input tensor with arbitrary shape.

Outputs:
  • out: output tensor with the same shape as data.

References

Layer Normalization

Examples

>>> # Input of shape (2, 5)
>>> x = mx.np.array([[1, 2, 3, 4, 5], [1, 1, 2, 2, 2]])
>>> # Layer normalization is calculated with the above formula
>>> layer = LayerNorm()
>>> layer.initialize(device=mx.cpu(0))
>>> layer(x)
[[-1.41421    -0.707105    0.          0.707105    1.41421   ]
 [-1.2247195  -1.2247195   0.81647956  0.81647956  0.81647956]]
apply(fn)

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

collect_params(select=None)

Returns a Dict containing this Block and all of its children’s Parameters(default), also can returns the select Dict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1.weight’, ‘conv1.bias’, ‘fc.weight’, ‘fc.bias’]:

model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected Dict

export(path, epoch=0, remove_amp_cast=True)

Export HybridBlock to json format that can be loaded by gluon.SymbolBlock.imports or the C++ interface.

Note

When there are only one input, it will have name data. When there Are more than one inputs, they will be named as data0, data1, etc.

Parameters
  • path (str or None) – Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters.

  • epoch (int) – Epoch number of saved model.

  • remove_amp_cast (bool, optional) – Whether to remove the amp_cast and amp_multicast operators, before saving the model.

Returns

  • symbol_filename (str) – Filename to which model symbols were saved, including path prefix.

  • params_filename (str) – Filename to which model parameters were saved, including path prefix.

forward(data)[source]

Overrides the forward computation. Arguments must be mxnet.numpy.ndarray.

hybridize(active=True, partition_if_dynamic=True, static_alloc=False, static_shape=False, inline_limit=2, forward_bulk_size=None, backward_bulk_size=None)

Activates or deactivates HybridBlock s recursively. Has no effect on non-hybrid children.

Parameters
  • active (bool, default True) – Whether to turn hybrid on or off.

  • partition_if_dynamic (bool, default False) – whether to partition the graph when dynamic shape op exists

  • static_alloc (bool, default False) – Statically allocate memory to improve speed. Memory usage may increase.

  • static_shape (bool, default False) – Optimize for invariant input shapes between iterations. Must also set static_alloc to True. Change of input shapes is still allowed but slower.

  • inline_limit (optional int, default 2) – Maximum number of operators that can be inlined.

  • forward_bulk_size (optional int, default None) – Segment size of bulk execution during forward pass.

  • backward_bulk_size (optional int, default None) – Segment size of bulk execution during backward pass.

infer_shape(data, *args)[source]

Infers shape of Parameters from inputs.

infer_type(*args)

Infers data type of Parameters from inputs.

initialize(init=<mxnet.initializer.Uniform object>, device=None, verbose=False, force_reinit=False)

Initializes Parameter s of this