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# to you under the Apache License, Version 2.0 (the
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#
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# coding: utf-8
# pylint: disable= arguments-differ, too-many-lines
"""Base container class for all neural network models."""
__all__ = ['Block', 'HybridBlock', 'SymbolBlock']
import threading
import copy
import warnings
import re
from collections import OrderedDict
from ..base import mx_real_t, MXNetError
from .. import symbol, ndarray, initializer
from ..symbol import Symbol
from ..ndarray import NDArray
from .. import name as _name
from .parameter import Parameter, ParameterDict, DeferredInitializationError
from .utils import _indent, _brief_print_list, HookHandle
class _BlockScope(object):
"""Scope for collecting child `Block` s."""
_current = threading.local()
def __init__(self, block):
self._block = block
self._counter = {}
self._old_scope = None
self._name_scope = None
@staticmethod
def create(prefix, params, hint):
"""Creates prefix and params for new `Block`."""
current = getattr(_BlockScope._current, "value", None)
if current is None:
if prefix is None:
if not hasattr(_name.NameManager._current, "value"):
_name.NameManager._current.value = _name.NameManager()
prefix = _name.NameManager._current.value.get(None, hint) + '_'
if params is None:
params = ParameterDict(prefix)
else:
params = ParameterDict(params.prefix, params)
return prefix, params
if prefix is None:
count = current._counter.get(hint, 0)
prefix = '%s%d_'%(hint, count)
current._counter[hint] = count + 1
if params is None:
parent = current._block.params
params = ParameterDict(parent.prefix+prefix, parent._shared)
else:
params = ParameterDict(params.prefix, params)
return current._block.prefix+prefix, params
def __enter__(self):
if self._block._empty_prefix:
return self
self._old_scope = getattr(_BlockScope._current, "value", None)
_BlockScope._current.value = self
self._name_scope = _name.Prefix(self._block.prefix)
self._name_scope.__enter__()
return self
def __exit__(self, ptype, value, trace):
if self._block._empty_prefix:
return
self._name_scope.__exit__(ptype, value, trace)
self._name_scope = None
_BlockScope._current.value = self._old_scope
def _flatten(args, inout_str):
if isinstance(args, NDArray):
return [args], int(0)
if isinstance(args, Symbol):
length = len(args.list_outputs())
length = length if length > 1 else 0
return [args], int(length)
assert isinstance(args, (list, tuple)), \
"HybridBlock %s must be (nested) list of Symbol or NDArray, " \
"but got %s of type %s"%(inout_str, str(args), str(type(args)))
flat = []
fmts = []
for i in args:
arg, fmt = _flatten(i, inout_str)
flat.extend(arg)
fmts.append(fmt)
return flat, fmts
def _regroup(args, fmt):
if isinstance(fmt, int):
if fmt == 0:
return args[0], args[1:]
return args[:fmt], args[fmt:]
assert isinstance(args, (list, tuple)), \
"HybridBlock output must be (nested) list of Symbol or NDArray, " \
"but got %s of type %s"%(str(args), str(type(args)))
ret = []
for i in fmt:
res, args = _regroup(args, i)
ret.append(res)
return ret, args
[docs]class Block(object):
"""Base class for all neural network layers and models. Your models should
subclass this class.
:py:class:`Block` can be nested recursively in a tree structure. You can create and
assign child :py:class:`Block` as regular attributes::
from mxnet.gluon import Block, nn
from mxnet import ndarray as F
class Model(Block):
def __init__(self, **kwargs):
super(Model, self).__init__(**kwargs)
# use name_scope to give child Blocks appropriate names.
with self.name_scope():
self.dense0 = nn.Dense(20)
self.dense1 = nn.Dense(20)
def forward(self, x):
x = F.relu(self.dense0(x))
return F.relu(self.dense1(x))
model = Model()
model.initialize(ctx=mx.cpu(0))
model(F.zeros((10, 10), ctx=mx.cpu(0)))
Child :py:class:`Block` assigned this way will be registered and :py:meth:`collect_params`
will collect their Parameters recursively. You can also manually register
child blocks with :py:meth:`register_child`.
Parameters
----------
prefix : str
Prefix acts like a name space. All children blocks created in parent block's
:py:meth:`name_scope` will have parent block's prefix in their name.
Please refer to
`naming tutorial `_
for more info on prefix and naming.
params : ParameterDict or None
:py:class:`ParameterDict` for sharing weights with the new :py:class:`Block`. For example,
if you want ``dense1`` to share ``dense0``'s weights, you can do::
dense0 = nn.Dense(20)
dense1 = nn.Dense(20, params=dense0.collect_params())
"""
def __init__(self, prefix=None, params=None):
self._empty_prefix = prefix == ''
self._prefix, self._params = _BlockScope.create(prefix, params, self._alias())
self._name = self._prefix[:-1] if self._prefix.endswith('_') else self._prefix
self._scope = _BlockScope(self)
self._children = OrderedDict()
self._reg_params = {}
self._forward_hooks = OrderedDict()
self._forward_pre_hooks = OrderedDict()
def __repr__(self):
s = '{name}(\n{modstr}\n)'
modstr = '\n'.join([' ({key}): {block}'.format(key=key,
block=_indent(block.__repr__(), 2))
for key, block in self.__dict__.items() if isinstance(block, Block)])
return s.format(name=self.__class__.__name__, modstr=modstr)
[docs] def __setattr__(self, name, value):
"""Registers parameters."""
if hasattr(self, name):
existing = getattr(self, name)
if isinstance(existing, (Parameter, Block)) and not isinstance(value, type(existing)):
raise TypeError('Changing attribute type for {name} from {type1} to {type2}' \
'is not allowed.'.format(
name=name, type1=type(existing), type2=type(value)))
if isinstance(value, Block):
self.register_child(value, name)
elif isinstance(value, Parameter):
assert name not in self._reg_params, \
"Overriding Parameter attribute %s is not allowed. " \
"If you want to share parameters between blocks, please set " \
"'params' at Block construction instead."
self._reg_params[name] = value
super(Block, self).__setattr__(name, value)
def _check_container_with_block(self):
children = set(self._children.values())
def _find_unregistered_block_in_container(data):
# Find whether a nested container structure contains Blocks
if isinstance(data, (list, tuple)):
for ele in data:
if _find_unregistered_block_in_container(ele):
return True
return False
elif isinstance(data, dict):
for _, v in data.items():
if _find_unregistered_block_in_container(v):
return True
return False
elif isinstance(data, Block):
return not data in children
else:
return False
for k, v in self.__dict__.items():
if isinstance(v, (list, tuple, dict)) and not (k.startswith('__') or k == '_children'):
if _find_unregistered_block_in_container(v):
warnings.warn('"{name}" is an unregistered container with Blocks. '
'Note that Blocks inside the list, tuple or dict will not be '
'registered automatically. Make sure to register them using '
'register_child() or switching to '
'nn.Sequential/nn.HybridSequential instead. '
.format(name=self.__class__.__name__ + "." + k), stacklevel=3)
def _alias(self):
return self.__class__.__name__.lower()
@property
def prefix(self):
"""Prefix of this :py:class:`Block`."""
return self._prefix
@property
def name(self):
"""Name of this :py:class:`Block`, without '_' in the end."""
return self._name
[docs] def name_scope(self):
"""Returns a name space object managing a child :py:class:`Block` and parameter
names. Should be used within a ``with`` statement::
with self.name_scope():
self.dense = nn.Dense(20)
Please refer to
`naming tutorial `_
for more info on prefix and naming.
"""
return self._scope
@property
def params(self):
"""Returns this :py:class:`Block`'s parameter dictionary (does not include its
children's parameters)."""
return self._params
[docs] def collect_params(self, select=None):
"""Returns a :py:class:`ParameterDict` containing this :py:class:`Block` and all of its
children's Parameters(default), also can returns the select :py:class:`ParameterDict`
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
-------
The selected :py:class:`ParameterDict`
"""
# We need to check here because blocks inside containers are not supported.
self._check_container_with_block()
ret = ParameterDict(self._params.prefix)
if not select:
ret.update(self.params)
else:
pattern = re.compile(select)
ret.update({name:value for name, value in self.params.items() if pattern.match(name)})
for cld in self._children.values():
ret.update(cld.collect_params(select=select))
return ret
def _collect_params_with_prefix(self, prefix=''):
if prefix:
prefix += '.'
ret = {prefix + key : val for key, val in self._reg_params.items()}
for name, child in self._children.items():
ret.update(child._collect_params_with_prefix(prefix + name))
return ret
[docs] def save_parameters(self, filename):
"""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 :py:meth:`HybridBlock.export`.
Parameters
----------
filename : str
Path to file.
References
----------
`Saving and Loading Gluon Models \
`_
"""
params = self._collect_params_with_prefix()
arg_dict = {key : val._reduce() for key, val in params.items()}
ndarray.save(filename, arg_dict)
[docs] def save_params(self, filename):
"""[Deprecated] Please use save_parameters. Note that if you want load
from SymbolBlock later, please use export instead.
Save parameters to file.
filename : str
Path to file.
"""
warnings.warn("save_params is deprecated. Please use save_parameters. "
"Note that if you want load from SymbolBlock later, please "
"use export instead. For details, see "
"/tutorials/gluon/save_lo"
"ad_params.html")
try:
self.collect_params().save(filename, strip_prefix=self.prefix)
except ValueError as e:
raise ValueError('%s\nsave_params is deprecated. Using ' \
'save_parameters may resolve this error.'%e.message)
[docs] def load_parameters(self, filename, ctx=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.
ctx : Context or list of Context, default cpu()
Context(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 \
`_
"""
loaded = ndarray.load(filename)
params = self._collect_params_with_prefix()
if not loaded and not params:
return
if not any('.' in i for i in loaded.keys()):
# legacy loading
del loaded
self.collect_params().load(
filename, ctx, allow_missing, ignore_extra, self.prefix,
cast_dtype=cast_dtype, dtype_source=dtype_source)
return
if not allow_missing:
for name in params.keys():
assert name in loaded, \
"Parameter '%s' is missing in file '%s', which contains parameters: %s. " \
"Set allow_missing=True to ignore missing parameters."%(
name, filename, _brief_print_list(loaded.keys()))
for name in loaded:
if not ignore_extra and name not in params:
raise ValueError(
"Parameter '%s' loaded from file '%s' is not present in ParameterDict, " \
"which contains parameters %s. Set ignore_extra=True to ignore. "%(
name, filename, _brief_print_list(self._params.keys())))
if name in params:
params[name]._load_init(loaded[name], ctx, cast_dtype=cast_dtype, dtype_source=dtype_source)
[docs] def load_params(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""[Deprecated] Please use load_parameters.
Load parameters from file.
filename : str
Path to parameter file.
ctx : Context or list of Context, default cpu()
Context(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.
"""
warnings.warn("load_params is deprecated. Please use load_parameters.")
self.load_parameters(filename, ctx, allow_missing, ignore_extra)
[docs] def register_child(self, block, name=None):
"""Registers block as a child of self. :py:class:`Block` s assigned to self as
attributes will be registered automatically."""
if name is None:
name = str(len(self._children))
self._children[name] = block
[docs] def register_forward_pre_hook(self, hook):
r"""Registers a forward pre-hook on the block.
The hook function is called immediately before :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form `hook(block, input) -> None`.
Returns
-------
:class:`mxnet.gluon.utils.HookHandle`
"""
handle = HookHandle()
handle.attach(self._forward_pre_hooks, hook)
return handle
[docs] def register_forward_hook(self, hook):
r"""Registers a forward hook on the block.
The hook function is called immediately after :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form `hook(block, input, output) -> None`.
Returns
-------
:class:`mxnet.gluon.utils.HookHandle`
"""
handle = HookHandle()
handle.attach(self._forward_hooks, hook)
return handle
[docs] def apply(self, fn):
r"""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
-------
this block
"""
for cld in self._children.values():
cld.apply(fn)
fn(self)
return self
[docs] def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes :py:class:`Parameter` s of this :py:class:`Block` and its children.
Equivalent to ``block.collect_params().initialize(...)``
Parameters
----------
init : Initializer
Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``.
Otherwise, :py:meth:`Parameter.init` takes precedence.
ctx : Context or list of Context
Keeps a copy of Parameters on one or many context(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.
"""
self.collect_params().initialize(init, ctx, verbose, force_reinit)
[docs] def hybridize(self, active=True, **kwargs):
"""Activates or deactivates :py:class:`HybridBlock` s recursively. Has no effect on
non-hybrid children.
Parameters
----------
active : bool, default True
Whether to turn hybrid on or off.
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.
"""
for cld in self._children.values():
cld.hybridize(active, **kwargs)
[docs] def cast(self, dtype):
"""Cast this Block to use another data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
for child in self._children.values():
child.cast(dtype)
for _, param in self.params.items():
param.cast(dtype)
[docs] def __call__(self, *args):
"""Calls forward. Only accepts positional arguments."""
for hook in self._forward_pre_hooks.values():
hook(self, args)
out = self.forward(*args)
for hook in self._forward_hooks.values():
hook(self, args, out)
return out
[docs] def forward(self, *args):
"""Overrides to implement forward computation using :py:class:`NDArray`. Only
accepts positional arguments.
Parameters
----------
*args : list of NDArray
Input tensors.
"""
# pylint: disable= invalid-name
raise NotImplementedError
[docs] def summary(self, *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
:class:`mxnet.ndarray.NDArray` is supported.
"""
summary = OrderedDict()
seen = set()
hooks = []
def _get_shape_str(args):
def flatten(args):
if not isinstance(args, (list, tuple)):
return [args], int(0)
flat = []
fmts = []
for i in args:
arg, fmt = flatten(i)
flat.extend(arg)
fmts.append(fmt)
return flat, fmts
def regroup(args, fmt):
if isinstance(fmt, int):
if fmt == 0:
return args[0], args[1:]
return args[:fmt], args[fmt:]
ret = []
for i in fmt:
res, args = regroup(args, i)
ret.append(res)
return ret, args
flat_args, fmts = flatten(args)
flat_arg_shapes = [x.shape if isinstance(x, ndarray.NDArray) else x
for x in flat_args]
shapes = regroup(flat_arg_shapes, fmts)[0]
if isinstance(shapes, list):
shape_str = str(shapes)[1:-1]
else:
shape_str = str(shapes)
return shape_str.replace('L', '')
def _register_summary_hook(block):
assert not isinstance(block, HybridBlock) or not block._active, \
'"{}" must not be hybridized to print summary.'.format(block.name)
def _summary_hook(block, _, outputs):
class_name = block.__class__.__name__
block_idx = len(summary) - 1
m_key = '%s-%i' % (class_name, block_idx+1)
summary[m_key] = OrderedDict()
summary[m_key]['output_shape'] = _get_shape_str(outputs)
params = 0
summary[m_key]['trainable'] = 0
summary[m_key]['shared'] = 0
for p in block.params.values():
params += p.data().size
summary[m_key]['trainable'] += 0 if p.grad_req == 'null' else p.data().size
if p in seen:
summary[m_key]['shared'] += p.data().size
else:
seen.add(p)
summary[m_key]['n_params'] = params
from .nn.basic_layers import Sequential, HybridSequential
if not isinstance(block, (Sequential, HybridSequential)):
hooks.append(block.register_forward_hook(_summary_hook))
summary['Input'] = OrderedDict()
summary['Input']['output_shape'] = _get_shape_str(inputs)
summary['Input']['n_params'] = 0
summary['Input']['trainable'] = 0
summary['Input']['shared'] = 0
try:
self.apply(_register_summary_hook)
self(*inputs)
line_format = '{:>20} {:>42} {:>15}'
print('-'*80)
print(line_format.format('Layer (type)', 'Output Shape', 'Param #'))
print('='*80)
total_params = 0
trainable_params = 0
shared_params = 0
for layer in summary:
print(line_format.format(layer,
str(summary[layer]['output_shape']),
summary[layer]['n_params']))
total_params += summary[layer]['n_params']
trainable_params += summary[layer]['trainable']
shared_params += summary[layer]['shared']
print('='*80)
print('Parameters in forward computation graph, duplicate included')
print(' Total params: ' + str(total_params))
print(' Trainable params: ' + str(trainable_params))
print(' Non-trainable params: ' + str(total_params - trainable_params))
print('Shared params in forward computation graph: ' + str(shared_params))
print('Unique parameters in model: ' + str(total_params - shared_params))
print('-'*80)
finally:
for h in hooks:
h.detach()
[docs]class HybridBlock(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)
# use name_scope to give child Blocks appropriate names.
with self.name_scope():
self.dense0 = nn.Dense(20)
self.dense1 = nn.Dense(20)
def hybrid_forward(self, F, x):
x = F.relu(self.dense0(x))
return F.relu(self.dense1(x))
model = Model()
model.initialize(ctx=mx.cpu(0))
model.hybridize()
model(mx.nd.zeros((10, 10), ctx=mx.cpu(0)))
Forward computation in :py:class:`HybridBlock` must be static to work with :py:class:`Symbol` s,
i.e. you cannot call :py:meth:`NDArray.asnumpy`, :py:attr:`NDArray.shape`,
:py:attr:`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 :py:meth:`hybridize()`, :py:class:`HybridBlock` works just like normal
:py:class:`Block`. After activation, :py:class:`HybridBlock` will create a symbolic graph
representing the forward computation and cache it. On subsequent forwards,
the cached graph will be used instead of :py:meth:`hybrid_forward`.
Please see references for detailed tutorial.
References
----------
`Hybrid - Faster training and easy deployment
`_
"""
def __init__(self, prefix=None, params=None):
super(HybridBlock, self).__init__(prefix=prefix, params=params)
self._cached_graph = ()
self._cached_op = None
self._out_format = None
self._in_format = None
self._active = False
self._flags = []
[docs] def __setattr__(self, name, value):
"""Registers parameters."""
super(HybridBlock, self).__setattr__(name, value)
if isinstance(value, HybridBlock):
self._clear_cached_op()
def _get_graph(self, *args):
if not self._cached_graph:
args, self._in_format = _flatten(args, "input")
if len(args) > 1:
inputs = [symbol.var('data%d'%i) for i in range(len(args))]
else:
inputs = [symbol.var('data')]
grouped_inputs = _regroup(inputs, self._in_format)[0]
params = {i: j.var() for i, j in self._reg_params.items()}
with self.name_scope():
out = self.hybrid_forward(symbol, *grouped_inputs, **params) # pylint: disable=no-value-for-parameter
out, self._out_format = _flatten(out, "output")
self._cached_graph = inputs, symbol.Group(out)
return self._cached_graph
def _build_cache(self, *args):
data, out = self._get_graph(*args)
data_names = {data.name : i for i, data in enumerate(data)}
params = self.collect_params()
input_names = out.list_inputs()
param_names = set(params.keys())
expected_names = set(input_names)
for name in expected_names:
assert name in param_names or name in data_names, \
"Unknown input to HybridBlock: %s"%name
used_data_names = [i for i in data_names if i in expected_names]
if len(used_data_names) != len(data_names):
unused = ', '.join(['%d-th'%i for name, i in data_names.items()
if name not in expected_names])
warnings.warn("The %s input to HybridBlock is not used by any "
"computation. Is this intended?"%unused, stacklevel=4)
used_param_names = [i for i in param_names if i in expected_names]
if len(used_param_names) != len(param_names):
unused = ', '.join(list(param_names - set(used_param_names)))
warnings.warn("Parameter %s is not used by any computation. "
"Is this intended?"%unused, stacklevel=4)
data_indices = []
param_indices = []
self._cached_op_args = []
for i, name in enumerate(input_names):
if name in data_names:
data_indices.append(i)
self._cached_op_args.append((True, data_names[name]))
else:
param_indices.append(i)
self._cached_op_args.append((False, params[name]))
flags = [('data_indices', data_indices), ('param_indices', param_indices)] + \
self._flags
self._cached_op = ndarray.CachedOp(out, flags)
def _deferred_infer_shape(self, *args):
try:
self.infer_shape(*args)
except Exception as e:
error_msg = "Deferred initialization failed because shape"\
" cannot be inferred. {}".format(e)
raise ValueError(error_msg)
def _call_cached_op(self, *args):
if self._cached_op is None:
self._build_cache(*args)
args, fmt = _flatten(args, "input")
assert fmt == self._in_format, "Invalid input format"
try:
cargs = [args[i] if is_arg else i.data()
for is_arg, i in self._cached_op_args]
except DeferredInitializationError:
self._deferred_infer_shape(*args)
cargs = []
for is_arg, i in self._cached_op_args:
if is_arg:
cargs.append(args[i])
else:
i._finish_deferred_init()
cargs.append(i.data())
out = self._cached_op(*cargs)
if isinstance(out, NDArray):
out = [out]
return _regroup(out, self._out_format)[0]
def _clear_cached_op(self):
self._cached_graph = ()
self._cached_op = None
def register_child(self, block, name=None):
if not isinstance(block, HybridBlock):
raise ValueError(
"Children of HybridBlock must also be HybridBlock, " \
"but %s has type %s. If you are using Sequential, " \
"please try HybridSequential instead."%(
str(block), str(type(block))))
super(HybridBlock, self).register_child(block, name)
self._clear_cached_op()
def hybridize(self, active=True, **kwargs):
self._active = active
self._flags = list(kwargs.items())
self._clear_cached_op()
if active and self._forward_hooks or self._forward_pre_hooks:
warnings.warn('"{}" is being hybridized while still having forward hook/pre-hook. '
'If "{}" is a child of HybridBlock, the hooks will not take effect.')
super(HybridBlock, self).hybridize(active, **kwargs)
def cast(self, dtype):
self._clear_cached_op()
super(HybridBlock, self).cast(dtype)
def _infer_attrs(self, infer_fn, attr, *args):
"""Generic infer attributes."""
inputs, out = self._get_graph(*args)
args, _ = _flatten(args, "input")
with warnings.catch_warnings(record=True) as w:
arg_attrs, _, aux_attrs = getattr(out, infer_fn)(
**{i.name: getattr(j, attr) for i, j in zip(inputs, args)})
if arg_attrs is None:
raise ValueError(w[0].message)
sdict = {i: j for i, j in zip(out.list_arguments(), arg_attrs)}
sdict.update({name : attr for name, attr in \
zip(out.list_auxiliary_states(), aux_attrs)})
for i in self.collect_params().values():
setattr(i, attr, sdict[i.name])
[docs] def infer_shape(self, *args):
"""Infers shape of Parameters from inputs."""
self._infer_attrs('infer_shape', 'shape', *args)
[docs] def infer_type(self, *args):
"""Infers data type of Parameters from inputs."""
self._infer_attrs('infer_type', 'dtype', *args)
[docs] def export(self, path, epoch=0, remove_amp_cast=True):
"""Export HybridBlock to json format that can be loaded by
`SymbolBlock.imports`, `mxnet.mod.Module` 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
Path to save model. Two files `path-symbol.json` and `path-xxxx.params`
will be created, where xxxx is the 4 digits epoch number.
epoch : int
Epoch number of saved model.
"""
if not self._cached_graph:
raise RuntimeError(
"Please first call block.hybridize() and then run forward with "
"this block at least once before calling export.")
sym = self._cached_graph[1]
sym.save('%s-symbol.json'%path, remove_amp_cast=remove_amp_cast)
arg_names = set(sym.list_arguments())
aux_names = set(sym.list_auxiliary_states())
arg_dict = {}
for name, param in self.collect_params().items():
if name in arg_names:
arg_dict['arg:%s'%name] = param._reduce()
else:
assert name in aux_names
arg_dict['aux:%s'%name] = param._reduce()
ndarray.save('%s-%04d.params'%(path, epoch), arg_dict)
[docs] def forward(self, x, *args):
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`."""
if isinstance(x, NDArray):
with x.context as ctx:
if self._active:
return self._call_cached_op(x, *args)
try:
params = {i: j.data(ctx) for i, j in self._reg_params.items()}
except DeferredInitializationError:
self._deferred_infer_shape(x, *args)
for _, i in self.params.items():
i._finish_deferred_init()
params = {i: j.data(ctx) for i, j in self._reg_params.items()}
return self.hybrid_forward(ndarray, x, *args, **params)
assert isinstance(x, Symbol), \
"HybridBlock requires the first argument to forward be either " \
"Symbol or NDArray, but got %s"%type(x)
params = {i: j.var() for i, j in self._reg_params.items()}
with self.name_scope():
return self.hybrid_forward(symbol, x, *args, **params)
[docs] def hybrid_forward(self, F, x, *args, **kwargs):
"""Overrides to construct symbolic graph for this `Block`.
Parameters
----------
x : Symbol or NDArray
The first input tensor.
*args : list of Symbol or list of NDArray
Additional input tensors.
"""
# pylint: disable= invalid-name
raise NotImplementedError
def _common_prefix(names):
"""Get the common prefix for all names"""
if not names:
return ''
prefix = names[0]
for name in names:
i = 0
while i < len(prefix) and i < len(name) and prefix[i] == name[i]:
i += 1
prefix = prefix[:i]
return prefix
[docs]class SymbolBlock(HybridBlock):
"""Construct block from symbol. This is useful for using pre-trained models
as feature extractors. For example, you may want to extract the output
from fc2 layer in AlexNet.
Parameters
----------
outputs : Symbol or list of Symbol
The desired output for SymbolBlock.
inputs : Symbol or list of Symbol
The Variables in output's argument that should be used as inputs.
params : ParameterDict
Parameter dictionary for arguments and auxililary states of outputs
that are not inputs.
Examples
--------
>>> # To extract the feature from fc1 and fc2 layers of AlexNet:
>>> alexnet = gluon.model_zoo.vision.alexnet(pretrained=True, ctx=mx.cpu(),
prefix='model_')
>>> inputs = mx.sym.var('data')
>>> out = alexnet(inputs)
>>> internals = out.get_internals()
>>> print(internals.list_outputs())
['data', ..., 'model_dense0_relu_fwd_output', ..., 'model_dense1_relu_fwd_output', ...]
>>> outputs = [internals['model_dense0_relu_fwd_output'],
internals['model_dense1_relu_fwd_output']]
>>> # Create SymbolBlock that shares parameters with alexnet
>>> feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params())
>>> x = mx.nd.random.normal(shape=(16, 3, 224, 224))
>>> print(feat_model(x))
"""
@staticmethod
[docs] def imports(symbol_file, input_names, param_file=None, ctx=None):
"""Import model previously saved by `HybridBlock.export` or
`Module.save_checkpoint` as a SymbolBlock for use in Gluon.
Parameters
----------
symbol_file : str
Path to symbol file.
input_names : list of str
List of input variable names
param_file : str, optional
Path to parameter file.
ctx : Context, default None
The context to initialize SymbolBlock on.
Returns
-------
SymbolBlock
SymbolBlock loaded from symbol and parameter files.
Examples
--------
>>> net1 = gluon.model_zoo.vision.resnet18_v1(
... prefix='resnet', pretrained=True)
>>> net1.hybridize()
>>> x = mx.nd.random.normal(shape=(1, 3, 32, 32))
>>> out1 = net1(x)
>>> net1.export('net1', epoch=1)
>>>
>>> net2 = gluon.SymbolBlock.imports(
... 'net1-symbol.json', ['data'], 'net1-0001.params')
>>> out2 = net2(x)
"""
sym = symbol.load(symbol_file)
if isinstance(input_names, str):
input_names = [input_names]
if param_file is None:
# Get a valid type inference by using fp32
inputs = [symbol.var(i, dtype=mx_real_t) for i in input_names]
else:
# Do not specify type, rely on saved params type instead
inputs = [symbol.var(i) for i in input_names]
ret = SymbolBlock(sym, inputs)
if param_file is not None:
ret.collect_params().load(param_file, ctx=ctx, cast_dtype=True, dtype_source='saved')
return ret
def __repr__(self):
s = '{name}(\n{modstr}\n)'
modstr = '\n'.join(['{block} : {numinputs} -> {numoutputs}'.format(block=self._cached_graph[1],
numinputs=len(self._cached_graph[0]),
numoutputs=len(self._cached_graph[1].
list_outputs()))])
return s.format(name=self.__class__.__name__,
modstr=modstr)
def __init__(self, outputs, inputs, params=None):
super(SymbolBlock, self).__init__(prefix=None, params=None)
self._prefix = ''
self._params = ParameterDict('', params)
if isinstance(inputs, symbol.Symbol) and len(inputs.list_outputs()) == 1:
inputs = [inputs]
if isinstance(outputs, (list, tuple)) and len(outputs) == 1:
outputs = outputs[0]
syms, self._in_format = _flatten(inputs, "input")
out, self._out_format = _flatten(outputs, "output")
out = symbol.Group(out)
input_names = set()
for i in syms:
assert len(i.get_internals().list_outputs()) == 1, \
"Input symbols must be variable, but %s is an output of operators"%str(i)
input_names.add(i.name)
# check if any symbol is row_sparse
row_sparse_storage = ndarray.ndarray._STORAGE_TYPE_STR_TO_ID['row_sparse']
for i in out:
for j in i.get_internals():
assert(j.attr("__storage_type__") != str(row_sparse_storage)), \
"SymbolBlock doesn't support Parameter '%s' because its storage " \
"type is 'row_sparse'." % j.name
# Infer type of parameters. Without this, every parameter will be created with
# default type i.e., fp32
arg_params = out.list_arguments()
aux_params = out.list_auxiliary_states()
arg_types, aux_types = _infer_param_types(syms, out, arg_params, aux_params)
for i, arg in enumerate(arg_params):
if arg not in input_names:
self.params.get(arg, allow_deferred_init=True, dtype=arg_types[i])
for i, aux in enumerate(aux_params):
if aux not in input_names:
self.params.get(aux, grad_req='null', allow_deferred_init=True, dtype=aux_types[i])
self._cached_graph = syms, out
len_prefix = len(_common_prefix(list(self._params.keys())))
self._reg_params = {key[len_prefix:]: val for key, val in self._params.items()}
def forward(self, x, *args):
if isinstance(x, NDArray):
with x.context:
return self._call_cached_op(x, *args)
assert isinstance(x, Symbol), \
"HybridBlock requires the first argument to forward be either " \
"Symbol or NDArray, but got %s"%type(x)
args, in_fmt = _flatten([x] + list(args), "input")
assert in_fmt == self._in_format, "Invalid input format"
ret = copy.copy(self._cached_graph[1])
ret._compose(**{k.name: v for k, v in zip(self._cached_graph[0], args)})
return _regroup(list(ret), self._out_format)[0]
def _clear_cached_op(self):
tmp = self._cached_graph
super(SymbolBlock, self)._clear_cached_op()
self._cached_graph = tmp
def cast(self, dtype):
self._clear_cached_op()
super(SymbolBlock, self).cast(dtype)
def hybrid_forward(self, F, x, *args, **kwargs):
raise NotImplementedError
def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t):
"""Utility function that helps in inferring DType of args and auxs params
from given input param.
Parameters
----------
in_params: List of Symbol
List of input symbol variables.
out_params: Symbol
Output symbol variable.
arg_params: List of Str
List of names of argument parametrs.
aux_params: List of Str
List of names of auxiliary parameters.
default_dtype: numpy.dtype or str, default 'float32'
Default data type for arg_params and aux_params, if unable to infer the type.
Returns
-------
arg_types: List of numpy.dtype
List of arg_params type. Order is same as arg_params.
Defaults to 'float32', if unable to infer type.
aux_types: List of numpy.dtype
List of aux_params type. Order is same as aux_params.
Defaults to 'float32', if unable to infer type.
"""
arg_types = None
aux_types = None
# Get Input symbol details. This will be used to infer types of
# other parameters.
input_sym_names = [in_param.name for in_param in in_params]
# Try to infer input types. If not successful, we will set default dtype.
# If successful, we will try to infer other params in the graph.
input_sym_arg_types = []
can_infer_input_type = True
for in_param in in_params:
input_sym_arg_type = in_param.infer_type()[0]
if not input_sym_arg_type or len(input_sym_arg_type) < 1:
can_infer_input_type = False
break
else:
input_sym_arg_types.append(in_param.infer_type()[0][0])
# Try to infer types of other parameters.
if can_infer_input_type:
params = {k:v for k, v in zip(input_sym_names, input_sym_arg_types)}
try:
arg_types, _, aux_types = out_params.infer_type(**params)
except MXNetError:
# Cannot infer type with current input
arg_types, aux_types = None, None
if arg_types is None or len(arg_types) != len(arg_params):
arg_types = []
for _ in arg_params:
arg_types.append(default_dtype)
if aux_types is None or len(aux_types) != len(aux_params):
aux_types = []
for _ in aux_params:
aux_types.append(default_dtype)
return (arg_types, aux_types)
