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# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# KIND, either express or implied. See the License for the
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# under the License.
"""Tools for testing."""
# pylint: disable=too-many-lines
from __future__ import absolute_import, print_function, division
import time
import gzip
import struct
import traceback
import numbers
import subprocess
import sys
import os
import errno
import logging
import bz2
import zipfile
from contextlib import contextmanager
import numpy as np
import numpy.testing as npt
import numpy.random as rnd
try:
import scipy.stats as ss
except ImportError:
ss = None
try:
import requests
except ImportError:
# in rare cases requests may be not installed
pass
import mxnet as mx
from .context import Context, current_context
from .ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
from .ndarray import array
from .symbol import Symbol
[docs]def default_context():
"""Get default context for regression test."""
# _TODO: get context from environment variable to support
# testing with GPUs
return current_context()
[docs]def set_default_context(ctx):
"""Set default context."""
Context._default_ctx.value = ctx
[docs]def default_dtype():
"""Get default data type for regression test."""
# _TODO: get default dtype from environment variable
return np.float32
[docs]def get_atol(atol=None):
"""Get default numerical threshold for regression test."""
# _TODO: get from env variable, different threshold might
# be needed for different device and dtype
return 1e-20 if atol is None else atol
[docs]def get_rtol(rtol=None):
"""Get default numerical threshold for regression test."""
# _TODO: get from env variable, different threshold might
# be needed for different device and dtype
return 1e-5 if rtol is None else rtol
[docs]def random_arrays(*shapes):
"""Generate some random numpy arrays."""
arrays = [np.random.randn(*s).astype(default_dtype())
for s in shapes]
if len(arrays) == 1:
return arrays[0]
return arrays
[docs]def random_sample(population, k):
"""Return a k length list of the elements chosen from the population sequence."""
assert 0 <= k <= len(population)
population_copy = population[:]
np.random.shuffle(population_copy)
return population_copy[0:k]
def _validate_csr_generation_inputs(num_rows, num_cols, density,
distribution="uniform"):
"""Validates inputs for csr generation helper functions
"""
total_nnz = int(num_rows * num_cols * density)
if density < 0 or density > 1:
raise ValueError("density has to be between 0 and 1")
if num_rows <= 0 or num_cols <= 0:
raise ValueError("num_rows or num_cols should be greater than 0")
if distribution == "powerlaw":
if total_nnz < 2 * num_rows:
raise ValueError("not supported for this density: %s"
" for this shape (%s, %s)"
" Please keep :"
" num_rows * num_cols * density >= 2 * num_rows"
% (density, num_rows, num_cols))
[docs]def shuffle_csr_column_indices(csr):
"""Shuffle CSR column indices per row
This allows validation of unordered column indices, which is not a requirement
for a valid CSR matrix
"""
row_count = len(csr.indptr) - 1
for i in range(row_count):
start_index = csr.indptr[i]
end_index = csr.indptr[i + 1]
sublist = np.array(csr.indices[start_index : end_index])
np.random.shuffle(sublist)
csr.indices[start_index : end_index] = sublist
def _get_uniform_dataset_csr(num_rows, num_cols, density=0.1, dtype=None,
data_init=None, shuffle_csr_indices=False):
"""Returns CSRNDArray with uniform distribution
This generates a csr matrix with totalnnz unique randomly chosen numbers
from num_rows*num_cols and arranges them in the 2d array in the
following way:
row_index = (random_number_generated / num_rows)
col_index = random_number_generated - row_index * num_cols
"""
_validate_csr_generation_inputs(num_rows, num_cols, density,
distribution="uniform")
try:
from scipy import sparse as spsp
csr = spsp.rand(num_rows, num_cols, density, dtype=dtype, format="csr")
if data_init is not None:
csr.data.fill(data_init)
if shuffle_csr_indices is True:
shuffle_csr_column_indices(csr)
result = mx.nd.sparse.csr_matrix((csr.data, csr.indices, csr.indptr),
shape=(num_rows, num_cols), dtype=dtype)
except ImportError:
assert(data_init is None), \
"data_init option is not supported when scipy is absent"
assert(not shuffle_csr_indices), \
"shuffle_csr_indices option is not supported when scipy is absent"
# scipy not available. try to generate one from a dense array
dns = mx.nd.random.uniform(shape=(num_rows, num_cols), dtype=dtype)
masked_dns = dns * (dns < density)
result = masked_dns.tostype('csr')
return result
def _get_powerlaw_dataset_csr(num_rows, num_cols, density=0.1, dtype=None):
"""Returns CSRNDArray with powerlaw distribution
with exponentially increasing number of non zeros in each row.
Not supported for cases where total_nnz < 2*num_rows. This is because
the algorithm first tries to ensure that there are rows with no zeros by
putting non zeros at beginning of each row.
"""
_validate_csr_generation_inputs(num_rows, num_cols, density,
distribution="powerlaw")
total_nnz = int(num_rows * num_cols * density)
unused_nnz = total_nnz
output_arr = np.zeros((num_rows, num_cols), dtype=dtype)
# Start with ones on each row so that no row is empty
for row in range(num_rows):
output_arr[row][0] = 1 + rnd.uniform(0.001, 2)
unused_nnz = unused_nnz - 1
if unused_nnz <= 0:
return mx.nd.array(output_arr).tostype("csr")
# Populate rest of matrix with 2^i items in ith row.
# if we have used all total nnz return the sparse matrix
# else if we reached max column size then fill up full columns until we use all nnz
col_max = 2
for row in range(num_rows):
col_limit = min(num_cols, col_max)
# In case col_limit reached assign same value to all elements, which is much faster
if col_limit == num_cols and unused_nnz > col_limit:
output_arr[row] = 1 + rnd.uniform(0.001, 2)
unused_nnz = unused_nnz - col_limit + 1
if unused_nnz <= 0:
return mx.nd.array(output_arr).tostype("csr")
else:
continue
for col_index in range(1, col_limit):
output_arr[row][col_index] = 1 + rnd.uniform(0.001, 2)
unused_nnz = unused_nnz - 1
if unused_nnz <= 0:
return mx.nd.array(output_arr).tostype("csr")
col_max = col_max * 2
if unused_nnz > 0:
raise ValueError("not supported for this density: %s"
" for this shape (%s,%s)" % (density, num_rows, num_cols))
else:
return mx.nd.array(output_arr).tostype("csr")
[docs]def assign_each(the_input, function):
"""Return ndarray composed of passing each array value through some function"""
if function is None:
output = np.array(the_input)
else:
it_input = np.nditer(the_input, flags=['f_index'])
output = np.zeros(the_input.shape)
it_out = np.nditer(output, flags=['f_index'], op_flags=['writeonly'])
while not it_input.finished:
val_input = it_input[0]
it_out[0] = function(val_input)
it_input.iternext()
it_out.iternext()
return output
[docs]def assign_each2(input1, input2, function):
"""Return ndarray composed of passing two array values through some function"""
if function is None:
output = np.array(input1)
else:
assert input1.shape == input2.shape
it_input1 = np.nditer(input1, flags=['f_index'])
it_input2 = np.nditer(input2, flags=['f_index'])
output = np.zeros(input1.shape)
it_out = np.nditer(output, flags=['f_index'], op_flags=['writeonly'])
while not it_input1.finished:
val_input1 = it_input1[0]
val_input2 = it_input2[0]
it_out[0] = function(val_input1, val_input2)
it_input1.iternext()
it_input2.iternext()
it_out.iternext()
return output
[docs]def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=None,
data_init=None, rsp_indices=None, modifier_func=None,
shuffle_csr_indices=False):
"""Generate a random sparse ndarray. Returns the ndarray, value(np) and indices(np)
Parameters
----------
shape: list or tuple
stype: str
valid values: "csr" or "row_sparse"
density: float, optional
should be between 0 and 1
distribution: str, optional
valid values: "uniform" or "powerlaw"
dtype: numpy.dtype, optional
default value is None
Returns
-------
Result of type CSRNDArray or RowSparseNDArray
Examples
--------
Below is an example of the powerlaw distribution with csr as the stype.
It calculates the nnz using the shape and density.
It fills up the ndarray with exponentially increasing number of elements.
If there are enough unused_nnzs, n+1th row will have twice more nnzs compared to nth row.
else, remaining unused_nnzs will be used in n+1th row
If number of cols is too small and we have already reached column size it will fill up
all following columns in all followings rows until we reach the required density.
>>> csr_arr, _ = rand_sparse_ndarray(shape=(5, 16), stype="csr",
density=0.50, distribution="powerlaw")
>>> indptr = csr_arr.indptr.asnumpy()
>>> indices = csr_arr.indices.asnumpy()
>>> data = csr_arr.data.asnumpy()
>>> row2nnz = len(data[indptr[1]:indptr[2]])
>>> row3nnz = len(data[indptr[2]:indptr[3]])
>>> assert(row3nnz == 2*row2nnz)
>>> row4nnz = len(data[indptr[3]:indptr[4]])
>>> assert(row4nnz == 2*row3nnz)
"""
density = rnd.rand() if density is None else density
dtype = default_dtype() if dtype is None else dtype
distribution = "uniform" if distribution is None else distribution
if stype == 'row_sparse':
assert (distribution == "uniform"), \
"Distribution %s not supported for row_sparse" % (distribution)
# sample index
if rsp_indices is not None:
indices = rsp_indices
assert(len(indices) <= shape[0])
else:
idx_sample = rnd.rand(shape[0])
indices = np.argwhere(idx_sample < density).flatten()
if indices.shape[0] == 0:
result = mx.nd.zeros(shape, stype='row_sparse', dtype=dtype)
return result, (np.array([], dtype=dtype), np.array([]))
# generate random values
val = rnd.rand(indices.shape[0], *shape[1:]).astype(dtype)
# Allow caller to override or adjust random values
if data_init is not None:
val.fill(data_init)
if modifier_func is not None:
val = assign_each(val, modifier_func)
arr = mx.nd.sparse.row_sparse_array((val, indices), shape=shape, dtype=dtype)
return arr, (val, indices)
elif stype == 'csr':
assert len(shape) == 2
if distribution == "uniform":
csr = _get_uniform_dataset_csr(shape[0], shape[1], density,
data_init=data_init,
shuffle_csr_indices=shuffle_csr_indices, dtype=dtype)
return csr, (csr.indptr, csr.indices, csr.data)
elif distribution == "powerlaw":
csr = _get_powerlaw_dataset_csr(shape[0], shape[1], density=density, dtype=dtype)
return csr, (csr.indptr, csr.indices, csr.data)
else:
assert(False), "Distribution not supported: %s" % (distribution)
return False
else:
assert(False), "unknown storage type"
return False
def rand_ndarray(shape, stype='default', density=None, dtype=None,
modifier_func=None, shuffle_csr_indices=False, distribution=None):
if stype == 'default':
arr = mx.nd.array(random_arrays(shape), dtype=dtype)
else:
arr, _ = rand_sparse_ndarray(shape, stype, density=density,
modifier_func=modifier_func, dtype=dtype,
shuffle_csr_indices=shuffle_csr_indices,
distribution=distribution)
return arr
[docs]def create_sparse_array(shape, stype, data_init=None, rsp_indices=None,
dtype=None, modifier_func=None, density=.5,
shuffle_csr_indices=False):
"""Create a sparse array, For Rsp, assure indices are in a canonical format"""
if stype == 'row_sparse':
if rsp_indices is not None:
arr_indices = np.asarray(rsp_indices)
arr_indices.sort()
else:
arr_indices = None
arr_data, (_, _) = rand_sparse_ndarray(shape, stype,
density=density,
data_init=data_init,
rsp_indices=arr_indices,
dtype=dtype,
modifier_func=modifier_func)
elif stype == 'csr':
arr_data, (_, _, _) = rand_sparse_ndarray(shape,
stype,
density=density,
data_init=data_init,
dtype=dtype,
modifier_func=modifier_func,
shuffle_csr_indices=shuffle_csr_indices)
else:
msg = "Unknown storage type: " + stype
raise AssertionError(msg)
return arr_data
[docs]def create_sparse_array_zd(shape, stype, density, data_init=None,
rsp_indices=None, dtype=None, modifier_func=None,
shuffle_csr_indices=False):
"""Create sparse array, using only rsp_indices to determine density"""
if stype == 'row_sparse':
density = 0.0
if rsp_indices is not None:
assert len(rsp_indices) <= shape[0]
return create_sparse_array(shape, stype,
data_init=data_init,
rsp_indices=rsp_indices,
dtype=dtype,
modifier_func=modifier_func,
density=density,
shuffle_csr_indices=shuffle_csr_indices)
def rand_shape_2d(dim0=10, dim1=10):
return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1)
def rand_shape_3d(dim0=10, dim1=10, dim2=10):
return rnd.randint(1, dim0 + 1), rnd.randint(1, dim1 + 1), rnd.randint(1, dim2 + 1)
def rand_shape_nd(num_dim, dim=10):
return tuple(rnd.randint(1, dim+1, size=num_dim))
[docs]def np_reduce(dat, axis, keepdims, numpy_reduce_func):
"""Compatible reduce for old version of NumPy.
Parameters
----------
dat : np.ndarray
Same as NumPy.
axis : None or int or list-like
Same as NumPy.
keepdims : bool
Same as NumPy.
numpy_reduce_func : function
A NumPy reducing function like ``np.sum`` or ``np.max``.
"""
if isinstance(axis, int):
axis = [axis]
else:
axis = list(axis) if axis is not None else range(len(dat.shape))
ret = dat
for i in reversed(sorted(axis)):
ret = numpy_reduce_func(ret, axis=i)
if keepdims:
keepdims_shape = list(dat.shape)
for i in axis:
keepdims_shape[i] = 1
ret = ret.reshape(tuple(keepdims_shape))
return ret
[docs]def find_max_violation(a, b, rtol=None, atol=None):
"""Finds and returns the location of maximum violation."""
rtol = get_rtol(rtol)
atol = get_atol(atol)
diff = np.abs(a-b)
tol = atol + rtol*np.abs(b)
violation = diff/(tol+1e-20)
loc = np.argmax(violation)
idx = np.unravel_index(loc, violation.shape)
return idx, np.max(violation)
[docs]def same(a, b):
"""Test if two NumPy arrays are the same.
Parameters
----------
a : np.ndarray
b : np.ndarray
"""
return np.array_equal(a, b)
[docs]def almost_equal(a, b, rtol=None, atol=None, equal_nan=False):
"""Test if two numpy arrays are almost equal."""
# pylint: disable=unexpected-keyword-arg
return np.allclose(a, b, rtol=get_rtol(rtol), atol=get_atol(atol), equal_nan=equal_nan)
# pylint: enable=unexpected-keyword-arg
[docs]def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=False):
"""Test that two numpy arrays are almost equal. Raise exception message if not.
Parameters
----------
a : np.ndarray
b : np.ndarray
threshold : None or float
The checking threshold. Default threshold will be used if set to ``None``.
"""
rtol = get_rtol(rtol)
atol = get_atol(atol)
if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
return
index, rel = find_max_violation(a, b, rtol, atol)
np.set_printoptions(threshold=4, suppress=True)
msg = npt.build_err_msg([a, b],
err_msg="Error %f exceeds tolerance rtol=%f, atol=%f. "
" Location of maximum error:%s, a=%f, b=%f"
% (rel, rtol, atol, str(index), a[index], b[index]),
names=names)
raise AssertionError(msg)
[docs]def almost_equal_ignore_nan(a, b, rtol=None, atol=None):
"""Test that two NumPy arrays are almost equal (ignoring NaN in either array).
Combines a relative and absolute measure of approximate eqality.
If either the relative or absolute check passes, the arrays are considered equal.
Including an absolute check resolves issues with the relative check where all
array values are close to zero.
Parameters
----------
a : np.ndarray
b : np.ndarray
rtol : None or float
The relative threshold. Default threshold will be used if set to ``None``.
atol : None or float
The absolute threshold. Default threshold will be used if set to ``None``.
"""
a = np.copy(a)
b = np.copy(b)
nan_mask = np.logical_or(np.isnan(a), np.isnan(b))
a[nan_mask] = 0
b[nan_mask] = 0
return almost_equal(a, b, rtol, atol)
[docs]def assert_almost_equal_ignore_nan(a, b, rtol=None, atol=None, names=('a', 'b')):
"""Test that two NumPy arrays are almost equal (ignoring NaN in either array).
Combines a relative and absolute measure of approximate eqality.
If either the relative or absolute check passes, the arrays are considered equal.
Including an absolute check resolves issues with the relative check where all
array values are close to zero.
Parameters
----------
a : np.ndarray
b : np.ndarray
rtol : None or float
The relative threshold. Default threshold will be used if set to ``None``.
atol : None or float
The absolute threshold. Default threshold will be used if set to ``None``.
"""
a = np.copy(a)
b = np.copy(b)
nan_mask = np.logical_or(np.isnan(a), np.isnan(b))
a[nan_mask] = 0
b[nan_mask] = 0
assert_almost_equal(a, b, rtol, atol, names)
[docs]def assert_exception(f, exception_type, *args, **kwargs):
"""Test that function f will throw an exception of type given by `exception_type`"""
try:
f(*args, **kwargs)
assert(False)
except exception_type:
return
[docs]def retry(n):
"""Retry n times before failing for stochastic test cases."""
assert n > 0
def decorate(f):
"""Decorate a test case."""
def wrapper(*args, **kwargs):
"""Wrapper for tests function."""
for _ in range(n):
try:
f(*args, **kwargs)
except AssertionError as e:
err = e
continue
return
raise err
return wrapper
return decorate
[docs]def simple_forward(sym, ctx=None, is_train=False, **inputs):
"""A simple forward function for a symbol.
Primarily used in doctest to test the functionality of a symbol.
Takes NumPy arrays as inputs and outputs are also converted to NumPy arrays.
Parameters
----------
ctx : Context
If ``None``, will take the default context.
inputs : keyword arguments
Mapping each input name to a NumPy array.
Returns
-------
The result as a numpy array. Multiple results will
be returned as a list of NumPy arrays.
"""
ctx = ctx or default_context()
inputs = {k: array(v) for k, v in inputs.items()}
exe = sym.bind(ctx, args=inputs)
exe.forward(is_train=is_train)
outputs = [x.asnumpy() for x in exe.outputs]
if len(outputs) == 1:
outputs = outputs[0]
return outputs
def _parse_location(sym, location, ctx, dtype=default_dtype()):
"""Parses the given location to a dictionary.
Arguments of the provided op `sym` are used as dictionary keys
and elements of `location` are used as values.
Parameters
----------
sym : Symbol
Symbol containing op
location : list or tuple or dict
Argument values location
- if type is list or tuple of `np.ndarray`
inner elements are arrays correspoding to
``sym.list_arguments()``.
- if type is dict of str -> `np.ndarray`
maps the name of arguments to the corresponding `np.ndarray`.
*In either case, value of all the arguments must be provided.*
ctx : Context
Device context.
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
Returns
-------
dict
Dictionary with `sym` arguments as keys and `location` elements as
values.
Examples
-------
>>> a = mx.symbol.Variable('a')
>>> b = mx.symbol.Variable('b')
>>> l1 = np.ndarray([2,3])
>>> l2 = np.ndarray([3,4])
>>> _parse_location(a * b, [l1, l2], None)
{'a': , 'b': }
>>> _parse_location(a * b, {'a': l1, 'b': l2}, None)
{'a': , 'b': }
>>> _parse_location(a * b, {'a': l1}, None)
ValueError: Symbol arguments and keys of the given location do not match.
"""
assert isinstance(location, (dict, list, tuple))
assert dtype in (np.float16, np.float32, np.float64)
if isinstance(location, dict):
if set(location.keys()) != set(sym.list_arguments()):
raise ValueError("Symbol arguments and keys of the given location do not match."
"symbol args:%s, location.keys():%s"
% (str(set(sym.list_arguments())), str(set(location.keys()))))
else:
location = {k: v for k, v in zip(sym.list_arguments(), location)}
location = {k: mx.nd.array(v, ctx=ctx, dtype=dtype) if isinstance(v, np.ndarray) \
else v for k, v in location.items()}
return location
def _parse_aux_states(sym, aux_states, ctx, dtype=default_dtype()):
"""Parses the given auxiliary states to a dictionary.
Auxiliary states of the provided op `sym` are used as dictionary
keys and elements of `aux_states` are used as values.
Parameters
----------
sym : Symbol
Symbol containing op
aux_states : None or list or dict
Aux states
- if type is list or tuple of `np.ndarray`
inner elements are arrays correspoding to
``sym.list_auxiliary_states()``.
- if type is dict of str -> `np.ndarray`
maps the name of arguments to the corresponding `np.ndarray`.
*In either case, all aux states of `sym` must be provided.*
ctx : Context
Device context.
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
Returns
-------
dict
Dictionary with `sym` aux states as keys and `aux_states` elements
as values.
Examples
-------
>>> data = mx.symbol.Variable('data')
>>> weight = mx.sym.Variable(name='fc1_weight')
>>> fc1 = mx.symbol.FullyConnected(data = data, weight=weight, name='fc1', num_hidden=128)
>>> fc2 = mx.symbol.BatchNorm(fc1, name='batchnorm0')
>>> mean_states = np.ones(3)
>>> var_states = np.ones(3)
>>> _parse_aux_states(fc2, [mean_states, var_states], None)
{'batchnorm0_moving_var': , 'batchnorm0_moving_mean': }
>>> _parse_aux_states(fc2, {'batchnorm0_moving_var': mean_states,
... 'batchnorm0_moving_mean': var_states}, None)
{'batchnorm0_moving_var': , 'batchnorm0_moving_mean': }
>>> _parse_aux_states(fc2, {'batchnorm0_moving_var': mean_states}, None)
ValueError: Symbol aux_states names and given aux_states do not match.
"""
assert dtype in (np.float16, np.float32, np.float64)
if aux_states is not None:
if isinstance(aux_states, dict):
if set(aux_states.keys()) != set(sym.list_auxiliary_states()):
raise ValueError("Symbol aux_states names and given aux_states do not match."
"symbol aux_names:%s, aux_states.keys:%s"
% (str(set(sym.list_auxiliary_states())),
str(set(aux_states.keys()))))
elif isinstance(aux_states, (list, tuple)):
aux_names = sym.list_auxiliary_states()
aux_states = {k:v for k, v in zip(aux_names, aux_states)}
aux_states = {k: mx.nd.array(v, ctx=ctx, dtype=dtype) for k, v in aux_states.items()}
return aux_states
[docs]def numeric_grad(executor, location, aux_states=None, eps=1e-4,
use_forward_train=True, dtype=default_dtype()):
"""Calculates a numeric gradient via finite difference method.
Class based on Theano's `theano.gradient.numeric_grad` [1]
Parameters
----------
executor : Executor
Executor that computes the forward pass.
location : list of numpy.ndarray or dict of str to numpy.ndarray
Argument values used as location to compute gradient
Maps the name of arguments to the corresponding numpy.ndarray.
Value of all the arguments must be provided.
aux_states : None or list of numpy.ndarray or dict of str to numpy.ndarray, optional
Auxiliary states values used as location to compute gradient
Maps the name of aux_states to the corresponding numpy.ndarray.
Value of all the auxiliary arguments must be provided.
eps : float, optional
Epsilon for the finite-difference method.
use_forward_train : bool, optional
Whether to use `is_train=True` in testing.
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
References
---------
..[1] https://github.com/Theano/Theano/blob/master/theano/gradient.py
"""
def as_stype(var, stype, dtype):
return mx.nd.cast_storage(mx.nd.array(var, dtype=dtype), stype=stype)
assert dtype in (np.float16, np.float32, np.float64)
approx_grads = {k: np.zeros(v.shape, dtype=dtype)
for k, v in location.items()}
for k, v in location.items():
stype = executor.arg_dict[k].stype
if stype == 'default':
executor.arg_dict[k][:] = as_stype(v, stype, dtype=dtype)
for k in location:
location[k] = np.asarray(location[k], order='C')
for k, v in location.items():
if v.dtype.kind != 'f':
continue
stype = executor.arg_dict[k].stype
old_value = v.copy()
for i in range(np.prod(v.shape)):
# inplace update
v.ravel()[i] += eps/2.0
executor.arg_dict[k][:] = as_stype(v, stype, dtype=dtype)
if aux_states is not None:
for key, val in aux_states.items():
executor.aux_dict[key][:] = val
executor.forward(is_train=use_forward_train)
f_peps = executor.outputs[0].asnumpy()
v.ravel()[i] -= eps
executor.arg_dict[k][:] = as_stype(v, stype, dtype=dtype)
if aux_states is not None:
for key, val in aux_states.items():
adstype = executor.aux_dict[key].stype
executor.aux_dict[key][:] = as_stype(val, adstype, dtype=dtype)
executor.forward(is_train=use_forward_train)
f_neps = executor.outputs[0].asnumpy()
approx_grad = (f_peps - f_neps).sum() / eps
approx_grads[k].ravel()[i] = approx_grad
v.ravel()[i] = old_value.ravel()[i]
# copy back the original value
executor.arg_dict[k][:] = as_stype(old_value, stype, dtype=dtype)
return approx_grads
[docs]def check_numeric_gradient(sym, location, aux_states=None, numeric_eps=1e-3, rtol=1e-2,
atol=None, grad_nodes=None, use_forward_train=True, ctx=None,
grad_stype_dict=None, dtype=default_dtype()):
"""Verify an operation by checking backward pass via finite difference method.
Based on Theano's `theano.gradient.verify_grad` [1]
Parameters
----------
sym : Symbol
Symbol containing op to test
location : list or tuple or dict
Argument values used as location to compute gradient
- if type is list of numpy.ndarray, \
inner elements should have the same order as mxnet.sym.list_arguments().
- if type is dict of str -> numpy.ndarray, \
maps the name of arguments to the corresponding numpy.ndarray.
*In either case, value of all the arguments must be provided.*
aux_states : list or tuple or dict, optional
The auxiliary states required when generating the executor for the symbol.
numeric_eps : float, optional
Delta for the finite difference method that approximates the gradient.
check_eps : float, optional
relative error eps used when comparing numeric grad to symbolic grad.
grad_nodes : None or list or tuple or dict, optional
Names of the nodes to check gradient on
use_forward_train : bool
Whether to use is_train=True when computing the finite-difference.
ctx : Context, optional
Check the gradient computation on the specified device.
grad_stype_dict : dict of str->str, optional
Storage type dictionary for gradient ndarrays.
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
References
---------
[1] https://github.com/Theano/Theano/blob/master/theano/gradient.py
"""
assert dtype in (np.float16, np.float32, np.float64)
# cannot use finite differences with small eps without high precision
if dtype in (np.float32, np.float16):
assert numeric_eps >= 1e-5
if ctx is None:
ctx = default_context()
def random_projection(shape):
"""Get a random weight matrix with not too small elements
Parameters
----------
shape : list or tuple
"""
# random_projection should not have elements too small,
# otherwise too much precision is lost in numerical gradient
plain = np.random.rand(*shape) + 0.1
return plain
location = _parse_location(sym=sym, location=location, ctx=ctx, dtype=dtype)
location_npy = {k:v.asnumpy() for k, v in location.items()}
aux_states = _parse_aux_states(sym=sym, aux_states=aux_states, ctx=ctx,
dtype=dtype)
if aux_states is not None:
aux_states_npy = {k: v.asnumpy() for k, v in aux_states.items()}
else:
aux_states_npy = None
if grad_nodes is None:
grad_nodes = sym.list_arguments()
grad_req = {k: 'write' for k in grad_nodes}
elif isinstance(grad_nodes, (list, tuple)):
grad_nodes = list(grad_nodes)
grad_req = {k: 'write' for k in grad_nodes}
elif isinstance(grad_nodes, dict):
grad_req = grad_nodes.copy()
grad_nodes = grad_nodes.keys()
else:
raise ValueError
input_shape = {k: v.shape for k, v in location.items()}
_, out_shape, _ = sym.infer_shape(**input_shape)
proj = mx.sym.Variable("__random_proj")
out = sym * proj
out = mx.sym.make_loss(out)
location = dict(list(location.items()) +
[("__random_proj", mx.nd.array(random_projection(out_shape[0]),
ctx=ctx, dtype=dtype))])
args_grad_npy = dict([(k, np.random.normal(0, 0.01, size=location[k].shape))
for k in grad_nodes]
+ [("__random_proj", np.random.normal(0, 0.01, size=out_shape[0]))])
args_grad = {k: mx.nd.array(v, ctx=ctx, dtype=dtype) for k, v in args_grad_npy.items()}
if grad_stype_dict is not None:
assert isinstance(grad_stype_dict, dict), "grad_stype_dict must be a dict"
for k, v in grad_stype_dict.items():
if k in args_grad and v in _STORAGE_TYPE_STR_TO_ID and v != 'default':
# create an uninitialized sparse ndarray for executor
# if the symbolic grad is expected to be zero, it should not be initialized at all
args_grad[k] = mx.nd.zeros(args_grad[k].shape, args_grad[k].context,
args_grad[k].dtype, v)
executor = out.bind(ctx, grad_req=grad_req,
args=location, args_grad=args_grad, aux_states=aux_states)
inps = executor.arg_arrays
if len(inps) != len(location):
raise ValueError("Executor arg_arrays and and location len do not match."
"Got %d inputs and %d locations"%(len(inps), len(location)))
assert len(executor.outputs) == 1
executor.forward(is_train=True)
executor.backward()
symbolic_grads = {k:executor.grad_dict[k].asnumpy() for k in grad_nodes}
numeric_gradients = numeric_grad(
executor, location_npy, aux_states_npy,
eps=numeric_eps, use_forward_train=use_forward_train, dtype=dtype)
for name in grad_nodes:
fd_grad = numeric_gradients[name]
orig_grad = args_grad_npy[name]
sym_grad = symbolic_grads[name]
if grad_req[name] == 'write':
assert_almost_equal(fd_grad, sym_grad, rtol, atol,
("NUMERICAL_%s"%name, "BACKWARD_%s"%name))
elif grad_req[name] == 'add':
assert_almost_equal(fd_grad, sym_grad - orig_grad, rtol, atol,
("NUMERICAL_%s"%name, "BACKWARD_%s"%name))
elif grad_req[name] == 'null':
assert_almost_equal(orig_grad, sym_grad, rtol, atol,
("NUMERICAL_%s"%name, "BACKWARD_%s"%name))
else:
raise ValueError("Invalid grad_req %s for argument %s"%(grad_req[name], name))
[docs]def check_symbolic_forward(sym, location, expected, rtol=1E-4, atol=None,
aux_states=None, ctx=None, equal_nan=False,
dtype=default_dtype()):
"""Compares a symbol's forward results with the expected ones.
Prints error messages if the forward results are not the same as the expected ones.
Parameters
---------
sym : Symbol
output symbol
location : list of np.ndarray or dict of str to np.ndarray
The evaluation point
- if type is list of np.ndarray
Contains all the numpy arrays corresponding to `sym.list_arguments()`.
- if type is dict of str to np.ndarray
Contains the mapping between argument names and their values.
expected : list of np.ndarray or dict of str to np.ndarray
The expected output value
- if type is list of np.ndarray
Contains arrays corresponding to exe.outputs.
- if type is dict of str to np.ndarray
Contains mapping between sym.list_output() and exe.outputs.
check_eps : float, optional
Relative error to check to.
aux_states : list of np.ndarray of dict, optional
- if type is list of np.ndarray
Contains all the NumPy arrays corresponding to sym.list_auxiliary_states
- if type is dict of str to np.ndarray
Contains the mapping between names of auxiliary states and their values.
ctx : Context, optional
running context
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
equal_nan: Boolean
if True, `nan` is a valid value for checking equivalency (ie `nan` == `nan`)
Example
-------
>>> shape = (2, 2)
>>> lhs = mx.symbol.Variable('lhs')
>>> rhs = mx.symbol.Variable('rhs')
>>> sym_dot = mx.symbol.dot(lhs, rhs)
>>> mat1 = np.array([[1, 2], [3, 4]])
>>> mat2 = np.array([[5, 6], [7, 8]])
>>> ret_expected = np.array([[19, 22], [43, 50]])
>>> check_symbolic_forward(sym_dot, [mat1, mat2], [ret_expected])
"""
assert dtype in (np.float16, np.float32, np.float64)
if ctx is None:
ctx = default_context()
location = _parse_location(sym=sym, location=location, ctx=ctx, dtype=dtype)
aux_states = _parse_aux_states(sym=sym, aux_states=aux_states, ctx=ctx,
dtype=dtype)
if isinstance(expected, dict):
expected = [expected[k] for k in sym.list_outputs()]
args_grad_data = {k:mx.nd.empty(v.shape, ctx=ctx, dtype=dtype) for k, v in location.items()}
executor = sym.bind(ctx=ctx, args=location, args_grad=args_grad_data, aux_states=aux_states)
for g in executor.grad_arrays:
g[:] = 0
executor.forward(is_train=False)
outputs = [x.asnumpy() for x in executor.outputs]
for output_name, expect, output in zip(sym.list_outputs(), expected, outputs):
assert_almost_equal(expect, output, rtol, atol,
("EXPECTED_%s"%output_name, "FORWARD_%s"%output_name),
equal_nan=equal_nan)
return executor.outputs
[docs]def check_symbolic_backward(sym, location, out_grads, expected, rtol=1e-5, atol=None,
aux_states=None, grad_req='write', ctx=None, grad_stypes=None,
equal_nan=False, dtype=default_dtype()):
"""Compares a symbol's backward results with the expected ones.
Prints error messages if the backward results are not the same as the expected results.
Parameters
---------
sym : Symbol
output symbol
location : list of np.ndarray or dict of str to np.ndarray
The evaluation point
- if type is list of np.ndarray
Contains all the NumPy arrays corresponding to ``mx.sym.list_arguments``.
- if type is dict of str to np.ndarray
Contains the mapping between argument names and their values.
out_grads : None or list of np.ndarray or dict of str to np.ndarray
NumPys arrays corresponding to sym.outputs for incomming gradient.
- if type is list of np.ndarray
Contains arrays corresponding to ``exe.outputs``.
- if type is dict of str to np.ndarray
contains mapping between mxnet.sym.list_output() and Executor.outputs
expected : list of np.ndarray or dict of str to np.ndarray
expected gradient values
- if type is list of np.ndarray
Contains arrays corresponding to exe.grad_arrays
- if type is dict of str to np.ndarray
Contains mapping between ``sym.list_arguments()`` and exe.outputs.
check_eps: float, optional
Relative error to check to.
aux_states : list of np.ndarray or dict of str to np.ndarray
grad_req : str or list of str or dict of str to str, optional
Gradient requirements. 'write', 'add' or 'null'.
ctx : Context, optional
Running context.
grad_stypes: dict of str->str
dictionary of mapping argument name to stype for the gradient
equal_nan: Boolean
if True, `nan` is a valid value for checking equivalency (ie `nan` == `nan`)
dtype: np.float16 or np.float32 or np.float64
Datatype for mx.nd.array.
Example
-------
>>> lhs = mx.symbol.Variable('lhs')
>>> rhs = mx.symbol.Variable('rhs')
>>> sym_add = mx.symbol.elemwise_add(lhs, rhs)
>>> mat1 = np.array([[1, 2], [3, 4]])
>>> mat2 = np.array([[5, 6], [7, 8]])
>>> grad1 = mx.nd.zeros(shape)
>>> grad2 = mx.nd.zeros(shape)
>>> exec_add = sym_add.bind(default_context(), args={'lhs': mat1, 'rhs': mat2},
... args_grad={'lhs': grad1, 'rhs': grad2}, grad_req={'lhs': 'write', 'rhs': 'write'})
>>> exec_add.forward(is_train=True)
>>> ograd = mx.nd.ones(shape)
>>> grad_expected = ograd.copy().asnumpy()
>>> check_symbolic_backward(sym_add, [mat1, mat2], [ograd], [grad_expected, grad_expected])
"""
assert dtype in (np.float16, np.float32, np.float64)
if ctx is None:
ctx = default_context()
location = _parse_location(sym=sym, location=location, ctx=ctx, dtype=dtype)
aux_states = _parse_aux_states(sym=sym, aux_states=aux_states, ctx=ctx,
dtype=dtype)
if isinstance(expected, (list, tuple)):
expected = {k:v for k, v in zip(sym.list_arguments(), expected)}
args_grad_npy = {k:np.random.normal(size=v.shape) for k, v in expected.items()}
args_grad_data = {}
for k, v in args_grad_npy.items():
nd = mx.nd.array(v, ctx=ctx, dtype=dtype)
if grad_stypes is not None and k in grad_stypes:
stype = grad_stypes[k]
if stype is not None and stype != 'default':
out = create_sparse_array(v.shape, stype, density=0.0)
else:
out = nd
args_grad_data[k] = out
else:
args_grad_data[k] = nd
if isinstance(grad_req, str):
grad_req = {k:grad_req for k in sym.list_arguments()}
elif isinstance(grad_req, (list, tuple)):
grad_req = {k:v for k, v in zip(sym.list_arguments(), grad_req)}
executor = sym.bind(ctx=ctx, args=location, args_grad=args_grad_data,
aux_states=aux_states, grad_req=grad_req)
executor.forward(is_train=True)
if isinstance(out_grads, (tuple, list)):
outg = list()
for arr in out_grads:
if isinstance(arr, np.ndarray):
outg.append(mx.nd.array(arr, ctx=ctx, dtype=dtype))
else:
outg.append(arr)
out_grads = outg
elif isinstance(out_grads, dict):
outg = dict()
for k, v in out_grads.items():
if isinstance(v, np.ndarray):
outg[k] = mx.nd.array(v, ctx=ctx, dtype=dtype)
else:
outg[k] = v
out_grads = outg
else:
assert out_grads is None
executor.backward(out_grads)
grads = {k: v.asnumpy() for k, v in args_grad_data.items()}
for name in expected:
if grad_req[name] == 'write':
assert_almost_equal(expected[name], grads[name], rtol, atol,
("EXPECTED_%s"%name, "BACKWARD_%s"%name),
equal_nan=equal_nan)
elif grad_req[name] == 'add':
assert_almost_equal(expected[name], grads[name] - args_grad_npy[name],
rtol, atol, ("EXPECTED_%s"%name, "BACKWARD_%s"%name),
equal_nan=equal_nan)
elif grad_req[name] == 'null':
assert_almost_equal(args_grad_npy[name], grads[name],
rtol, atol, ("EXPECTED_%s"%name, "BACKWARD_%s"%name),
equal_nan=equal_nan)
else:
raise ValueError("Invalid grad_req %s for argument %s"%(grad_req[name], name))
return args_grad_data
[docs]def check_speed(sym, location=None, ctx=None, N=20, grad_req=None, typ="whole",
**kwargs):
"""Check the running speed of a symbol.
Parameters
----------
sym : Symbol
Symbol to run the speed test.
location : none or dict of str to np.ndarray
Location to evaluate the inner executor.
ctx : Context
Running context.
N : int, optional
Repeat times.
grad_req : None or str or list of str or dict of str to str, optional
Gradient requirements.
typ : str, optional
"whole" or "forward"
- "whole"
Test the forward_backward speed.
- "forward"
Only test the forward speed.
"""
if ctx is None:
ctx = default_context()
if grad_req is None:
grad_req = 'write'
if location is None:
exe = sym.simple_bind(grad_req=grad_req, ctx=ctx, **kwargs)
location = {k: np.random.normal(size=arr.shape, scale=1.0) for k, arr in
exe.arg_dict.items()}
else:
assert isinstance(location, dict), "Expect dict, get \"location\"=%s" %str(location)
exe = sym.simple_bind(grad_req=grad_req, ctx=ctx,
**{k: v.shape for k, v in location.items()})
for name, iarr in location.items():
exe.arg_dict[name][:] = iarr.astype(exe.arg_dict[name].dtype)
if typ == "whole":
# Warm up
exe.forward(is_train=True)
exe.backward(out_grads=exe.outputs)
for output in exe.outputs:
output.wait_to_read()
# Test forward + backward
tic = time.time()
for _ in range(N):
exe.forward(is_train=True)
exe.backward(out_grads=exe.outputs)
mx.nd.waitall()
toc = time.time()
forward_backward_time = (toc - tic) * 1.0 / N
return forward_backward_time
elif typ == "forward":
# Warm up
exe.forward(is_train=False)
for output in exe.outputs:
output.wait_to_read()
# Test forward only
tic = time.time()
for _ in range(N):
exe.forward(is_train=False)
mx.nd.waitall()
toc = time.time()
forward_time = (toc - tic) * 1.0 / N
return forward_time
else:
raise ValueError('typ can only be "whole" or "forward".')
[docs]def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
arg_params=None, aux_params=None, tol=None,
raise_on_err=True, ground_truth=None, equal_nan=False,
use_uniform=False, rand_type=np.float64):
"""Check symbol gives the same output for different running context
Parameters
----------
sym : Symbol or list of Symbols
Symbol(s) to run the consistency test.
ctx_list : list
Running context. See example for more detail.
scale : float, optional
Standard deviation of the inner normal distribution. Used in initialization.
grad_req : str or list of str or dict of str to str
Gradient requirement.
use_unifrom: bool
Optional, When flag set to true,
random input data generated follows uniform distribution,
not normal distribution
rand_type: np.dtype
casts the randomly generated data to this type
Optional, when input data is passed via arg_params,
defaults to np.float64 (numpy float default)
Examples
--------
>>> # create the symbol
>>> sym = mx.sym.Convolution(num_filter=3, kernel=(3,3), name='conv')
>>> # initialize the running context
>>> ctx_list =\
[{'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float64}},\
{'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float32}},\
{'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float16}},\
{'ctx': mx.cpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float64}},\
{'ctx': mx.cpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float32}}]
>>> check_consistency(sym, ctx_list)
>>> sym = mx.sym.Concat(name='concat', num_args=2)
>>> ctx_list = \
[{'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\
'type_dict': {'concat_arg0': np.float64, 'concat_arg1': np.float64}},\
{'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\
'type_dict': {'concat_arg0': np.float32, 'concat_arg1': np.float32}},\
{'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\
'type_dict': {'concat_arg0': np.float16, 'concat_arg1': np.float16}},\
{'ctx': mx.cpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\
'type_dict': {'concat_arg0': np.float64, 'concat_arg1': np.float64}},\
{'ctx': mx.cpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\
'type_dict': {'concat_arg0': np.float32, 'concat_arg1': np.float32}}]
>>> check_consistency(sym, ctx_list)
"""
if tol is None:
tol = {np.dtype(np.float16): 1e-1,
np.dtype(np.float32): 1e-3,
np.dtype(np.float64): 1e-5,
np.dtype(np.uint8): 0,
np.dtype(np.int32): 0,
np.dtype(np.int64): 0}
elif isinstance(tol, numbers.Number):
tol = {np.dtype(np.float16): tol,
np.dtype(np.float32): tol,
np.dtype(np.float64): tol,
np.dtype(np.uint8): tol,
np.dtype(np.int32): tol,
np.dtype(np.int64): tol}
assert len(ctx_list) > 1
if isinstance(sym, Symbol):
sym = [sym]*len(ctx_list)
else:
assert len(sym) == len(ctx_list)
output_names = sym[0].list_outputs()
arg_names = sym[0].list_arguments()
exe_list = []
for s, ctx in zip(sym, ctx_list):
assert s.list_arguments() == arg_names
assert s.list_outputs() == output_names
exe_list.append(s.simple_bind(grad_req=grad_req, **ctx))
arg_params = {} if arg_params is None else arg_params
aux_params = {} if aux_params is None else aux_params
for n, arr in exe_list[0].arg_dict.items():
if n not in arg_params:
if use_uniform:
arg_params[n] = np.random.uniform(low=-0.92, high=0.92,
size=arr.shape).astype(rand_type)
else:
arg_params[n] = np.random.normal(size=arr.shape,
scale=scale).astype(rand_type)
for n, arr in exe_list[0].aux_dict.items():
if n not in aux_params:
aux_params[n] = 0
for exe in exe_list:
for name, arr in exe.arg_dict.items():
arr[:] = arg_params[name]
for name, arr in exe.aux_dict.items():
arr[:] = aux_params[name]
# We need to initialize the gradient arrays if it's add.
if (grad_req == "add"):
for arr in exe.grad_arrays:
arr[:] = np.zeros(arr.shape, dtype=arr.dtype)
dtypes = [np.dtype(exe.outputs[0].dtype) for exe in exe_list]
max_idx = np.argmax(dtypes)
gt = ground_truth
if gt is None:
gt = exe_list[max_idx].output_dict.copy()
if grad_req != 'null':
gt.update(exe_list[max_idx].grad_dict)
# test
for exe in exe_list:
exe.forward(is_train=False)
for i, exe in enumerate(exe_list):
if i == max_idx:
continue
for name, arr in zip(output_names, exe.outputs):
gtarr = gt[name].astype(dtypes[i]).asnumpy()
arr = arr.asnumpy()
try:
assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
equal_nan=equal_nan)
except AssertionError as e:
print('Predict Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
traceback.print_exc()
if raise_on_err:
raise e
else:
print(str(e))
# train
if grad_req != 'null':
for exe in exe_list:
exe.forward(is_train=True)
exe.backward(exe.outputs)
for i, exe in enumerate(exe_list):
if i == max_idx:
continue
curr = zip(output_names + arg_names, exe.outputs + exe.grad_arrays)
for name, arr in curr:
if gt[name] is None:
assert arr is None
continue
gtarr = gt[name].astype(dtypes[i]).asnumpy()
arr = arr.asnumpy()
try:
assert_almost_equal(arr, gtarr, rtol=tol[dtypes[i]], atol=tol[dtypes[i]],
equal_nan=equal_nan)
except AssertionError as e:
print('Train Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
traceback.print_exc()
if raise_on_err:
raise e
else:
print(str(e))
return gt
[docs]def list_gpus():
"""Return a list of GPUs
Returns
-------
list of int:
If there are n GPUs, then return a list [0,1,...,n-1]. Otherwise returns
[].
"""
re = ''
nvidia_smi = ['nvidia-smi', '/usr/bin/nvidia-smi', '/usr/local/nvidia/bin/nvidia-smi']
for cmd in nvidia_smi:
try:
re = subprocess.check_output([cmd, "-L"], universal_newlines=True)
except (subprocess.CalledProcessError, OSError):
pass
return range(len([i for i in re.split('\n') if 'GPU' in i]))
[docs]def download(url, fname=None, dirname=None, overwrite=False, retries=5):
"""Download an given URL
Parameters
----------
url : str
URL to download
fname : str, optional
filename of the downloaded file. If None, then will guess a filename
from url.
dirname : str, optional
output directory name. If None, then guess from fname or use the current
directory
overwrite : bool, optional
Default is false, which means skipping download if the local file
exists. If true, then download the url to overwrite the local file if
exists.
retries : integer, default 5
The number of times to attempt the download in case of failure or non 200 return codes
Returns
-------
str
The filename of the downloaded file
"""
assert retries >= 0, "Number of retries should be at least 0"
if fname is None:
fname = url.split('/')[-1]
if dirname is None:
dirname = os.path.dirname(fname)
else:
fname = os.path.join(dirname, fname)
if dirname != "":
if not os.path.exists(dirname):
try:
logging.info('create directory %s', dirname)
os.makedirs(dirname)
except OSError as exc:
if exc.errno != errno.EEXIST:
raise OSError('failed to create ' + dirname)
if not overwrite and os.path.exists(fname):
logging.info("%s exists, skipping download", fname)
return fname
while retries+1 > 0:
# Disable pyling too broad Exception
# pylint: disable=W0703
try:
r = requests.get(url, stream=True)
assert r.status_code == 200, "failed to open %s" % url
with open(fname, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
break
except Exception as e:
retries -= 1
if retries <= 0:
raise e
else:
print("download failed, retrying, {} attempt{} left"
.format(retries, 's' if retries > 1 else ''))
logging.info("downloaded %s into %s successfully", url, fname)
return fname
[docs]def get_mnist():
"""Download and load the MNIST dataset
Returns
-------
dict
A dict containing the data
"""
def read_data(label_url, image_url):
with gzip.open(mx.test_utils.download(label_url)) as flbl:
struct.unpack(">II", flbl.read(8))
label = np.frombuffer(flbl.read(), dtype=np.int8)
with gzip.open(mx.test_utils.download(image_url), 'rb') as fimg:
_, _, rows, cols = struct.unpack(">IIII", fimg.read(16))
image = np.frombuffer(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols)
image = image.reshape(image.shape[0], 1, 28, 28).astype(np.float32)/255
return (label, image)
# changed to mxnet.io for more stable hosting
# path = 'http://yann.lecun.com/exdb/mnist/'
path = 'http://data.mxnet.io/data/mnist/'
(train_lbl, train_img) = read_data(
path+'train-labels-idx1-ubyte.gz', path+'train-images-idx3-ubyte.gz')
(test_lbl, test_img) = read_data(
path+'t10k-labels-idx1-ubyte.gz', path+'t10k-images-idx3-ubyte.gz')
return {'train_data':train_img, 'train_label':train_lbl,
'test_data':test_img, 'test_label':test_lbl}
[docs]def get_mnist_pkl():
"""Downloads MNIST dataset as a pkl.gz into a directory in the current directory
with the name `data`
"""
if not os.path.isdir("data"):
os.makedirs('data')
if not os.path.exists('data/mnist.pkl.gz'):
download('http://deeplearning.net/data/mnist/mnist.pkl.gz',
dirname='data')
[docs]def get_mnist_ubyte():
"""Downloads ubyte version of the MNIST dataset into a directory in the current directory
with the name `data` and extracts all files in the zip archive to this directory.
"""
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train-images-idx3-ubyte')) or \
(not os.path.exists('data/train-labels-idx1-ubyte')) or \
(not os.path.exists('data/t10k-images-idx3-ubyte')) or \
(not os.path.exists('data/t10k-labels-idx1-ubyte')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
[docs]def get_cifar10():
"""Downloads CIFAR10 dataset into a directory in the current directory with the name `data`,
and then extracts all files into the directory `data/cifar`.
"""
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/cifar/train.rec')) or \
(not os.path.exists('data/cifar/test.rec')) or \
(not os.path.exists('data/cifar/train.lst')) or \
(not os.path.exists('data/cifar/test.lst')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
[docs]def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
"""Returns training and validation iterators for MNIST dataset
"""
get_mnist_ubyte()
flat = False if len(input_shape) == 3 else True
train_dataiter = mx.io.MNISTIter(
image="data/train-images-idx3-ubyte",
label="data/train-labels-idx1-ubyte",
input_shape=input_shape,
batch_size=batch_size,
shuffle=True,
flat=flat,
num_parts=num_parts,
part_index=part_index)
val_dataiter = mx.io.MNISTIter(
image="data/t10k-images-idx3-ubyte",
label="data/t10k-labels-idx1-ubyte",
input_shape=input_shape,
batch_size=batch_size,
flat=flat,
num_parts=num_parts,
part_index=part_index)
return (train_dataiter, val_dataiter)
[docs]def get_zip_data(data_dir, url, data_origin_name):
"""Download and extract zip data.
Parameters
----------
data_dir : str
Absolute or relative path of the directory name to store zip files
url : str
URL to download data from
data_origin_name : str
Name of the downloaded zip file
Examples
--------
>>> get_zip_data("data_dir",
"http://files.grouplens.org/datasets/movielens/ml-10m.zip",
"ml-10m.zip")
"""
data_origin_name = os.path.join(data_dir, data_origin_name)
if not os.path.exists(data_origin_name):
download(url, dirname=data_dir, overwrite=False)
zip_file = zipfile.ZipFile(data_origin_name)
zip_file.extractall(path=data_dir)
[docs]def get_bz2_data(data_dir, data_name, url, data_origin_name):
"""Download and extract bz2 data.
Parameters
----------
data_dir : str
Absolute or relative path of the directory name to store bz2 files
data_name : str
Name of the output file in which bz2 contents will be extracted
url : str
URL to download data from
data_origin_name : str
Name of the downloaded b2 file
Examples
--------
>>> get_bz2_data("data_dir", "kdda.t",
"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2",
"kdda.t.bz2")
"""
data_name = os.path.join(data_dir, data_name)
data_origin_name = os.path.join(data_dir, data_origin_name)
if not os.path.exists(data_name):
download(url, fname=data_origin_name, dirname=data_dir, overwrite=False)
bz_file = bz2.BZ2File(data_origin_name, 'rb')
with open(data_name, 'wb') as fout:
for line in bz_file:
fout.write(line)
bz_file.close()
os.remove(data_origin_name)
[docs]def set_env_var(key, val, default_val=""):
"""Set environment variable
Parameters
----------
key : str
Env var to set
val : str
New value assigned to the env var
default_val : str, optional
Default value returned if the env var doesn't exist
Returns
-------
str
The value of env var before it is set to the new value
"""
prev_val = os.environ.get(key, default_val)
os.environ[key] = val
return prev_val
[docs]def same_array(array1, array2):
"""Check whether two NDArrays sharing the same memory block
Parameters
----------
array1 : NDArray
First NDArray to be checked
array2 : NDArray
Second NDArray to be checked
Returns
-------
bool
Whether two NDArrays share the same memory
"""
array1[:] += 1
if not same(array1.asnumpy(), array2.asnumpy()):
array1[:] -= 1
return False
array1[:] -= 1
return same(array1.asnumpy(), array2.asnumpy())
@contextmanager
[docs]def discard_stderr():
"""
Discards error output of a routine if invoked as:
with discard_stderr():
...
"""
with open(os.devnull, 'w') as bit_bucket:
try:
stderr_fileno = sys.stderr.fileno()
old_stderr = os.dup(stderr_fileno)
try:
os.dup2(bit_bucket.fileno(), stderr_fileno)
yield
finally:
os.dup2(old_stderr, stderr_fileno)
except AttributeError:
# On some systems is stderr not a file descriptor but actually a virtual pipeline
# that can not be copied
yield
[docs]class DummyIter(mx.io.DataIter):
"""A dummy iterator that always returns the same batch of data
(the first data batch of the real data iter). This is usually used for speed testing.
Parameters
----------
real_iter: mx.io.DataIter
The real data iterator where the first batch of data comes from
"""
def __init__(self, real_iter):
super(DummyIter, self).__init__()
self.real_iter = real_iter
self.provide_data = real_iter.provide_data
self.provide_label = real_iter.provide_label
self.batch_size = real_iter.batch_size
self.the_batch = next(real_iter)
def __iter__(self):
return self
[docs] def next(self):
"""Get a data batch from iterator. The first data batch of real iter is always returned.
StopIteration will never be raised.
Returns
-------
DataBatch
The data of next batch.
"""
return self.the_batch
[docs]def gen_buckets_probs_with_ppf(ppf, nbuckets):
"""Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)
is specified.
Parameters
----------
ppf : function
The Quantile function that takes a probability and maps it back to a value.
It's the inverse of the cdf function
nbuckets : int
size of the buckets
Returns
-------
buckets : list of tuple
The generated buckets
probs : list
The generate probabilities
"""
assert nbuckets > 0
probs = [1.0 / nbuckets for _ in range(nbuckets)]
buckets = [(ppf(i / float(nbuckets)), ppf((i + 1) / float(nbuckets))) for i in range(nbuckets)]
return buckets, probs
[docs]def mean_check(generator, mu, sigma, nsamples=1000000):
"""Test the generator by matching the mean.
We test the sample mean by checking if it falls inside the range
(mu - 3 * sigma / sqrt(n), mu + 3 * sigma / sqrt(n))
References::
@incollection{goucher2009beautiful,
title={Beautiful Testing: Leading Professionals Reveal How They Improve Software},
author={Goucher, Adam and Riley, Tim},
year={2009},
chapter=10
}
Examples::
generator = lambda x: np.random.normal(0, 1.0, size=x)
mean_check_ret = mean_check(generator, 0, 1.0)
Parameters
----------
generator : function
The generator function. It's expected to generate N i.i.d samples by calling generator(N).
mu : float
sigma : float
nsamples : int
Returns
-------
ret : bool
Whether the mean test succeeds
"""
samples = np.array(generator(nsamples))
sample_mean = samples.mean()
ret = (sample_mean > mu - 3 * sigma / np.sqrt(nsamples)) and\
(sample_mean < mu + 3 * sigma / np.sqrt(nsamples))
return ret
[docs]def get_im2rec_path(home_env="MXNET_HOME"):
"""Get path to the im2rec.py tool
Parameters
----------
home_env : str
Env variable that holds the path to the MXNET folder
Returns
-------
str
The path to im2rec.py
"""
# Check first if the path to MXNET is passed as an env variable
if home_env in os.environ:
mxnet_path = os.environ[home_env]
else:
# Else use currently imported mxnet as reference
mxnet_path = os.path.dirname(mx.__file__)
# If MXNet was installed through pip, the location of im2rec.py
im2rec_path = os.path.join(mxnet_path, 'tools', 'im2rec.py')
if os.path.isfile(im2rec_path):
return im2rec_path
# If MXNet has been built locally
im2rec_path = os.path.join(mxnet_path, '..', '..', 'tools', 'im2rec.py')
if os.path.isfile(im2rec_path):
return im2rec_path
raise IOError('Could not find path to tools/im2rec.py')
[docs]def var_check(generator, sigma, nsamples=1000000):
"""Test the generator by matching the variance.
It will need a large number of samples and is not recommended to use
We test the sample variance by checking if it falls inside the range
(sigma^2 - 3 * sqrt(2 * sigma^4 / (n-1)), sigma^2 + 3 * sqrt(2 * sigma^4 / (n-1)))
References::
@incollection{goucher2009beautiful,
title={Beautiful Testing: Leading Professionals Reveal How They Improve Software},
author={Goucher, Adam and Riley, Tim},
year={2009},
chapter=10
}
Examples::
generator = lambda x: np.random.normal(0, 1.0, size=x)
var_check_ret = var_check(generator, 0, 1.0)
Parameters
----------
generator : function
The generator function. It's expected to generate N i.i.d samples by calling generator(N).
sigma : float
nsamples : int
Returns
-------
ret : bool
Whether the variance test succeeds
"""
samples = np.array(generator(nsamples))
sample_var = samples.var(ddof=1)
ret = (sample_var > sigma ** 2 - 3 * np.sqrt(2 * sigma ** 4 / (nsamples - 1))) and\
(sample_var < sigma ** 2 + 3 * np.sqrt(2 * sigma ** 4 / (nsamples - 1)))
return ret
[docs]def chi_square_check(generator, buckets, probs, nsamples=1000000):
"""Run the chi-square test for the generator. The generator can be both continuous and discrete.
If the generator is continuous, the buckets should contain tuples of (range_min, range_max) \
and the probs should be the corresponding ideal probability within the specific ranges. \
Otherwise, the buckets should be the possible output of the discrete distribution and the \
probs should be groud-truth probability.
Usually the user is required to specify the probs parameter.
After obtatining the p value, we could further use the standard p > 0.05 threshold to get \
the final result.
Examples::
buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.norm.ppf(x, 0, 1), 5)
generator = lambda x: np.random.normal(0, 1.0, size=x)
p = chi_square_check(generator=generator, buckets=buckets, probs=probs)
assert(p > 0.05)
Parameters
----------
generator: function
A function that is assumed to generate i.i.d samples from a specific distribution.
generator(N) should generate N random samples.
buckets: list of tuple or list of number
The buckets to run the chi-square the test. Make sure that the buckets cover
the whole range of the distribution. Also, the buckets must be in ascending order and have
no intersection
probs: list or tuple
The ground-truth probability of the random value fall in a specific bucket.
nsamples:int
The number of samples to generate for the testing
Returns
-------
p : float
p value that the generator has the expected distribution.
A higher value indicates a larger confidence
obs_freq : list
Observed frequency of buckets
expected_freq : list
The expected (ground-truth) frequency of the buckets
"""
if not ss:
raise ImportError("scipy is not available."
" Please check if the scipy python bindings are installed.")
assert isinstance(buckets, list)
samples = generator(nsamples)
assert len(probs) == len(buckets)
if isinstance(buckets[0], (list, tuple)):
# Check whether the buckets are valid and fill them into a npy array
continuous_dist = True
buckets_npy = np.zeros((len(buckets) * 2, ), dtype=np.float32)
for i, _ in enumerate(buckets):
assert(buckets[i][0] <= buckets[i][1])
if i < len(buckets) - 1:
assert(buckets[i][1] <= buckets[i + 1][0])
buckets_npy[i * 2] = buckets[i][0]
buckets_npy[i * 2 + 1] = buckets[i][1]
else:
continuous_dist = False
buckets_npy = np.array(buckets)
expected_freq = (nsamples * np.array(probs, dtype=np.float32)).astype(np.int32)
if continuous_dist:
sample_bucket_ids = np.searchsorted(buckets_npy, samples, side='right')
else:
sample_bucket_ids = samples
if continuous_dist:
sample_bucket_ids = sample_bucket_ids // 2
obs_freq = np.zeros(shape=len(buckets), dtype=np.int)
for i in range(len(buckets)):
obs_freq[i] = (sample_bucket_ids == i).sum()
_, p = ss.chisquare(f_obs=obs_freq, f_exp=expected_freq)
return p, obs_freq, expected_freq
[docs]def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, success_rate=0.15):
"""Verify whether the generator is correct using chi-square testing.
The test is repeated for "nrepeat" times and we check if the success rate is
above the threshold (25% by default).
Parameters
----------
generator: function
A function that is assumed to generate i.i.d samples from a specific distribution.
generator(N) should generate N random samples.
buckets: list of tuple or list of number
The buckets to run the chi-square the test. Make sure that the buckets cover
the whole range of the distribution. Also, the buckets must be in ascending order and
have no intersection
probs: list or tuple
The ground-truth probability of the random value fall in a specific bucket.
nsamples: int
The number of samples to generate for the testing
nrepeat: int
The times to repeat the test
success_rate: float
The desired success rate
Returns
-------
cs_ret_l: list
The p values of the chi-square test.
"""
cs_ret_l = []
obs_freq_l = []
expected_freq_l = []
for _ in range(nrepeat):
cs_ret, obs_freq, expected_freq = chi_square_check(generator=generator, buckets=buckets,
probs=probs, nsamples=nsamples)
cs_ret_l.append(cs_ret)
obs_freq_l.append(obs_freq)
expected_freq_l.append(expected_freq)
success_num = (np.array(cs_ret_l) > 0.05).sum()
if success_num < nrepeat * success_rate:
raise AssertionError("Generator test fails, Chi-square p=%s, obs_freq=%s, expected_freq=%s."
"\nbuckets=%s, probs=%s"
% (str(cs_ret_l), str(obs_freq_l), str(expected_freq_l),
str(buckets), str(probs)))
return cs_ret_l
[docs]def compare_ndarray_tuple(t1, t2, rtol=None, atol=None):
"""Compare ndarray tuple."""
if t1 is not None and t2 is not None:
if isinstance(t1, tuple):
for s1, s2 in zip(t1, t2):
compare_ndarray_tuple(s1, s2, rtol, atol)
else:
assert_almost_equal(t1.asnumpy(), t2.asnumpy(), rtol=rtol, atol=atol)
[docs]def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='default',
rtol=1e-4, atol=1e-5, compare_states=True):
"""Compare opt1 and opt2."""
if w_stype == 'default':
w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
w1 = w2.copyto(default_context())
elif w_stype == 'row_sparse' or w_stype == 'csr':
w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
w1 = w2.copyto(default_context()).tostype('default')
else:
raise Exception("type not supported yet")
if g_stype == 'default':
g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
g1 = g2.copyto(default_context())
elif g_stype == 'row_sparse' or g_stype == 'csr':
g2 = rand_ndarray(shape, g_stype, dtype=dtype)
g1 = g2.copyto(default_context()).tostype('default')
else:
raise Exception("type not supported yet")
state1 = opt1.create_state_multi_precision(0, w1)
state2 = opt2.create_state_multi_precision(0, w2)
if compare_states:
compare_ndarray_tuple(state1, state2)
opt1.update_multi_precision(0, w1, g1, state1)
opt2.update_multi_precision(0, w2, g2, state2)
if compare_states:
compare_ndarray_tuple(state1, state2, rtol=rtol, atol=atol)
assert_almost_equal(w1.asnumpy(), w2.asnumpy(), rtol=rtol, atol=atol)
[docs]class EnvManager(object):
"""Environment variable setter and unsetter via with idiom"""
def __init__(self, key, val):
self._key = key
self._next_val = val
self._prev_val = None
def __enter__(self):
self._prev_val = os.environ.get(self._key)
os.environ[self._key] = self._next_val
def __exit__(self, ptype, value, trace):
if self._prev_val:
os.environ[self._key] = self._prev_val
else:
del os.environ[self._key]
