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24,000 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | global_lppooling | def global_lppooling(attrs, inputs, proto_obj):
"""Performs global lp pooling on the input."""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
... | python | def global_lppooling(attrs, inputs, proto_obj):
"""Performs global lp pooling on the input."""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
... | [
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24,001 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | linalg_gemm | def linalg_gemm(attrs, inputs, proto_obj):
"""Performs general matrix multiplication and accumulation"""
trans_a = 0
trans_b = 0
alpha = 1
beta = 1
if 'transA' in attrs:
trans_a = attrs['transA']
if 'transB' in attrs:
trans_b = attrs['transB']
if 'alpha' in attrs:
... | python | def linalg_gemm(attrs, inputs, proto_obj):
"""Performs general matrix multiplication and accumulation"""
trans_a = 0
trans_b = 0
alpha = 1
beta = 1
if 'transA' in attrs:
trans_a = attrs['transA']
if 'transB' in attrs:
trans_b = attrs['transB']
if 'alpha' in attrs:
... | [
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24,002 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | local_response_norm | def local_response_norm(attrs, inputs, proto_obj):
"""Local Response Normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'bias': 'knorm',
'size' : 'nsize'})
return 'LRN', n... | python | def local_response_norm(attrs, inputs, proto_obj):
"""Local Response Normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'bias': 'knorm',
'size' : 'nsize'})
return 'LRN', n... | [
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24,003 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | dropout | def dropout(attrs, inputs, proto_obj):
"""Dropout Regularization."""
mode = 'training'
if 'is_test' in attrs and attrs['is_test'] == 0:
mode = 'always'
new_attrs = translation_utils._fix_attribute_names(attrs,
{'ratio': 'p'})
new_attrs =... | python | def dropout(attrs, inputs, proto_obj):
"""Dropout Regularization."""
mode = 'training'
if 'is_test' in attrs and attrs['is_test'] == 0:
mode = 'always'
new_attrs = translation_utils._fix_attribute_names(attrs,
{'ratio': 'p'})
new_attrs =... | [
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24,004 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reshape | def reshape(attrs, inputs, proto_obj):
"""Reshape the given array by the shape attribute."""
if len(inputs) == 1:
return 'reshape', attrs, inputs[0]
reshape_shape = list(proto_obj._params[inputs[1].name].asnumpy())
reshape_shape = [int(i) for i in reshape_shape]
new_attrs = {'shape': reshape... | python | def reshape(attrs, inputs, proto_obj):
"""Reshape the given array by the shape attribute."""
if len(inputs) == 1:
return 'reshape', attrs, inputs[0]
reshape_shape = list(proto_obj._params[inputs[1].name].asnumpy())
reshape_shape = [int(i) for i in reshape_shape]
new_attrs = {'shape': reshape... | [
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24,005 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | cast | def cast(attrs, inputs, proto_obj):
""" Cast input to a given dtype"""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
... | python | def cast(attrs, inputs, proto_obj):
""" Cast input to a given dtype"""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
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24,006 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | split | def split(attrs, inputs, proto_obj):
"""Splits an array along a particular axis into multiple sub-arrays."""
split_list = attrs.get('split') if 'split' in attrs else []
new_attrs = translation_utils._fix_attribute_names(attrs,
{'split' : 'num_outputs'})... | python | def split(attrs, inputs, proto_obj):
"""Splits an array along a particular axis into multiple sub-arrays."""
split_list = attrs.get('split') if 'split' in attrs else []
new_attrs = translation_utils._fix_attribute_names(attrs,
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24,007 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | _slice | def _slice(attrs, inputs, proto_obj):
"""Returns a slice of the input tensor along multiple axes."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis',
'ends' : 'end',
... | python | def _slice(attrs, inputs, proto_obj):
"""Returns a slice of the input tensor along multiple axes."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis',
'ends' : 'end',
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24,008 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | transpose | def transpose(attrs, inputs, proto_obj):
"""Transpose the input array."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'perm' : 'axes'})
return 'transpose', new_attrs, inputs | python | def transpose(attrs, inputs, proto_obj):
"""Transpose the input array."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'perm' : 'axes'})
return 'transpose', new_attrs, inputs | [
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24,009 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | squeeze | def squeeze(attrs, inputs, proto_obj):
"""Remove single-dimensional entries from the shape of a tensor."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis'})
return 'squeeze', new_attrs, inputs | python | def squeeze(attrs, inputs, proto_obj):
"""Remove single-dimensional entries from the shape of a tensor."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis'})
return 'squeeze', new_attrs, inputs | [
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24,010 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | unsqueeze | def unsqueeze(attrs, inputs, cls):
"""Inserts a new axis of size 1 into the array shape"""
# MXNet can only add one axis at a time.
mxnet_op = inputs[0]
for axis in attrs["axes"]:
mxnet_op = symbol.expand_dims(mxnet_op, axis=axis)
return mxnet_op, attrs, inputs | python | def unsqueeze(attrs, inputs, cls):
"""Inserts a new axis of size 1 into the array shape"""
# MXNet can only add one axis at a time.
mxnet_op = inputs[0]
for axis in attrs["axes"]:
mxnet_op = symbol.expand_dims(mxnet_op, axis=axis)
return mxnet_op, attrs, inputs | [
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24,011 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | flatten | def flatten(attrs, inputs, proto_obj):
"""Flattens the input array into a 2-D array by collapsing the higher dimensions."""
#Mxnet does not have axis support. By default uses axis=1
if 'axis' in attrs and attrs['axis'] != 1:
raise RuntimeError("Flatten operator only supports axis=1")
new_attrs =... | python | def flatten(attrs, inputs, proto_obj):
"""Flattens the input array into a 2-D array by collapsing the higher dimensions."""
#Mxnet does not have axis support. By default uses axis=1
if 'axis' in attrs and attrs['axis'] != 1:
raise RuntimeError("Flatten operator only supports axis=1")
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24,012 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_max | def reduce_max(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by maximum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'max', new_attrs, inputs | python | def reduce_max(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by maximum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'max', new_attrs, inputs | [
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24,013 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_mean | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | python | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs | [
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24,014 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_min | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | python | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs | [
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24,015 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_sum | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | python | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs | [
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24,016 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_prod | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | python | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs | [
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24,017 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_log_sum | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
... | python | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
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24,018 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_log_sum_exp | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep... | python | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
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24,019 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_sum_square | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | python | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs | [
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24,020 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_l1 | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'ord' : 1})
return 'n... | python | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
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24,021 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | reduce_l2 | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | python | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs | [
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24,022 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | max_roi_pooling | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
... | python | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
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24,023 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | depthtospace | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | python | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs | [
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24,024 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | spacetodepth | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | python | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs | [
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24,025 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | hardmax | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_... | python | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
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24,026 | apache/incubator-mxnet | ci/docker/qemu/runtime_functions.py | run_ut_python3_qemu_internal | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt") ... | python | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt") ... | [
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24,027 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _new_empty_handle | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | python | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl | [
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24,028 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _new_alloc_handle | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(... | python | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(... | [
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24,029 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _get_indexing_dispatch_code | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
... | python | def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
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24,030 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _get_oshape_of_gather_nd_op | def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
oshape.extend(... | python | def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
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24,031 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _get_dim_size | def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1)... | python | def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1)... | [
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24,032 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _get_broadcast_shape | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
... | python | def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
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24,033 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | ones | def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Def... | python | def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
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24,034 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | moveaxis | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original... | python | def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
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24,035 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | _ufunc_helper | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.... | python | def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
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24,036 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | modulo | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcasta... | python | def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
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24,037 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | power | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them h... | python | def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them h... | [
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24,038 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | maximum | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a com... | python | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a com... | [
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24,039 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | minimum | def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a com... | python | def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
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24,040 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | concatenate | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
T... | python | def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
T... | [
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24,041 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | imdecode | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
... | python | def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
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24,042 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | zeros | def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current d... | python | def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
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24,043 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | eye | def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
I... | python | def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
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Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
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24,044 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | to_dlpack_for_read | def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedT... | python | def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
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24,045 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | from_dlpack | def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
... | python | def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
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24,046 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | from_numpy | def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c... | python | def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
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24,047 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray._prepare_value_nd | def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape."""
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtyp... | python | def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape."""
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtyp... | [
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24,048 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.broadcast_to | def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, ... | python | def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
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24,049 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.shape | def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
"""
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)... | python | def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
"""
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)... | [
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24,050 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.context | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu... | python | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu... | [
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24,051 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.dtype | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int... | python | def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int... | [
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24,052 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.asnumpy | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., ... | python | def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
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>>> y
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24,053 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.astype | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
... | python | def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
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24,054 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.as_in_context | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
... | python | def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
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24,055 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.attach_grad | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'wr... | python | def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
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24,056 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.grad | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | python | def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) | [
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24,057 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.detach | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | python | def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) | [
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24,058 | apache/incubator-mxnet | python/mxnet/ndarray/ndarray.py | NDArray.backward | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to re... | python | def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
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24,059 | apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.build | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
... | python | def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
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24,060 | apache/incubator-mxnet | example/gluon/lipnet/utils/align.py | Align.word_frame_pos | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) | python | def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
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24,061 | apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.prepare_sparse_params | def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
if ... | python | def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
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24,062 | apache/incubator-mxnet | example/rnn/large_word_lm/custom_module.py | CustomModule.rescale_grad | def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param... | python | def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param... | [
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24,063 | apache/incubator-mxnet | python/mxnet/symbol_doc.py | _build_doc | def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_v... | python | def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
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24,064 | apache/incubator-mxnet | python/mxnet/symbol_doc.py | SymbolDoc.get_output_shape | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | python | def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) | [
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24,065 | apache/incubator-mxnet | python/mxnet/context.py | num_gpus | def num_gpus():
"""Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
"""
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value | python | def num_gpus():
"""Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
"""
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value | [
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Will raise an exception on any CUDA error.
Returns
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24,066 | apache/incubator-mxnet | python/mxnet/context.py | gpu_memory_info | def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
(free, total) : ... | python | def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
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24,067 | apache/incubator-mxnet | python/mxnet/context.py | current_context | def current_context():
"""Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
Examples
-------
>>> mx.current_context()
cpu(0)
>>> with... | python | def current_context():
"""Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
Examples
-------
>>> mx.current_context()
cpu(0)
>>> with... | [
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24,068 | apache/incubator-mxnet | python/setup.py | config_cython | def config_cython():
"""Try to configure cython and return cython configuration"""
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
try:
... | python | def config_cython():
"""Try to configure cython and return cython configuration"""
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
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24,069 | apache/incubator-mxnet | python/mxnet/_ctypes/symbol.py | SymbolBase._compose | def _compose(self, *args, **kwargs):
"""Compose symbol on inputs.
This call mutates the current symbol.
Parameters
----------
args:
provide positional arguments
kwargs:
provide keyword arguments
Returns
-------
the resul... | python | def _compose(self, *args, **kwargs):
"""Compose symbol on inputs.
This call mutates the current symbol.
Parameters
----------
args:
provide positional arguments
kwargs:
provide keyword arguments
Returns
-------
the resul... | [
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24,070 | apache/incubator-mxnet | python/mxnet/_ctypes/symbol.py | SymbolBase._set_attr | def _set_attr(self, **kwargs):
"""Set the attribute of the symbol.
Parameters
----------
**kwargs
The attributes to set
"""
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))... | python | def _set_attr(self, **kwargs):
"""Set the attribute of the symbol.
Parameters
----------
**kwargs
The attributes to set
"""
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))... | [
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24,071 | apache/incubator-mxnet | example/ssd/symbol/symbol_factory.py | get_symbol_train | def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
"""
if ne... | python | def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
"""
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name for the base network symbol
data_shape : int
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kwargs : dict
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24,072 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._set_trainer | def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set th... | python | def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
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24,073 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._get_row_sparse | def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id... | python | def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id... | [
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24,074 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._finish_deferred_init | def _finish_deferred_init(self):
"""Finishes deferred initialization."""
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return
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self._deferred_init = ()
assert self.shape is not None and np.prod(self.shape) > 0, \
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"""Finishes deferred initialization."""
if not self._deferred_init:
return
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24,075 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._init_impl | def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
while len(dev_list) <= ctx.device_id:
de... | python | def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
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24,076 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._init_grad | def _init_grad(self):
"""Initialize grad buffers."""
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24,077 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter._reduce | def _reduce(self):
"""Reduce data from multiple context to cpu."""
ctx = context.cpu()
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block = self.list_data()
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else:
# fetch all rows for 'row_sparse' para... | python | def _reduce(self):
"""Reduce data from multiple context to cpu."""
ctx = context.cpu()
if self._stype == 'default':
block = self.list_data()
data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
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24,078 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.reset_ctx | def reset_ctx(self, ctx):
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Parameters
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ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
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24,079 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.set_data | def set_data(self, data):
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"""Sets this parameter's value on all contexts."""
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24,080 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.row_sparse_data | def row_sparse_data(self, row_id):
"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
Parameters
----------
row_... | python | def row_sparse_data(self, row_id):
"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
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24,081 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_row_sparse_data | def list_row_sparse_data(self, row_id):
"""Returns copies of the 'row_sparse' parameter on all contexts, in the same order
as creation. The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized before.
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24,082 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.grad | def grad(self, ctx=None):
"""Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
"""
if self._data is not None and self._grad is None:
raise RuntimeError(
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"""Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
"""
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24,083 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.list_ctx | def list_ctx(self):
"""Returns a list of contexts this parameter is initialized on."""
if self._data is None:
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return self._deferred_init[1]
raise RuntimeError("Parameter '%s' has not been initialized"%self.name)
return self._ctx_lis... | python | def list_ctx(self):
"""Returns a list of contexts this parameter is initialized on."""
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raise RuntimeError("Parameter '%s' has not been initialized"%self.name)
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24,084 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.zero_grad | def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
parameter is uninitialized or doesn't require gradient."""
if self._grad is None:
return
for i in self._grad:
ndarray.zeros_like(i, out=i) | python | def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
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return
for i in self._grad:
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24,085 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.var | def var(self):
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init=self.init, sty... | python | def var(self):
"""Returns a symbol representing this parameter."""
if self._var is None:
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24,086 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | Parameter.cast | def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self.dtype = dtype
if self._data is None:
return
with autograd.pause():
... | python | def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self.dtype = dtype
if self._data is None:
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Parameters
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dtype : str or numpy.dtype
The new data type. | [
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24,087 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.update | def update(self, other):
"""Copies all Parameters in ``other`` to self."""
for k, v in other.items():
if k in self._params:
assert self._params[k] is v, \
"Cannot update self with other because they have different " \
"Parameters with t... | python | def update(self, other):
"""Copies all Parameters in ``other`` to self."""
for k, v in other.items():
if k in self._params:
assert self._params[k] is v, \
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24,088 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.setattr | def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
model.collect_params().setattr('grad_req', 'null')
or change the learning rate multiplier::
... | python | def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
model.collect_params().setattr('grad_req', 'null')
or change the learning rate multiplier::
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24,089 | apache/incubator-mxnet | python/mxnet/gluon/parameter.py | ParameterDict.save | def save(self, filename, strip_prefix=''):
"""Save parameters to file.
Parameters
----------
filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving.
"""
arg_dict = {}
f... | python | def save(self, filename, strip_prefix=''):
"""Save parameters to file.
Parameters
----------
filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving.
"""
arg_dict = {}
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24,090 | apache/incubator-mxnet | python/mxnet/torch.py | _init_torch_module | def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module."""
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modul... | python | def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module."""
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modul... | [
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24,091 | apache/incubator-mxnet | python/mxnet/recordio.py | pack | def pack(header, s):
"""Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
Returns
-------
s ... | python | def pack(header, s):
"""Pack a string into MXImageRecord.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
s : str
Raw image string to be packed.
Returns
-------
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24,092 | apache/incubator-mxnet | python/mxnet/recordio.py | unpack | def unpack(s):
"""Unpack a MXImageRecord to string.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
Returns
-------
header : IRHeader
Header of the image record.
s : str
Unpacked string.
Examples
--------
>>> record = mx.record... | python | def unpack(s):
"""Unpack a MXImageRecord to string.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
Returns
-------
header : IRHeader
Header of the image record.
s : str
Unpacked string.
Examples
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24,093 | apache/incubator-mxnet | python/mxnet/recordio.py | unpack_img | def unpack_img(s, iscolor=-1):
"""Unpack a MXImageRecord to image.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
iscolor : int
Image format option for ``cv2.imdecode``.
Returns
-------
header : IRHeader
Header of the image record.
img... | python | def unpack_img(s, iscolor=-1):
"""Unpack a MXImageRecord to image.
Parameters
----------
s : str
String buffer from ``MXRecordIO.read``.
iscolor : int
Image format option for ``cv2.imdecode``.
Returns
-------
header : IRHeader
Header of the image record.
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24,094 | apache/incubator-mxnet | python/mxnet/recordio.py | pack_img | def pack_img(header, img, quality=95, img_fmt='.jpg'):
"""Pack an image into ``MXImageRecord``.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
img : numpy.ndarray
Image to be ... | python | def pack_img(header, img, quality=95, img_fmt='.jpg'):
"""Pack an image into ``MXImageRecord``.
Parameters
----------
header : IRHeader
Header of the image record.
``header.label`` can be a number or an array. See more detail in ``IRHeader``.
img : numpy.ndarray
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24,095 | apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.open | def open(self):
"""Opens the record file."""
if self.flag == "w":
check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle)))
self.writable = True
elif self.flag == "r":
check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.byref(self.handle... | python | def open(self):
"""Opens the record file."""
if self.flag == "w":
check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle)))
self.writable = True
elif self.flag == "r":
check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.byref(self.handle... | [
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24,096 | apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO._check_pid | def _check_pid(self, allow_reset=False):
"""Check process id to ensure integrity, reset if in new process."""
if not self.pid == current_process().pid:
if allow_reset:
self.reset()
else:
raise RuntimeError("Forbidden operation in multiple processes... | python | def _check_pid(self, allow_reset=False):
"""Check process id to ensure integrity, reset if in new process."""
if not self.pid == current_process().pid:
if allow_reset:
self.reset()
else:
raise RuntimeError("Forbidden operation in multiple processes... | [
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24,097 | apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.write | def write(self, buf):
"""Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : ... | python | def write(self, buf):
"""Inserts a string buffer as a record.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'w')
>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
Parameters
----------
buf : ... | [
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>>> for i in range(5):
... record.write('record_%d'%i)
>>> record.close()
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24,098 | apache/incubator-mxnet | python/mxnet/recordio.py | MXRecordIO.read | def read(self):
"""Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
... print(item)
record_0
record_1
record_2
record_3
... | python | def read(self):
"""Returns record as a string.
Examples
---------
>>> record = mx.recordio.MXRecordIO('tmp.rec', 'r')
>>> for i in range(5):
... item = record.read()
... print(item)
record_0
record_1
record_2
record_3
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24,099 | apache/incubator-mxnet | python/mxnet/recordio.py | MXIndexedRecordIO.seek | def seek(self, idx):
"""Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
reader pointer position. It doesn't return anything."""
assert not self.writable
self._check_pid(allow_reset=True)
pos = ctypes.c_siz... | python | def seek(self, idx):
"""Sets the current read pointer position.
This function is internally called by `read_idx(idx)` to find the current
reader pointer position. It doesn't return anything."""
assert not self.writable
self._check_pid(allow_reset=True)
pos = ctypes.c_siz... | [
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