partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | get_squeezenet | r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper.
SqueezeNet 1.1 model from the `official SqueezeNet repo
<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Parameters
----------
version : str
Version of squeezenet. Options are '1.0', '1.1'.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters. | python/mxnet/gluon/model_zoo/vision/squeezenet.py | def get_squeezenet(version, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper.
SqueezeNet 1.1 model from the `official SqueezeNet repo
<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Parameters
----------
version : str
Version of squeezenet. Options are '1.0', '1.1'.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
net = SqueezeNet(version, **kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_parameters(get_model_file('squeezenet%s'%version, root=root), ctx=ctx)
return net | def get_squeezenet(version, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""SqueezeNet model from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
and <0.5MB model size" <https://arxiv.org/abs/1602.07360>`_ paper.
SqueezeNet 1.1 model from the `official SqueezeNet repo
<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
than SqueezeNet 1.0, without sacrificing accuracy.
Parameters
----------
version : str
Version of squeezenet. Options are '1.0', '1.1'.
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
net = SqueezeNet(version, **kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_parameters(get_model_file('squeezenet%s'%version, root=root), ctx=ctx)
return net | [
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train | parse_helper | Helper function to parse operator attributes in required format. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def parse_helper(attrs, attrs_name, alt_value=None):
"""Helper function to parse operator attributes in required format."""
tuple_re = re.compile('\([0-9L|,| ]+\)')
if not attrs:
return alt_value
attrs_str = None if attrs.get(attrs_name) is None else str(attrs.get(attrs_name))
if attrs_str is None:
return alt_value
attrs_match = tuple_re.search(attrs_str)
if attrs_match is not None:
if attrs_match.span() == (0, len(attrs_str)):
dims = eval(attrs_str)
return dims
else:
raise AttributeError("Malformed %s dimensions: %s" % (attrs_name, str(attrs_str)))
return alt_value | def parse_helper(attrs, attrs_name, alt_value=None):
"""Helper function to parse operator attributes in required format."""
tuple_re = re.compile('\([0-9L|,| ]+\)')
if not attrs:
return alt_value
attrs_str = None if attrs.get(attrs_name) is None else str(attrs.get(attrs_name))
if attrs_str is None:
return alt_value
attrs_match = tuple_re.search(attrs_str)
if attrs_match is not None:
if attrs_match.span() == (0, len(attrs_str)):
dims = eval(attrs_str)
return dims
else:
raise AttributeError("Malformed %s dimensions: %s" % (attrs_name, str(attrs_str)))
return alt_value | [
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train | transform_padding | Helper function to convert padding format for pad operator. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def transform_padding(pad_width):
"""Helper function to convert padding format for pad operator.
"""
num_pad_values = len(pad_width)
onnx_pad_width = [0]*num_pad_values
start_index = 0
# num_pad_values will always be multiple of 2
end_index = int(num_pad_values/2)
for idx in range(0, num_pad_values):
if idx % 2 == 0:
onnx_pad_width[start_index] = pad_width[idx]
start_index += 1
else:
onnx_pad_width[end_index] = pad_width[idx]
end_index += 1
return onnx_pad_width | def transform_padding(pad_width):
"""Helper function to convert padding format for pad operator.
"""
num_pad_values = len(pad_width)
onnx_pad_width = [0]*num_pad_values
start_index = 0
# num_pad_values will always be multiple of 2
end_index = int(num_pad_values/2)
for idx in range(0, num_pad_values):
if idx % 2 == 0:
onnx_pad_width[start_index] = pad_width[idx]
start_index += 1
else:
onnx_pad_width[end_index] = pad_width[idx]
end_index += 1
return onnx_pad_width | [
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train | convert_string_to_list | Helper function to convert string to list.
Used to convert shape attribute string to list format. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_string_to_list(string_val):
"""Helper function to convert string to list.
Used to convert shape attribute string to list format.
"""
result_list = []
list_string = string_val.split(',')
for val in list_string:
val = str(val.strip())
val = val.replace("(", "")
val = val.replace(")", "")
val = val.replace("L", "")
val = val.replace("[", "")
val = val.replace("]", "")
if val not in ("", "None"):
result_list.append(int(val))
return result_list | def convert_string_to_list(string_val):
"""Helper function to convert string to list.
Used to convert shape attribute string to list format.
"""
result_list = []
list_string = string_val.split(',')
for val in list_string:
val = str(val.strip())
val = val.replace("(", "")
val = val.replace(")", "")
val = val.replace("L", "")
val = val.replace("[", "")
val = val.replace("]", "")
if val not in ("", "None"):
result_list.append(int(val))
return result_list | [
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train | get_inputs | Helper function to get inputs | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def get_inputs(node, kwargs):
"""Helper function to get inputs"""
name = node["name"]
proc_nodes = kwargs["proc_nodes"]
index_lookup = kwargs["index_lookup"]
inputs = node["inputs"]
attrs = node.get("attrs", {})
input_nodes = []
for ip in inputs:
input_node_id = index_lookup[ip[0]]
input_nodes.append(proc_nodes[input_node_id].name)
return name, input_nodes, attrs | def get_inputs(node, kwargs):
"""Helper function to get inputs"""
name = node["name"]
proc_nodes = kwargs["proc_nodes"]
index_lookup = kwargs["index_lookup"]
inputs = node["inputs"]
attrs = node.get("attrs", {})
input_nodes = []
for ip in inputs:
input_node_id = index_lookup[ip[0]]
input_nodes.append(proc_nodes[input_node_id].name)
return name, input_nodes, attrs | [
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train | create_basic_op_node | Helper function to create a basic operator
node that doesn't contain op specific attrs | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def create_basic_op_node(op_name, node, kwargs):
"""Helper function to create a basic operator
node that doesn't contain op specific attrs"""
name, input_nodes, _ = get_inputs(node, kwargs)
node = onnx.helper.make_node(
op_name,
input_nodes,
[name],
name=name
)
return [node] | def create_basic_op_node(op_name, node, kwargs):
"""Helper function to create a basic operator
node that doesn't contain op specific attrs"""
name, input_nodes, _ = get_inputs(node, kwargs)
node = onnx.helper.make_node(
op_name,
input_nodes,
[name],
name=name
)
return [node] | [
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train | convert_weights_and_inputs | Helper function to convert weights and inputs. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_weights_and_inputs(node, **kwargs):
"""Helper function to convert weights and inputs.
"""
name, _, _ = get_inputs(node, kwargs)
if kwargs["is_input"] is False:
weights = kwargs["weights"]
initializer = kwargs["initializer"]
np_arr = weights[name]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
dims = np.shape(np_arr)
tensor_node = onnx.helper.make_tensor_value_info(name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=name,
data_type=data_type,
dims=dims,
vals=np_arr.flatten().tolist(),
raw=False,
)
)
return [tensor_node]
else:
tval_node = onnx.helper.make_tensor_value_info(name, kwargs["in_type"], kwargs["in_shape"])
return [tval_node] | def convert_weights_and_inputs(node, **kwargs):
"""Helper function to convert weights and inputs.
"""
name, _, _ = get_inputs(node, kwargs)
if kwargs["is_input"] is False:
weights = kwargs["weights"]
initializer = kwargs["initializer"]
np_arr = weights[name]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
dims = np.shape(np_arr)
tensor_node = onnx.helper.make_tensor_value_info(name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=name,
data_type=data_type,
dims=dims,
vals=np_arr.flatten().tolist(),
raw=False,
)
)
return [tensor_node]
else:
tval_node = onnx.helper.make_tensor_value_info(name, kwargs["in_type"], kwargs["in_shape"])
return [tval_node] | [
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train | convert_convolution | Map MXNet's convolution operator attributes to onnx's Conv operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_convolution(node, **kwargs):
"""Map MXNet's convolution operator attributes to onnx's Conv operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "dilate", [1, 1]))
pad_dims = pad_dims + pad_dims
conv_node = onnx.helper.make_node(
"Conv",
inputs=input_nodes,
outputs=[name],
kernel_shape=kernel_dims,
strides=stride_dims,
dilations=dilations,
pads=pad_dims,
group=num_group,
name=name
)
return [conv_node] | def convert_convolution(node, **kwargs):
"""Map MXNet's convolution operator attributes to onnx's Conv operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "dilate", [1, 1]))
pad_dims = pad_dims + pad_dims
conv_node = onnx.helper.make_node(
"Conv",
inputs=input_nodes,
outputs=[name],
kernel_shape=kernel_dims,
strides=stride_dims,
dilations=dilations,
pads=pad_dims,
group=num_group,
name=name
)
return [conv_node] | [
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train | convert_deconvolution | Map MXNet's deconvolution operator attributes to onnx's ConvTranspose operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_deconvolution(node, **kwargs):
"""Map MXNet's deconvolution operator attributes to onnx's ConvTranspose operator
and return the created node.
"""
name, inputs, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "dilate", [1, 1]))
adj_dims = list(parse_helper(attrs, "adj", [0, 0]))
pad_dims = pad_dims + pad_dims
deconv_node = onnx.helper.make_node(
"ConvTranspose",
inputs=inputs,
outputs=[name],
kernel_shape=kernel_dims,
strides=stride_dims,
dilations=dilations,
output_padding=adj_dims,
pads=pad_dims,
group=num_group,
name=name
)
return [deconv_node] | def convert_deconvolution(node, **kwargs):
"""Map MXNet's deconvolution operator attributes to onnx's ConvTranspose operator
and return the created node.
"""
name, inputs, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "dilate", [1, 1]))
adj_dims = list(parse_helper(attrs, "adj", [0, 0]))
pad_dims = pad_dims + pad_dims
deconv_node = onnx.helper.make_node(
"ConvTranspose",
inputs=inputs,
outputs=[name],
kernel_shape=kernel_dims,
strides=stride_dims,
dilations=dilations,
output_padding=adj_dims,
pads=pad_dims,
group=num_group,
name=name
)
return [deconv_node] | [
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train | convert_crop | Map MXNet's crop operator attributes to onnx's Crop operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_crop(node, **kwargs):
"""Map MXNet's crop operator attributes to onnx's Crop operator
and return the created node.
"""
name, inputs, attrs = get_inputs(node, kwargs)
num_inputs = len(inputs)
y, x = list(parse_helper(attrs, "offset", [0, 0]))
h, w = list(parse_helper(attrs, "h_w", [0, 0]))
if num_inputs > 1:
h, w = kwargs["out_shape"][-2:]
border = [x, y, x + w, y + h]
crop_node = onnx.helper.make_node(
"Crop",
inputs=[inputs[0]],
outputs=[name],
border=border,
scale=[1, 1],
name=name
)
logging.warning(
"Using an experimental ONNX operator: Crop. " \
"Its definition can change.")
return [crop_node] | def convert_crop(node, **kwargs):
"""Map MXNet's crop operator attributes to onnx's Crop operator
and return the created node.
"""
name, inputs, attrs = get_inputs(node, kwargs)
num_inputs = len(inputs)
y, x = list(parse_helper(attrs, "offset", [0, 0]))
h, w = list(parse_helper(attrs, "h_w", [0, 0]))
if num_inputs > 1:
h, w = kwargs["out_shape"][-2:]
border = [x, y, x + w, y + h]
crop_node = onnx.helper.make_node(
"Crop",
inputs=[inputs[0]],
outputs=[name],
border=border,
scale=[1, 1],
name=name
)
logging.warning(
"Using an experimental ONNX operator: Crop. " \
"Its definition can change.")
return [crop_node] | [
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train | convert_fully_connected | Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_fully_connected(node, **kwargs):
"""Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
no_bias = get_boolean_attribute_value(attrs, "no_bias")
fcnode = []
op_name = "flatten_" + str(kwargs["idx"])
flatten_node = onnx.helper.make_node(
'Flatten',
inputs=[input_nodes[0]],
outputs=[op_name],
name=op_name
)
input_nodes[0] = op_name
fcnode.append(flatten_node)
if no_bias:
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
bias_name = "bias" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(bias_name, data_type, (1,))
initializer.append(
onnx.helper.make_tensor(
name=bias_name,
data_type=data_type,
dims=(1,),
vals=[0],
raw=False,
)
)
input_nodes.append(bias_name)
fcnode.append(tensor_node)
node = onnx.helper.make_node(
"Gemm",
input_nodes, # input (A, B, C) - C can be in place
[name], # output
alpha=1.0,
beta=1.0,
transA=False,
transB=True,
name=name
)
fcnode.append(node)
return fcnode | def convert_fully_connected(node, **kwargs):
"""Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
no_bias = get_boolean_attribute_value(attrs, "no_bias")
fcnode = []
op_name = "flatten_" + str(kwargs["idx"])
flatten_node = onnx.helper.make_node(
'Flatten',
inputs=[input_nodes[0]],
outputs=[op_name],
name=op_name
)
input_nodes[0] = op_name
fcnode.append(flatten_node)
if no_bias:
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
bias_name = "bias" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(bias_name, data_type, (1,))
initializer.append(
onnx.helper.make_tensor(
name=bias_name,
data_type=data_type,
dims=(1,),
vals=[0],
raw=False,
)
)
input_nodes.append(bias_name)
fcnode.append(tensor_node)
node = onnx.helper.make_node(
"Gemm",
input_nodes, # input (A, B, C) - C can be in place
[name], # output
alpha=1.0,
beta=1.0,
transA=False,
transB=True,
name=name
)
fcnode.append(node)
return fcnode | [
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train | convert_batchnorm | Map MXNet's BatchNorm operator attributes to onnx's BatchNormalization operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_batchnorm(node, **kwargs):
"""Map MXNet's BatchNorm operator attributes to onnx's BatchNormalization operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
momentum = float(attrs.get("momentum", 0.9))
eps = float(attrs.get("eps", 0.001))
bn_node = onnx.helper.make_node(
"BatchNormalization",
input_nodes,
[name],
name=name,
epsilon=eps,
momentum=momentum,
# MXNet computes mean and variance per feature for batchnorm
# Default for onnx is across all spatial features. So disabling the parameter.
spatial=0
)
return [bn_node] | def convert_batchnorm(node, **kwargs):
"""Map MXNet's BatchNorm operator attributes to onnx's BatchNormalization operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
momentum = float(attrs.get("momentum", 0.9))
eps = float(attrs.get("eps", 0.001))
bn_node = onnx.helper.make_node(
"BatchNormalization",
input_nodes,
[name],
name=name,
epsilon=eps,
momentum=momentum,
# MXNet computes mean and variance per feature for batchnorm
# Default for onnx is across all spatial features. So disabling the parameter.
spatial=0
)
return [bn_node] | [
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train | convert_activation | Map MXNet's Activation operator attributes to onnx's Tanh/Relu operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_activation(node, **kwargs):
"""Map MXNet's Activation operator attributes to onnx's Tanh/Relu operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
act_type = attrs["act_type"]
# Creating a dictionary here, but if this titlecase pattern
# mxnet_name.title()
act_types = {
"tanh": "Tanh",
"relu": "Relu",
"sigmoid": "Sigmoid",
"softrelu": "Softplus",
"softsign": "Softsign"
}
act_name = act_types.get(act_type)
if act_name:
node = onnx.helper.make_node(
act_name,
input_nodes,
[name],
name=name
)
else:
raise AttributeError(
"Activation %s not implemented or recognized in the converter" % act_type
)
return [node] | def convert_activation(node, **kwargs):
"""Map MXNet's Activation operator attributes to onnx's Tanh/Relu operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
act_type = attrs["act_type"]
# Creating a dictionary here, but if this titlecase pattern
# mxnet_name.title()
act_types = {
"tanh": "Tanh",
"relu": "Relu",
"sigmoid": "Sigmoid",
"softrelu": "Softplus",
"softsign": "Softsign"
}
act_name = act_types.get(act_type)
if act_name:
node = onnx.helper.make_node(
act_name,
input_nodes,
[name],
name=name
)
else:
raise AttributeError(
"Activation %s not implemented or recognized in the converter" % act_type
)
return [node] | [
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train | convert_pad | Map MXNet's pad operator attributes to onnx's Pad operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_pad(node, **kwargs):
"""Map MXNet's pad operator attributes to onnx's Pad operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mxnet_pad_width = convert_string_to_list(attrs.get("pad_width"))
onnx_pad_width = transform_padding(mxnet_pad_width)
pad_mode = attrs.get("mode")
if pad_mode == "constant":
pad_value = float(attrs.get("constant_value")) \
if "constant_value" in attrs else 0.0
node = onnx.helper.make_node(
'Pad',
inputs=input_nodes,
outputs=[name],
mode='constant',
value=pad_value,
pads=onnx_pad_width,
name=name
)
else:
node = onnx.helper.make_node(
'Pad',
inputs=input_nodes,
outputs=[name],
mode=pad_mode,
pads=onnx_pad_width,
name=name
)
return [node] | def convert_pad(node, **kwargs):
"""Map MXNet's pad operator attributes to onnx's Pad operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mxnet_pad_width = convert_string_to_list(attrs.get("pad_width"))
onnx_pad_width = transform_padding(mxnet_pad_width)
pad_mode = attrs.get("mode")
if pad_mode == "constant":
pad_value = float(attrs.get("constant_value")) \
if "constant_value" in attrs else 0.0
node = onnx.helper.make_node(
'Pad',
inputs=input_nodes,
outputs=[name],
mode='constant',
value=pad_value,
pads=onnx_pad_width,
name=name
)
else:
node = onnx.helper.make_node(
'Pad',
inputs=input_nodes,
outputs=[name],
mode=pad_mode,
pads=onnx_pad_width,
name=name
)
return [node] | [
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train | create_helper_trans_node | create extra transpose node for dot operator | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def create_helper_trans_node(op_name, input_node, node_name):
"""create extra transpose node for dot operator"""
node_name = op_name + "_" + node_name
trans_node = onnx.helper.make_node(
'Transpose',
inputs=[input_node],
outputs=[node_name],
name=node_name
)
return trans_node | def create_helper_trans_node(op_name, input_node, node_name):
"""create extra transpose node for dot operator"""
node_name = op_name + "_" + node_name
trans_node = onnx.helper.make_node(
'Transpose',
inputs=[input_node],
outputs=[node_name],
name=node_name
)
return trans_node | [
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train | convert_dot | Map MXNet's dot operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_dot(node, **kwargs):
"""Map MXNet's dot operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes."""
name, input_nodes, attrs = get_inputs(node, kwargs)
input_node_a = input_nodes[0]
input_node_b = input_nodes[1]
trans_a_node = None
trans_b_node = None
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str(kwargs["idx"])
if trans_a:
trans_a_node = create_helper_trans_node(op_name, input_nodes[0], 'a')
input_node_a = op_name+"_a"
if trans_b:
trans_b_node = create_helper_trans_node(op_name, input_nodes[1], 'b')
input_node_b = op_name+"_b"
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[input_node_a, input_node_b],
outputs=[name],
name=name
)
if not trans_a and not trans_b:
return [matmul_node]
elif trans_a and not trans_b:
return [trans_a_node, matmul_node]
elif trans_b and not trans_a:
return [trans_b_node, matmul_node]
else:
return [trans_a_node, trans_b_node, matmul_node] | def convert_dot(node, **kwargs):
"""Map MXNet's dot operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes."""
name, input_nodes, attrs = get_inputs(node, kwargs)
input_node_a = input_nodes[0]
input_node_b = input_nodes[1]
trans_a_node = None
trans_b_node = None
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str(kwargs["idx"])
if trans_a:
trans_a_node = create_helper_trans_node(op_name, input_nodes[0], 'a')
input_node_a = op_name+"_a"
if trans_b:
trans_b_node = create_helper_trans_node(op_name, input_nodes[1], 'b')
input_node_b = op_name+"_b"
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[input_node_a, input_node_b],
outputs=[name],
name=name
)
if not trans_a and not trans_b:
return [matmul_node]
elif trans_a and not trans_b:
return [trans_a_node, matmul_node]
elif trans_b and not trans_a:
return [trans_b_node, matmul_node]
else:
return [trans_a_node, trans_b_node, matmul_node] | [
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train | convert_linalg_gemm2 | Map MXNet's _linalg_gemm2 operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes.
Return multiple nodes created. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_linalg_gemm2(node, **kwargs):
"""Map MXNet's _linalg_gemm2 operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes.
Return multiple nodes created.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Getting the attributes and assigning default values.
alpha = float(attrs.get("alpha", 1.0))
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str(kwargs["idx"])
if alpha == 1.0 and trans_a == 0 and trans_b == 0:
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=input_nodes,
outputs=[name],
name=name
)
return [matmul_node]
elif trans_a == 1 and trans_b == 0:
op_name = "transpose" + str(kwargs["idx"])
node_name = op_name+"_a"
trans_a_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[0]],
outputs=[op_name+"_a"],
name=node_name
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[node_name, input_nodes[1]],
outputs=[name],
name=name
)
return [trans_a_node, matmul_node]
elif trans_a == 0 and trans_b == 1:
node_name = op_name + "_b"
trans_b_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[1]],
outputs=[op_name+"_b"],
name=node_name
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[input_nodes[0], node_name],
outputs=[name],
name=name
)
return [trans_b_node, matmul_node]
else:
node_name_a = op_name+"_a"
trans_a_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[0]],
outputs=[op_name+"_a"],
name=node_name_a
)
node_name_b = op_name + "_b"
trans_b_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[1]],
outputs=[op_name+"_b"],
name=node_name_b
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=input_nodes,
outputs=[name],
name=name
)
return [trans_a_node, trans_b_node, matmul_node] | def convert_linalg_gemm2(node, **kwargs):
"""Map MXNet's _linalg_gemm2 operator attributes to onnx's
MatMul and Transpose operators based on the values set for
transpose_a, transpose_b attributes.
Return multiple nodes created.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Getting the attributes and assigning default values.
alpha = float(attrs.get("alpha", 1.0))
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str(kwargs["idx"])
if alpha == 1.0 and trans_a == 0 and trans_b == 0:
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=input_nodes,
outputs=[name],
name=name
)
return [matmul_node]
elif trans_a == 1 and trans_b == 0:
op_name = "transpose" + str(kwargs["idx"])
node_name = op_name+"_a"
trans_a_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[0]],
outputs=[op_name+"_a"],
name=node_name
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[node_name, input_nodes[1]],
outputs=[name],
name=name
)
return [trans_a_node, matmul_node]
elif trans_a == 0 and trans_b == 1:
node_name = op_name + "_b"
trans_b_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[1]],
outputs=[op_name+"_b"],
name=node_name
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=[input_nodes[0], node_name],
outputs=[name],
name=name
)
return [trans_b_node, matmul_node]
else:
node_name_a = op_name+"_a"
trans_a_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[0]],
outputs=[op_name+"_a"],
name=node_name_a
)
node_name_b = op_name + "_b"
trans_b_node = onnx.helper.make_node(
'Transpose',
inputs=[input_nodes[1]],
outputs=[op_name+"_b"],
name=node_name_b
)
matmul_node = onnx.helper.make_node(
'MatMul',
inputs=input_nodes,
outputs=[name],
name=name
)
return [trans_a_node, trans_b_node, matmul_node] | [
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train | convert_pooling | Map MXNet's Pooling operator attributes to onnx's
MaxPool/AveragePool/GlobalMaxPool/GlobalAveragePool operators
based on the input node's attributes and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_pooling(node, **kwargs):
"""Map MXNet's Pooling operator attributes to onnx's
MaxPool/AveragePool/GlobalMaxPool/GlobalAveragePool operators
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
kernel = eval(attrs["kernel"])
pool_type = attrs["pool_type"] if attrs.get("pool_type") else "max"
stride = eval(attrs["stride"]) if attrs.get("stride") else (1, 1)
global_pool = get_boolean_attribute_value(attrs, "global_pool")
p_value = attrs.get('p_value', 'None')
pooling_convention = attrs.get('pooling_convention', 'valid')
if pooling_convention == 'full':
pooling_warning = "Pooling: ONNX currently doesn't support pooling_convention. " \
"This might lead to shape or accuracy issues. " \
"https://github.com/onnx/onnx/issues/549"
logging.warning(pooling_warning)
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
pad_dims = pad_dims + pad_dims
pool_types = {"max": "MaxPool", "avg": "AveragePool", "lp": "LpPool"}
global_pool_types = {"max": "GlobalMaxPool", "avg": "GlobalAveragePool",
"lp": "GlobalLpPool"}
if pool_type == 'lp' and p_value == 'None':
raise AttributeError('ONNX requires a p value for LpPool and GlobalLpPool')
if global_pool:
if pool_type == 'lp':
node = onnx.helper.make_node(
global_pool_types[pool_type],
input_nodes, # input
[name],
p=int(p_value),
name=name
)
else:
node = onnx.helper.make_node(
global_pool_types[pool_type],
input_nodes, # input
[name],
name=name
)
else:
if pool_type == 'lp':
node = onnx.helper.make_node(
pool_types[pool_type],
input_nodes, # input
[name],
p=int(p_value),
kernel_shape=kernel,
pads=pad_dims,
strides=stride,
name=name
)
else:
node = onnx.helper.make_node(
pool_types[pool_type],
input_nodes, # input
[name],
kernel_shape=kernel,
pads=pad_dims,
strides=stride,
name=name
)
return [node] | def convert_pooling(node, **kwargs):
"""Map MXNet's Pooling operator attributes to onnx's
MaxPool/AveragePool/GlobalMaxPool/GlobalAveragePool operators
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
kernel = eval(attrs["kernel"])
pool_type = attrs["pool_type"] if attrs.get("pool_type") else "max"
stride = eval(attrs["stride"]) if attrs.get("stride") else (1, 1)
global_pool = get_boolean_attribute_value(attrs, "global_pool")
p_value = attrs.get('p_value', 'None')
pooling_convention = attrs.get('pooling_convention', 'valid')
if pooling_convention == 'full':
pooling_warning = "Pooling: ONNX currently doesn't support pooling_convention. " \
"This might lead to shape or accuracy issues. " \
"https://github.com/onnx/onnx/issues/549"
logging.warning(pooling_warning)
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
pad_dims = pad_dims + pad_dims
pool_types = {"max": "MaxPool", "avg": "AveragePool", "lp": "LpPool"}
global_pool_types = {"max": "GlobalMaxPool", "avg": "GlobalAveragePool",
"lp": "GlobalLpPool"}
if pool_type == 'lp' and p_value == 'None':
raise AttributeError('ONNX requires a p value for LpPool and GlobalLpPool')
if global_pool:
if pool_type == 'lp':
node = onnx.helper.make_node(
global_pool_types[pool_type],
input_nodes, # input
[name],
p=int(p_value),
name=name
)
else:
node = onnx.helper.make_node(
global_pool_types[pool_type],
input_nodes, # input
[name],
name=name
)
else:
if pool_type == 'lp':
node = onnx.helper.make_node(
pool_types[pool_type],
input_nodes, # input
[name],
p=int(p_value),
kernel_shape=kernel,
pads=pad_dims,
strides=stride,
name=name
)
else:
node = onnx.helper.make_node(
pool_types[pool_type],
input_nodes, # input
[name],
kernel_shape=kernel,
pads=pad_dims,
strides=stride,
name=name
)
return [node] | [
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train | convert_instancenorm | Map MXNet's InstanceNorm operator attributes to onnx's InstanceNormalization operator
based on the input node's attributes and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_instancenorm(node, **kwargs):
"""Map MXNet's InstanceNorm operator attributes to onnx's InstanceNormalization operator
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
eps = float(attrs.get("eps", 0.001))
node = onnx.helper.make_node(
'InstanceNormalization',
inputs=input_nodes,
outputs=[name],
name=name,
epsilon=eps)
return [node] | def convert_instancenorm(node, **kwargs):
"""Map MXNet's InstanceNorm operator attributes to onnx's InstanceNormalization operator
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
eps = float(attrs.get("eps", 0.001))
node = onnx.helper.make_node(
'InstanceNormalization',
inputs=input_nodes,
outputs=[name],
name=name,
epsilon=eps)
return [node] | [
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train | convert_leakyrelu | Map MXNet's LeakyReLU operator attributes to onnx's Elu/LeakyRelu/PRelu operators
based on the input node's attributes and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_leakyrelu(node, **kwargs):
"""Map MXNet's LeakyReLU operator attributes to onnx's Elu/LeakyRelu/PRelu operators
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
act_type = attrs.get("act_type", "leaky")
alpha = float(attrs.get("slope", 0.25))
act_name = {"elu": "Elu", "leaky": "LeakyRelu", "prelu": "PRelu",
"selu": "Selu"}
if act_type == "prelu" or act_type == "selu":
node = onnx.helper.make_node(
act_name[act_type],
inputs=input_nodes,
outputs=[name],
name=name)
else:
node = onnx.helper.make_node(
act_name[act_type],
inputs=input_nodes,
outputs=[name],
name=name,
alpha=alpha)
return [node] | def convert_leakyrelu(node, **kwargs):
"""Map MXNet's LeakyReLU operator attributes to onnx's Elu/LeakyRelu/PRelu operators
based on the input node's attributes and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
act_type = attrs.get("act_type", "leaky")
alpha = float(attrs.get("slope", 0.25))
act_name = {"elu": "Elu", "leaky": "LeakyRelu", "prelu": "PRelu",
"selu": "Selu"}
if act_type == "prelu" or act_type == "selu":
node = onnx.helper.make_node(
act_name[act_type],
inputs=input_nodes,
outputs=[name],
name=name)
else:
node = onnx.helper.make_node(
act_name[act_type],
inputs=input_nodes,
outputs=[name],
name=name,
alpha=alpha)
return [node] | [
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train | convert_softmax | Map MXNet's softmax operator attributes to onnx's Softmax operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_softmax(node, **kwargs):
"""Map MXNet's softmax operator attributes to onnx's Softmax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis", -1))
softmax_node = onnx.helper.make_node(
"Softmax",
input_nodes,
[name],
axis=axis,
name=name
)
return [softmax_node] | def convert_softmax(node, **kwargs):
"""Map MXNet's softmax operator attributes to onnx's Softmax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis", -1))
softmax_node = onnx.helper.make_node(
"Softmax",
input_nodes,
[name],
axis=axis,
name=name
)
return [softmax_node] | [
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train | convert_softmax_output | Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_softmax_output(node, **kwargs):
"""Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node.
"""
name = node["name"]
input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
input1 = kwargs["proc_nodes"][input1_idx]
softmax_node = onnx.helper.make_node(
"Softmax",
[input1.name],
[name],
axis=1,
name=name
)
return [softmax_node] | def convert_softmax_output(node, **kwargs):
"""Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node.
"""
name = node["name"]
input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
input1 = kwargs["proc_nodes"][input1_idx]
softmax_node = onnx.helper.make_node(
"Softmax",
[input1.name],
[name],
axis=1,
name=name
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return [softmax_node] | [
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train | convert_logistic_regression_output | Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_logistic_regression_output(node, **kwargs):
"""Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node.
"""
name = node["name"]
input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
input1 = kwargs["proc_nodes"][input1_idx]
sigmoid_node = onnx.helper.make_node(
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[input1.name],
[name],
name=name
)
return [sigmoid_node] | def convert_logistic_regression_output(node, **kwargs):
"""Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
and return the created node.
"""
name = node["name"]
input1_idx = kwargs["index_lookup"][node["inputs"][0][0]]
input1 = kwargs["proc_nodes"][input1_idx]
sigmoid_node = onnx.helper.make_node(
"Sigmoid",
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name=name
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return [sigmoid_node] | [
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train | convert_concat | Map MXNet's Concat operator attributes to onnx's Concat operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_concat(node, **kwargs):
"""Map MXNet's Concat operator attributes to onnx's Concat operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("dim", 1))
concat_node = onnx.helper.make_node(
"Concat",
input_nodes,
[name],
axis=axis,
name=name
)
return [concat_node] | def convert_concat(node, **kwargs):
"""Map MXNet's Concat operator attributes to onnx's Concat operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("dim", 1))
concat_node = onnx.helper.make_node(
"Concat",
input_nodes,
[name],
axis=axis,
name=name
)
return [concat_node] | [
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train | convert_transpose | Map MXNet's transpose operator attributes to onnx's Transpose operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_transpose(node, **kwargs):
"""Map MXNet's transpose operator attributes to onnx's Transpose operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = attrs.get("axes", ())
if axes:
axes = tuple(map(int, re.findall(r'\d+', axes)))
transpose_node = onnx.helper.make_node(
"Transpose",
input_nodes,
[name],
perm=axes,
name=name
)
else:
transpose_node = onnx.helper.make_node(
"Transpose",
input_nodes,
[name],
name=name
)
return [transpose_node] | def convert_transpose(node, **kwargs):
"""Map MXNet's transpose operator attributes to onnx's Transpose operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = attrs.get("axes", ())
if axes:
axes = tuple(map(int, re.findall(r'\d+', axes)))
transpose_node = onnx.helper.make_node(
"Transpose",
input_nodes,
[name],
perm=axes,
name=name
)
else:
transpose_node = onnx.helper.make_node(
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input_nodes,
[name],
name=name
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return [transpose_node] | [
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"... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | convert_lrn | Map MXNet's LRN operator attributes to onnx's LRN operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_lrn(node, **kwargs):
"""Map MXNet's LRN operator attributes to onnx's LRN operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
alpha = float(attrs.get("alpha", 0.0001))
beta = float(attrs.get("beta", 0.75))
bias = float(attrs.get("knorm", 1.0))
size = int(attrs.get("nsize"))
lrn_node = onnx.helper.make_node(
"LRN",
inputs=input_nodes,
outputs=[name],
name=name,
alpha=alpha,
beta=beta,
bias=bias,
size=size
)
return [lrn_node] | def convert_lrn(node, **kwargs):
"""Map MXNet's LRN operator attributes to onnx's LRN operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
alpha = float(attrs.get("alpha", 0.0001))
beta = float(attrs.get("beta", 0.75))
bias = float(attrs.get("knorm", 1.0))
size = int(attrs.get("nsize"))
lrn_node = onnx.helper.make_node(
"LRN",
inputs=input_nodes,
outputs=[name],
name=name,
alpha=alpha,
beta=beta,
bias=bias,
size=size
)
return [lrn_node] | [
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train | convert_l2normalization | Map MXNet's L2Normalization operator attributes to onnx's LpNormalization operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_l2normalization(node, **kwargs):
"""Map MXNet's L2Normalization operator attributes to onnx's LpNormalization operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mode = attrs.get("mode", "instance")
if mode != "channel":
raise AttributeError("L2Normalization: ONNX currently supports channel mode only")
l2norm_node = onnx.helper.make_node(
"LpNormalization",
input_nodes,
[name],
axis=1, # channel only
name=name
)
return [l2norm_node] | def convert_l2normalization(node, **kwargs):
"""Map MXNet's L2Normalization operator attributes to onnx's LpNormalization operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mode = attrs.get("mode", "instance")
if mode != "channel":
raise AttributeError("L2Normalization: ONNX currently supports channel mode only")
l2norm_node = onnx.helper.make_node(
"LpNormalization",
input_nodes,
[name],
axis=1, # channel only
name=name
)
return [l2norm_node] | [
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train | convert_dropout | Map MXNet's Dropout operator attributes to onnx's Dropout operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_dropout(node, **kwargs):
"""Map MXNet's Dropout operator attributes to onnx's Dropout operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
probability = float(attrs.get("p", 0.5))
dropout_node = onnx.helper.make_node(
"Dropout",
input_nodes,
[name],
ratio=probability,
name=name
)
return [dropout_node] | def convert_dropout(node, **kwargs):
"""Map MXNet's Dropout operator attributes to onnx's Dropout operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
probability = float(attrs.get("p", 0.5))
dropout_node = onnx.helper.make_node(
"Dropout",
input_nodes,
[name],
ratio=probability,
name=name
)
return [dropout_node] | [
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train | convert_clip | Map MXNet's Clip operator attributes to onnx's Clip operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_clip(node, **kwargs):
"""Map MXNet's Clip operator attributes to onnx's Clip operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
a_min = np.float(attrs.get('a_min', -np.inf))
a_max = np.float(attrs.get('a_max', np.inf))
clip_node = onnx.helper.make_node(
"Clip",
input_nodes,
[name],
name=name,
min=a_min,
max=a_max
)
return [clip_node] | def convert_clip(node, **kwargs):
"""Map MXNet's Clip operator attributes to onnx's Clip operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
a_min = np.float(attrs.get('a_min', -np.inf))
a_max = np.float(attrs.get('a_max', np.inf))
clip_node = onnx.helper.make_node(
"Clip",
input_nodes,
[name],
name=name,
min=a_min,
max=a_max
)
return [clip_node] | [
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train | scalar_op_helper | Helper function for scalar arithmetic operations | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def scalar_op_helper(node, op_name, **kwargs):
"""Helper function for scalar arithmetic operations"""
name, input_nodes, attrs = get_inputs(node, kwargs)
from onnx import numpy_helper
input_type = kwargs["in_type"]
scalar_value = np.array([attrs.get("scalar", 1)],
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
initializer = kwargs["initializer"]
flag = True
# If the input value is in initializer, just multiply with scalar input
# and create a new initializer
for i in initializer:
if i.name == input_nodes[0]:
if op_name == 'Mul':
new_initializer = numpy_helper.to_array(i) * scalar_value[0]
elif op_name == 'Sub':
if name.startswith("_rminusscalar"):
new_initializer = scalar_value[0] - numpy_helper.to_array(i)
else:
new_initializer = numpy_helper.to_array(i) - scalar_value[0]
elif op_name == 'Add':
new_initializer = numpy_helper.to_array(i) + scalar_value[0]
elif op_name == 'Div':
if name.startswith("_rdivscalar"):
new_initializer = scalar_value[0] / numpy_helper.to_array(i)
else:
new_initializer = numpy_helper.to_array(i) / scalar_value[0]
elif op_name == 'Pow':
new_initializer = numpy_helper.to_array(i) ** scalar_value[0]
flag = False
break
# else create a new tensor of the scalar value, add it in initializer
if flag is True:
dims = np.shape(scalar_value)
scalar_op_name = "scalar_op" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(scalar_op_name, input_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=scalar_op_name,
data_type=input_type,
dims=dims,
vals=scalar_value,
raw=False,
)
)
mul_node = onnx.helper.make_node(
op_name,
[input_nodes[0], scalar_op_name],
[name],
name=name
)
return [tensor_node, mul_node]
else:
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[new_initializer.dtype]
dims = np.shape(new_initializer)
new_a_node = input_nodes[0] + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(new_a_node, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=new_a_node,
data_type=data_type,
dims=dims,
vals=new_initializer,
raw=False,
)
)
return [tensor_node] | def scalar_op_helper(node, op_name, **kwargs):
"""Helper function for scalar arithmetic operations"""
name, input_nodes, attrs = get_inputs(node, kwargs)
from onnx import numpy_helper
input_type = kwargs["in_type"]
scalar_value = np.array([attrs.get("scalar", 1)],
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
initializer = kwargs["initializer"]
flag = True
# If the input value is in initializer, just multiply with scalar input
# and create a new initializer
for i in initializer:
if i.name == input_nodes[0]:
if op_name == 'Mul':
new_initializer = numpy_helper.to_array(i) * scalar_value[0]
elif op_name == 'Sub':
if name.startswith("_rminusscalar"):
new_initializer = scalar_value[0] - numpy_helper.to_array(i)
else:
new_initializer = numpy_helper.to_array(i) - scalar_value[0]
elif op_name == 'Add':
new_initializer = numpy_helper.to_array(i) + scalar_value[0]
elif op_name == 'Div':
if name.startswith("_rdivscalar"):
new_initializer = scalar_value[0] / numpy_helper.to_array(i)
else:
new_initializer = numpy_helper.to_array(i) / scalar_value[0]
elif op_name == 'Pow':
new_initializer = numpy_helper.to_array(i) ** scalar_value[0]
flag = False
break
# else create a new tensor of the scalar value, add it in initializer
if flag is True:
dims = np.shape(scalar_value)
scalar_op_name = "scalar_op" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(scalar_op_name, input_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=scalar_op_name,
data_type=input_type,
dims=dims,
vals=scalar_value,
raw=False,
)
)
mul_node = onnx.helper.make_node(
op_name,
[input_nodes[0], scalar_op_name],
[name],
name=name
)
return [tensor_node, mul_node]
else:
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[new_initializer.dtype]
dims = np.shape(new_initializer)
new_a_node = input_nodes[0] + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(new_a_node, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=new_a_node,
data_type=data_type,
dims=dims,
vals=new_initializer,
raw=False,
)
)
return [tensor_node] | [
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train | convert_argmax | Map MXNet's argmax operator attributes to onnx's ArgMax operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_argmax(node, **kwargs):
"""Map MXNet's argmax operator attributes to onnx's ArgMax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
keepdims = get_boolean_attribute_value(attrs, "keepdims")
node = onnx.helper.make_node(
'ArgMax',
inputs=input_nodes,
axis=axis,
keepdims=keepdims,
outputs=[name],
name=name
)
return [node] | def convert_argmax(node, **kwargs):
"""Map MXNet's argmax operator attributes to onnx's ArgMax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
keepdims = get_boolean_attribute_value(attrs, "keepdims")
node = onnx.helper.make_node(
'ArgMax',
inputs=input_nodes,
axis=axis,
keepdims=keepdims,
outputs=[name],
name=name
)
return [node] | [
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train | convert_reshape | Map MXNet's Reshape operator attributes to onnx's Reshape operator.
Converts output shape attribute to output shape tensor
and return multiple created nodes. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_reshape(node, **kwargs):
"""Map MXNet's Reshape operator attributes to onnx's Reshape operator.
Converts output shape attribute to output shape tensor
and return multiple created nodes.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
output_shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(output_shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
output_shape_name = "reshape_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=output_shape_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
not_supported_shape = [-2, -3, -4]
for val in output_shape_list:
if val in not_supported_shape:
raise AttributeError("Reshape: Shape value not supported in ONNX", val)
reshape_node = onnx.helper.make_node(
"Reshape",
input_nodes,
[name],
name=name
)
return [tensor_node, reshape_node] | def convert_reshape(node, **kwargs):
"""Map MXNet's Reshape operator attributes to onnx's Reshape operator.
Converts output shape attribute to output shape tensor
and return multiple created nodes.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
output_shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(output_shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
output_shape_name = "reshape_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=output_shape_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
not_supported_shape = [-2, -3, -4]
for val in output_shape_list:
if val in not_supported_shape:
raise AttributeError("Reshape: Shape value not supported in ONNX", val)
reshape_node = onnx.helper.make_node(
"Reshape",
input_nodes,
[name],
name=name
)
return [tensor_node, reshape_node] | [
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train | convert_cast | Map MXNet's Cast operator attributes to onnx's Cast operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_cast(node, **kwargs):
"""Map MXNet's Cast operator attributes to onnx's Cast operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = attrs["dtype"]
# dtype can be mapped only with types from TensorProto
# float32 is mapped to float and float64 to double in onnx
# following tensorproto mapping https://github.com/onnx/onnx/blob/master/onnx/mapping.py
if dtype == 'float32':
dtype = 'float'
elif dtype == 'float64':
dtype = 'double'
node = onnx.helper.make_node(
"Cast",
input_nodes,
[name],
to=getattr(onnx.TensorProto, dtype.upper()),
name=name,
)
return [node] | def convert_cast(node, **kwargs):
"""Map MXNet's Cast operator attributes to onnx's Cast operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = attrs["dtype"]
# dtype can be mapped only with types from TensorProto
# float32 is mapped to float and float64 to double in onnx
# following tensorproto mapping https://github.com/onnx/onnx/blob/master/onnx/mapping.py
if dtype == 'float32':
dtype = 'float'
elif dtype == 'float64':
dtype = 'double'
node = onnx.helper.make_node(
"Cast",
input_nodes,
[name],
to=getattr(onnx.TensorProto, dtype.upper()),
name=name,
)
return [node] | [
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train | convert_slice_axis | Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_slice_axis(node, **kwargs):
"""Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = int(attrs.get("axis"))
starts = int(attrs.get("begin"))
ends = int(attrs.get("end", None))
if not ends:
raise ValueError("Slice: ONNX doesnt't support 'None' in 'end' attribute")
node = onnx.helper.make_node(
"Slice",
input_nodes,
[name],
axes=[axes],
starts=[starts],
ends=[ends],
name=name,
)
return [node] | def convert_slice_axis(node, **kwargs):
"""Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = int(attrs.get("axis"))
starts = int(attrs.get("begin"))
ends = int(attrs.get("end", None))
if not ends:
raise ValueError("Slice: ONNX doesnt't support 'None' in 'end' attribute")
node = onnx.helper.make_node(
"Slice",
input_nodes,
[name],
axes=[axes],
starts=[starts],
ends=[ends],
name=name,
)
return [node] | [
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train | convert_slice_channel | Map MXNet's SliceChannel operator attributes to onnx's Squeeze or Split
operator based on squeeze_axis attribute
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_slice_channel(node, **kwargs):
"""Map MXNet's SliceChannel operator attributes to onnx's Squeeze or Split
operator based on squeeze_axis attribute
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
num_outputs = int(attrs.get("num_outputs"))
axis = int(attrs.get("axis", 1))
squeeze_axis = int(attrs.get("squeeze_axis", 0))
if squeeze_axis == 1 and num_outputs == 1:
node = onnx.helper.make_node(
"Squeeze",
input_nodes,
[name],
axes=[axis],
name=name,
)
return [node]
elif squeeze_axis == 0 and num_outputs > 1:
in_shape = kwargs.get('in_shape')[0]
split = in_shape[axis] // num_outputs
node = onnx.helper.make_node(
"Split",
input_nodes,
[name+'_output'+str(i) for i in range(num_outputs)],
axis=axis,
split=[split for _ in range(num_outputs)],
name=name,
)
return [node]
else:
raise NotImplementedError("SliceChannel operator with num_outputs>1 and"
"squeeze_axis true is not implemented.") | def convert_slice_channel(node, **kwargs):
"""Map MXNet's SliceChannel operator attributes to onnx's Squeeze or Split
operator based on squeeze_axis attribute
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
num_outputs = int(attrs.get("num_outputs"))
axis = int(attrs.get("axis", 1))
squeeze_axis = int(attrs.get("squeeze_axis", 0))
if squeeze_axis == 1 and num_outputs == 1:
node = onnx.helper.make_node(
"Squeeze",
input_nodes,
[name],
axes=[axis],
name=name,
)
return [node]
elif squeeze_axis == 0 and num_outputs > 1:
in_shape = kwargs.get('in_shape')[0]
split = in_shape[axis] // num_outputs
node = onnx.helper.make_node(
"Split",
input_nodes,
[name+'_output'+str(i) for i in range(num_outputs)],
axis=axis,
split=[split for _ in range(num_outputs)],
name=name,
)
return [node]
else:
raise NotImplementedError("SliceChannel operator with num_outputs>1 and"
"squeeze_axis true is not implemented.") | [
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train | convert_expand_dims | Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_expand_dims(node, **kwargs):
"""Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
node = onnx.helper.make_node(
"Unsqueeze",
input_nodes,
[name],
axes=[axis],
name=name,
)
return [node] | def convert_expand_dims(node, **kwargs):
"""Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
node = onnx.helper.make_node(
"Unsqueeze",
input_nodes,
[name],
axes=[axis],
name=name,
)
return [node] | [
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train | convert_squeeze | Map MXNet's squeeze operator attributes to onnx's squeeze operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_squeeze(node, **kwargs):
"""Map MXNet's squeeze operator attributes to onnx's squeeze operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = attrs.get("axis", None)
if not axis:
raise AttributeError("Squeeze: Missing axis attribute: ONNX currently requires axis to "
"be specified for squeeze operator")
axis = convert_string_to_list(axis)
node = onnx.helper.make_node(
"Squeeze",
input_nodes,
[name],
axes=axis,
name=name,
)
return [node] | def convert_squeeze(node, **kwargs):
"""Map MXNet's squeeze operator attributes to onnx's squeeze operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = attrs.get("axis", None)
if not axis:
raise AttributeError("Squeeze: Missing axis attribute: ONNX currently requires axis to "
"be specified for squeeze operator")
axis = convert_string_to_list(axis)
node = onnx.helper.make_node(
"Squeeze",
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[name],
axes=axis,
name=name,
)
return [node] | [
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train | convert_depthtospace | Map MXNet's depth_to_space operator attributes to onnx's
DepthToSpace operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_depthtospace(node, **kwargs):
"""Map MXNet's depth_to_space operator attributes to onnx's
DepthToSpace operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
blksize = int(attrs.get("block_size", 0))
node = onnx.helper.make_node(
"DepthToSpace",
input_nodes,
[name],
blocksize=blksize,
name=name,
)
return [node] | def convert_depthtospace(node, **kwargs):
"""Map MXNet's depth_to_space operator attributes to onnx's
DepthToSpace operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
blksize = int(attrs.get("block_size", 0))
node = onnx.helper.make_node(
"DepthToSpace",
input_nodes,
[name],
blocksize=blksize,
name=name,
)
return [node] | [
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train | convert_square | Map MXNet's square operator attributes to onnx's Pow operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_square(node, **kwargs):
"""Map MXNet's square operator attributes to onnx's Pow operator
and return the created node.
"""
name, input_nodes, _ = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
power2_name = "square_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(power2_name, data_type, (1,))
initializer.append(
onnx.helper.make_tensor(
name=power2_name,
data_type=data_type,
dims=(1,),
vals=[2],
raw=False,
)
)
input_nodes.append(power2_name)
node = onnx.helper.make_node(
"Pow",
input_nodes,
[name],
name=name
)
return [tensor_node, node] | def convert_square(node, **kwargs):
"""Map MXNet's square operator attributes to onnx's Pow operator
and return the created node.
"""
name, input_nodes, _ = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
power2_name = "square_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(power2_name, data_type, (1,))
initializer.append(
onnx.helper.make_tensor(
name=power2_name,
data_type=data_type,
dims=(1,),
vals=[2],
raw=False,
)
)
input_nodes.append(power2_name)
node = onnx.helper.make_node(
"Pow",
input_nodes,
[name],
name=name
)
return [tensor_node, node] | [
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train | convert_sum | Map MXNet's sum operator attributes to onnx's ReduceSum operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_sum(node, **kwargs):
"""Map MXNet's sum operator attributes to onnx's ReduceSum operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
if axes:
node = onnx.helper.make_node(
'ReduceSum',
inputs=input_nodes,
outputs=[name],
axes=axes,
keepdims=keepdims,
name=name
)
else:
node = onnx.helper.make_node(
'ReduceSum',
inputs=input_nodes,
outputs=[name],
keepdims=keepdims,
name=name
)
return [node] | def convert_sum(node, **kwargs):
"""Map MXNet's sum operator attributes to onnx's ReduceSum operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
if axes:
node = onnx.helper.make_node(
'ReduceSum',
inputs=input_nodes,
outputs=[name],
axes=axes,
keepdims=keepdims,
name=name
)
else:
node = onnx.helper.make_node(
'ReduceSum',
inputs=input_nodes,
outputs=[name],
keepdims=keepdims,
name=name
)
return [node] | [
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train | convert_hardsigmoid | Map MXNet's hard_sigmoid operator attributes to onnx's HardSigmoid operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_hardsigmoid(node, **kwargs):
"""Map MXNet's hard_sigmoid operator attributes to onnx's HardSigmoid operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
alpha = float(attrs.get("alpha", 0.2))
beta = float(attrs.get("beta", 0.5))
node = onnx.helper.make_node(
'HardSigmoid',
input_nodes,
[name],
alpha=alpha,
beta=beta,
name=name
)
return [node] | def convert_hardsigmoid(node, **kwargs):
"""Map MXNet's hard_sigmoid operator attributes to onnx's HardSigmoid operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
alpha = float(attrs.get("alpha", 0.2))
beta = float(attrs.get("beta", 0.5))
node = onnx.helper.make_node(
'HardSigmoid',
input_nodes,
[name],
alpha=alpha,
beta=beta,
name=name
)
return [node] | [
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train | convert_logsoftmax | Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_logsoftmax(node, **kwargs):
"""Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to int
axis = int(attrs.get("axis", -1))
temp = attrs.get("temperature", 'None')
if temp != 'None':
raise AttributeError("LogSoftMax: ONNX supports only temperature=None")
node = onnx.helper.make_node(
'LogSoftmax',
input_nodes,
[name],
axis=axis,
name=name
)
return [node] | def convert_logsoftmax(node, **kwargs):
"""Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to int
axis = int(attrs.get("axis", -1))
temp = attrs.get("temperature", 'None')
if temp != 'None':
raise AttributeError("LogSoftMax: ONNX supports only temperature=None")
node = onnx.helper.make_node(
'LogSoftmax',
input_nodes,
[name],
axis=axis,
name=name
)
return [node] | [
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train | convert_norm | Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_norm(node, **kwargs):
"""Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
ord = int(attrs.get("ord", 2))
onnx_op_name = "ReduceL1" if ord == 1 else "ReduceL2"
if axes:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
axes=axes,
keepdims=keepdims,
name=name
)
return [reduce_node]
else:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
keepdims=keepdims,
name=name
)
return [reduce_node] | def convert_norm(node, **kwargs):
"""Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators
and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
ord = int(attrs.get("ord", 2))
onnx_op_name = "ReduceL1" if ord == 1 else "ReduceL2"
if axes:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
axes=axes,
keepdims=keepdims,
name=name
)
return [reduce_node]
else:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
keepdims=keepdims,
name=name
)
return [reduce_node] | [
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train | convert_multinomial | Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_multinomial(node, **kwargs):
"""Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
sample_size = convert_string_to_list(attrs.get("shape", '1'))
if len(sample_size) < 2:
sample_size = sample_size[-1]
else:
raise AttributeError("ONNX currently supports integer sample_size only")
node = onnx.helper.make_node(
"Multinomial",
input_nodes,
[name],
dtype=dtype,
sample_size=sample_size,
name=name,
)
return [node] | def convert_multinomial(node, **kwargs):
"""Map MXNet's multinomial operator attributes to onnx's
Multinomial operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
sample_size = convert_string_to_list(attrs.get("shape", '1'))
if len(sample_size) < 2:
sample_size = sample_size[-1]
else:
raise AttributeError("ONNX currently supports integer sample_size only")
node = onnx.helper.make_node(
"Multinomial",
input_nodes,
[name],
dtype=dtype,
sample_size=sample_size,
name=name,
)
return [node] | [
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train | convert_random_uniform | Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_random_uniform(node, **kwargs):
"""Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
low = float(attrs.get("low", 0))
high = float(attrs.get("high", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomUniform',
input_nodes,
[name],
low=low,
high=high,
dtype=dtype,
shape=shape,
name=name
)
return [node] | def convert_random_uniform(node, **kwargs):
"""Map MXNet's random_uniform operator attributes to onnx's RandomUniform
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
low = float(attrs.get("low", 0))
high = float(attrs.get("high", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomUniform',
input_nodes,
[name],
low=low,
high=high,
dtype=dtype,
shape=shape,
name=name
)
return [node] | [
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train | convert_random_normal | Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_random_normal(node, **kwargs):
"""Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
mean = float(attrs.get("loc", 0))
scale = float(attrs.get("scale", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomNormal',
input_nodes,
[name],
mean=mean,
scale=scale,
dtype=dtype,
shape=shape,
name=name
)
return [node] | def convert_random_normal(node, **kwargs):
"""Map MXNet's random_normal operator attributes to onnx's RandomNormal
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
mean = float(attrs.get("loc", 0))
scale = float(attrs.get("scale", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomNormal',
input_nodes,
[name],
mean=mean,
scale=scale,
dtype=dtype,
shape=shape,
name=name
)
return [node] | [
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train | convert_roipooling | Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_roipooling(node, **kwargs):
"""Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
scale = float(attrs.get("spatial_scale"))
node = onnx.helper.make_node(
'MaxRoiPool',
input_nodes,
[name],
pooled_shape=pooled_shape,
spatial_scale=scale,
name=name
)
return [node] | def convert_roipooling(node, **kwargs):
"""Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
scale = float(attrs.get("spatial_scale"))
node = onnx.helper.make_node(
'MaxRoiPool',
input_nodes,
[name],
pooled_shape=pooled_shape,
spatial_scale=scale,
name=name
)
return [node] | [
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train | convert_tile | Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_tile(node, **kwargs):
"""Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
reps_list = convert_string_to_list(attrs["reps"])
initializer = kwargs["initializer"]
reps_shape_np = np.array(reps_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[reps_shape_np.dtype]
dims = np.shape(reps_shape_np)
output_shape_name = "reps_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=reps_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
tile_node = onnx.helper.make_node(
"Tile",
input_nodes,
[name],
name=name
)
return [tensor_node, tile_node] | def convert_tile(node, **kwargs):
"""Map MXNet's Tile operator attributes to onnx's Tile
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
reps_list = convert_string_to_list(attrs["reps"])
initializer = kwargs["initializer"]
reps_shape_np = np.array(reps_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[reps_shape_np.dtype]
dims = np.shape(reps_shape_np)
output_shape_name = "reps_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=reps_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
tile_node = onnx.helper.make_node(
"Tile",
input_nodes,
[name],
name=name
)
return [tensor_node, tile_node] | [
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train | convert_broadcast_to | Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node. | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | def convert_broadcast_to(node, **kwargs):
"""Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
output_shape_name = "expand_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=shape_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
expand_node = onnx.helper.make_node(
"Expand",
input_nodes,
[name],
name=name
)
return [tensor_node, expand_node] | def convert_broadcast_to(node, **kwargs):
"""Map MXNet's broadcast_to operator attributes to onnx's Expand
operator and return the created node.
"""
name, input_nodes, attrs = get_inputs(node, kwargs)
shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
output_shape_name = "expand_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=shape_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
expand_node = onnx.helper.make_node(
"Expand",
input_nodes,
[name],
name=name
)
return [tensor_node, expand_node] | [
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train | Base.exe | Get the current executor
Returns
-------
exe : mxnet.executor.Executor | example/reinforcement-learning/dqn/base.py | def exe(self):
"""Get the current executor
Returns
-------
exe : mxnet.executor.Executor
"""
return self._buckets[self.curr_bucket_key]['exe'][tuple(self.data_shapes.items())] | def exe(self):
"""Get the current executor
Returns
-------
exe : mxnet.executor.Executor
"""
return self._buckets[self.curr_bucket_key]['exe'][tuple(self.data_shapes.items())] | [
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train | Base.compute_internal | View the internal symbols using the forward function.
:param sym_name:
:param bucket_kwargs:
:param input_dict:
:return: | example/reinforcement-learning/dqn/base.py | def compute_internal(self, sym_name, bucket_kwargs=None, **arg_dict):
"""
View the internal symbols using the forward function.
:param sym_name:
:param bucket_kwargs:
:param input_dict:
:return:
"""
data_shapes = {k: v.shape for k, v in arg_dict.items()}
self.switch_bucket(bucket_kwargs=bucket_kwargs,
data_shapes=data_shapes)
internal_sym = self.sym.get_internals()[sym_name]
data_inputs = {k: mx.nd.empty(v, ctx=self.ctx)
for k, v in self.data_shapes.items()
if k in internal_sym.list_arguments()}
params = {k: v for k, v in self.params.items() if
k in internal_sym.list_arguments()}
aux_states = {k: v for k, v in self.aux_states.items()
if k in internal_sym.list_auxiliary_states()}
exe = internal_sym.bind(ctx=self.ctx,
args=dict(params, **data_inputs),
args_grad=None,
grad_req='null',
aux_states=aux_states,
shared_exec=self.exe)
for k, v in arg_dict.items():
exe.arg_dict[k][:] = v
exe.forward(is_train=False)
assert 1 == len(exe.outputs)
for output in exe.outputs:
output.wait_to_read()
return exe.outputs[0] | def compute_internal(self, sym_name, bucket_kwargs=None, **arg_dict):
"""
View the internal symbols using the forward function.
:param sym_name:
:param bucket_kwargs:
:param input_dict:
:return:
"""
data_shapes = {k: v.shape for k, v in arg_dict.items()}
self.switch_bucket(bucket_kwargs=bucket_kwargs,
data_shapes=data_shapes)
internal_sym = self.sym.get_internals()[sym_name]
data_inputs = {k: mx.nd.empty(v, ctx=self.ctx)
for k, v in self.data_shapes.items()
if k in internal_sym.list_arguments()}
params = {k: v for k, v in self.params.items() if
k in internal_sym.list_arguments()}
aux_states = {k: v for k, v in self.aux_states.items()
if k in internal_sym.list_auxiliary_states()}
exe = internal_sym.bind(ctx=self.ctx,
args=dict(params, **data_inputs),
args_grad=None,
grad_req='null',
aux_states=aux_states,
shared_exec=self.exe)
for k, v in arg_dict.items():
exe.arg_dict[k][:] = v
exe.forward(is_train=False)
assert 1 == len(exe.outputs)
for output in exe.outputs:
output.wait_to_read()
return exe.outputs[0] | [
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train | init_from_fcnxs | use zero initialization for better convergence, because it tends to oputut 0,
and the label 0 stands for background, which may occupy most size of one image. | example/fcn-xs/init_fcnxs.py | def init_from_fcnxs(ctx, fcnxs_symbol, fcnxs_args_from, fcnxs_auxs_from):
""" use zero initialization for better convergence, because it tends to oputut 0,
and the label 0 stands for background, which may occupy most size of one image.
"""
fcnxs_args = fcnxs_args_from.copy()
fcnxs_auxs = fcnxs_auxs_from.copy()
for k,v in fcnxs_args.items():
if(v.context != ctx):
fcnxs_args[k] = mx.nd.zeros(v.shape, ctx)
v.copyto(fcnxs_args[k])
for k,v in fcnxs_auxs.items():
if(v.context != ctx):
fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx)
v.copyto(fcnxs_auxs[k])
data_shape=(1,3,500,500)
arg_names = fcnxs_symbol.list_arguments()
arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape)
rest_params = {}
deconv_params = {}
# this is fcn8s init from fcn16s
if 'score_pool3_weight' in arg_names:
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes)
if x[0] in ['score_pool3_bias', 'score_pool3_weight']])
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \
in ["bigscore_weight", 'score4_weight']])
# this is fcn16s init from fcn32s
elif 'score_pool4_weight' in arg_names:
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes)
if x[0] in ['score_pool4_weight', 'score_pool4_bias']])
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \
in ["bigscore_weight", 'score2_weight']])
# this is fcn32s init
else:
logging.error("you are init the fcn32s model, so you should use init_from_vgg16()")
sys.exit()
fcnxs_args.update(rest_params)
for k, v in deconv_params.items():
filt = upsample_filt(v[3])
initw = np.zeros(v)
initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing
fcnxs_args[k] = mx.nd.array(initw, ctx)
return fcnxs_args, fcnxs_auxs | def init_from_fcnxs(ctx, fcnxs_symbol, fcnxs_args_from, fcnxs_auxs_from):
""" use zero initialization for better convergence, because it tends to oputut 0,
and the label 0 stands for background, which may occupy most size of one image.
"""
fcnxs_args = fcnxs_args_from.copy()
fcnxs_auxs = fcnxs_auxs_from.copy()
for k,v in fcnxs_args.items():
if(v.context != ctx):
fcnxs_args[k] = mx.nd.zeros(v.shape, ctx)
v.copyto(fcnxs_args[k])
for k,v in fcnxs_auxs.items():
if(v.context != ctx):
fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx)
v.copyto(fcnxs_auxs[k])
data_shape=(1,3,500,500)
arg_names = fcnxs_symbol.list_arguments()
arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape)
rest_params = {}
deconv_params = {}
# this is fcn8s init from fcn16s
if 'score_pool3_weight' in arg_names:
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes)
if x[0] in ['score_pool3_bias', 'score_pool3_weight']])
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \
in ["bigscore_weight", 'score4_weight']])
# this is fcn16s init from fcn32s
elif 'score_pool4_weight' in arg_names:
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes)
if x[0] in ['score_pool4_weight', 'score_pool4_bias']])
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \
in ["bigscore_weight", 'score2_weight']])
# this is fcn32s init
else:
logging.error("you are init the fcn32s model, so you should use init_from_vgg16()")
sys.exit()
fcnxs_args.update(rest_params)
for k, v in deconv_params.items():
filt = upsample_filt(v[3])
initw = np.zeros(v)
initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing
fcnxs_args[k] = mx.nd.array(initw, ctx)
return fcnxs_args, fcnxs_auxs | [
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"v... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | residual_unit | Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator | example/image-classification/symbols/resnext.py | def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, num_group=32, bn_mom=0.9, workspace=256, memonger=False):
"""Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
if bottle_neck:
# the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.5), num_group=num_group, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
workspace=workspace, name=name + '_conv3')
bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
if dim_match:
shortcut = data
else:
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_sc')
shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')
if memonger:
shortcut._set_attr(mirror_stage='True')
eltwise = bn3 + shortcut
return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')
else:
conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
if dim_match:
shortcut = data
else:
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_sc')
shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')
if memonger:
shortcut._set_attr(mirror_stage='True')
eltwise = bn2 + shortcut
return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu') | def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, num_group=32, bn_mom=0.9, workspace=256, memonger=False):
"""Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
if bottle_neck:
# the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
conv1 = mx.sym.Convolution(data=data, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.5), num_group=num_group, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
conv3 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
workspace=workspace, name=name + '_conv3')
bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
if dim_match:
shortcut = data
else:
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_sc')
shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')
if memonger:
shortcut._set_attr(mirror_stage='True')
eltwise = bn3 + shortcut
return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu')
else:
conv1 = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
conv2 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
if dim_match:
shortcut = data
else:
shortcut_conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_sc')
shortcut = mx.sym.BatchNorm(data=shortcut_conv, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc_bn')
if memonger:
shortcut._set_attr(mirror_stage='True')
eltwise = bn2 + shortcut
return mx.sym.Activation(data=eltwise, act_type='relu', name=name + '_relu') | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | resnext | Return ResNeXt symbol of
Parameters
----------
units : list
Number of units in each stage
num_stages : int
Number of stage
filter_list : list
Channel size of each stage
num_classes : int
Ouput size of symbol
num_groupes: int
Number of conv groups
dataset : str
Dataset type, only cifar10 and imagenet supports
workspace : int
Workspace used in convolution operator
dtype : str
Precision (float32 or float16) | example/image-classification/symbols/resnext.py | def resnext(units, num_stages, filter_list, num_classes, num_group, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False):
"""Return ResNeXt symbol of
Parameters
----------
units : list
Number of units in each stage
num_stages : int
Number of stage
filter_list : list
Channel size of each stage
num_classes : int
Ouput size of symbol
num_groupes: int
Number of conv groups
dataset : str
Dataset type, only cifar10 and imagenet supports
workspace : int
Workspace used in convolution operator
dtype : str
Precision (float32 or float16)
"""
num_unit = len(units)
assert(num_unit == num_stages)
data = mx.sym.Variable(name='data')
if dtype == 'float32':
data = mx.sym.identity(data=data, name='id')
else:
if dtype == 'float16':
data = mx.sym.Cast(data=data, dtype=np.float16)
data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
(nchannel, height, width) = image_shape
if height <= 32: # such as cifar10
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
no_bias=True, name="conv0", workspace=workspace)
else: # often expected to be 224 such as imagenet
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
no_bias=True, name="conv0", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
for i in range(num_stages):
body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, num_group=num_group,
bn_mom=bn_mom, workspace=workspace, memonger=memonger)
for j in range(units[i]-1):
body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger)
pool1 = mx.sym.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.sym.Flatten(data=pool1)
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
if dtype == 'float16':
fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
return mx.sym.SoftmaxOutput(data=fc1, name='softmax') | def resnext(units, num_stages, filter_list, num_classes, num_group, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False):
"""Return ResNeXt symbol of
Parameters
----------
units : list
Number of units in each stage
num_stages : int
Number of stage
filter_list : list
Channel size of each stage
num_classes : int
Ouput size of symbol
num_groupes: int
Number of conv groups
dataset : str
Dataset type, only cifar10 and imagenet supports
workspace : int
Workspace used in convolution operator
dtype : str
Precision (float32 or float16)
"""
num_unit = len(units)
assert(num_unit == num_stages)
data = mx.sym.Variable(name='data')
if dtype == 'float32':
data = mx.sym.identity(data=data, name='id')
else:
if dtype == 'float16':
data = mx.sym.Cast(data=data, dtype=np.float16)
data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
(nchannel, height, width) = image_shape
if height <= 32: # such as cifar10
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1),
no_bias=True, name="conv0", workspace=workspace)
else: # often expected to be 224 such as imagenet
body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
no_bias=True, name="conv0", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
for i in range(num_stages):
body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, num_group=num_group,
bn_mom=bn_mom, workspace=workspace, memonger=memonger)
for j in range(units[i]-1):
body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger)
pool1 = mx.sym.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.sym.Flatten(data=pool1)
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
if dtype == 'float16':
fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
return mx.sym.SoftmaxOutput(data=fc1, name='softmax') | [
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train | get_symbol | Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
Original author Wei Wu | example/image-classification/symbols/resnext.py | def get_symbol(num_classes, num_layers, image_shape, num_group=32, conv_workspace=256, dtype='float32', **kwargs):
"""
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
Original author Wei Wu
"""
image_shape = [int(l) for l in image_shape.split(',')]
(nchannel, height, width) = image_shape
if height <= 32:
num_stages = 3
if (num_layers-2) % 9 == 0 and num_layers >= 164:
per_unit = [(num_layers-2)//9]
filter_list = [16, 64, 128, 256]
bottle_neck = True
elif (num_layers-2) % 6 == 0 and num_layers < 164:
per_unit = [(num_layers-2)//6]
filter_list = [16, 16, 32, 64]
bottle_neck = False
else:
raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
units = per_unit * num_stages
else:
if num_layers >= 50:
filter_list = [64, 256, 512, 1024, 2048]
bottle_neck = True
else:
filter_list = [64, 64, 128, 256, 512]
bottle_neck = False
num_stages = 4
if num_layers == 18:
units = [2, 2, 2, 2]
elif num_layers == 34:
units = [3, 4, 6, 3]
elif num_layers == 50:
units = [3, 4, 6, 3]
elif num_layers == 101:
units = [3, 4, 23, 3]
elif num_layers == 152:
units = [3, 8, 36, 3]
elif num_layers == 200:
units = [3, 24, 36, 3]
elif num_layers == 269:
units = [3, 30, 48, 8]
else:
raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
return resnext(units = units,
num_stages = num_stages,
filter_list = filter_list,
num_classes = num_classes,
num_group = num_group,
image_shape = image_shape,
bottle_neck = bottle_neck,
workspace = conv_workspace,
dtype = dtype) | def get_symbol(num_classes, num_layers, image_shape, num_group=32, conv_workspace=256, dtype='float32', **kwargs):
"""
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
Original author Wei Wu
"""
image_shape = [int(l) for l in image_shape.split(',')]
(nchannel, height, width) = image_shape
if height <= 32:
num_stages = 3
if (num_layers-2) % 9 == 0 and num_layers >= 164:
per_unit = [(num_layers-2)//9]
filter_list = [16, 64, 128, 256]
bottle_neck = True
elif (num_layers-2) % 6 == 0 and num_layers < 164:
per_unit = [(num_layers-2)//6]
filter_list = [16, 16, 32, 64]
bottle_neck = False
else:
raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
units = per_unit * num_stages
else:
if num_layers >= 50:
filter_list = [64, 256, 512, 1024, 2048]
bottle_neck = True
else:
filter_list = [64, 64, 128, 256, 512]
bottle_neck = False
num_stages = 4
if num_layers == 18:
units = [2, 2, 2, 2]
elif num_layers == 34:
units = [3, 4, 6, 3]
elif num_layers == 50:
units = [3, 4, 6, 3]
elif num_layers == 101:
units = [3, 4, 23, 3]
elif num_layers == 152:
units = [3, 8, 36, 3]
elif num_layers == 200:
units = [3, 24, 36, 3]
elif num_layers == 269:
units = [3, 30, 48, 8]
else:
raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
return resnext(units = units,
num_stages = num_stages,
filter_list = filter_list,
num_classes = num_classes,
num_group = num_group,
image_shape = image_shape,
bottle_neck = bottle_neck,
workspace = conv_workspace,
dtype = dtype) | [
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train | var | Creates a symbolic variable with specified name.
Example
-------
>>> data = mx.sym.Variable('data', attr={'a': 'b'})
>>> data
<Symbol data>
>>> csr_data = mx.sym.Variable('csr_data', stype='csr')
>>> csr_data
<Symbol csr_data>
>>> row_sparse_weight = mx.sym.Variable('weight', stype='row_sparse')
>>> row_sparse_weight
<Symbol weight>
Parameters
----------
name : str
Variable name.
attr : Dict of strings
Additional attributes to set on the variable. Format {string : string}.
shape : tuple
The shape of a variable. If specified, this will be used during the shape inference.
If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.
lr_mult : float
The learning rate multiplier for input variable.
wd_mult : float
Weight decay multiplier for input variable.
dtype : str or numpy.dtype
The dtype for input variable. If not specified, this value will be inferred.
init : initializer (mxnet.init.*)
Initializer for this variable to (optionally) override the default initializer.
stype : str
The storage type of the variable, such as 'row_sparse', 'csr', 'default', etc
kwargs : Additional attribute variables
Additional attributes must start and end with double underscores.
Returns
-------
variable : Symbol
A symbol corresponding to an input to the computation graph. | python/mxnet/symbol/symbol.py | def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None,
init=None, stype=None, **kwargs):
"""Creates a symbolic variable with specified name.
Example
-------
>>> data = mx.sym.Variable('data', attr={'a': 'b'})
>>> data
<Symbol data>
>>> csr_data = mx.sym.Variable('csr_data', stype='csr')
>>> csr_data
<Symbol csr_data>
>>> row_sparse_weight = mx.sym.Variable('weight', stype='row_sparse')
>>> row_sparse_weight
<Symbol weight>
Parameters
----------
name : str
Variable name.
attr : Dict of strings
Additional attributes to set on the variable. Format {string : string}.
shape : tuple
The shape of a variable. If specified, this will be used during the shape inference.
If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.
lr_mult : float
The learning rate multiplier for input variable.
wd_mult : float
Weight decay multiplier for input variable.
dtype : str or numpy.dtype
The dtype for input variable. If not specified, this value will be inferred.
init : initializer (mxnet.init.*)
Initializer for this variable to (optionally) override the default initializer.
stype : str
The storage type of the variable, such as 'row_sparse', 'csr', 'default', etc
kwargs : Additional attribute variables
Additional attributes must start and end with double underscores.
Returns
-------
variable : Symbol
A symbol corresponding to an input to the computation graph.
"""
if not isinstance(name, string_types):
raise TypeError('Expect a string for variable `name`')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateVariable(c_str(name), ctypes.byref(handle)))
ret = Symbol(handle)
if not hasattr(AttrScope._current, "value"):
AttrScope._current.value = AttrScope()
attr = AttrScope._current.value.get(attr)
attr = {} if attr is None else attr
if shape is not None:
attr['__shape__'] = str(shape)
if lr_mult is not None:
attr['__lr_mult__'] = str(lr_mult)
if wd_mult is not None:
attr['__wd_mult__'] = str(wd_mult)
if dtype is not None:
attr['__dtype__'] = str(_DTYPE_NP_TO_MX[_numpy.dtype(dtype).type])
if init is not None:
if not isinstance(init, string_types):
init = init.dumps()
attr['__init__'] = init
if stype is not None:
attr['__storage_type__'] = str(_STORAGE_TYPE_STR_TO_ID[stype])
for k, v in kwargs.items():
if k.startswith('__') and k.endswith('__'):
attr[k] = str(v)
else:
raise ValueError('Attribute name=%s is not supported.'
' Additional attributes must start and end with double underscores,'
' e.g, __yourattr__' % k)
ret._set_attr(**attr)
return ret | def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None,
init=None, stype=None, **kwargs):
"""Creates a symbolic variable with specified name.
Example
-------
>>> data = mx.sym.Variable('data', attr={'a': 'b'})
>>> data
<Symbol data>
>>> csr_data = mx.sym.Variable('csr_data', stype='csr')
>>> csr_data
<Symbol csr_data>
>>> row_sparse_weight = mx.sym.Variable('weight', stype='row_sparse')
>>> row_sparse_weight
<Symbol weight>
Parameters
----------
name : str
Variable name.
attr : Dict of strings
Additional attributes to set on the variable. Format {string : string}.
shape : tuple
The shape of a variable. If specified, this will be used during the shape inference.
If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.
lr_mult : float
The learning rate multiplier for input variable.
wd_mult : float
Weight decay multiplier for input variable.
dtype : str or numpy.dtype
The dtype for input variable. If not specified, this value will be inferred.
init : initializer (mxnet.init.*)
Initializer for this variable to (optionally) override the default initializer.
stype : str
The storage type of the variable, such as 'row_sparse', 'csr', 'default', etc
kwargs : Additional attribute variables
Additional attributes must start and end with double underscores.
Returns
-------
variable : Symbol
A symbol corresponding to an input to the computation graph.
"""
if not isinstance(name, string_types):
raise TypeError('Expect a string for variable `name`')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateVariable(c_str(name), ctypes.byref(handle)))
ret = Symbol(handle)
if not hasattr(AttrScope._current, "value"):
AttrScope._current.value = AttrScope()
attr = AttrScope._current.value.get(attr)
attr = {} if attr is None else attr
if shape is not None:
attr['__shape__'] = str(shape)
if lr_mult is not None:
attr['__lr_mult__'] = str(lr_mult)
if wd_mult is not None:
attr['__wd_mult__'] = str(wd_mult)
if dtype is not None:
attr['__dtype__'] = str(_DTYPE_NP_TO_MX[_numpy.dtype(dtype).type])
if init is not None:
if not isinstance(init, string_types):
init = init.dumps()
attr['__init__'] = init
if stype is not None:
attr['__storage_type__'] = str(_STORAGE_TYPE_STR_TO_ID[stype])
for k, v in kwargs.items():
if k.startswith('__') and k.endswith('__'):
attr[k] = str(v)
else:
raise ValueError('Attribute name=%s is not supported.'
' Additional attributes must start and end with double underscores,'
' e.g, __yourattr__' % k)
ret._set_attr(**attr)
return ret | [
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train | Group | Creates a symbol that contains a collection of other symbols, grouped together.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> mx.sym.Group([a,b])
<Symbol Grouped>
Parameters
----------
symbols : list
List of symbols to be grouped.
Returns
-------
sym : Symbol
A group symbol. | python/mxnet/symbol/symbol.py | def Group(symbols):
"""Creates a symbol that contains a collection of other symbols, grouped together.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> mx.sym.Group([a,b])
<Symbol Grouped>
Parameters
----------
symbols : list
List of symbols to be grouped.
Returns
-------
sym : Symbol
A group symbol.
"""
if not symbols or any(not isinstance(sym, Symbol) for sym in symbols):
raise TypeError('Expected a list of symbols as input')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateGroup(
mx_uint(len(symbols)),
c_handle_array(symbols), ctypes.byref(handle)))
return Symbol(handle) | def Group(symbols):
"""Creates a symbol that contains a collection of other symbols, grouped together.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> mx.sym.Group([a,b])
<Symbol Grouped>
Parameters
----------
symbols : list
List of symbols to be grouped.
Returns
-------
sym : Symbol
A group symbol.
"""
if not symbols or any(not isinstance(sym, Symbol) for sym in symbols):
raise TypeError('Expected a list of symbols as input')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateGroup(
mx_uint(len(symbols)),
c_handle_array(symbols), ctypes.byref(handle)))
return Symbol(handle) | [
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train | load | Loads symbol from a JSON file.
You can also use pickle to do the job if you only work on python.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file, examples:
- `s3://my-bucket/path/my-s3-symbol`
- `hdfs://my-bucket/path/my-hdfs-symbol`
- `/path-to/my-local-symbol`
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.save : Used to save symbol into file. | python/mxnet/symbol/symbol.py | def load(fname):
"""Loads symbol from a JSON file.
You can also use pickle to do the job if you only work on python.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file, examples:
- `s3://my-bucket/path/my-s3-symbol`
- `hdfs://my-bucket/path/my-hdfs-symbol`
- `/path-to/my-local-symbol`
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.save : Used to save symbol into file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromFile(c_str(fname), ctypes.byref(handle)))
return Symbol(handle) | def load(fname):
"""Loads symbol from a JSON file.
You can also use pickle to do the job if you only work on python.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file, examples:
- `s3://my-bucket/path/my-s3-symbol`
- `hdfs://my-bucket/path/my-hdfs-symbol`
- `/path-to/my-local-symbol`
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.save : Used to save symbol into file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromFile(c_str(fname), ctypes.byref(handle)))
return Symbol(handle) | [
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train | load_json | Loads symbol from json string.
Parameters
----------
json_str : str
A JSON string.
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.tojson : Used to save symbol into json string. | python/mxnet/symbol/symbol.py | def load_json(json_str):
"""Loads symbol from json string.
Parameters
----------
json_str : str
A JSON string.
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.tojson : Used to save symbol into json string.
"""
if not isinstance(json_str, string_types):
raise TypeError('fname required to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
return Symbol(handle) | def load_json(json_str):
"""Loads symbol from json string.
Parameters
----------
json_str : str
A JSON string.
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.tojson : Used to save symbol into json string.
"""
if not isinstance(json_str, string_types):
raise TypeError('fname required to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
return Symbol(handle) | [
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train | pow | Returns element-wise result of base element raised to powers from exp element.
Both inputs can be Symbol or scalar number.
Broadcasting is not supported. Use `broadcast_pow` instead.
`sym.pow` is being deprecated, please use `sym.power` instead.
Parameters
---------
base : Symbol or scalar
The base symbol
exp : Symbol or scalar
The exponent symbol
Returns
-------
Symbol or scalar
The bases in x raised to the exponents in y.
Examples
--------
>>> mx.sym.pow(2, 3)
8
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.pow(x, 2)
>>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
array([ 1., 4.], dtype=float32)
>>> z = mx.sym.pow(3, y)
>>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 27.], dtype=float32)
>>> z = mx.sym.pow(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 64.], dtype=float32) | python/mxnet/symbol/symbol.py | def pow(base, exp):
"""Returns element-wise result of base element raised to powers from exp element.
Both inputs can be Symbol or scalar number.
Broadcasting is not supported. Use `broadcast_pow` instead.
`sym.pow` is being deprecated, please use `sym.power` instead.
Parameters
---------
base : Symbol or scalar
The base symbol
exp : Symbol or scalar
The exponent symbol
Returns
-------
Symbol or scalar
The bases in x raised to the exponents in y.
Examples
--------
>>> mx.sym.pow(2, 3)
8
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.pow(x, 2)
>>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
array([ 1., 4.], dtype=float32)
>>> z = mx.sym.pow(3, y)
>>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 27.], dtype=float32)
>>> z = mx.sym.pow(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 64.], dtype=float32)
"""
if isinstance(base, Symbol) and isinstance(exp, Symbol):
return _internal._Power(base, exp)
if isinstance(base, Symbol) and isinstance(exp, Number):
return _internal._PowerScalar(base, scalar=exp)
if isinstance(base, Number) and isinstance(exp, Symbol):
return _internal._RPowerScalar(exp, scalar=base)
if isinstance(base, Number) and isinstance(exp, Number):
return base**exp
else:
raise TypeError('types (%s, %s) not supported' % (str(type(base)), str(type(exp)))) | def pow(base, exp):
"""Returns element-wise result of base element raised to powers from exp element.
Both inputs can be Symbol or scalar number.
Broadcasting is not supported. Use `broadcast_pow` instead.
`sym.pow` is being deprecated, please use `sym.power` instead.
Parameters
---------
base : Symbol or scalar
The base symbol
exp : Symbol or scalar
The exponent symbol
Returns
-------
Symbol or scalar
The bases in x raised to the exponents in y.
Examples
--------
>>> mx.sym.pow(2, 3)
8
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.pow(x, 2)
>>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
array([ 1., 4.], dtype=float32)
>>> z = mx.sym.pow(3, y)
>>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 27.], dtype=float32)
>>> z = mx.sym.pow(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 64.], dtype=float32)
"""
if isinstance(base, Symbol) and isinstance(exp, Symbol):
return _internal._Power(base, exp)
if isinstance(base, Symbol) and isinstance(exp, Number):
return _internal._PowerScalar(base, scalar=exp)
if isinstance(base, Number) and isinstance(exp, Symbol):
return _internal._RPowerScalar(exp, scalar=base)
if isinstance(base, Number) and isinstance(exp, Number):
return base**exp
else:
raise TypeError('types (%s, %s) not supported' % (str(type(base)), str(type(exp)))) | [
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train | maximum | Returns element-wise maximum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise maximum of the input symbols.
Examples
--------
>>> mx.sym.maximum(2, 3.5)
3.5
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.maximum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 4., 5., 4., 10.], dtype=float32)
>>> z = mx.sym.maximum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10., 4.], dtype=float32) | python/mxnet/symbol/symbol.py | def maximum(left, right):
"""Returns element-wise maximum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise maximum of the input symbols.
Examples
--------
>>> mx.sym.maximum(2, 3.5)
3.5
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.maximum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 4., 5., 4., 10.], dtype=float32)
>>> z = mx.sym.maximum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10., 4.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Maximum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MaximumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MaximumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left > right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | def maximum(left, right):
"""Returns element-wise maximum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise maximum of the input symbols.
Examples
--------
>>> mx.sym.maximum(2, 3.5)
3.5
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.maximum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 4., 5., 4., 10.], dtype=float32)
>>> z = mx.sym.maximum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10., 4.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Maximum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MaximumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MaximumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left > right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | [
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train | minimum | Returns element-wise minimum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise minimum of the input symbols.
Examples
--------
>>> mx.sym.minimum(2, 3.5)
2
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.minimum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 3., 4., 2., 4.], dtype=float32)
>>> z = mx.sym.minimum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 3., 2.], dtype=float32) | python/mxnet/symbol/symbol.py | def minimum(left, right):
"""Returns element-wise minimum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise minimum of the input symbols.
Examples
--------
>>> mx.sym.minimum(2, 3.5)
2
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.minimum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 3., 4., 2., 4.], dtype=float32)
>>> z = mx.sym.minimum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 3., 2.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Minimum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MinimumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MinimumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left < right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | def minimum(left, right):
"""Returns element-wise minimum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise minimum of the input symbols.
Examples
--------
>>> mx.sym.minimum(2, 3.5)
2
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.minimum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 3., 4., 2., 4.], dtype=float32)
>>> z = mx.sym.minimum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 3., 2.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Minimum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MinimumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MinimumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left < right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | [
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train | hypot | Given the "legs" of a right triangle, returns its hypotenuse.
Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First leg of the triangle(s).
right : Symbol or scalar
Second leg of the triangle(s).
Returns
-------
Symbol or scalar
The hypotenuse of the triangle(s)
Examples
--------
>>> mx.sym.hypot(3, 4)
5.0
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.hypot(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2]))[0].asnumpy()
array([ 5., 6.40312433, 4.47213602], dtype=float32)
>>> z = mx.sym.hypot(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10.44030666, 4.47213602], dtype=float32) | python/mxnet/symbol/symbol.py | def hypot(left, right):
"""Given the "legs" of a right triangle, returns its hypotenuse.
Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First leg of the triangle(s).
right : Symbol or scalar
Second leg of the triangle(s).
Returns
-------
Symbol or scalar
The hypotenuse of the triangle(s)
Examples
--------
>>> mx.sym.hypot(3, 4)
5.0
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.hypot(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2]))[0].asnumpy()
array([ 5., 6.40312433, 4.47213602], dtype=float32)
>>> z = mx.sym.hypot(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10.44030666, 4.47213602], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Hypot(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._HypotScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._HypotScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return _numpy.hypot(left, right)
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | def hypot(left, right):
"""Given the "legs" of a right triangle, returns its hypotenuse.
Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First leg of the triangle(s).
right : Symbol or scalar
Second leg of the triangle(s).
Returns
-------
Symbol or scalar
The hypotenuse of the triangle(s)
Examples
--------
>>> mx.sym.hypot(3, 4)
5.0
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.hypot(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2]))[0].asnumpy()
array([ 5., 6.40312433, 4.47213602], dtype=float32)
>>> z = mx.sym.hypot(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10.44030666, 4.47213602], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Hypot(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._HypotScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._HypotScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return _numpy.hypot(left, right)
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) | [
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train | eye | Returns a new symbol of 2-D shpae, filled 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
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol. | python/mxnet/symbol/symbol.py | def eye(N, M=0, k=0, dtype=None, **kwargs):
"""Returns a new symbol of 2-D shpae, filled 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
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol.
"""
if dtype is None:
dtype = _numpy.float32
return _internal._eye(N, M, k, dtype=dtype, **kwargs) | def eye(N, M=0, k=0, dtype=None, **kwargs):
"""Returns a new symbol of 2-D shpae, filled 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
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol.
"""
if dtype is None:
dtype = _numpy.float32
return _internal._eye(N, M, k, dtype=dtype, **kwargs) | [
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train | zeros | Returns a new symbol of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol. | python/mxnet/symbol/symbol.py | def zeros(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol.
"""
if dtype is None:
dtype = _numpy.float32
return _internal._zeros(shape=shape, dtype=dtype, **kwargs) | def zeros(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol.
"""
if dtype is None:
dtype = _numpy.float32
return _internal._zeros(shape=shape, dtype=dtype, **kwargs) | [
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train | ones | Returns a new symbol of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol | python/mxnet/symbol/symbol.py | def ones(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._ones(shape=shape, dtype=dtype, **kwargs) | def ones(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._ones(shape=shape, dtype=dtype, **kwargs) | [
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train | full | Returns a new array of given shape and type, filled with the given value `val`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
val : scalar
Fill value.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol | python/mxnet/symbol/symbol.py | def full(shape, val, dtype=None, **kwargs):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
val : scalar
Fill value.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._full(shape=shape, dtype=dtype, value=float(val), **kwargs) | def full(shape, val, dtype=None, **kwargs):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
val : scalar
Fill value.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._full(shape=shape, dtype=dtype, value=float(val), **kwargs) | [
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train | arange | Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns a `Symbol`.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
End of interval. The interval does not include this value.
step : number, optional
Spacing between values.
repeat : int, optional
"The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol | python/mxnet/symbol/symbol.py | def arange(start, stop=None, step=1.0, repeat=1, infer_range=False, name=None, dtype=None):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns a `Symbol`.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
End of interval. The interval does not include this value.
step : number, optional
Spacing between values.
repeat : int, optional
"The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
infer_range=infer_range, name=name, dtype=dtype) | def arange(start, stop=None, step=1.0, repeat=1, infer_range=False, name=None, dtype=None):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns a `Symbol`.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
End of interval. The interval does not include this value.
step : number, optional
Spacing between values.
repeat : int, optional
"The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
infer_range=infer_range, name=name, dtype=dtype) | [
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train | histogram | Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), required if bins is an integer
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
out : Symbol
The created Symbol | python/mxnet/symbol/symbol.py | def histogram(a, bins=10, range=None, **kwargs):
"""Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), required if bins is an integer
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
out : Symbol
The created Symbol
"""
if isinstance(bins, Symbol):
return _internal._histogram(data=a, bins=bins, **kwargs)
elif isinstance(bins, integer_types):
if range is None:
raise ValueError("null range is not supported in symbol mode")
return _internal._histogram(data=a, bin_cnt=bins, range=range, **kwargs)
raise ValueError("bins argument should be either an integer or an NDArray") | def histogram(a, bins=10, range=None, **kwargs):
"""Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), required if bins is an integer
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
out : Symbol
The created Symbol
"""
if isinstance(bins, Symbol):
return _internal._histogram(data=a, bins=bins, **kwargs)
elif isinstance(bins, integer_types):
if range is None:
raise ValueError("null range is not supported in symbol mode")
return _internal._histogram(data=a, bin_cnt=bins, range=range, **kwargs)
raise ValueError("bins argument should be either an integer or an NDArray") | [
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train | split_v2 | Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol | python/mxnet/symbol/symbol.py | def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol
"""
indices = []
sections = 0
if isinstance(indices_or_sections, int):
sections = indices_or_sections
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis, sections) | def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol
"""
indices = []
sections = 0
if isinstance(indices_or_sections, int):
sections = indices_or_sections
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis, sections) | [
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train | Symbol.name | Gets name string from the symbol, this function only works for non-grouped symbol.
Returns
-------
value : str
The name of this symbol, returns ``None`` for grouped symbol. | python/mxnet/symbol/symbol.py | def name(self):
"""Gets name string from the symbol, this function only works for non-grouped symbol.
Returns
-------
value : str
The name of this symbol, returns ``None`` for grouped symbol.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetName(
self.handle, ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None | def name(self):
"""Gets name string from the symbol, this function only works for non-grouped symbol.
Returns
-------
value : str
The name of this symbol, returns ``None`` for grouped symbol.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetName(
self.handle, ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None | [
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train | Symbol.attr | Returns the attribute string for corresponding input key from the symbol.
This function only works for non-grouped symbols.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.attr('mood')
'angry'
Parameters
----------
key : str
The key corresponding to the desired attribute.
Returns
-------
value : str
The desired attribute value, returns ``None`` if the attribute does not exist. | python/mxnet/symbol/symbol.py | def attr(self, key):
"""Returns the attribute string for corresponding input key from the symbol.
This function only works for non-grouped symbols.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.attr('mood')
'angry'
Parameters
----------
key : str
The key corresponding to the desired attribute.
Returns
-------
value : str
The desired attribute value, returns ``None`` if the attribute does not exist.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetAttr(
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None | def attr(self, key):
"""Returns the attribute string for corresponding input key from the symbol.
This function only works for non-grouped symbols.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.attr('mood')
'angry'
Parameters
----------
key : str
The key corresponding to the desired attribute.
Returns
-------
value : str
The desired attribute value, returns ``None`` if the attribute does not exist.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetAttr(
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None | [
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train | Symbol.list_attr | Gets all attributes from the symbol.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.list_attr()
{'mood': 'angry'}
Returns
-------
ret : Dict of str to str
A dictionary mapping attribute keys to values. | python/mxnet/symbol/symbol.py | def list_attr(self, recursive=False):
"""Gets all attributes from the symbol.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.list_attr()
{'mood': 'angry'}
Returns
-------
ret : Dict of str to str
A dictionary mapping attribute keys to values.
"""
if recursive:
raise DeprecationWarning("Symbol.list_attr with recursive=True has been deprecated. "
"Please use attr_dict instead.")
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttrShallow
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
return {py_str(pairs[i * 2]): py_str(pairs[i * 2 + 1]) for i in range(size.value)} | def list_attr(self, recursive=False):
"""Gets all attributes from the symbol.
Example
-------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.list_attr()
{'mood': 'angry'}
Returns
-------
ret : Dict of str to str
A dictionary mapping attribute keys to values.
"""
if recursive:
raise DeprecationWarning("Symbol.list_attr with recursive=True has been deprecated. "
"Please use attr_dict instead.")
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttrShallow
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
return {py_str(pairs[i * 2]): py_str(pairs[i * 2 + 1]) for i in range(size.value)} | [
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train | Symbol.attr_dict | Recursively gets all attributes from the symbol and its children.
Example
-------
>>> a = mx.sym.Variable('a', attr={'a1':'a2'})
>>> b = mx.sym.Variable('b', attr={'b1':'b2'})
>>> c = a+b
>>> c.attr_dict()
{'a': {'a1': 'a2'}, 'b': {'b1': 'b2'}}
Returns
-------
ret : Dict of str to dict
There is a key in the returned dict for every child with non-empty attribute set.
For each symbol, the name of the symbol is its key in the dict
and the correspond value is that symbol's attribute list (itself a dictionary). | python/mxnet/symbol/symbol.py | def attr_dict(self):
"""Recursively gets all attributes from the symbol and its children.
Example
-------
>>> a = mx.sym.Variable('a', attr={'a1':'a2'})
>>> b = mx.sym.Variable('b', attr={'b1':'b2'})
>>> c = a+b
>>> c.attr_dict()
{'a': {'a1': 'a2'}, 'b': {'b1': 'b2'}}
Returns
-------
ret : Dict of str to dict
There is a key in the returned dict for every child with non-empty attribute set.
For each symbol, the name of the symbol is its key in the dict
and the correspond value is that symbol's attribute list (itself a dictionary).
"""
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttr
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
ret = {}
for i in range(size.value):
name, key = py_str(pairs[i * 2]).split('$')
val = py_str(pairs[i * 2 + 1])
if name not in ret:
ret[name] = {}
ret[name][key] = val
return ret | def attr_dict(self):
"""Recursively gets all attributes from the symbol and its children.
Example
-------
>>> a = mx.sym.Variable('a', attr={'a1':'a2'})
>>> b = mx.sym.Variable('b', attr={'b1':'b2'})
>>> c = a+b
>>> c.attr_dict()
{'a': {'a1': 'a2'}, 'b': {'b1': 'b2'}}
Returns
-------
ret : Dict of str to dict
There is a key in the returned dict for every child with non-empty attribute set.
For each symbol, the name of the symbol is its key in the dict
and the correspond value is that symbol's attribute list (itself a dictionary).
"""
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttr
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
ret = {}
for i in range(size.value):
name, key = py_str(pairs[i * 2]).split('$')
val = py_str(pairs[i * 2 + 1])
if name not in ret:
ret[name] = {}
ret[name][key] = val
return ret | [
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train | Symbol._set_attr | Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set | python/mxnet/symbol/symbol.py | def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set
"""
for key, value in kwargs.items():
if not isinstance(value, string_types):
raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value)))) | def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set
"""
for key, value in kwargs.items():
if not isinstance(value, string_types):
raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value)))) | [
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train | Symbol.get_internals | Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of
outputs of all of the internal nodes.
Consider the following code:
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> d = c.get_internals()
>>> d
<Symbol Grouped>
>>> d.list_outputs()
['a', 'b', '_plus4_output']
Returns
-------
sgroup : Symbol
A symbol group containing all internal and leaf nodes of the computation graph
used to compute the symbol. | python/mxnet/symbol/symbol.py | def get_internals(self):
"""Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of
outputs of all of the internal nodes.
Consider the following code:
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> d = c.get_internals()
>>> d
<Symbol Grouped>
>>> d.list_outputs()
['a', 'b', '_plus4_output']
Returns
-------
sgroup : Symbol
A symbol group containing all internal and leaf nodes of the computation graph
used to compute the symbol.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetInternals(
self.handle, ctypes.byref(handle)))
return Symbol(handle=handle) | def get_internals(self):
"""Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of
outputs of all of the internal nodes.
Consider the following code:
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> d = c.get_internals()
>>> d
<Symbol Grouped>
>>> d.list_outputs()
['a', 'b', '_plus4_output']
Returns
-------
sgroup : Symbol
A symbol group containing all internal and leaf nodes of the computation graph
used to compute the symbol.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetInternals(
self.handle, ctypes.byref(handle)))
return Symbol(handle=handle) | [
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train | Symbol.get_children | Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.Variable('z')
>>> a = y+z
>>> b = x+a
>>> b.get_children()
<Symbol Grouped>
>>> b.get_children().list_outputs()
['x', '_plus10_output']
>>> b.get_children().get_children().list_outputs()
['y', 'z']
Returns
-------
sgroup : Symbol or None
The children of the head node. If the symbol has no
inputs then ``None`` will be returned. | python/mxnet/symbol/symbol.py | def get_children(self):
"""Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.Variable('z')
>>> a = y+z
>>> b = x+a
>>> b.get_children()
<Symbol Grouped>
>>> b.get_children().list_outputs()
['x', '_plus10_output']
>>> b.get_children().get_children().list_outputs()
['y', 'z']
Returns
-------
sgroup : Symbol or None
The children of the head node. If the symbol has no
inputs then ``None`` will be returned.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetChildren(
self.handle, ctypes.byref(handle)))
ret = Symbol(handle=handle)
if len(ret.list_outputs()) == 0:
return None
return ret | def get_children(self):
"""Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.Variable('z')
>>> a = y+z
>>> b = x+a
>>> b.get_children()
<Symbol Grouped>
>>> b.get_children().list_outputs()
['x', '_plus10_output']
>>> b.get_children().get_children().list_outputs()
['y', 'z']
Returns
-------
sgroup : Symbol or None
The children of the head node. If the symbol has no
inputs then ``None`` will be returned.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetChildren(
self.handle, ctypes.byref(handle)))
ret = Symbol(handle=handle)
if len(ret.list_outputs()) == 0:
return None
return ret | [
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train | Symbol.list_arguments | Lists all the arguments in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_arguments
['a', 'b']
Returns
-------
args : list of string
List containing the names of all the arguments required to compute the symbol. | python/mxnet/symbol/symbol.py | def list_arguments(self):
"""Lists all the arguments in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_arguments
['a', 'b']
Returns
-------
args : list of string
List containing the names of all the arguments required to compute the symbol.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListArguments(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | def list_arguments(self):
"""Lists all the arguments in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_arguments
['a', 'b']
Returns
-------
args : list of string
List containing the names of all the arguments required to compute the symbol.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListArguments(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | [
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train | Symbol.list_outputs | Lists all the outputs in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_outputs()
['_plus12_output']
Returns
-------
list of str
List of all the outputs.
For most symbols, this list contains only the name of this symbol.
For symbol groups, this is a list with the names of all symbols
in the group. | python/mxnet/symbol/symbol.py | def list_outputs(self):
"""Lists all the outputs in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_outputs()
['_plus12_output']
Returns
-------
list of str
List of all the outputs.
For most symbols, this list contains only the name of this symbol.
For symbol groups, this is a list with the names of all symbols
in the group.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListOutputs(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | def list_outputs(self):
"""Lists all the outputs in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_outputs()
['_plus12_output']
Returns
-------
list of str
List of all the outputs.
For most symbols, this list contains only the name of this symbol.
For symbol groups, this is a list with the names of all symbols
in the group.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListOutputs(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | [
"Lists",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L734-L757 | [
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train | Symbol.list_auxiliary_states | Lists all the auxiliary states in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_auxiliary_states()
[]
Example of auxiliary states in `BatchNorm`.
>>> 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')
>>> fc2.list_auxiliary_states()
['batchnorm0_moving_mean', 'batchnorm0_moving_var']
Returns
-------
aux_states : list of str
List of the auxiliary states in input symbol.
Notes
-----
Auxiliary states are special states of symbols that do not correspond to an argument,
and are not updated by gradient descent. Common examples of auxiliary states
include the `moving_mean` and `moving_variance` in `BatchNorm`.
Most operators do not have auxiliary states. | python/mxnet/symbol/symbol.py | def list_auxiliary_states(self):
"""Lists all the auxiliary states in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_auxiliary_states()
[]
Example of auxiliary states in `BatchNorm`.
>>> 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')
>>> fc2.list_auxiliary_states()
['batchnorm0_moving_mean', 'batchnorm0_moving_var']
Returns
-------
aux_states : list of str
List of the auxiliary states in input symbol.
Notes
-----
Auxiliary states are special states of symbols that do not correspond to an argument,
and are not updated by gradient descent. Common examples of auxiliary states
include the `moving_mean` and `moving_variance` in `BatchNorm`.
Most operators do not have auxiliary states.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListAuxiliaryStates(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | def list_auxiliary_states(self):
"""Lists all the auxiliary states in the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_auxiliary_states()
[]
Example of auxiliary states in `BatchNorm`.
>>> 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')
>>> fc2.list_auxiliary_states()
['batchnorm0_moving_mean', 'batchnorm0_moving_var']
Returns
-------
aux_states : list of str
List of the auxiliary states in input symbol.
Notes
-----
Auxiliary states are special states of symbols that do not correspond to an argument,
and are not updated by gradient descent. Common examples of auxiliary states
include the `moving_mean` and `moving_variance` in `BatchNorm`.
Most operators do not have auxiliary states.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListAuxiliaryStates(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L779-L815 | [
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"(",... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.list_inputs | Lists all arguments and auxiliary states of this Symbol.
Returns
-------
inputs : list of str
List of all inputs.
Examples
--------
>>> bn = mx.sym.BatchNorm(name='bn')
>>> bn.list_arguments()
['bn_data', 'bn_gamma', 'bn_beta']
>>> bn.list_auxiliary_states()
['bn_moving_mean', 'bn_moving_var']
>>> bn.list_inputs()
['bn_data', 'bn_gamma', 'bn_beta', 'bn_moving_mean', 'bn_moving_var'] | python/mxnet/symbol/symbol.py | def list_inputs(self):
"""Lists all arguments and auxiliary states of this Symbol.
Returns
-------
inputs : list of str
List of all inputs.
Examples
--------
>>> bn = mx.sym.BatchNorm(name='bn')
>>> bn.list_arguments()
['bn_data', 'bn_gamma', 'bn_beta']
>>> bn.list_auxiliary_states()
['bn_moving_mean', 'bn_moving_var']
>>> bn.list_inputs()
['bn_data', 'bn_gamma', 'bn_beta', 'bn_moving_mean', 'bn_moving_var']
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.NNSymbolListInputNames(
self.handle, 0, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | def list_inputs(self):
"""Lists all arguments and auxiliary states of this Symbol.
Returns
-------
inputs : list of str
List of all inputs.
Examples
--------
>>> bn = mx.sym.BatchNorm(name='bn')
>>> bn.list_arguments()
['bn_data', 'bn_gamma', 'bn_beta']
>>> bn.list_auxiliary_states()
['bn_moving_mean', 'bn_moving_var']
>>> bn.list_inputs()
['bn_data', 'bn_gamma', 'bn_beta', 'bn_moving_mean', 'bn_moving_var']
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.NNSymbolListInputNames(
self.handle, 0, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] | [
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".... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.infer_type | Infers the type of all arguments and all outputs, given the known types
for some arguments.
This function takes the known types of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing types.
Inconsistencies in the known types will cause an error to be raised.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_types, out_types, aux_types = c.infer_type(a='float32')
>>> arg_types
[<type 'numpy.float32'>, <type 'numpy.float32'>]
>>> out_types
[<type 'numpy.float32'>]
>>> aux_types
[]
Parameters
----------
*args :
Type of known arguments in a positional way.
Unknown type can be marked as None.
**kwargs :
Keyword arguments of known types.
Returns
-------
arg_types : list of numpy.dtype or None
List of argument types.
The order is same as the order of list_arguments().
out_types : list of numpy.dtype or None
List of output types.
The order is same as the order of list_outputs().
aux_types : list of numpy.dtype or None
List of auxiliary state types.
The order is same as the order of list_auxiliary_states(). | python/mxnet/symbol/symbol.py | def infer_type(self, *args, **kwargs):
"""Infers the type of all arguments and all outputs, given the known types
for some arguments.
This function takes the known types of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing types.
Inconsistencies in the known types will cause an error to be raised.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_types, out_types, aux_types = c.infer_type(a='float32')
>>> arg_types
[<type 'numpy.float32'>, <type 'numpy.float32'>]
>>> out_types
[<type 'numpy.float32'>]
>>> aux_types
[]
Parameters
----------
*args :
Type of known arguments in a positional way.
Unknown type can be marked as None.
**kwargs :
Keyword arguments of known types.
Returns
-------
arg_types : list of numpy.dtype or None
List of argument types.
The order is same as the order of list_arguments().
out_types : list of numpy.dtype or None
List of output types.
The order is same as the order of list_outputs().
aux_types : list of numpy.dtype or None
List of auxiliary state types.
The order is same as the order of list_auxiliary_states().
"""
try:
res = self._infer_type_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_type_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, dtype in zip(arg_names, arg_shapes):
if not dtype:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append('%s: %s' % (name, str(dtype)))
warnings.warn(
"Cannot decide type for the following arguments. " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_type error. Arguments:")
for i, arg in enumerate(args):
print(" #%d: %s" % (i, arg))
for k, v in kwargs.items():
print(" %s: %s" % (k, v))
raise | def infer_type(self, *args, **kwargs):
"""Infers the type of all arguments and all outputs, given the known types
for some arguments.
This function takes the known types of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing types.
Inconsistencies in the known types will cause an error to be raised.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_types, out_types, aux_types = c.infer_type(a='float32')
>>> arg_types
[<type 'numpy.float32'>, <type 'numpy.float32'>]
>>> out_types
[<type 'numpy.float32'>]
>>> aux_types
[]
Parameters
----------
*args :
Type of known arguments in a positional way.
Unknown type can be marked as None.
**kwargs :
Keyword arguments of known types.
Returns
-------
arg_types : list of numpy.dtype or None
List of argument types.
The order is same as the order of list_arguments().
out_types : list of numpy.dtype or None
List of output types.
The order is same as the order of list_outputs().
aux_types : list of numpy.dtype or None
List of auxiliary state types.
The order is same as the order of list_auxiliary_states().
"""
try:
res = self._infer_type_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_type_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, dtype in zip(arg_names, arg_shapes):
if not dtype:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append('%s: %s' % (name, str(dtype)))
warnings.warn(
"Cannot decide type for the following arguments. " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_type error. Arguments:")
for i, arg in enumerate(args):
print(" #%d: %s" % (i, arg))
for k, v in kwargs.items():
print(" %s: %s" % (k, v))
raise | [
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"Non... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol._infer_type_impl | The actual implementation for calling type inference API. | python/mxnet/symbol/symbol.py | def _infer_type_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling type inference API."""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
types either by positional or kwargs way.')
sdata = []
if len(args) != 0:
keys = c_array(ctypes.c_char_p, [])
for s in args:
if s is not None:
s = _numpy.dtype(s).type
if s not in _DTYPE_NP_TO_MX:
raise TypeError('Argument need to be one of ' + str(_DTYPE_NP_TO_MX))
sdata.append(_DTYPE_NP_TO_MX[s])
else:
sdata.append(-1)
else:
str_keys = []
for k, v in kwargs.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
str_keys.append(k)
sdata.append(_DTYPE_NP_TO_MX[v])
keys = c_str_array(str_keys)
arg_type_size = mx_uint()
arg_type_data = ctypes.POINTER(ctypes.c_int)()
out_type_size = mx_uint()
out_type_data = ctypes.POINTER(ctypes.c_int)()
aux_type_size = mx_uint()
aux_type_data = ctypes.POINTER(ctypes.c_int)()
complete = ctypes.c_int()
if partial:
infer_func = _LIB.MXSymbolInferTypePartial
else:
infer_func = _LIB.MXSymbolInferType
check_call(infer_func(
self.handle,
mx_uint(len(sdata)),
keys,
c_array_buf(ctypes.c_int, array('i', sdata)),
ctypes.byref(arg_type_size),
ctypes.byref(arg_type_data),
ctypes.byref(out_type_size),
ctypes.byref(out_type_data),
ctypes.byref(aux_type_size),
ctypes.byref(aux_type_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_types = [
_DTYPE_MX_TO_NP[arg_type_data[i]] for i in range(arg_type_size.value)]
out_types = [
_DTYPE_MX_TO_NP[out_type_data[i]] for i in range(out_type_size.value)]
aux_types = [
_DTYPE_MX_TO_NP[aux_type_data[i]] for i in range(aux_type_size.value)]
return (arg_types, out_types, aux_types)
else:
return (None, None, None) | def _infer_type_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling type inference API."""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
types either by positional or kwargs way.')
sdata = []
if len(args) != 0:
keys = c_array(ctypes.c_char_p, [])
for s in args:
if s is not None:
s = _numpy.dtype(s).type
if s not in _DTYPE_NP_TO_MX:
raise TypeError('Argument need to be one of ' + str(_DTYPE_NP_TO_MX))
sdata.append(_DTYPE_NP_TO_MX[s])
else:
sdata.append(-1)
else:
str_keys = []
for k, v in kwargs.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
str_keys.append(k)
sdata.append(_DTYPE_NP_TO_MX[v])
keys = c_str_array(str_keys)
arg_type_size = mx_uint()
arg_type_data = ctypes.POINTER(ctypes.c_int)()
out_type_size = mx_uint()
out_type_data = ctypes.POINTER(ctypes.c_int)()
aux_type_size = mx_uint()
aux_type_data = ctypes.POINTER(ctypes.c_int)()
complete = ctypes.c_int()
if partial:
infer_func = _LIB.MXSymbolInferTypePartial
else:
infer_func = _LIB.MXSymbolInferType
check_call(infer_func(
self.handle,
mx_uint(len(sdata)),
keys,
c_array_buf(ctypes.c_int, array('i', sdata)),
ctypes.byref(arg_type_size),
ctypes.byref(arg_type_data),
ctypes.byref(out_type_size),
ctypes.byref(out_type_data),
ctypes.byref(aux_type_size),
ctypes.byref(aux_type_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_types = [
_DTYPE_MX_TO_NP[arg_type_data[i]] for i in range(arg_type_size.value)]
out_types = [
_DTYPE_MX_TO_NP[out_type_data[i]] for i in range(out_type_size.value)]
aux_types = [
_DTYPE_MX_TO_NP[aux_type_data[i]] for i in range(aux_type_size.value)]
return (arg_types, out_types, aux_types)
else:
return (None, None, None) | [
"The",
"actual",
"implementation",
"for",
"calling",
"type",
"inference",
"API",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L958-L1015 | [
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"Valu... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.infer_shape | Infers the shapes of all arguments and all outputs given the known shapes of
some arguments.
This function takes the known shapes of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing shapes.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_shapes, out_shapes, aux_shapes = c.infer_shape(a=(3,3))
>>> arg_shapes
[(3L, 3L), (3L, 3L)]
>>> out_shapes
[(3L, 3L)]
>>> aux_shapes
[]
>>> c.infer_shape(a=(0,3)) # 0s in shape means unknown dimensions. So, returns None.
(None, None, None)
Inconsistencies in the known shapes will cause an error to be raised.
See the following example:
>>> data = mx.sym.Variable('data')
>>> out = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=1000)
>>> out = mx.sym.Activation(data=out, act_type='relu')
>>> out = mx.sym.FullyConnected(data=out, name='fc2', num_hidden=10)
>>> weight_shape= (1, 100)
>>> data_shape = (100, 100)
>>> out.infer_shape(data=data_shape, fc1_weight=weight_shape)
Error in operator fc1: Shape inconsistent, Provided=(1,100), inferred shape=(1000,100)
Parameters
----------
*args :
Shape of arguments in a positional way.
Unknown shape can be marked as None.
**kwargs :
Keyword arguments of the known shapes.
Returns
-------
arg_shapes : list of tuple or None
List of argument shapes.
The order is same as the order of list_arguments().
out_shapes : list of tuple or None
List of output shapes.
The order is same as the order of list_outputs().
aux_shapes : list of tuple or None
List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states(). | python/mxnet/symbol/symbol.py | def infer_shape(self, *args, **kwargs):
"""Infers the shapes of all arguments and all outputs given the known shapes of
some arguments.
This function takes the known shapes of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing shapes.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_shapes, out_shapes, aux_shapes = c.infer_shape(a=(3,3))
>>> arg_shapes
[(3L, 3L), (3L, 3L)]
>>> out_shapes
[(3L, 3L)]
>>> aux_shapes
[]
>>> c.infer_shape(a=(0,3)) # 0s in shape means unknown dimensions. So, returns None.
(None, None, None)
Inconsistencies in the known shapes will cause an error to be raised.
See the following example:
>>> data = mx.sym.Variable('data')
>>> out = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=1000)
>>> out = mx.sym.Activation(data=out, act_type='relu')
>>> out = mx.sym.FullyConnected(data=out, name='fc2', num_hidden=10)
>>> weight_shape= (1, 100)
>>> data_shape = (100, 100)
>>> out.infer_shape(data=data_shape, fc1_weight=weight_shape)
Error in operator fc1: Shape inconsistent, Provided=(1,100), inferred shape=(1000,100)
Parameters
----------
*args :
Shape of arguments in a positional way.
Unknown shape can be marked as None.
**kwargs :
Keyword arguments of the known shapes.
Returns
-------
arg_shapes : list of tuple or None
List of argument shapes.
The order is same as the order of list_arguments().
out_shapes : list of tuple or None
List of output shapes.
The order is same as the order of list_outputs().
aux_shapes : list of tuple or None
List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().
"""
try:
res = self._infer_shape_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_shape_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, shape in zip(arg_names, arg_shapes):
if is_np_compat():
shape_is_none = not shape or -1 in shape
else:
shape_is_none = not shape or 0 in shape
if shape_is_none:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append('%s: %s' % (name, str(shape)))
warnings.warn(
"Cannot decide shape for the following arguments " +
"(0s in shape means unknown dimensions). " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_shape error. Arguments:")
for i, arg in enumerate(args):
print(" #%d: %s" % (i, arg))
for k, v in kwargs.items():
print(" %s: %s" % (k, v))
raise | def infer_shape(self, *args, **kwargs):
"""Infers the shapes of all arguments and all outputs given the known shapes of
some arguments.
This function takes the known shapes of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing shapes.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_shapes, out_shapes, aux_shapes = c.infer_shape(a=(3,3))
>>> arg_shapes
[(3L, 3L), (3L, 3L)]
>>> out_shapes
[(3L, 3L)]
>>> aux_shapes
[]
>>> c.infer_shape(a=(0,3)) # 0s in shape means unknown dimensions. So, returns None.
(None, None, None)
Inconsistencies in the known shapes will cause an error to be raised.
See the following example:
>>> data = mx.sym.Variable('data')
>>> out = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=1000)
>>> out = mx.sym.Activation(data=out, act_type='relu')
>>> out = mx.sym.FullyConnected(data=out, name='fc2', num_hidden=10)
>>> weight_shape= (1, 100)
>>> data_shape = (100, 100)
>>> out.infer_shape(data=data_shape, fc1_weight=weight_shape)
Error in operator fc1: Shape inconsistent, Provided=(1,100), inferred shape=(1000,100)
Parameters
----------
*args :
Shape of arguments in a positional way.
Unknown shape can be marked as None.
**kwargs :
Keyword arguments of the known shapes.
Returns
-------
arg_shapes : list of tuple or None
List of argument shapes.
The order is same as the order of list_arguments().
out_shapes : list of tuple or None
List of output shapes.
The order is same as the order of list_outputs().
aux_shapes : list of tuple or None
List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().
"""
try:
res = self._infer_shape_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_shape_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, shape in zip(arg_names, arg_shapes):
if is_np_compat():
shape_is_none = not shape or -1 in shape
else:
shape_is_none = not shape or 0 in shape
if shape_is_none:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append('%s: %s' % (name, str(shape)))
warnings.warn(
"Cannot decide shape for the following arguments " +
"(0s in shape means unknown dimensions). " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_shape error. Arguments:")
for i, arg in enumerate(args):
print(" #%d: %s" % (i, arg))
for k, v in kwargs.items():
print(" %s: %s" % (k, v))
raise | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L1018-L1102 | [
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"N... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol._infer_shape_impl | The actual implementation for calling shape inference API. | python/mxnet/symbol/symbol.py | def _infer_shape_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling shape inference API."""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
shapes either by positional or kwargs way.')
sdata = []
indptr = [0]
if len(args) != 0:
keys = c_array(ctypes.c_char_p, [])
for i, s in enumerate(args):
if s is not None:
if not isinstance(s, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
"but argument %d is %s." % (i, type(s)))
sdata.extend(s)
indptr.append(len(sdata))
else:
str_keys = []
for k, v in kwargs.items():
if not isinstance(v, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
"but '%s' is %s." % (k, type(v)))
str_keys.append(k)
sdata.extend(v)
indptr.append(len(sdata))
keys = c_str_array(str_keys)
arg_shape_size = mx_uint()
arg_shape_ndim = ctypes.POINTER(mx_int)()
arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
out_shape_size = mx_uint()
out_shape_ndim = ctypes.POINTER(mx_int)()
out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
aux_shape_size = mx_uint()
aux_shape_ndim = ctypes.POINTER(mx_int)()
aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
complete = ctypes.c_int()
if partial:
infer_func = _LIB.MXSymbolInferShapePartialEx
else:
infer_func = _LIB.MXSymbolInferShapeEx
check_call(infer_func(
self.handle,
mx_uint(len(indptr) - 1),
keys,
c_array_buf(mx_uint, array('I', indptr)),
c_array_buf(mx_int, array('i', sdata)),
ctypes.byref(arg_shape_size),
ctypes.byref(arg_shape_ndim),
ctypes.byref(arg_shape_data),
ctypes.byref(out_shape_size),
ctypes.byref(out_shape_ndim),
ctypes.byref(out_shape_data),
ctypes.byref(aux_shape_size),
ctypes.byref(aux_shape_ndim),
ctypes.byref(aux_shape_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_shapes = [tuple(arg_shape_data[i][:arg_shape_ndim[i]])
if arg_shape_ndim[i] >= 0 else None
for i in range(arg_shape_size.value)]
out_shapes = [tuple(out_shape_data[i][:out_shape_ndim[i]])
if out_shape_ndim[i] >= 0 else None
for i in range(out_shape_size.value)]
aux_shapes = [tuple(aux_shape_data[i][:aux_shape_ndim[i]])
if aux_shape_ndim[i] >= 0 else None
for i in range(aux_shape_size.value)]
return (arg_shapes, out_shapes, aux_shapes)
else:
return (None, None, None) | def _infer_shape_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling shape inference API."""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
shapes either by positional or kwargs way.')
sdata = []
indptr = [0]
if len(args) != 0:
keys = c_array(ctypes.c_char_p, [])
for i, s in enumerate(args):
if s is not None:
if not isinstance(s, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
"but argument %d is %s." % (i, type(s)))
sdata.extend(s)
indptr.append(len(sdata))
else:
str_keys = []
for k, v in kwargs.items():
if not isinstance(v, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
"but '%s' is %s." % (k, type(v)))
str_keys.append(k)
sdata.extend(v)
indptr.append(len(sdata))
keys = c_str_array(str_keys)
arg_shape_size = mx_uint()
arg_shape_ndim = ctypes.POINTER(mx_int)()
arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
out_shape_size = mx_uint()
out_shape_ndim = ctypes.POINTER(mx_int)()
out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
aux_shape_size = mx_uint()
aux_shape_ndim = ctypes.POINTER(mx_int)()
aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
complete = ctypes.c_int()
if partial:
infer_func = _LIB.MXSymbolInferShapePartialEx
else:
infer_func = _LIB.MXSymbolInferShapeEx
check_call(infer_func(
self.handle,
mx_uint(len(indptr) - 1),
keys,
c_array_buf(mx_uint, array('I', indptr)),
c_array_buf(mx_int, array('i', sdata)),
ctypes.byref(arg_shape_size),
ctypes.byref(arg_shape_ndim),
ctypes.byref(arg_shape_data),
ctypes.byref(out_shape_size),
ctypes.byref(out_shape_ndim),
ctypes.byref(out_shape_data),
ctypes.byref(aux_shape_size),
ctypes.byref(aux_shape_ndim),
ctypes.byref(aux_shape_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_shapes = [tuple(arg_shape_data[i][:arg_shape_ndim[i]])
if arg_shape_ndim[i] >= 0 else None
for i in range(arg_shape_size.value)]
out_shapes = [tuple(out_shape_data[i][:out_shape_ndim[i]])
if out_shape_ndim[i] >= 0 else None
for i in range(out_shape_size.value)]
aux_shapes = [tuple(aux_shape_data[i][:aux_shape_ndim[i]])
if aux_shape_ndim[i] >= 0 else None
for i in range(aux_shape_size.value)]
return (arg_shapes, out_shapes, aux_shapes)
else:
return (None, None, None) | [
"The",
"actual",
"implementation",
"for",
"calling",
"shape",
"inference",
"API",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L1153-L1222 | [
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train | Symbol.save | Saves symbol to a file.
You can also use pickle to do the job if you only work on python.
The advantage of `load`/`save` functions is that the file contents are language agnostic.
This means the model saved by one language binding can be loaded by a different
language binding of `MXNet`.
You also get the benefit of being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file.
- "s3://my-bucket/path/my-s3-symbol"
- "hdfs://my-bucket/path/my-hdfs-symbol"
- "/path-to/my-local-symbol"
See Also
--------
symbol.load : Used to load symbol from file. | python/mxnet/symbol/symbol.py | def save(self, fname):
"""Saves symbol to a file.
You can also use pickle to do the job if you only work on python.
The advantage of `load`/`save` functions is that the file contents are language agnostic.
This means the model saved by one language binding can be loaded by a different
language binding of `MXNet`.
You also get the benefit of being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file.
- "s3://my-bucket/path/my-s3-symbol"
- "hdfs://my-bucket/path/my-hdfs-symbol"
- "/path-to/my-local-symbol"
See Also
--------
symbol.load : Used to load symbol from file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
check_call(_LIB.MXSymbolSaveToFile(self.handle, c_str(fname))) | def save(self, fname):
"""Saves symbol to a file.
You can also use pickle to do the job if you only work on python.
The advantage of `load`/`save` functions is that the file contents are language agnostic.
This means the model saved by one language binding can be loaded by a different
language binding of `MXNet`.
You also get the benefit of being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file.
- "s3://my-bucket/path/my-s3-symbol"
- "hdfs://my-bucket/path/my-hdfs-symbol"
- "/path-to/my-local-symbol"
See Also
--------
symbol.load : Used to load symbol from file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
check_call(_LIB.MXSymbolSaveToFile(self.handle, c_str(fname))) | [
"Saves",
"symbol",
"to",
"a",
"file",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L1278-L1302 | [
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... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.tojson | Saves symbol to a JSON string.
See Also
--------
symbol.load_json : Used to load symbol from JSON string. | python/mxnet/symbol/symbol.py | def tojson(self):
"""Saves symbol to a JSON string.
See Also
--------
symbol.load_json : Used to load symbol from JSON string.
"""
json_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolSaveToJSON(self.handle, ctypes.byref(json_str)))
return py_str(json_str.value) | def tojson(self):
"""Saves symbol to a JSON string.
See Also
--------
symbol.load_json : Used to load symbol from JSON string.
"""
json_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolSaveToJSON(self.handle, ctypes.byref(json_str)))
return py_str(json_str.value) | [
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train | Symbol._get_ndarray_inputs | Helper function to get NDArray lists handles from various inputs.
Parameters
----------
arg_key : str
The name of argument, used for error message.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbols.
If type is list of NDArray, the position is in the same order of arg_names.
If type is dict of str to NDArray, then it maps the name of arguments
to the corresponding NDArray,
args_names : list of string
List of argument names.
allow_missing : boolean
Whether missing argument is allowed.
When allowed, the missing handle will be set to None(null)
Returns
-------
handles : list of NDArrayHandle
The positional list of NDArrayHandles generated from input. | python/mxnet/symbol/symbol.py | def _get_ndarray_inputs(arg_key, args, arg_names, allow_missing):
"""Helper function to get NDArray lists handles from various inputs.
Parameters
----------
arg_key : str
The name of argument, used for error message.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbols.
If type is list of NDArray, the position is in the same order of arg_names.
If type is dict of str to NDArray, then it maps the name of arguments
to the corresponding NDArray,
args_names : list of string
List of argument names.
allow_missing : boolean
Whether missing argument is allowed.
When allowed, the missing handle will be set to None(null)
Returns
-------
handles : list of NDArrayHandle
The positional list of NDArrayHandles generated from input.
"""
# setup args
arg_handles = []
arg_arrays = []
if isinstance(args, list):
if len(args) != len(arg_names):
raise ValueError('Length of %s does not match the number of arguments' % arg_key)
for narr in args:
if narr is None and allow_missing:
arg_handles.append(None)
elif not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
else:
arg_handles.append(narr.handle)
arg_arrays = args
elif isinstance(args, dict):
for name in arg_names:
if name in args:
narr = args[name]
if not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
arg_handles.append(narr.handle)
arg_arrays.append(narr)
else:
if allow_missing:
arg_handles.append(None)
arg_arrays.append(None)
else:
raise ValueError('key `%s` is missing in `%s`' % (name, arg_key))
else:
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
return c_array(NDArrayHandle, arg_handles), arg_arrays | def _get_ndarray_inputs(arg_key, args, arg_names, allow_missing):
"""Helper function to get NDArray lists handles from various inputs.
Parameters
----------
arg_key : str
The name of argument, used for error message.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbols.
If type is list of NDArray, the position is in the same order of arg_names.
If type is dict of str to NDArray, then it maps the name of arguments
to the corresponding NDArray,
args_names : list of string
List of argument names.
allow_missing : boolean
Whether missing argument is allowed.
When allowed, the missing handle will be set to None(null)
Returns
-------
handles : list of NDArrayHandle
The positional list of NDArrayHandles generated from input.
"""
# setup args
arg_handles = []
arg_arrays = []
if isinstance(args, list):
if len(args) != len(arg_names):
raise ValueError('Length of %s does not match the number of arguments' % arg_key)
for narr in args:
if narr is None and allow_missing:
arg_handles.append(None)
elif not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
else:
arg_handles.append(narr.handle)
arg_arrays = args
elif isinstance(args, dict):
for name in arg_names:
if name in args:
narr = args[name]
if not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
arg_handles.append(narr.handle)
arg_arrays.append(narr)
else:
if allow_missing:
arg_handles.append(None)
arg_arrays.append(None)
else:
raise ValueError('key `%s` is missing in `%s`' % (name, arg_key))
else:
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
return c_array(NDArrayHandle, arg_handles), arg_arrays | [
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train | Symbol.simple_bind | Bind current symbol to get an executor, allocate all the arguments needed.
Allows specifying data types.
This function simplifies the binding procedure. You need to specify only input data shapes.
Before binding the executor, the function allocates arguments and auxiliary states
that were not explicitly specified. Allows specifying data types.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.FullyConnected(x, num_hidden=4)
>>> exe = y.simple_bind(mx.cpu(), x=(5,4), grad_req='null')
>>> exe.forward()
[<NDArray 5x4 @cpu(0)>]
>>> exe.outputs[0].asnumpy()
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
>>> exe.arg_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
>>> exe.grad_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
grad_req: string
{'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means every time gradient is written to specified `args_grad` NDArray.
- 'add' means every time gradient is added to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
type_dict : Dict of str->numpy.dtype
Input type dictionary, name->dtype
stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_arg_names : List of string
The argument names whose `NDArray` of shared_exec can be reused for initializing
the current executor.
shared_exec : Executor
The executor whose arg_arrays, arg_arrays, grad_arrays, and aux_arrays can be
reused for initializing the current executor.
shared_buffer : Dict of string to `NDArray`
The dict mapping argument names to the `NDArray` that can be reused for initializing
the current executor. This buffer will be checked for reuse if one argument name
of the current executor is not found in `shared_arg_names`. The `NDArray` s are
expected have default storage type.
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
The generated executor | python/mxnet/symbol/symbol.py | def simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=None,
group2ctx=None, shared_arg_names=None, shared_exec=None,
shared_buffer=None, **kwargs):
"""Bind current symbol to get an executor, allocate all the arguments needed.
Allows specifying data types.
This function simplifies the binding procedure. You need to specify only input data shapes.
Before binding the executor, the function allocates arguments and auxiliary states
that were not explicitly specified. Allows specifying data types.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.FullyConnected(x, num_hidden=4)
>>> exe = y.simple_bind(mx.cpu(), x=(5,4), grad_req='null')
>>> exe.forward()
[<NDArray 5x4 @cpu(0)>]
>>> exe.outputs[0].asnumpy()
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
>>> exe.arg_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
>>> exe.grad_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
grad_req: string
{'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means every time gradient is written to specified `args_grad` NDArray.
- 'add' means every time gradient is added to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
type_dict : Dict of str->numpy.dtype
Input type dictionary, name->dtype
stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_arg_names : List of string
The argument names whose `NDArray` of shared_exec can be reused for initializing
the current executor.
shared_exec : Executor
The executor whose arg_arrays, arg_arrays, grad_arrays, and aux_arrays can be
reused for initializing the current executor.
shared_buffer : Dict of string to `NDArray`
The dict mapping argument names to the `NDArray` that can be reused for initializing
the current executor. This buffer will be checked for reuse if one argument name
of the current executor is not found in `shared_arg_names`. The `NDArray` s are
expected have default storage type.
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
The generated executor
"""
# data types
num_provided_arg_types = 0
provided_arg_type_names = ctypes.POINTER(ctypes.c_char_p)() # provided type argument names
provided_arg_type_data = ctypes.POINTER(mx_uint)() # provided types
if type_dict is not None:
provided_arg_type_names = []
provided_arg_type_data = []
for k, v in type_dict.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
provided_arg_type_names.append(k)
provided_arg_type_data.append(_DTYPE_NP_TO_MX[v])
num_provided_arg_types = mx_uint(len(provided_arg_type_names))
provided_arg_type_names = c_str_array(provided_arg_type_names)
provided_arg_type_data = c_array_buf(ctypes.c_int, array('i', provided_arg_type_data))
# storage types
num_provided_arg_stypes = 0
# provided storage type argument names
provided_arg_stype_names = ctypes.POINTER(ctypes.c_char_p)()
provided_arg_stype_data = ctypes.POINTER(mx_uint)() # provided storage types
if stype_dict is not None:
provided_arg_stype_names = []
provided_arg_stype_data = []
for k, v in stype_dict.items():
if v in _STORAGE_TYPE_STR_TO_ID:
provided_arg_stype_names.append(k)
provided_arg_stype_data.append(_STORAGE_TYPE_STR_TO_ID[v])
num_provided_arg_stypes = mx_uint(len(provided_arg_stype_names))
provided_arg_stype_names = c_str_array(provided_arg_stype_names)
provided_arg_stype_data = c_array_buf(ctypes.c_int, array('i', provided_arg_stype_data))
provided_arg_shape_data = [] # shape data
# argument shape index in sdata,
# e.g. [sdata[indptr[0]], sdata[indptr[1]]) is the shape of the first arg
provided_arg_shape_idx = [0]
provided_arg_shape_names = [] # provided argument names
for k, v in kwargs.items():
# if k not in listed_arguments and k not in listed_aux_states:
# raise ValueError('arg name %s is not valid', k)
if isinstance(v, tuple):
provided_arg_shape_names.append(k)
provided_arg_shape_data.extend(v)
provided_arg_shape_idx.append(len(provided_arg_shape_data))
provided_req_type_list_len = 0
provided_grad_req_types = ctypes.POINTER(ctypes.c_char_p)()
provided_grad_req_names = ctypes.POINTER(ctypes.c_char_p)()
if grad_req is not None:
if isinstance(grad_req, string_types):
# use provided_req_type_list_len = 0 to indicate this situation
provided_req_type_list_len = 0
provided_grad_req_types = [grad_req]
elif isinstance(grad_req, list):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty list')
provided_grad_req_types = grad_req
provided_req_type_list_len = len(provided_grad_req_types)
elif isinstance(grad_req, dict):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty dict')
provided_grad_req_names = []
provided_grad_req_types = []
for k, v in grad_req.items():
provided_grad_req_names.append(k)
provided_grad_req_types.append(v)
provided_grad_req_names = c_str_array(provided_grad_req_names)
provided_req_type_list_len = len(provided_grad_req_types)
provided_grad_req_types = c_str_array(provided_grad_req_types)
num_ctx_map_keys = mx_uint(0)
ctx_map_keys = ctypes.POINTER(ctypes.c_char_p)()
ctx_map_dev_types = ctypes.POINTER(ctypes.c_int)()
ctx_map_dev_ids = ctypes.POINTER(ctypes.c_int)()
if group2ctx is not None:
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
for key, val in group2ctx.items():
ctx_map_keys.append(key)
ctx_map_dev_types.append(val.device_typeid)
ctx_map_dev_ids.append(val.device_id)
num_ctx_map_keys = mx_uint(len(ctx_map_keys))
ctx_map_keys = c_str_array(ctx_map_keys)
ctx_map_dev_types = c_array(ctypes.c_int, array('i', ctx_map_dev_types))
ctx_map_dev_ids = c_array(ctypes.c_int, array('i', ctx_map_dev_ids))
# prepare param names
shared_arg_name_list = []
if shared_arg_names is not None:
if not isinstance(shared_arg_names, list):
raise ValueError('shared_arg_names in simple_bind must be a list or None')
shared_arg_name_list = shared_arg_names
# prepare shared_buffer
if shared_buffer is None:
shared_buffer_len = ctypes.c_int(-1)
shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
else:
if not isinstance(shared_buffer, dict):
raise ValueError('shared_buffer in simple_bind must be dict or None')
buffer_names = shared_buffer.keys()
buffer_arrays = shared_buffer.values()
for v in buffer_arrays:
assert(v.stype == 'default'), \
"shared_buffer is expected to only contain NDArrays with default storage"
shared_buffer_names = c_str_array(buffer_names)
shared_buffer_len = ctypes.c_int(len(buffer_arrays))
shared_buffer_handles = c_handle_array(buffer_arrays)
updated_shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
updated_shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
# prepare shared_exec_handle
shared_exec_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
# prepare current executor handle
exe_handle = ExecutorHandle()
# prepare current executor's in_args, arg_grads, and aux_states
num_in_args = ctypes.c_uint()
in_arg_handles = ctypes.POINTER(NDArrayHandle)()
arg_grad_handles = ctypes.POINTER(NDArrayHandle)()
num_aux_states = ctypes.c_uint()
aux_state_handles = ctypes.POINTER(NDArrayHandle)()
try:
check_call(_LIB.MXExecutorSimpleBindEx(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
num_ctx_map_keys,
ctx_map_keys,
ctx_map_dev_types,
ctx_map_dev_ids,
mx_uint(provided_req_type_list_len),
provided_grad_req_names,
provided_grad_req_types,
mx_uint(len(provided_arg_shape_names)),
c_str_array(provided_arg_shape_names),
c_array_buf(mx_int,
array('I', provided_arg_shape_data)),
c_array_buf(mx_uint,
array('i', provided_arg_shape_idx)),
num_provided_arg_types,
provided_arg_type_names,
provided_arg_type_data,
num_provided_arg_stypes,
provided_arg_stype_names,
provided_arg_stype_data,
mx_uint(len(shared_arg_name_list)),
c_str_array(shared_arg_name_list),
ctypes.byref(shared_buffer_len),
shared_buffer_names,
shared_buffer_handles,
ctypes.byref(updated_shared_buffer_names),
ctypes.byref(updated_shared_buffer_handles),
ctypes.byref(num_in_args),
ctypes.byref(in_arg_handles),
ctypes.byref(arg_grad_handles),
ctypes.byref(num_aux_states),
ctypes.byref(aux_state_handles),
shared_exec_handle,
ctypes.byref(exe_handle)))
except MXNetError as e:
error_msg = "simple_bind error. Arguments:\n"
for k, v in kwargs.items():
error_msg += "%s: %s\n" % (k, v)
error_msg += "%s" % e
raise RuntimeError(error_msg)
# update shared_buffer
if shared_buffer is not None:
for i in range(shared_buffer_len.value):
k = py_str(updated_shared_buffer_names[i])
v = NDArray(NDArrayHandle(updated_shared_buffer_handles[i]))
shared_buffer[k] = v
# create in_args, arg_grads, and aux_states for the current executor
arg_arrays = [_ndarray_cls(NDArrayHandle(in_arg_handles[i]))
for i in range(num_in_args.value)]
grad_arrays = [_ndarray_cls(NDArrayHandle(arg_grad_handles[i]))
if arg_grad_handles[i] is not None
else None for i in range(num_in_args.value)]
aux_arrays = [_ndarray_cls(NDArrayHandle(aux_state_handles[i]))
for i in range(num_aux_states.value)]
executor = Executor(exe_handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = arg_arrays
executor.grad_arrays = grad_arrays
executor.aux_arrays = aux_arrays
return executor | def simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=None,
group2ctx=None, shared_arg_names=None, shared_exec=None,
shared_buffer=None, **kwargs):
"""Bind current symbol to get an executor, allocate all the arguments needed.
Allows specifying data types.
This function simplifies the binding procedure. You need to specify only input data shapes.
Before binding the executor, the function allocates arguments and auxiliary states
that were not explicitly specified. Allows specifying data types.
Example
-------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.FullyConnected(x, num_hidden=4)
>>> exe = y.simple_bind(mx.cpu(), x=(5,4), grad_req='null')
>>> exe.forward()
[<NDArray 5x4 @cpu(0)>]
>>> exe.outputs[0].asnumpy()
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
>>> exe.arg_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
>>> exe.grad_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
grad_req: string
{'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means every time gradient is written to specified `args_grad` NDArray.
- 'add' means every time gradient is added to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
type_dict : Dict of str->numpy.dtype
Input type dictionary, name->dtype
stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_arg_names : List of string
The argument names whose `NDArray` of shared_exec can be reused for initializing
the current executor.
shared_exec : Executor
The executor whose arg_arrays, arg_arrays, grad_arrays, and aux_arrays can be
reused for initializing the current executor.
shared_buffer : Dict of string to `NDArray`
The dict mapping argument names to the `NDArray` that can be reused for initializing
the current executor. This buffer will be checked for reuse if one argument name
of the current executor is not found in `shared_arg_names`. The `NDArray` s are
expected have default storage type.
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
The generated executor
"""
# data types
num_provided_arg_types = 0
provided_arg_type_names = ctypes.POINTER(ctypes.c_char_p)() # provided type argument names
provided_arg_type_data = ctypes.POINTER(mx_uint)() # provided types
if type_dict is not None:
provided_arg_type_names = []
provided_arg_type_data = []
for k, v in type_dict.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
provided_arg_type_names.append(k)
provided_arg_type_data.append(_DTYPE_NP_TO_MX[v])
num_provided_arg_types = mx_uint(len(provided_arg_type_names))
provided_arg_type_names = c_str_array(provided_arg_type_names)
provided_arg_type_data = c_array_buf(ctypes.c_int, array('i', provided_arg_type_data))
# storage types
num_provided_arg_stypes = 0
# provided storage type argument names
provided_arg_stype_names = ctypes.POINTER(ctypes.c_char_p)()
provided_arg_stype_data = ctypes.POINTER(mx_uint)() # provided storage types
if stype_dict is not None:
provided_arg_stype_names = []
provided_arg_stype_data = []
for k, v in stype_dict.items():
if v in _STORAGE_TYPE_STR_TO_ID:
provided_arg_stype_names.append(k)
provided_arg_stype_data.append(_STORAGE_TYPE_STR_TO_ID[v])
num_provided_arg_stypes = mx_uint(len(provided_arg_stype_names))
provided_arg_stype_names = c_str_array(provided_arg_stype_names)
provided_arg_stype_data = c_array_buf(ctypes.c_int, array('i', provided_arg_stype_data))
provided_arg_shape_data = [] # shape data
# argument shape index in sdata,
# e.g. [sdata[indptr[0]], sdata[indptr[1]]) is the shape of the first arg
provided_arg_shape_idx = [0]
provided_arg_shape_names = [] # provided argument names
for k, v in kwargs.items():
# if k not in listed_arguments and k not in listed_aux_states:
# raise ValueError('arg name %s is not valid', k)
if isinstance(v, tuple):
provided_arg_shape_names.append(k)
provided_arg_shape_data.extend(v)
provided_arg_shape_idx.append(len(provided_arg_shape_data))
provided_req_type_list_len = 0
provided_grad_req_types = ctypes.POINTER(ctypes.c_char_p)()
provided_grad_req_names = ctypes.POINTER(ctypes.c_char_p)()
if grad_req is not None:
if isinstance(grad_req, string_types):
# use provided_req_type_list_len = 0 to indicate this situation
provided_req_type_list_len = 0
provided_grad_req_types = [grad_req]
elif isinstance(grad_req, list):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty list')
provided_grad_req_types = grad_req
provided_req_type_list_len = len(provided_grad_req_types)
elif isinstance(grad_req, dict):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty dict')
provided_grad_req_names = []
provided_grad_req_types = []
for k, v in grad_req.items():
provided_grad_req_names.append(k)
provided_grad_req_types.append(v)
provided_grad_req_names = c_str_array(provided_grad_req_names)
provided_req_type_list_len = len(provided_grad_req_types)
provided_grad_req_types = c_str_array(provided_grad_req_types)
num_ctx_map_keys = mx_uint(0)
ctx_map_keys = ctypes.POINTER(ctypes.c_char_p)()
ctx_map_dev_types = ctypes.POINTER(ctypes.c_int)()
ctx_map_dev_ids = ctypes.POINTER(ctypes.c_int)()
if group2ctx is not None:
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
for key, val in group2ctx.items():
ctx_map_keys.append(key)
ctx_map_dev_types.append(val.device_typeid)
ctx_map_dev_ids.append(val.device_id)
num_ctx_map_keys = mx_uint(len(ctx_map_keys))
ctx_map_keys = c_str_array(ctx_map_keys)
ctx_map_dev_types = c_array(ctypes.c_int, array('i', ctx_map_dev_types))
ctx_map_dev_ids = c_array(ctypes.c_int, array('i', ctx_map_dev_ids))
# prepare param names
shared_arg_name_list = []
if shared_arg_names is not None:
if not isinstance(shared_arg_names, list):
raise ValueError('shared_arg_names in simple_bind must be a list or None')
shared_arg_name_list = shared_arg_names
# prepare shared_buffer
if shared_buffer is None:
shared_buffer_len = ctypes.c_int(-1)
shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
else:
if not isinstance(shared_buffer, dict):
raise ValueError('shared_buffer in simple_bind must be dict or None')
buffer_names = shared_buffer.keys()
buffer_arrays = shared_buffer.values()
for v in buffer_arrays:
assert(v.stype == 'default'), \
"shared_buffer is expected to only contain NDArrays with default storage"
shared_buffer_names = c_str_array(buffer_names)
shared_buffer_len = ctypes.c_int(len(buffer_arrays))
shared_buffer_handles = c_handle_array(buffer_arrays)
updated_shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
updated_shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
# prepare shared_exec_handle
shared_exec_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
# prepare current executor handle
exe_handle = ExecutorHandle()
# prepare current executor's in_args, arg_grads, and aux_states
num_in_args = ctypes.c_uint()
in_arg_handles = ctypes.POINTER(NDArrayHandle)()
arg_grad_handles = ctypes.POINTER(NDArrayHandle)()
num_aux_states = ctypes.c_uint()
aux_state_handles = ctypes.POINTER(NDArrayHandle)()
try:
check_call(_LIB.MXExecutorSimpleBindEx(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
num_ctx_map_keys,
ctx_map_keys,
ctx_map_dev_types,
ctx_map_dev_ids,
mx_uint(provided_req_type_list_len),
provided_grad_req_names,
provided_grad_req_types,
mx_uint(len(provided_arg_shape_names)),
c_str_array(provided_arg_shape_names),
c_array_buf(mx_int,
array('I', provided_arg_shape_data)),
c_array_buf(mx_uint,
array('i', provided_arg_shape_idx)),
num_provided_arg_types,
provided_arg_type_names,
provided_arg_type_data,
num_provided_arg_stypes,
provided_arg_stype_names,
provided_arg_stype_data,
mx_uint(len(shared_arg_name_list)),
c_str_array(shared_arg_name_list),
ctypes.byref(shared_buffer_len),
shared_buffer_names,
shared_buffer_handles,
ctypes.byref(updated_shared_buffer_names),
ctypes.byref(updated_shared_buffer_handles),
ctypes.byref(num_in_args),
ctypes.byref(in_arg_handles),
ctypes.byref(arg_grad_handles),
ctypes.byref(num_aux_states),
ctypes.byref(aux_state_handles),
shared_exec_handle,
ctypes.byref(exe_handle)))
except MXNetError as e:
error_msg = "simple_bind error. Arguments:\n"
for k, v in kwargs.items():
error_msg += "%s: %s\n" % (k, v)
error_msg += "%s" % e
raise RuntimeError(error_msg)
# update shared_buffer
if shared_buffer is not None:
for i in range(shared_buffer_len.value):
k = py_str(updated_shared_buffer_names[i])
v = NDArray(NDArrayHandle(updated_shared_buffer_handles[i]))
shared_buffer[k] = v
# create in_args, arg_grads, and aux_states for the current executor
arg_arrays = [_ndarray_cls(NDArrayHandle(in_arg_handles[i]))
for i in range(num_in_args.value)]
grad_arrays = [_ndarray_cls(NDArrayHandle(arg_grad_handles[i]))
if arg_grad_handles[i] is not None
else None for i in range(num_in_args.value)]
aux_arrays = [_ndarray_cls(NDArrayHandle(aux_state_handles[i]))
for i in range(num_aux_states.value)]
executor = Executor(exe_handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = arg_arrays
executor.grad_arrays = grad_arrays
executor.aux_arrays = aux_arrays
return executor | [
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"sha... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.bind | Binds the current symbol to an executor and returns it.
We first declare the computation and then bind to the data to run.
This function returns an executor which provides method `forward()` method for evaluation
and a `outputs()` method to get all the results.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
<Symbol _plus1>
>>> ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]), 'b' : mx.nd.ones([2,3])})
>>> ex.forward()
[<NDArray 2x3 @cpu(0)>]
>>> ex.outputs[0].asnumpy()
[[ 2. 2. 2.]
[ 2. 2. 2.]]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbol.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding `NDArray`.
- In either case, all the arguments must be provided.
args_grad : list of NDArray or dict of str to `NDArray`, optional
When specified, `args_grad` provides NDArrays to hold
the result of gradient value in backward.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding NDArray.
- When the type is a dict of str to `NDArray`, one only need to provide the dict
for required argument gradient.
Only the specified argument gradient will be calculated.
grad_req : {'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means everytime gradient is write to specified `args_grad` `NDArray`.
- 'add' means everytime gradient is add to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
aux_states : list of `NDArray`, or dict of str to `NDArray`, optional
Input auxiliary states to the symbol, only needed when the output of
`list_auxiliary_states()` is not empty.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_auxiliary_states()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of
`auxiliary_states` to the corresponding `NDArray`,
- In either case, all the auxiliary states need to be provided.
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_exec : mx.executor.Executor
Executor to share memory with. This is intended for runtime reshaping, variable length
sequences, etc. The returned executor shares state with `shared_exec`, and should not be
used in parallel with it.
Returns
-------
executor : Executor
The generated executor
Notes
-----
Auxiliary states are the special states of symbols that do not correspond
to an argument, and do not have gradient but are still useful
for the specific operations. Common examples of auxiliary states include
the `moving_mean` and `moving_variance` states in `BatchNorm`.
Most operators do not have auxiliary states and in those cases,
this parameter can be safely ignored.
One can give up gradient by using a dict in `args_grad` and only specify
gradient they interested in. | python/mxnet/symbol/symbol.py | def bind(self, ctx, args, args_grad=None, grad_req='write',
aux_states=None, group2ctx=None, shared_exec=None):
"""Binds the current symbol to an executor and returns it.
We first declare the computation and then bind to the data to run.
This function returns an executor which provides method `forward()` method for evaluation
and a `outputs()` method to get all the results.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
<Symbol _plus1>
>>> ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]), 'b' : mx.nd.ones([2,3])})
>>> ex.forward()
[<NDArray 2x3 @cpu(0)>]
>>> ex.outputs[0].asnumpy()
[[ 2. 2. 2.]
[ 2. 2. 2.]]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbol.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding `NDArray`.
- In either case, all the arguments must be provided.
args_grad : list of NDArray or dict of str to `NDArray`, optional
When specified, `args_grad` provides NDArrays to hold
the result of gradient value in backward.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding NDArray.
- When the type is a dict of str to `NDArray`, one only need to provide the dict
for required argument gradient.
Only the specified argument gradient will be calculated.
grad_req : {'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means everytime gradient is write to specified `args_grad` `NDArray`.
- 'add' means everytime gradient is add to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
aux_states : list of `NDArray`, or dict of str to `NDArray`, optional
Input auxiliary states to the symbol, only needed when the output of
`list_auxiliary_states()` is not empty.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_auxiliary_states()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of
`auxiliary_states` to the corresponding `NDArray`,
- In either case, all the auxiliary states need to be provided.
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_exec : mx.executor.Executor
Executor to share memory with. This is intended for runtime reshaping, variable length
sequences, etc. The returned executor shares state with `shared_exec`, and should not be
used in parallel with it.
Returns
-------
executor : Executor
The generated executor
Notes
-----
Auxiliary states are the special states of symbols that do not correspond
to an argument, and do not have gradient but are still useful
for the specific operations. Common examples of auxiliary states include
the `moving_mean` and `moving_variance` states in `BatchNorm`.
Most operators do not have auxiliary states and in those cases,
this parameter can be safely ignored.
One can give up gradient by using a dict in `args_grad` and only specify
gradient they interested in.
"""
# pylint: disable=too-many-locals, too-many-branches
if not isinstance(ctx, Context):
raise TypeError("Context type error")
listed_arguments = self.list_arguments()
args_handle, args = self._get_ndarray_inputs('args', args, listed_arguments, False)
# setup args gradient
if args_grad is None:
args_grad_handle = c_array(NDArrayHandle, [None] * len(args))
else:
args_grad_handle, args_grad = self._get_ndarray_inputs(
'args_grad', args_grad, listed_arguments, True)
if aux_states is None:
aux_states = []
aux_args_handle, aux_states = self._get_ndarray_inputs(
'aux_states', aux_states, self.list_auxiliary_states(), False)
# setup requirements
if isinstance(grad_req, string_types):
if grad_req not in _GRAD_REQ_MAP:
raise ValueError('grad_req must be in %s' % str(_GRAD_REQ_MAP))
reqs_array = c_array_buf(mx_uint,
array('I', [_GRAD_REQ_MAP[grad_req]] * len(listed_arguments)))
elif isinstance(grad_req, list):
reqs_array = c_array_buf(mx_uint,
array('I', [_GRAD_REQ_MAP[item] for item in grad_req]))
elif isinstance(grad_req, dict):
req_array = []
for name in listed_arguments:
if name in grad_req:
req_array.append(_GRAD_REQ_MAP[grad_req[name]])
else:
req_array.append(0)
reqs_array = c_array_buf(mx_uint, array('I', req_array))
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
if group2ctx:
for key, val in group2ctx.items():
ctx_map_keys.append(key)
ctx_map_dev_types.append(val.device_typeid)
ctx_map_dev_ids.append(val.device_id)
handle = ExecutorHandle()
shared_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
check_call(_LIB.MXExecutorBindEX(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
mx_uint(len(ctx_map_keys)),
c_str_array(ctx_map_keys),
c_array_buf(ctypes.c_int, array('i', ctx_map_dev_types)),
c_array_buf(ctypes.c_int, array('i', ctx_map_dev_ids)),
mx_uint(len(args)),
args_handle,
args_grad_handle,
reqs_array,
mx_uint(len(aux_states)),
aux_args_handle,
shared_handle,
ctypes.byref(handle)))
executor = Executor(handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = args
executor.grad_arrays = args_grad
executor.aux_arrays = aux_states
return executor | def bind(self, ctx, args, args_grad=None, grad_req='write',
aux_states=None, group2ctx=None, shared_exec=None):
"""Binds the current symbol to an executor and returns it.
We first declare the computation and then bind to the data to run.
This function returns an executor which provides method `forward()` method for evaluation
and a `outputs()` method to get all the results.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
<Symbol _plus1>
>>> ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]), 'b' : mx.nd.ones([2,3])})
>>> ex.forward()
[<NDArray 2x3 @cpu(0)>]
>>> ex.outputs[0].asnumpy()
[[ 2. 2. 2.]
[ 2. 2. 2.]]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbol.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding `NDArray`.
- In either case, all the arguments must be provided.
args_grad : list of NDArray or dict of str to `NDArray`, optional
When specified, `args_grad` provides NDArrays to hold
the result of gradient value in backward.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding NDArray.
- When the type is a dict of str to `NDArray`, one only need to provide the dict
for required argument gradient.
Only the specified argument gradient will be calculated.
grad_req : {'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means everytime gradient is write to specified `args_grad` `NDArray`.
- 'add' means everytime gradient is add to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
aux_states : list of `NDArray`, or dict of str to `NDArray`, optional
Input auxiliary states to the symbol, only needed when the output of
`list_auxiliary_states()` is not empty.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_auxiliary_states()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of
`auxiliary_states` to the corresponding `NDArray`,
- In either case, all the auxiliary states need to be provided.
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_exec : mx.executor.Executor
Executor to share memory with. This is intended for runtime reshaping, variable length
sequences, etc. The returned executor shares state with `shared_exec`, and should not be
used in parallel with it.
Returns
-------
executor : Executor
The generated executor
Notes
-----
Auxiliary states are the special states of symbols that do not correspond
to an argument, and do not have gradient but are still useful
for the specific operations. Common examples of auxiliary states include
the `moving_mean` and `moving_variance` states in `BatchNorm`.
Most operators do not have auxiliary states and in those cases,
this parameter can be safely ignored.
One can give up gradient by using a dict in `args_grad` and only specify
gradient they interested in.
"""
# pylint: disable=too-many-locals, too-many-branches
if not isinstance(ctx, Context):
raise TypeError("Context type error")
listed_arguments = self.list_arguments()
args_handle, args = self._get_ndarray_inputs('args', args, listed_arguments, False)
# setup args gradient
if args_grad is None:
args_grad_handle = c_array(NDArrayHandle, [None] * len(args))
else:
args_grad_handle, args_grad = self._get_ndarray_inputs(
'args_grad', args_grad, listed_arguments, True)
if aux_states is None:
aux_states = []
aux_args_handle, aux_states = self._get_ndarray_inputs(
'aux_states', aux_states, self.list_auxiliary_states(), False)
# setup requirements
if isinstance(grad_req, string_types):
if grad_req not in _GRAD_REQ_MAP:
raise ValueError('grad_req must be in %s' % str(_GRAD_REQ_MAP))
reqs_array = c_array_buf(mx_uint,
array('I', [_GRAD_REQ_MAP[grad_req]] * len(listed_arguments)))
elif isinstance(grad_req, list):
reqs_array = c_array_buf(mx_uint,
array('I', [_GRAD_REQ_MAP[item] for item in grad_req]))
elif isinstance(grad_req, dict):
req_array = []
for name in listed_arguments:
if name in grad_req:
req_array.append(_GRAD_REQ_MAP[grad_req[name]])
else:
req_array.append(0)
reqs_array = c_array_buf(mx_uint, array('I', req_array))
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
if group2ctx:
for key, val in group2ctx.items():
ctx_map_keys.append(key)
ctx_map_dev_types.append(val.device_typeid)
ctx_map_dev_ids.append(val.device_id)
handle = ExecutorHandle()
shared_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
check_call(_LIB.MXExecutorBindEX(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
mx_uint(len(ctx_map_keys)),
c_str_array(ctx_map_keys),
c_array_buf(ctypes.c_int, array('i', ctx_map_dev_types)),
c_array_buf(ctypes.c_int, array('i', ctx_map_dev_ids)),
mx_uint(len(args)),
args_handle,
args_grad_handle,
reqs_array,
mx_uint(len(aux_states)),
aux_args_handle,
shared_handle,
ctypes.byref(handle)))
executor = Executor(handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = args
executor.grad_arrays = args_grad
executor.aux_arrays = aux_states
return executor | [
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"returns",
"it",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/symbol/symbol.py#L1639-L1795 | [
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"# pylint: disable=too-many-local... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | Symbol.gradient | Gets the autodiff of current symbol.
This function can only be used if current symbol is a loss function.
.. note:: This function is currently not implemented.
Parameters
----------
wrt : Array of String
keyword arguments of the symbol that the gradients are taken.
Returns
-------
grad : Symbol
A gradient Symbol with returns to be the corresponding gradients. | python/mxnet/symbol/symbol.py | def gradient(self, wrt):
"""Gets the autodiff of current symbol.
This function can only be used if current symbol is a loss function.
.. note:: This function is currently not implemented.
Parameters
----------
wrt : Array of String
keyword arguments of the symbol that the gradients are taken.
Returns
-------
grad : Symbol
A gradient Symbol with returns to be the corresponding gradients.
"""
handle = SymbolHandle()
c_wrt = c_str_array(wrt)
check_call(_LIB.MXSymbolGrad(self.handle,
mx_uint(len(wrt)),
c_wrt,
ctypes.byref(handle)))
return Symbol(handle) | def gradient(self, wrt):
"""Gets the autodiff of current symbol.
This function can only be used if current symbol is a loss function.
.. note:: This function is currently not implemented.
Parameters
----------
wrt : Array of String
keyword arguments of the symbol that the gradients are taken.
Returns
-------
grad : Symbol
A gradient Symbol with returns to be the corresponding gradients.
"""
handle = SymbolHandle()
c_wrt = c_str_array(wrt)
check_call(_LIB.MXSymbolGrad(self.handle,
mx_uint(len(wrt)),
c_wrt,
ctypes.byref(handle)))
return Symbol(handle) | [
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train | Symbol.eval | Evaluates a symbol given arguments.
The `eval` method combines a call to `bind` (which returns an executor)
with a call to `forward` (executor method).
For the common use case, where you might repeatedly evaluate with same arguments,
eval is slow.
In that case, you should call `bind` once and then repeatedly call forward.
This function allows simpler syntax for less cumbersome introspection.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
>>> ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
>>> ex
[<NDArray 2x3 @cpu(0)>]
>>> ex[0].asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
Parameters
----------
ctx : Context
The device context the generated executor to run on.
kwargs : Keyword arguments of type `NDArray`
Input arguments to the symbol. All the arguments must be provided.
Returns
----------
result : a list of NDArrays corresponding to the values taken by each symbol when
evaluated on given args. When called on a single symbol (not a group),
the result will be a list with one element. | python/mxnet/symbol/symbol.py | def eval(self, ctx=None, **kwargs):
"""Evaluates a symbol given arguments.
The `eval` method combines a call to `bind` (which returns an executor)
with a call to `forward` (executor method).
For the common use case, where you might repeatedly evaluate with same arguments,
eval is slow.
In that case, you should call `bind` once and then repeatedly call forward.
This function allows simpler syntax for less cumbersome introspection.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
>>> ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
>>> ex
[<NDArray 2x3 @cpu(0)>]
>>> ex[0].asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
Parameters
----------
ctx : Context
The device context the generated executor to run on.
kwargs : Keyword arguments of type `NDArray`
Input arguments to the symbol. All the arguments must be provided.
Returns
----------
result : a list of NDArrays corresponding to the values taken by each symbol when
evaluated on given args. When called on a single symbol (not a group),
the result will be a list with one element.
"""
if ctx is None:
ctx = current_context()
return self.bind(ctx, kwargs).forward() | def eval(self, ctx=None, **kwargs):
"""Evaluates a symbol given arguments.
The `eval` method combines a call to `bind` (which returns an executor)
with a call to `forward` (executor method).
For the common use case, where you might repeatedly evaluate with same arguments,
eval is slow.
In that case, you should call `bind` once and then repeatedly call forward.
This function allows simpler syntax for less cumbersome introspection.
Example
-------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
>>> ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
>>> ex
[<NDArray 2x3 @cpu(0)>]
>>> ex[0].asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
Parameters
----------
ctx : Context
The device context the generated executor to run on.
kwargs : Keyword arguments of type `NDArray`
Input arguments to the symbol. All the arguments must be provided.
Returns
----------
result : a list of NDArrays corresponding to the values taken by each symbol when
evaluated on given args. When called on a single symbol (not a group),
the result will be a list with one element.
"""
if ctx is None:
ctx = current_context()
return self.bind(ctx, kwargs).forward() | [
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train | Symbol.get_backend_symbol | Return symbol for target backend.
Parameters
----------
backend : str
The backend names.
Returns
-------
out : Symbol
The created Symbol for target backend. | python/mxnet/symbol/symbol.py | def get_backend_symbol(self, backend):
"""Return symbol for target backend.
Parameters
----------
backend : str
The backend names.
Returns
-------
out : Symbol
The created Symbol for target backend.
"""
out = SymbolHandle()
check_call(_LIB.MXGenBackendSubgraph(self.handle, c_str(backend), ctypes.byref(out)))
return Symbol(out) | def get_backend_symbol(self, backend):
"""Return symbol for target backend.
Parameters
----------
backend : str
The backend names.
Returns
-------
out : Symbol
The created Symbol for target backend.
"""
out = SymbolHandle()
check_call(_LIB.MXGenBackendSubgraph(self.handle, c_str(backend), ctypes.byref(out)))
return Symbol(out) | [
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train | PixelShuffle1D.hybrid_forward | Perform pixel-shuffling on the input. | python/mxnet/gluon/contrib/nn/basic_layers.py | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
f = self._factor
# (N, C*f, W)
x = F.reshape(x, (0, -4, -1, f, 0)) # (N, C, f, W)
x = F.transpose(x, (0, 1, 3, 2)) # (N, C, W, f)
x = F.reshape(x, (0, 0, -3)) # (N, C, W*f)
return x | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
f = self._factor
# (N, C*f, W)
x = F.reshape(x, (0, -4, -1, f, 0)) # (N, C, f, W)
x = F.transpose(x, (0, 1, 3, 2)) # (N, C, W, f)
x = F.reshape(x, (0, 0, -3)) # (N, C, W*f)
return x | [
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train | PixelShuffle2D.hybrid_forward | Perform pixel-shuffling on the input. | python/mxnet/gluon/contrib/nn/basic_layers.py | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
f1, f2 = self._factors
# (N, f1*f2*C, H, W)
x = F.reshape(x, (0, -4, -1, f1 * f2, 0, 0)) # (N, C, f1*f2, H, W)
x = F.reshape(x, (0, 0, -4, f1, f2, 0, 0)) # (N, C, f1, f2, H, W)
x = F.transpose(x, (0, 1, 4, 2, 5, 3)) # (N, C, H, f1, W, f2)
x = F.reshape(x, (0, 0, -3, -3)) # (N, C, H*f1, W*f2)
return x | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
f1, f2 = self._factors
# (N, f1*f2*C, H, W)
x = F.reshape(x, (0, -4, -1, f1 * f2, 0, 0)) # (N, C, f1*f2, H, W)
x = F.reshape(x, (0, 0, -4, f1, f2, 0, 0)) # (N, C, f1, f2, H, W)
x = F.transpose(x, (0, 1, 4, 2, 5, 3)) # (N, C, H, f1, W, f2)
x = F.reshape(x, (0, 0, -3, -3)) # (N, C, H*f1, W*f2)
return x | [
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train | PixelShuffle3D.hybrid_forward | Perform pixel-shuffling on the input. | python/mxnet/gluon/contrib/nn/basic_layers.py | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
# `transpose` doesn't support 8D, need other implementation
f1, f2, f3 = self._factors
# (N, C*f1*f2*f3, D, H, W)
x = F.reshape(x, (0, -4, -1, f1 * f2 * f3, 0, 0, 0)) # (N, C, f1*f2*f3, D, H, W)
x = F.swapaxes(x, 2, 3) # (N, C, D, f1*f2*f3, H, W)
x = F.reshape(x, (0, 0, 0, -4, f1, f2*f3, 0, 0)) # (N, C, D, f1, f2*f3, H, W)
x = F.reshape(x, (0, 0, -3, 0, 0, 0)) # (N, C, D*f1, f2*f3, H, W)
x = F.swapaxes(x, 3, 4) # (N, C, D*f1, H, f2*f3, W)
x = F.reshape(x, (0, 0, 0, 0, -4, f2, f3, 0)) # (N, C, D*f1, H, f2, f3, W)
x = F.reshape(x, (0, 0, 0, -3, 0, 0)) # (N, C, D*f1, H*f2, f3, W)
x = F.swapaxes(x, 4, 5) # (N, C, D*f1, H*f2, W, f3)
x = F.reshape(x, (0, 0, 0, 0, -3)) # (N, C, D*f1, H*f2, W*f3)
return x | def hybrid_forward(self, F, x):
"""Perform pixel-shuffling on the input."""
# `transpose` doesn't support 8D, need other implementation
f1, f2, f3 = self._factors
# (N, C*f1*f2*f3, D, H, W)
x = F.reshape(x, (0, -4, -1, f1 * f2 * f3, 0, 0, 0)) # (N, C, f1*f2*f3, D, H, W)
x = F.swapaxes(x, 2, 3) # (N, C, D, f1*f2*f3, H, W)
x = F.reshape(x, (0, 0, 0, -4, f1, f2*f3, 0, 0)) # (N, C, D, f1, f2*f3, H, W)
x = F.reshape(x, (0, 0, -3, 0, 0, 0)) # (N, C, D*f1, f2*f3, H, W)
x = F.swapaxes(x, 3, 4) # (N, C, D*f1, H, f2*f3, W)
x = F.reshape(x, (0, 0, 0, 0, -4, f2, f3, 0)) # (N, C, D*f1, H, f2, f3, W)
x = F.reshape(x, (0, 0, 0, -3, 0, 0)) # (N, C, D*f1, H*f2, f3, W)
x = F.swapaxes(x, 4, 5) # (N, C, D*f1, H*f2, W, f3)
x = F.reshape(x, (0, 0, 0, 0, -3)) # (N, C, D*f1, H*f2, W*f3)
return x | [
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train | retry | Retry calling the decorated function using an exponential backoff.
http://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/
original from: http://wiki.python.org/moin/PythonDecoratorLibrary#Retry
:param target_exception: the exception to check. may be a tuple of
exceptions to check
:type target_exception: Exception or tuple
:param tries: number of times to try (not retry) before giving up
:type tries: int
:param delay_s: initial delay between retries in seconds
:type delay_s: int
:param backoff: backoff multiplier e.g. value of 2 will double the delay
each retry
:type backoff: int | ci/util.py | def retry(target_exception, tries=4, delay_s=1, backoff=2):
"""Retry calling the decorated function using an exponential backoff.
http://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/
original from: http://wiki.python.org/moin/PythonDecoratorLibrary#Retry
:param target_exception: the exception to check. may be a tuple of
exceptions to check
:type target_exception: Exception or tuple
:param tries: number of times to try (not retry) before giving up
:type tries: int
:param delay_s: initial delay between retries in seconds
:type delay_s: int
:param backoff: backoff multiplier e.g. value of 2 will double the delay
each retry
:type backoff: int
"""
import time
from functools import wraps
def decorated_retry(f):
@wraps(f)
def f_retry(*args, **kwargs):
mtries, mdelay = tries, delay_s
while mtries > 1:
try:
return f(*args, **kwargs)
except target_exception as e:
logging.warning("Exception: %s, Retrying in %d seconds...", str(e), mdelay)
time.sleep(mdelay)
mtries -= 1
mdelay *= backoff
return f(*args, **kwargs)
return f_retry # true decorator
return decorated_retry | def retry(target_exception, tries=4, delay_s=1, backoff=2):
"""Retry calling the decorated function using an exponential backoff.
http://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/
original from: http://wiki.python.org/moin/PythonDecoratorLibrary#Retry
:param target_exception: the exception to check. may be a tuple of
exceptions to check
:type target_exception: Exception or tuple
:param tries: number of times to try (not retry) before giving up
:type tries: int
:param delay_s: initial delay between retries in seconds
:type delay_s: int
:param backoff: backoff multiplier e.g. value of 2 will double the delay
each retry
:type backoff: int
"""
import time
from functools import wraps
def decorated_retry(f):
@wraps(f)
def f_retry(*args, **kwargs):
mtries, mdelay = tries, delay_s
while mtries > 1:
try:
return f(*args, **kwargs)
except target_exception as e:
logging.warning("Exception: %s, Retrying in %d seconds...", str(e), mdelay)
time.sleep(mdelay)
mtries -= 1
mdelay *= backoff
return f(*args, **kwargs)
return f_retry # true decorator
return decorated_retry | [
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train | load_model | Returns a module loaded with the provided model.
Parameters
----------
model_name: str
Prefix of the MXNet model name as stored on the local directory.
epoch_num : int
Epoch number of model we would like to load.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module | tools/coreml/converter/utils.py | def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gpus=''):
"""Returns a module loaded with the provided model.
Parameters
----------
model_name: str
Prefix of the MXNet model name as stored on the local directory.
epoch_num : int
Epoch number of model we would like to load.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module
"""
sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, epoch_num)
mod = create_module(sym, data_shapes, label_shapes, label_names, gpus)
mod.set_params(
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True
)
return mod | def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gpus=''):
"""Returns a module loaded with the provided model.
Parameters
----------
model_name: str
Prefix of the MXNet model name as stored on the local directory.
epoch_num : int
Epoch number of model we would like to load.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module
"""
sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, epoch_num)
mod = create_module(sym, data_shapes, label_shapes, label_names, gpus)
mod.set_params(
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True
)
return mod | [
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train | create_module | Creates a new MXNet module.
Parameters
----------
sym : Symbol
An MXNet symbol.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module | tools/coreml/converter/utils.py | def create_module(sym, data_shapes, label_shapes, label_names, gpus=''):
"""Creates a new MXNet module.
Parameters
----------
sym : Symbol
An MXNet symbol.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module
"""
if gpus == '':
devices = mx.cpu()
else:
devices = [mx.gpu(int(i)) for i in gpus.split(',')]
data_names = [data_shape[0] for data_shape in data_shapes]
mod = mx.mod.Module(
symbol=sym,
data_names=data_names,
context=devices,
label_names=label_names
)
mod.bind(
for_training=False,
data_shapes=data_shapes,
label_shapes=label_shapes
)
return mod | def create_module(sym, data_shapes, label_shapes, label_names, gpus=''):
"""Creates a new MXNet module.
Parameters
----------
sym : Symbol
An MXNet symbol.
input_shape: tuple
The shape of the input data in the form of (batch_size, channels, height, width)
files: list of strings
List of URLs pertaining to files that need to be downloaded in order to use the model.
data_shapes: list of tuples.
List of tuples where each tuple is a pair of input variable name and its shape.
label_shapes: list of (str, tuple)
Typically is ``data_iter.provide_label``.
label_names: list of str
Name of the output labels in the MXNet symbolic graph.
gpus: str
Comma separated string of gpu ids on which inferences are executed. E.g. 3,5,6 would refer to GPUs 3, 5 and 6.
If empty, we use CPU.
Returns
-------
MXNet module
"""
if gpus == '':
devices = mx.cpu()
else:
devices = [mx.gpu(int(i)) for i in gpus.split(',')]
data_names = [data_shape[0] for data_shape in data_shapes]
mod = mx.mod.Module(
symbol=sym,
data_names=data_names,
context=devices,
label_names=label_names
)
mod.bind(
for_training=False,
data_shapes=data_shapes,
label_shapes=label_shapes
)
return mod | [
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"a",
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"MXNet",
"module",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/tools/coreml/converter/utils.py#L68-L117 | [
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".... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | evaluate_net | evalute network given validation record file
Parameters:
----------
net : str or None
Network name or use None to load from json without modifying
path_imgrec : str
path to the record validation file
path_imglist : str
path to the list file to replace labels in record file, optional
num_classes : int
number of classes, not including background
mean_pixels : tuple
(mean_r, mean_g, mean_b)
data_shape : tuple or int
(3, height, width) or height/width
model_prefix : str
model prefix of saved checkpoint
epoch : int
load model epoch
ctx : mx.ctx
mx.gpu() or mx.cpu()
batch_size : int
validation batch size
nms_thresh : float
non-maximum suppression threshold
force_nms : boolean
whether suppress different class objects
ovp_thresh : float
AP overlap threshold for true/false postives
use_difficult : boolean
whether to use difficult objects in evaluation if applicable
class_names : comma separated str
class names in string, must correspond to num_classes if set
voc07_metric : boolean
whether to use 11-point evluation as in VOC07 competition | example/ssd/evaluate/evaluate_net.py | def evaluate_net(net, path_imgrec, num_classes, num_batch, mean_pixels, data_shape,
model_prefix, epoch, ctx=mx.cpu(), batch_size=32,
path_imglist="", nms_thresh=0.45, force_nms=False,
ovp_thresh=0.5, use_difficult=False, class_names=None,
voc07_metric=False):
"""
evalute network given validation record file
Parameters:
----------
net : str or None
Network name or use None to load from json without modifying
path_imgrec : str
path to the record validation file
path_imglist : str
path to the list file to replace labels in record file, optional
num_classes : int
number of classes, not including background
mean_pixels : tuple
(mean_r, mean_g, mean_b)
data_shape : tuple or int
(3, height, width) or height/width
model_prefix : str
model prefix of saved checkpoint
epoch : int
load model epoch
ctx : mx.ctx
mx.gpu() or mx.cpu()
batch_size : int
validation batch size
nms_thresh : float
non-maximum suppression threshold
force_nms : boolean
whether suppress different class objects
ovp_thresh : float
AP overlap threshold for true/false postives
use_difficult : boolean
whether to use difficult objects in evaluation if applicable
class_names : comma separated str
class names in string, must correspond to num_classes if set
voc07_metric : boolean
whether to use 11-point evluation as in VOC07 competition
"""
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# args
if isinstance(data_shape, int):
data_shape = (3, data_shape, data_shape)
assert len(data_shape) == 3 and data_shape[0] == 3
model_prefix += '_' + str(data_shape[1])
# iterator
eval_iter = DetRecordIter(path_imgrec, batch_size, data_shape, mean_pixels=mean_pixels,
path_imglist=path_imglist, **cfg.valid)
# model params
load_net, args, auxs = mx.model.load_checkpoint(model_prefix, epoch)
# network
if net is None:
net = load_net
else:
net = get_symbol(net, data_shape[1], num_classes=num_classes,
nms_thresh=nms_thresh, force_suppress=force_nms)
if not 'label' in net.list_arguments():
label = mx.sym.Variable(name='label')
net = mx.sym.Group([net, label])
# init module
mod = mx.mod.Module(net, label_names=('label',), logger=logger, context=ctx,
fixed_param_names=net.list_arguments())
mod.bind(data_shapes=eval_iter.provide_data, label_shapes=eval_iter.provide_label)
mod.set_params(args, auxs, allow_missing=False, force_init=True)
# run evaluation
if voc07_metric:
metric = VOC07MApMetric(ovp_thresh, use_difficult, class_names)
else:
metric = MApMetric(ovp_thresh, use_difficult, class_names)
num = num_batch * batch_size
data = [mx.random.uniform(-1.0, 1.0, shape=shape, ctx=ctx) for _, shape in mod.data_shapes]
batch = mx.io.DataBatch(data, []) # empty label
dry_run = 5 # use 5 iterations to warm up
for i in range(dry_run):
mod.forward(batch, is_train=False)
for output in mod.get_outputs():
output.wait_to_read()
tic = time.time()
results = mod.score(eval_iter, metric, num_batch=num_batch)
speed = num / (time.time() - tic)
if logger is not None:
logger.info('Finished inference with %d images' % num)
logger.info('Finished with %f images per second', speed)
for k, v in results:
print("{}: {}".format(k, v)) | def evaluate_net(net, path_imgrec, num_classes, num_batch, mean_pixels, data_shape,
model_prefix, epoch, ctx=mx.cpu(), batch_size=32,
path_imglist="", nms_thresh=0.45, force_nms=False,
ovp_thresh=0.5, use_difficult=False, class_names=None,
voc07_metric=False):
"""
evalute network given validation record file
Parameters:
----------
net : str or None
Network name or use None to load from json without modifying
path_imgrec : str
path to the record validation file
path_imglist : str
path to the list file to replace labels in record file, optional
num_classes : int
number of classes, not including background
mean_pixels : tuple
(mean_r, mean_g, mean_b)
data_shape : tuple or int
(3, height, width) or height/width
model_prefix : str
model prefix of saved checkpoint
epoch : int
load model epoch
ctx : mx.ctx
mx.gpu() or mx.cpu()
batch_size : int
validation batch size
nms_thresh : float
non-maximum suppression threshold
force_nms : boolean
whether suppress different class objects
ovp_thresh : float
AP overlap threshold for true/false postives
use_difficult : boolean
whether to use difficult objects in evaluation if applicable
class_names : comma separated str
class names in string, must correspond to num_classes if set
voc07_metric : boolean
whether to use 11-point evluation as in VOC07 competition
"""
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# args
if isinstance(data_shape, int):
data_shape = (3, data_shape, data_shape)
assert len(data_shape) == 3 and data_shape[0] == 3
model_prefix += '_' + str(data_shape[1])
# iterator
eval_iter = DetRecordIter(path_imgrec, batch_size, data_shape, mean_pixels=mean_pixels,
path_imglist=path_imglist, **cfg.valid)
# model params
load_net, args, auxs = mx.model.load_checkpoint(model_prefix, epoch)
# network
if net is None:
net = load_net
else:
net = get_symbol(net, data_shape[1], num_classes=num_classes,
nms_thresh=nms_thresh, force_suppress=force_nms)
if not 'label' in net.list_arguments():
label = mx.sym.Variable(name='label')
net = mx.sym.Group([net, label])
# init module
mod = mx.mod.Module(net, label_names=('label',), logger=logger, context=ctx,
fixed_param_names=net.list_arguments())
mod.bind(data_shapes=eval_iter.provide_data, label_shapes=eval_iter.provide_label)
mod.set_params(args, auxs, allow_missing=False, force_init=True)
# run evaluation
if voc07_metric:
metric = VOC07MApMetric(ovp_thresh, use_difficult, class_names)
else:
metric = MApMetric(ovp_thresh, use_difficult, class_names)
num = num_batch * batch_size
data = [mx.random.uniform(-1.0, 1.0, shape=shape, ctx=ctx) for _, shape in mod.data_shapes]
batch = mx.io.DataBatch(data, []) # empty label
dry_run = 5 # use 5 iterations to warm up
for i in range(dry_run):
mod.forward(batch, is_train=False)
for output in mod.get_outputs():
output.wait_to_read()
tic = time.time()
results = mod.score(eval_iter, metric, num_batch=num_batch)
speed = num / (time.time() - tic)
if logger is not None:
logger.info('Finished inference with %d images' % num)
logger.info('Finished with %f images per second', speed)
for k, v in results:
print("{}: {}".format(k, v)) | [
"evalute",
"network",
"given",
"validation",
"record",
"file"
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/ssd/evaluate/evaluate_net.py#L34-L133 | [
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"path_i... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
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