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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | load_model | Loads a serialized ModelProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@return
Loaded in-memory ModelProto | onnx/__init__.py | def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto
'''
Loads a serialized ModelProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@ret... | def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto
'''
Loads a serialized ModelProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
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train | load_tensor | Loads a serialized TensorProto into memory
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format is for future use
@return
Loaded in-memory TensorProto | onnx/__init__.py | def load_tensor(f, format=None): # type: (Union[IO[bytes], Text], Optional[Any]) -> TensorProto
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Loads a serialized TensorProto into memory
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f can be a file-like object (has "read" function) or a string containing a file name
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Loads a serialized TensorProto into memory
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f can be a file-like object (has "read" function) or a string containing a file name
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train | save_model | Saves the ModelProto to the specified path.
@params
proto should be a in-memory ModelProto
f can be a file-like object (has "write" function) or a string containing a file name
format is for future use | onnx/__init__.py | def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None
'''
Saves the ModelProto to the specified path.
@params
proto should be a in-memory ModelProto
f can be a file-like object (has "write" function) or a string containing a file n... | def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None
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Saves the ModelProto to the specified path.
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train | polish_model | This function combines several useful utility functions together. | onnx/utils.py | def polish_model(model): # type: (ModelProto) -> ModelProto
'''
This function combines several useful utility functions together.
'''
onnx.checker.check_model(model)
onnx.helper.strip_doc_string(model)
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model = onnx.optimizer.optimize(mode... | def polish_model(model): # type: (ModelProto) -> ModelProto
'''
This function combines several useful utility functions together.
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train | dynamic_unroll | Unrolls an RNN cell across time steps.
Currently, 'TNC' is a preferred layout. unroll on the input of this layout
runs much faster.
Parameters
----------
cell : an object whose base class is RNNCell.
The RNN cell to run on the input sequence.
inputs : Symbol
It should have shap... | python/mxnet/gluon/contrib/rnn/rnn_cell.py | def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0,
layout='TNC', valid_length=None):
"""Unrolls an RNN cell across time steps.
Currently, 'TNC' is a preferred layout. unroll on the input of this layout
runs much faster.
Parameters
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cell : ... | def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0,
layout='TNC', valid_length=None):
"""Unrolls an RNN cell across time steps.
Currently, 'TNC' is a preferred layout. unroll on the input of this layout
runs much faster.
Parameters
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train | VariationalDropoutCell.unroll | Unrolls an RNN cell across time steps.
Parameters
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length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
If `inputs` is a single Symbol (usually the output
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valid_length=None):
"""Unrolls an RNN cell across time steps.
Parameters
----------
length : int
Number of steps to unroll.
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Parameters
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Number of steps to unroll.
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train | _fix_attribute_names | Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
Returns
-------
:return new_attr : dict Converted dict of operator attributes. | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _fix_attribute_names(attrs, change_map):
"""
Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
Returns
-------
:retur... | def _fix_attribute_names(attrs, change_map):
"""
Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
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train | _remove_attributes | Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes. | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _remove_attributes(attrs, remove_list):
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Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes.
"""
... | def _remove_attributes(attrs, remove_list):
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Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes.
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train | _add_extra_attributes | :param attrs: Current Attribute list
:param extraAttrMap: Additional attributes to be added
:return: new_attr | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _add_extra_attributes(attrs, extra_attr_map):
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:param extraAttrMap: Additional attributes to be added
:return: new_attr
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return attrs | def _add_extra_attributes(attrs, extra_attr_map):
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:param extraAttrMap: Additional attributes to be added
:return: new_attr
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train | _pad_sequence_fix | Changing onnx's pads sequence to match with mxnet's pad_width
mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
onnx: (x1_begin, x2_begin, ... , xn_end, xn_end) | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _pad_sequence_fix(attr, kernel_dim=None):
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mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
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new_attr = ()
if len(attr) % 2 == 0:
for index in range(int(len(attr) / 2)):
... | def _pad_sequence_fix(attr, kernel_dim=None):
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mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
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new_attr = ()
if len(attr) % 2 == 0:
for index in range(int(len(attr) / 2)):
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train | _fix_pooling | onnx pooling operator supports asymmetrical padding
Adding pad operator before pooling in mxnet to work with onnx | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _fix_pooling(pool_type, inputs, new_attr):
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stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
padding = new_attr.get('pad')
p_value = new_attr.get('p_value')
... | def _fix_pooling(pool_type, inputs, new_attr):
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Adding pad operator before pooling in mxnet to work with onnx"""
stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
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p_value = new_attr.get('p_value')
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train | _fix_bias | A workaround for 'use_bias' attribute since onnx don't provide this attribute,
we have to check the number of inputs to decide it. | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _fix_bias(op_name, attrs, num_inputs):
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attrs['no_bias'] = False
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if num_inputs == 3:
attrs['no_bias'] = False
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train | _fix_broadcast | A workaround to reshape bias term to (1, num_channel). | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj):
"""A workaround to reshape bias term to (1, num_channel)."""
if int(len(proto_obj._params)) > 0:
assert len(list(inputs)) == 2
input0_shape = get_input_shape(inputs[0], proto_obj)
#creating reshape shape
reshape_sha... | def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj):
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if int(len(proto_obj._params)) > 0:
assert len(list(inputs)) == 2
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#creating reshape shape
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train | _fix_channels | A workaround for getting 'channels' or 'units' since onnx don't provide
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"""A workaround for getting 'channels' or 'units' since onnx don't provide
these attributes. We check the shape of weights provided to get the number.
"""
weight_name = inputs[1].name
if not weight_name in proto_obj._params:
raise ValueEr... | def _fix_channels(op_name, attrs, inputs, proto_obj):
"""A workaround for getting 'channels' or 'units' since onnx don't provide
these attributes. We check the shape of weights provided to get the number.
"""
weight_name = inputs[1].name
if not weight_name in proto_obj._params:
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train | _fix_gemm | Using FullyConnected operator in place of linalg_gemm to perform same operation | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def _fix_gemm(op_name, inputs, old_attr, proto_obj):
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train | get_input_shape | Helper function to obtain the shape of an array | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | def get_input_shape(sym, proto_obj):
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model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')]
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train | imresize | r"""Resize image with OpenCV.
.. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built
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Parameters
----------
src : NDArray
source image
w : int, required
Width of resized image.
h : int, required
... | python/mxnet/image/image.py | def imresize(src, w, h, *args, **kwargs):
r"""Resize image with OpenCV.
.. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built
with USE_OPENCV=1 for `imresize` to work.
Parameters
----------
src : NDArray
source image
w : int, required
... | def imresize(src, w, h, *args, **kwargs):
r"""Resize image with OpenCV.
.. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built
with USE_OPENCV=1 for `imresize` to work.
Parameters
----------
src : NDArray
source image
w : int, required
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train | imdecode | Decode an image to an NDArray.
.. note:: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes/bytearray or numpy.ndarray
Binary image data as string or numpy ndarray.
flag : in... | python/mxnet/image/image.py | def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray.
.. note:: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes/bytearray or numpy.ndarray
Binary image dat... | def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray.
.. note:: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes/bytearray or numpy.ndarray
Binary image dat... | [
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train | scale_down | Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in (width, height) format.
size : tupl... | python/mxnet/image/image.py | def scale_down(src_size, size):
"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
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"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in ... | [
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train | copyMakeBorder | Pad image border with OpenCV.
Parameters
----------
src : NDArray
source image
top : int, required
Top margin.
bot : int, required
Bottom margin.
left : int, required
Left margin.
right : int, required
Right margin.
type : int, optional, default='... | python/mxnet/image/image.py | def copyMakeBorder(src, top, bot, left, right, *args, **kwargs):
"""Pad image border with OpenCV.
Parameters
----------
src : NDArray
source image
top : int, required
Top margin.
bot : int, required
Bottom margin.
left : int, required
Left margin.
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Parameters
----------
src : NDArray
source image
top : int, required
Top margin.
bot : int, required
Bottom margin.
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Left margin.
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train | _get_interp_method | Get the interpolation method for resize functions.
The major purpose of this function is to wrap a random interp method selection
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Parameters
----------
interp : int
interpolation method for all resizing operations
Possible values:
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"""Get the interpolation method for resize functions.
The major purpose of this function is to wrap a random interp method selection
and a auto-estimation method.
Parameters
----------
interp : int
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"""Get the interpolation method for resize functions.
The major purpose of this function is to wrap a random interp method selection
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Parameters
----------
interp : int
interpolation method for all resizing operation... | [
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train | resize_short | Resizes shorter edge to size.
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MXNet must have been built with OpenCV for `resize_short` to work.
Resizes the original image by setting the shorter edge to size
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Resizing function is called... | python/mxnet/image/image.py | def resize_short(src, size, interp=2):
"""Resizes shorter edge to size.
.. note:: `resize_short` uses OpenCV (not the CV2 Python library).
MXNet must have been built with OpenCV for `resize_short` to work.
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train | fixed_crop | Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
w : int
Width of the cropping area
h : int
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"""Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
w : int
... | def fixed_crop(src, x0, y0, w, h, size=None, interp=2):
"""Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
w : int
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train | center_crop | Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
than `size`.
.. note:: This requires MXNet to be compiled with USE_OPENCV.
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src : NDArray
Binary source image data.
siz... | python/mxnet/image/image.py | def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
than `size`.
.. note:: This requires MXNet to be compiled with USE_OPENCV.
Parameters
----------
src : NDAr... | def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
than `size`.
.. note:: This requires MXNet to be compiled with USE_OPENCV.
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----------
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train | color_normalize | Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
Returns
-------
NDArray
An `NDArray` containing the normalized image. | python/mxnet/image/image.py | def color_normalize(src, mean, std=None):
"""Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
Returns
-------
NDArray
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"""Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
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train | random_size_crop | Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or tuple of (float, float)
If tuple, minimum area and maximum area t... | python/mxnet/image/image.py | def random_size_crop(src, size, area, ratio, interp=2, **kwargs):
"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or ... | def random_size_crop(src, size, area, ratio, interp=2, **kwargs):
"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or ... | [
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train | CreateAugmenter | Creates an augmenter list.
Parameters
----------
data_shape : tuple of int
Shape for output data
resize : int
Resize shorter edge if larger than 0 at the begining
rand_crop : bool
Whether to enable random cropping other than center crop
rand_resize : bool
Whether... | python/mxnet/image/image.py | def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False,
mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0,
pca_noise=0, rand_gray=0, inter_method=2):
"""Creates an augmenter list.
Parameters
----------
dat... | def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False,
mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0,
pca_noise=0, rand_gray=0, inter_method=2):
"""Creates an augmenter list.
Parameters
----------
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train | Augmenter.dumps | Saves the Augmenter to string
Returns
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JSON formatted string that describes the Augmenter. | python/mxnet/image/image.py | def dumps(self):
"""Saves the Augmenter to string
Returns
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JSON formatted string that describes the Augmenter.
"""
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JSON formatted string that describes the Augmenter.
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train | SequentialAug.dumps | Override the default to avoid duplicate dump. | python/mxnet/image/image.py | def dumps(self):
"""Override the default to avoid duplicate dump."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] | def dumps(self):
"""Override the default to avoid duplicate dump."""
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train | ImageIter.reset | Resets the iterator to the beginning of the data. | python/mxnet/image/image.py | def reset(self):
"""Resets the iterator to the beginning of the data."""
if self.seq is not None and self.shuffle:
random.shuffle(self.seq)
if self.last_batch_handle != 'roll_over' or \
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train | ImageIter.hard_reset | Resets the iterator and ignore roll over data | python/mxnet/image/image.py | def hard_reset(self):
"""Resets the iterator and ignore roll over data"""
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train | ImageIter.next_sample | Helper function for reading in next sample. | python/mxnet/image/image.py | def next_sample(self):
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train | ImageIter._batchify | Helper function for batchifying data | python/mxnet/image/image.py | def _batchify(self, batch_data, batch_label, start=0):
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i = start
batch_size = self.batch_size
try:
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label, s = self.next_sample()
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try:
while i < batch_size:
label, s = self.next_sample()
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train | ImageIter.imdecode | Decodes a string or byte string to an NDArray.
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train | ImageIter.read_image | Reads an input image `fname` and returns the decoded raw bytes.
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Examples
--------
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"""
with open(os.path.join(self.path_root, fname), 'rb') as fin:
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train | facc | evaluate accuracy | example/gluon/sn_gan/train.py | def facc(label, pred):
""" evaluate accuracy """
pred = pred.ravel()
label = label.ravel()
return ((pred > 0.5) == label).mean() | def facc(label, pred):
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train | word_to_vector | Convert character vectors to integer vectors. | example/gluon/lipnet/utils/common.py | def word_to_vector(word):
"""
Convert character vectors to integer vectors.
"""
vector = []
for char in list(word):
vector.append(char2int(char))
return vector | def word_to_vector(word):
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Convert character vectors to integer vectors.
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train | vector_to_word | Convert integer vectors to character vectors. | example/gluon/lipnet/utils/common.py | def vector_to_word(vector):
"""
Convert integer vectors to character vectors.
"""
word = ""
for vec in vector:
word = word + int2char(vec)
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"""
Convert integer vectors to character vectors.
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train | char_conv | Convert integer vectors to character vectors for batch. | example/gluon/lipnet/utils/common.py | def char_conv(out):
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Convert integer vectors to character vectors for batch.
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for i in range(out.shape[0]):
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for j in range(out.shape[1]):
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Convert integer vectors to character vectors for batch.
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train | add_pooling_with_padding_types | Add a pooling layer to the model.
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Par... | tools/coreml/converter/_add_pooling.py | def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width,
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padding_top = 0, padding_bottom = 0, padding_left = 0, padding_right = 0,
same_padding_asymmetry_mode = 'BOTTOM_RIGHT_HEAVY',
exclude_pad_a... | def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width,
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train | get_frames | Get path to all the frame in view SAX and contain complete frames | example/kaggle-ndsb2/Preprocessing.py | def get_frames(root_path):
"""Get path to all the frame in view SAX and contain complete frames"""
ret = []
for root, _, files in os.walk(root_path):
root=root.replace('\\','/')
files=[s for s in files if ".dcm" in s]
if len(files) == 0 or not files[0].endswith(".dcm") or root.find("sax") ... | def get_frames(root_path):
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train | write_data_csv | Write data to csv file | example/kaggle-ndsb2/Preprocessing.py | def write_data_csv(fname, frames, preproc):
"""Write data to csv file"""
fdata = open(fname, "w")
dr = Parallel()(delayed(get_data)(lst,preproc) for lst in frames)
data,result = zip(*dr)
for entry in data:
fdata.write(','.join(entry)+'\r\n')
print("All finished, %d slices in total" % len(data))
... | def write_data_csv(fname, frames, preproc):
"""Write data to csv file"""
fdata = open(fname, "w")
dr = Parallel()(delayed(get_data)(lst,preproc) for lst in frames)
data,result = zip(*dr)
for entry in data:
fdata.write(','.join(entry)+'\r\n')
print("All finished, %d slices in total" % len(data))
... | [
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train | crop_resize | crop center and resize | example/kaggle-ndsb2/Preprocessing.py | def crop_resize(img, size):
"""crop center and resize"""
if img.shape[0] < img.shape[1]:
img = img.T
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short_egde = min(img.shape[:2])
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xx = int((img.shape[1] - short_egde) / 2)
crop_img = img[yy : yy + short_egde, xx : xx + ... | def crop_resize(img, size):
"""crop center and resize"""
if img.shape[0] < img.shape[1]:
img = img.T
# we crop image from center
short_egde = min(img.shape[:2])
yy = int((img.shape[0] - short_egde) / 2)
xx = int((img.shape[1] - short_egde) / 2)
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train | get_generator | construct and return generator | example/gluon/sn_gan/model.py | def get_generator():
""" construct and return generator """
g_net = gluon.nn.Sequential()
with g_net.name_scope():
g_net.add(gluon.nn.Conv2DTranspose(
channels=512, kernel_size=4, strides=1, padding=0, use_bias=False))
g_net.add(gluon.nn.BatchNorm())
g_net.add(gluon.nn.L... | def get_generator():
""" construct and return generator """
g_net = gluon.nn.Sequential()
with g_net.name_scope():
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channels=512, kernel_size=4, strides=1, padding=0, use_bias=False))
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train | get_descriptor | construct and return descriptor | example/gluon/sn_gan/model.py | def get_descriptor(ctx):
""" construct and return descriptor """
d_net = gluon.nn.Sequential()
with d_net.name_scope():
d_net.add(SNConv2D(num_filter=64, kernel_size=4, strides=2, padding=1, in_channels=3, ctx=ctx))
d_net.add(gluon.nn.LeakyReLU(0.2))
d_net.add(SNConv2D(num_filter=1... | def get_descriptor(ctx):
""" construct and return descriptor """
d_net = gluon.nn.Sequential()
with d_net.name_scope():
d_net.add(SNConv2D(num_filter=64, kernel_size=4, strides=2, padding=1, in_channels=3, ctx=ctx))
d_net.add(gluon.nn.LeakyReLU(0.2))
d_net.add(SNConv2D(num_filter=1... | [
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train | SNConv2D._spectral_norm | spectral normalization | example/gluon/sn_gan/model.py | def _spectral_norm(self):
""" spectral normalization """
w = self.params.get('weight').data(self.ctx)
w_mat = nd.reshape(w, [w.shape[0], -1])
_u = self.u.data(self.ctx)
_v = None
for _ in range(POWER_ITERATION):
_v = nd.L2Normalization(nd.dot(_u, w_mat))
... | def _spectral_norm(self):
""" spectral normalization """
w = self.params.get('weight').data(self.ctx)
w_mat = nd.reshape(w, [w.shape[0], -1])
_u = self.u.data(self.ctx)
_v = None
for _ in range(POWER_ITERATION):
_v = nd.L2Normalization(nd.dot(_u, w_mat))
... | [
"spectral",
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train | conv_output_length | Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
Params:
input_length (int): Length of the input sequence.
filter_size (int): Width of the convolution kernel.
... | example/speech_recognition/stt_utils.py | def conv_output_length(input_length, filter_size, border_mode, stride,
dilation=1):
""" Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
Params:
i... | def conv_output_length(input_length, filter_size, border_mode, stride,
dilation=1):
""" Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
Params:
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train | spectrogram | Compute the spectrogram for a real signal.
The parameters follow the naming convention of
matplotlib.mlab.specgram
Args:
samples (1D array): input audio signal
fft_length (int): number of elements in fft window
sample_rate (scalar): sample rate
hop_length (int): hop length (r... | example/speech_recognition/stt_utils.py | def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128):
"""
Compute the spectrogram for a real signal.
The parameters follow the naming convention of
matplotlib.mlab.specgram
Args:
samples (1D array): input audio signal
fft_length (int): number of elements in fft win... | def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128):
"""
Compute the spectrogram for a real signal.
The parameters follow the naming convention of
matplotlib.mlab.specgram
Args:
samples (1D array): input audio signal
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train | spectrogram_from_file | Calculate the log of linear spectrogram from FFT energy
Params:
filename (str): Path to the audio file
step (int): Step size in milliseconds between windows
window (int): FFT window size in milliseconds
max_freq (int): Only FFT bins corresponding to frequencies between
[0... | example/speech_recognition/stt_utils.py | def spectrogram_from_file(filename, step=10, window=20, max_freq=None,
eps=1e-14, overwrite=False, save_feature_as_csvfile=False):
""" Calculate the log of linear spectrogram from FFT energy
Params:
filename (str): Path to the audio file
step (int): Step size in millise... | def spectrogram_from_file(filename, step=10, window=20, max_freq=None,
eps=1e-14, overwrite=False, save_feature_as_csvfile=False):
""" Calculate the log of linear spectrogram from FFT energy
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filename (str): Path to the audio file
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train | RandCropper.sample | generate random cropping boxes according to parameters
if satifactory crops generated, apply to ground-truth as well
Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
----------
list of (crop_box, label) tuples, if fail... | example/ssd/tools/rand_sampler.py | def sample(self, label):
"""
generate random cropping boxes according to parameters
if satifactory crops generated, apply to ground-truth as well
Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
----------
... | def sample(self, label):
"""
generate random cropping boxes according to parameters
if satifactory crops generated, apply to ground-truth as well
Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
----------
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train | RandCropper._check_satisfy | check if overlap with any gt box is larger than threshold | example/ssd/tools/rand_sampler.py | def _check_satisfy(self, rand_box, gt_boxes):
"""
check if overlap with any gt box is larger than threshold
"""
l, t, r, b = rand_box
num_gt = gt_boxes.shape[0]
ls = np.ones(num_gt) * l
ts = np.ones(num_gt) * t
rs = np.ones(num_gt) * r
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num_gt = gt_boxes.shape[0]
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train | RandPadder.sample | generate random padding boxes according to parameters
if satifactory padding generated, apply to ground-truth as well
Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
----------
list of (crop_box, label) tuples, if fai... | example/ssd/tools/rand_sampler.py | def sample(self, label):
"""
generate random padding boxes according to parameters
if satifactory padding generated, apply to ground-truth as well
Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
----------
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generate random padding boxes according to parameters
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Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
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train | measure_cost | Measure time cost of running a function | benchmark/python/sparse/dot.py | def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, *args, **kwargs):
"""Measure time cost of running a function
"""
mx.nd.waitall()
args_list = []
for arg in args:
args_list.append(arg)
start = time.time()
if scipy_trans_lhs:
args_list[0] = np.transpose(args_... | def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, *args, **kwargs):
"""Measure time cost of running a function
"""
mx.nd.waitall()
args_list = []
for arg in args:
args_list.append(arg)
start = time.time()
if scipy_trans_lhs:
args_list[0] = np.transpose(args_... | [
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train | COCO.info | Print information about the annotation file.
:return: | example/ssd/dataset/pycocotools/coco.py | def info(self):
"""
Print information about the annotation file.
:return:
"""
for key, value in self.dataset['info'].items():
print('{}: {}'.format(key, value)) | def info(self):
"""
Print information about the annotation file.
:return:
"""
for key, value in self.dataset['info'].items():
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train | COCO.getCatIds | filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
:param catIds (int array) : get cats for given cat ids
:return: ids (int array) : integer array of cat ids | example/ssd/dataset/pycocotools/coco.py | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
"""
filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
:param catIds (int array) : get cats for given... | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
"""
filtering parameters. default skips that filter.
:param catNms (str array) : get cats for given cat names
:param supNms (str array) : get cats for given supercategory names
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train | COCO.loadAnns | Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects | example/ssd/dataset/pycocotools/coco.py | def loadAnns(self, ids=[]):
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:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects
"""
if type(ids) == list:
return [self.anns[id] for id in ids]
elif type(ids)... | def loadAnns(self, ids=[]):
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Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
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train | COCO.loadCats | Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects | example/ssd/dataset/pycocotools/coco.py | def loadCats(self, ids=[]):
"""
Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects
"""
if type(ids) == list:
return [self.cats[id] for id in ids]
elif type(ids)... | def loadCats(self, ids=[]):
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Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects
"""
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train | COCO.loadImgs | Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects | example/ssd/dataset/pycocotools/coco.py | def loadImgs(self, ids=[]):
"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
"""
if type(ids) == list:
return [self.imgs[id] for id in ids]
elif type(ids) ... | def loadImgs(self, ids=[]):
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Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
"""
if type(ids) == list:
return [self.imgs[id] for id in ids]
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train | COCO.showAnns | Display the specified annotations.
:param anns (array of object): annotations to display
:return: None | example/ssd/dataset/pycocotools/coco.py | def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
:return: None
"""
if len(anns) == 0:
return 0
if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
datasetType = 'inst... | def showAnns(self, anns):
"""
Display the specified annotations.
:param anns (array of object): annotations to display
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train | COCO.download | Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return: | example/ssd/dataset/pycocotools/coco.py | def download(self, tarDir = None, imgIds = [] ):
'''
Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return:
'''
if tarDir is None:
print('Please specify targe... | def download(self, tarDir = None, imgIds = [] ):
'''
Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return:
'''
if tarDir is None:
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train | COCO.loadNumpyAnnotations | Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list) | example/ssd/dataset/pycocotools/coco.py | def loadNumpyAnnotations(self, data):
"""
Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list)
"""
print('Converting ndarray to lists...')
assert... | def loadNumpyAnnotations(self, data):
"""
Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list)
"""
print('Converting ndarray to lists...')
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train | COCO.annToRLE | Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array) | example/ssd/dataset/pycocotools/coco.py | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
if type(segm) == list:
... | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
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train | save_model | Save cnn model
Returns
----------
callback: A callback function that can be passed as epoch_end_callback to fit | example/cnn_chinese_text_classification/text_cnn.py | def save_model():
"""Save cnn model
Returns
----------
callback: A callback function that can be passed as epoch_end_callback to fit
"""
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period) | def save_model():
"""Save cnn model
Returns
----------
callback: A callback function that can be passed as epoch_end_callback to fit
"""
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
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train | highway | Construct highway net
Parameters
----------
data:
Returns
----------
Highway Networks | example/cnn_chinese_text_classification/text_cnn.py | def highway(data):
"""Construct highway net
Parameters
----------
data:
Returns
----------
Highway Networks
"""
_data = data
high_weight = mx.sym.Variable('high_weight')
high_bias = mx.sym.Variable('high_bias')
high_fc = mx.sym.FullyConnected(data=data, weight=high_weight... | def highway(data):
"""Construct highway net
Parameters
----------
data:
Returns
----------
Highway Networks
"""
_data = data
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train | train | Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
data_column_names: list of str
Defaults to ('data') for a typical model used in image classific... | example/cnn_chinese_text_classification/text_cnn.py | def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names):
"""Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
data_column_names: lis... | def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names):
"""Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
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train | default_batchify_fn | Collate data into batch. | python/mxnet/gluon/data/dataloader.py | def default_batchify_fn(data):
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elif isinstance(data[0], tuple):
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train | default_mp_batchify_fn | Collate data into batch. Use shared memory for stacking. | python/mxnet/gluon/data/dataloader.py | def default_mp_batchify_fn(data):
"""Collate data into batch. Use shared memory for stacking."""
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return nd.stack(*data, out=out)
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"""Collate data into batch. Use shared memory for stacking."""
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out = nd.empty((len(data),) + data[0].shape, dtype=data[0].dtype,
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train | _as_in_context | Move data into new context. | python/mxnet/gluon/data/dataloader.py | def _as_in_context(data, ctx):
"""Move data into new context."""
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elif isinstance(data, (list, tuple)):
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return data | def _as_in_context(data, ctx):
"""Move data into new context."""
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train | worker_loop_v1 | Worker loop for multiprocessing DataLoader. | python/mxnet/gluon/data/dataloader.py | def worker_loop_v1(dataset, key_queue, data_queue, batchify_fn):
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train | fetcher_loop_v1 | Fetcher loop for fetching data from queue and put in reorder dict. | python/mxnet/gluon/data/dataloader.py | def fetcher_loop_v1(data_queue, data_buffer, pin_memory=False,
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train | _worker_fn | Function for processing data in worker process. | python/mxnet/gluon/data/dataloader.py | def _worker_fn(samples, batchify_fn, dataset=None):
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train | ConnectionWrapper.send | Send object | python/mxnet/gluon/data/dataloader.py | def send(self, obj):
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train | _MultiWorkerIterV1._push_next | Assign next batch workload to workers. | python/mxnet/gluon/data/dataloader.py | def _push_next(self):
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train | _MultiWorkerIterV1.shutdown | Shutdown internal workers by pushing terminate signals. | python/mxnet/gluon/data/dataloader.py | def shutdown(self):
"""Shutdown internal workers by pushing terminate signals."""
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# send shutdown signal to the fetcher and join data queue first
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# otherwise, the the fet... | def shutdown(self):
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train | _MultiWorkerIter._push_next | Assign next batch workload to workers. | python/mxnet/gluon/data/dataloader.py | def _push_next(self):
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train | _ctype_key_value | Returns ctype arrays for the key-value args, and the whether string keys are used.
For internal use only. | python/mxnet/kvstore.py | def _ctype_key_value(keys, vals):
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train | _ctype_dict | Returns ctype arrays for keys and values(converted to strings) in a dictionary | python/mxnet/kvstore.py | def _ctype_dict(param_dict):
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train | _updater_wrapper | A wrapper for the user-defined handle. | python/mxnet/kvstore.py | def _updater_wrapper(updater):
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train | create | Creates a new KVStore.
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the KVStore also attempts to use GPU peer-to-peer communicat... | python/mxnet/kvstore.py | def create(name='local'):
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train | KVStore.init | Initializes a single or a sequence of key-value pairs into the store.
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When multiple workers invoke `init` for the same key, only
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after data has bee... | python/mxnet/kvstore.py | def init(self, key, value):
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train | KVStore.push | Pushes a single or a sequence of key-value pairs into the store.
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... | python/mxnet/kvstore.py | def push(self, key, value, priority=0):
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pushes to the same key, there ... | def push(self, key, value, priority=0):
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train | KVStore.pull | Pulls a single value or a sequence of values from the store.
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train | KVStore.row_sparse_pull | Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \
is invoked just once and the result is broadcast to all the rest of outputs.
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""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \
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""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \
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train | KVStore.set_gradient_compression | Specifies type of low-bit quantization for gradient compression \
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""" Specifies type of low-bit quantization for gradient compression \
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The technique works by t... | def set_gradient_compression(self, compression_params):
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train | KVStore.set_optimizer | Registers an optimizer with the kvstore.
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""" Registers an optimizer with the kvstore.
When using a single machine, this function updates the local optimizer.
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train | KVStore.type | Returns the type of this kvstore.
Returns
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type : str
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""" Returns the type of this kvstore.
Returns
-------
type : str
the string type
"""
kv_type = ctypes.c_char_p()
check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type)))
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""" Returns the type of this kvstore.
Returns
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type : str
the string type
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train | KVStore.rank | Returns the rank of this worker node.
Returns
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rank : int
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""" Returns the rank of this worker node.
Returns
-------
rank : int
The rank of this node, which is in range [0, num_workers())
"""
rank = ctypes.c_int()
check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank)))
retur... | def rank(self):
""" Returns the rank of this worker node.
Returns
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rank : int
The rank of this node, which is in range [0, num_workers())
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rank = ctypes.c_int()
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train | KVStore.num_workers | Returns the number of worker nodes.
Returns
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size :int
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"""Returns the number of worker nodes.
Returns
-------
size :int
The number of worker nodes.
"""
size = ctypes.c_int()
check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size)))
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"""Returns the number of worker nodes.
Returns
-------
size :int
The number of worker nodes.
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train | KVStore.save_optimizer_states | Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.
Parameters
----------
fname : str
Path to the output states file.
dump_optimizer : bool, default False
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"""Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.
Parameters
----------
fname : str
Path to the output states file.
dump_optimizer :... | def save_optimizer_states(self, fname, dump_optimizer=False):
"""Saves the optimizer (updater) state to a file. This is often used when checkpointing
the model during training.
Parameters
----------
fname : str
Path to the output states file.
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train | KVStore.load_optimizer_states | Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
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"""Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file.
"""
assert self._updater is not None, "Cannot load states for distributed training"
self._update... | def load_optimizer_states(self, fname):
"""Loads the optimizer (updater) state from the file.
Parameters
----------
fname : str
Path to input states file.
"""
assert self._updater is not None, "Cannot load states for distributed training"
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train | KVStore._set_updater | Sets a push updater into the store.
This function only changes the local store. When running on multiple machines one must
use `set_optimizer`.
Parameters
----------
updater : function
The updater function.
Examples
--------
>>> def update(k... | python/mxnet/kvstore.py | def _set_updater(self, updater):
"""Sets a push updater into the store.
This function only changes the local store. When running on multiple machines one must
use `set_optimizer`.
Parameters
----------
updater : function
The updater function.
Exampl... | def _set_updater(self, updater):
"""Sets a push updater into the store.
This function only changes the local store. When running on multiple machines one must
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Parameters
----------
updater : function
The updater function.
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train | KVStore._send_command_to_servers | Sends a command to all server nodes.
Sending command to a server node will cause that server node to invoke
``KVStoreServer.controller`` to execute the command.
This function returns after the command has been executed on all server
nodes.
Parameters
----------
... | python/mxnet/kvstore.py | def _send_command_to_servers(self, head, body):
"""Sends a command to all server nodes.
Sending command to a server node will cause that server node to invoke
``KVStoreServer.controller`` to execute the command.
This function returns after the command has been executed on all server
... | def _send_command_to_servers(self, head, body):
"""Sends a command to all server nodes.
Sending command to a server node will cause that server node to invoke
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This function returns after the command has been executed on all server
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train | SequentialModule.add | Add a module to the chain.
Parameters
----------
module : BaseModule
The new module to add.
kwargs : ``**keywords``
All the keyword arguments are saved as meta information
for the added module. The currently known meta includes
- `take_la... | python/mxnet/module/sequential_module.py | def add(self, module, **kwargs):
"""Add a module to the chain.
Parameters
----------
module : BaseModule
The new module to add.
kwargs : ``**keywords``
All the keyword arguments are saved as meta information
for the added module. The currently... | def add(self, module, **kwargs):
"""Add a module to the chain.
Parameters
----------
module : BaseModule
The new module to add.
kwargs : ``**keywords``
All the keyword arguments are saved as meta information
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train | SequentialModule.get_params | Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
is a merged dictionary of all the parameters in the modules. | python/mxnet/module/sequential_module.py | def get_params(self):
"""Gets current parameters.
Returns
-------
(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
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assert self.bin... | def get_params(self):
"""Gets current parameters.
Returns
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(arg_params, aux_params)
A pair of dictionaries each mapping parameter names to NDArray values. This
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train | SequentialModule.init_params | Initializes parameters.
Parameters
----------
initializer : Initializer
arg_params : dict
Default ``None``. Existing parameters. This has higher priority
than `initializer`.
aux_params : dict
Default ``None``. Existing auxiliary states. This h... | python/mxnet/module/sequential_module.py | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
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"""Initializes parameters.
Parameters
----------
initializer : Initializer
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----------
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train | SequentialModule.bind | Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.
Parameters
----------
data_shapes : list of (str, tuple)
Typically is `data_iter.provide_data`.
label_shapes : list of (str, tuple)
Typically i... | python/mxnet/module/sequential_module.py | def bind(self, data_shapes, label_shapes=None, for_training=True,
inputs_need_grad=False, force_rebind=False, shared_module=None,
grad_req='write'):
"""Binds the symbols to construct executors. This is necessary before one
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train | SequentialModule.init_optimizer | Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
optimizer : str or Optimizer
Default `'sgd'`
optimizer_params : dict
Default ``(('learning_rate', 0.01),)``. The default value is not a dict... | python/mxnet/module/sequential_module.py | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
... | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
... | [
"Installs",
"and",
"initializes",
"optimizers",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/module/sequential_module.py#L298-L325 | [
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train | SequentialModule.forward | Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Default is ``None``, in which case `is_train` is take as ``self.for_training``. | python/mxnet/module/sequential_module.py | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Default is ``None``, in which case `is_train` is take as ``self.for_training``.
"""
assert self.binded and self.params_i... | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Default is ``None``, in which case `is_train` is take as ``self.for_training``.
"""
assert self.binded and self.params_i... | [
"Forward",
"computation",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/module/sequential_module.py#L327-L356 | [
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"# make a shallow copy, just to maintain necessary properties (if any) like",
"# bucket_key, pad, etc.",
"d... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | SequentialModule.backward | Backward computation. | python/mxnet/module/sequential_module.py | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))):
module.backward(out_grads=out_grads)
if i_layer == 0:
brea... | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
for i_layer, module in reversed(list(zip(range(len(self._modules)), self._modules))):
module.backward(out_grads=out_grads)
if i_layer == 0:
brea... | [
"Backward",
"computation",
"."
] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/module/sequential_module.py#L358-L367 | [
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"(... | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
train | SequentialModule.update | Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle. | python/mxnet/module/sequential_module.py | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
for module in self._modules:
module.update() | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
for module in self._modules:
module.update() | [
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/module/sequential_module.py#L369-L376 | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 |
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