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apache/incubator-mxnet | python/mxnet/module/base_module.py | BaseModule.bind | 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
can perform computation with the module.
Param... | python | 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
can perform computation with the module.
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apache/incubator-mxnet | python/mxnet/libinfo.py | find_lib_path | def find_lib_path():
"""Find MXNet dynamic library files.
Returns
-------
lib_path : list(string)
List of all found path to the libraries.
"""
lib_from_env = os.environ.get('MXNET_LIBRARY_PATH')
if lib_from_env:
if os.path.isfile(lib_from_env):
if not os.path.isa... | python | def find_lib_path():
"""Find MXNet dynamic library files.
Returns
-------
lib_path : list(string)
List of all found path to the libraries.
"""
lib_from_env = os.environ.get('MXNET_LIBRARY_PATH')
if lib_from_env:
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apache/incubator-mxnet | python/mxnet/libinfo.py | find_include_path | def find_include_path():
"""Find MXNet included header files.
Returns
-------
incl_path : string
Path to the header files.
"""
incl_from_env = os.environ.get('MXNET_INCLUDE_PATH')
if incl_from_env:
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if not os.path.isabs(incl_from_e... | python | def find_include_path():
"""Find MXNet included header files.
Returns
-------
incl_path : string
Path to the header files.
"""
incl_from_env = os.environ.get('MXNET_INCLUDE_PATH')
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apache/incubator-mxnet | example/ctc/captcha_generator.py | CaptchaGen.image | def image(self, captcha_str):
"""Generate a greyscale captcha image representing number string
Parameters
----------
captcha_str: str
string a characters for captcha image
Returns
-------
numpy.ndarray
Generated greyscale image in np.ndar... | python | def image(self, captcha_str):
"""Generate a greyscale captcha image representing number string
Parameters
----------
captcha_str: str
string a characters for captcha image
Returns
-------
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apache/incubator-mxnet | example/ctc/captcha_generator.py | DigitCaptcha.get_rand | def get_rand(num_digit_min, num_digit_max):
"""Generates a character string of digits. Number of digits are
between self.num_digit_min and self.num_digit_max
Returns
-------
str
"""
buf = ""
max_len = random.randint(num_digit_min, num_digit_max)
fo... | python | def get_rand(num_digit_min, num_digit_max):
"""Generates a character string of digits. Number of digits are
between self.num_digit_min and self.num_digit_max
Returns
-------
str
"""
buf = ""
max_len = random.randint(num_digit_min, num_digit_max)
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apache/incubator-mxnet | example/ctc/captcha_generator.py | DigitCaptcha._gen_sample | def _gen_sample(self):
"""Generate a random captcha image sample
Returns
-------
(numpy.ndarray, str)
Tuple of image (numpy ndarray) and character string of digits used to generate the image
"""
num_str = self.get_rand(self.num_digit_min, self.num_digit_max)
... | python | def _gen_sample(self):
"""Generate a random captcha image sample
Returns
-------
(numpy.ndarray, str)
Tuple of image (numpy ndarray) and character string of digits used to generate the image
"""
num_str = self.get_rand(self.num_digit_min, self.num_digit_max)
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.register | def register(klass):
"""Registers a new optimizer.
Once an optimizer is registered, we can create an instance of this
optimizer with `create_optimizer` later.
Examples
--------
>>> @mx.optimizer.Optimizer.register
... class MyOptimizer(mx.optimizer.Optimizer):
... | python | def register(klass):
"""Registers a new optimizer.
Once an optimizer is registered, we can create an instance of this
optimizer with `create_optimizer` later.
Examples
--------
>>> @mx.optimizer.Optimizer.register
... class MyOptimizer(mx.optimizer.Optimizer):
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.create_optimizer | def create_optimizer(name, **kwargs):
"""Instantiates an optimizer with a given name and kwargs.
.. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.
Parameters
----------
name: str
Name of the optimizer. Should be the name
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"""Instantiates an optimizer with a given name and kwargs.
.. note:: We can use the alias `create` for ``Optimizer.create_optimizer``.
Parameters
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.create_state_multi_precision | def create_state_multi_precision(self, index, weight):
"""Creates auxiliary state for a given weight, including FP32 high
precision copy if original weight is FP16.
This method is provided to perform automatic mixed precision training
for optimizers that do not support it themselves.
... | python | def create_state_multi_precision(self, index, weight):
"""Creates auxiliary state for a given weight, including FP32 high
precision copy if original weight is FP16.
This method is provided to perform automatic mixed precision training
for optimizers that do not support it themselves.
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.update_multi_precision | def update_multi_precision(self, index, weight, grad, state):
"""Updates the given parameter using the corresponding gradient and state.
Mixed precision version.
Parameters
----------
index : int
The unique index of the parameter into the individual learning
... | python | def update_multi_precision(self, index, weight, grad, state):
"""Updates the given parameter using the corresponding gradient and state.
Mixed precision version.
Parameters
----------
index : int
The unique index of the parameter into the individual learning
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.set_lr_mult | def set_lr_mult(self, args_lr_mult):
"""Sets an individual learning rate multiplier for each parameter.
If you specify a learning rate multiplier for a parameter, then
the learning rate for the parameter will be set as the product of
the global learning rate `self.lr` and its multiplier... | python | def set_lr_mult(self, args_lr_mult):
"""Sets an individual learning rate multiplier for each parameter.
If you specify a learning rate multiplier for a parameter, then
the learning rate for the parameter will be set as the product of
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer.set_wd_mult | def set_wd_mult(self, args_wd_mult):
"""Sets an individual weight decay multiplier for each parameter.
By default, if `param_idx2name` was provided in the
constructor, the weight decay multipler is set as 0 for all
parameters whose name don't end with ``_weight`` or
``_gamma``.
... | python | def set_wd_mult(self, args_wd_mult):
"""Sets an individual weight decay multiplier for each parameter.
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constructor, the weight decay multipler is set as 0 for all
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer._set_current_context | def _set_current_context(self, device_id):
"""Sets the number of the currently handled device.
Parameters
----------
device_id : int
The number of current device.
"""
if device_id not in self._all_index_update_counts:
self._all_index_update_counts... | python | def _set_current_context(self, device_id):
"""Sets the number of the currently handled device.
Parameters
----------
device_id : int
The number of current device.
"""
if device_id not in self._all_index_update_counts:
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer._update_count | def _update_count(self, index):
"""Updates num_update.
Parameters
----------
index : int or list of int
The index to be updated.
"""
if not isinstance(index, (list, tuple)):
index = [index]
for idx in index:
if idx not in self.... | python | def _update_count(self, index):
"""Updates num_update.
Parameters
----------
index : int or list of int
The index to be updated.
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Optimizer._get_lrs | def _get_lrs(self, indices):
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Parameters
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indices : list of int
Indices corresponding to weights.
Returns
-------
lrs : list of float
Learning rates for those indices.
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Learning rates for those indices.
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Updater.sync_state_context | def sync_state_context(self, state, context):
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if isinstance(state, NDArray):
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elif isinstance(state, (tuple, list)):
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if isinstance(... | python | def sync_state_context(self, state, context):
"""sync state context."""
if isinstance(state, NDArray):
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Updater.set_states | def set_states(self, states):
"""Sets updater states."""
states = pickle.loads(states)
if isinstance(states, tuple) and len(states) == 2:
self.states, self.optimizer = states
else:
self.states = states
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"""Sets updater states."""
states = pickle.loads(states)
if isinstance(states, tuple) and len(states) == 2:
self.states, self.optimizer = states
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apache/incubator-mxnet | python/mxnet/optimizer/optimizer.py | Updater.get_states | def get_states(self, dump_optimizer=False):
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | preprocess | def preprocess(from_idx, to_idx, _params):
"""
Preprocess: Convert a video into the mouth images
"""
source_exts = '*.mpg'
src_path = _params['src_path']
tgt_path = _params['tgt_path']
face_predictor_path = './shape_predictor_68_face_landmarks.dat'
succ = set()
fail = set()
for ... | python | def preprocess(from_idx, to_idx, _params):
"""
Preprocess: Convert a video into the mouth images
"""
source_exts = '*.mpg'
src_path = _params['src_path']
tgt_path = _params['tgt_path']
face_predictor_path = './shape_predictor_68_face_landmarks.dat'
succ = set()
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.from_frames | def from_frames(self, path):
"""
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"""
frames_path = sorted([os.path.join(path, x) for x in os.listdir(path)])
frames = [ndimage.imread(frame_path) for frame_path in frames_path]
self.handle_type(frames)
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"""
Read from frames
"""
frames_path = sorted([os.path.join(path, x) for x in os.listdir(path)])
frames = [ndimage.imread(frame_path) for frame_path in frames_path]
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.from_video | def from_video(self, path):
"""
Read from videos
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frames = self.get_video_frames(path)
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Read from videos
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.handle_type | def handle_type(self, frames):
"""
Config video types
"""
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Config video types
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.process_frames_face | def process_frames_face(self, frames):
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.process_frames_mouth | def process_frames_mouth(self, frames):
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Preprocess from frames using mouth detector
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.get_frames_mouth | def get_frames_mouth(self, detector, predictor, frames):
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Get frames using mouth crop
"""
mouth_width = 100
mouth_height = 50
horizontal_pad = 0.19
normalize_ratio = None
mouth_frames = []
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dets = detector(frame, ... | python | def get_frames_mouth(self, detector, predictor, frames):
"""
Get frames using mouth crop
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mouth_width = 100
mouth_height = 50
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.get_video_frames | def get_video_frames(self, path):
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apache/incubator-mxnet | example/gluon/lipnet/utils/preprocess_data.py | Video.set_data | def set_data(self, frames):
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#frame H x W x C
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apache/incubator-mxnet | example/speech_recognition/stt_io_bucketingiter.py | BucketSTTIter.reset | def reset(self):
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apache/incubator-mxnet | example/speech_recognition/stt_io_bucketingiter.py | BucketSTTIter.next | def next(self):
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apache/incubator-mxnet | example/gluon/style_transfer/utils.py | subtract_imagenet_mean_preprocess_batch | def subtract_imagenet_mean_preprocess_batch(batch):
"""Subtract ImageNet mean pixel-wise from a BGR image."""
batch = F.swapaxes(batch,0, 1)
(r, g, b) = F.split(batch, num_outputs=3, axis=0)
r = r - 123.680
g = g - 116.779
b = b - 103.939
batch = F.concat(b, g, r, dim=0)
batch = F.swapax... | python | def subtract_imagenet_mean_preprocess_batch(batch):
"""Subtract ImageNet mean pixel-wise from a BGR image."""
batch = F.swapaxes(batch,0, 1)
(r, g, b) = F.split(batch, num_outputs=3, axis=0)
r = r - 123.680
g = g - 116.779
b = b - 103.939
batch = F.concat(b, g, r, dim=0)
batch = F.swapax... | [
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apache/incubator-mxnet | example/gluon/style_transfer/utils.py | imagenet_clamp_batch | def imagenet_clamp_batch(batch, low, high):
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""" Not necessary in practice """
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"""Create a linear regression network for performing SVRG optimization.
:return: an instance of mx.io.NDArrayIter
:return: an instance of mx.mod.svrgmodule for performing SVRG optimization
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acc = mx.metric.Accuracy()
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output = net(data)
predictions = nd.argmax(output, axis=1)
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"""Function to evaluate accuracy of any data iterator passed to it as an argument"""
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apache/incubator-mxnet | example/gluon/audio/urban_sounds/train.py | train | def train(train_dir=None, train_csv=None, epochs=30, batch_size=32):
"""Function responsible for running the training the model."""
if not train_dir or not os.path.exists(train_dir) or not train_csv:
warnings.warn("No train directory could be found ")
return
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"""Function responsible for running the training the model."""
if not train_dir or not os.path.exists(train_dir) or not train_csv:
warnings.warn("No train directory could be found ")
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apache/incubator-mxnet | python/mxnet/engine.py | set_bulk_size | def set_bulk_size(size):
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Parameters
----------
size : int
Maximum number of operators that can be bundled in ... | python | def set_bulk_size(size):
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apache/incubator-mxnet | example/gluon/lipnet/BeamSearch.py | applyLM | def applyLM(parentBeam, childBeam, classes, lm):
"""
calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars
"""
if lm and not childBeam.lmApplied:
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"""
calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars
"""
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apache/incubator-mxnet | example/gluon/lipnet/BeamSearch.py | addBeam | def addBeam(beamState, labeling):
"""
add beam if it does not yet exist
"""
if labeling not in beamState.entries:
beamState.entries[labeling] = BeamEntry() | python | def addBeam(beamState, labeling):
"""
add beam if it does not yet exist
"""
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apache/incubator-mxnet | example/gluon/lipnet/BeamSearch.py | ctcBeamSearch | def ctcBeamSearch(mat, classes, lm, k, beamWidth):
"""
beam search as described by the paper of Hwang et al. and the paper of Graves et al.
"""
blankIdx = len(classes)
maxT, maxC = mat.shape
# initialise beam state
last = BeamState()
labeling = ()
last.entries[labeling] = BeamEntry... | python | def ctcBeamSearch(mat, classes, lm, k, beamWidth):
"""
beam search as described by the paper of Hwang et al. and the paper of Graves et al.
"""
blankIdx = len(classes)
maxT, maxC = mat.shape
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apache/incubator-mxnet | example/gluon/lipnet/BeamSearch.py | BeamState.norm | def norm(self):
"""
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for (k, _) in self.entries.items():
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self.entries[k].prText = self.entries[k].prText ** (1.0 / (labelingLen if labelingLen else 1.0)) | python | def norm(self):
"""
length-normalise LM score
"""
for (k, _) in self.entries.items():
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apache/incubator-mxnet | example/image-classification/symbols/lenet.py | get_loc | def get_loc(data, attr={'lr_mult':'0.01'}):
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when num-epoch >=15
"""
loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2))
loc = mx.symbol.Activation(data = loc, act_type='relu')
l... | python | def get_loc(data, attr={'lr_mult':'0.01'}):
"""
the localisation network in lenet-stn, it will increase acc about more than 1%,
when num-epoch >=15
"""
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apache/incubator-mxnet | example/ssd/demo.py | get_detector | def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class,
nms_thresh=0.5, force_nms=True, nms_topk=400):
"""
wrapper for initialize a detector
Parameters:
----------
net : str
test network name
prefix : str
load model prefix
epoch : int
... | python | def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class,
nms_thresh=0.5, force_nms=True, nms_topk=400):
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wrapper for initialize a detector
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net : str
test network name
prefix : str
load model prefix
epoch : int
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apache/incubator-mxnet | example/ssd/demo.py | parse_class_names | def parse_class_names(class_names):
""" parse # classes and class_names if applicable """
if len(class_names) > 0:
if os.path.isfile(class_names):
# try to open it to read class names
with open(class_names, 'r') as f:
class_names = [l.strip() for l in f.readlines(... | python | def parse_class_names(class_names):
""" parse # classes and class_names if applicable """
if len(class_names) > 0:
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apache/incubator-mxnet | example/ssd/demo.py | parse_data_shape | def parse_data_shape(data_shape_str):
"""Parse string to tuple or int"""
ds = data_shape_str.strip().split(',')
if len(ds) == 1:
data_shape = (int(ds[0]), int(ds[0]))
elif len(ds) == 2:
data_shape = (int(ds[0]), int(ds[1]))
else:
raise ValueError("Unexpected data_shape: %s", ... | python | def parse_data_shape(data_shape_str):
"""Parse string to tuple or int"""
ds = data_shape_str.strip().split(',')
if len(ds) == 1:
data_shape = (int(ds[0]), int(ds[0]))
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apache/incubator-mxnet | example/kaggle-ndsb2/Train.py | CRPS | def CRPS(label, pred):
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""" Custom evaluation metric on CRPS.
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apache/incubator-mxnet | example/rcnn/symimdb/coco.py | coco._load_annotation | def _load_annotation(self, _coco, coco_ind_to_class_ind, index):
"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
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crowd instances are handled by marking their overlaps with all categories to -1
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"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
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apache/incubator-mxnet | example/rcnn/symimdb/coco.py | coco._write_coco_results | def _write_coco_results(self, _coco, detections):
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"category_id": 18,
"bbox": [258.15,41.29,348.26,243.78],
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"""
cats = [cat['name'] for cat in _coco.loadCats(_coco.getCatIds())]
class_to_coco... | python | def _write_coco_results(self, _coco, detections):
""" example results
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"category_id": 18,
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apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | rand_zipfian | def rand_zipfian(true_classes, num_sampled, range_max, ctx=None):
"""Draw random samples from an approximately log-uniform or Zipfian distribution.
This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).
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apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | foreach | def foreach(body, data, init_states):
"""Run a for loop with user-defined computation over NDArrays on dimension 0.
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body takes tw... | python | def foreach(body, data, init_states):
"""Run a for loop with user-defined computation over NDArrays on dimension 0.
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apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | while_loop | def while_loop(cond, func, loop_vars, max_iterations=None):
"""Run a while loop with user-defined computation and loop condition.
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"""Run a while loop with user-defined computation and loop condition.
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apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | cond | def cond(pred, then_func, else_func):
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"""Run an if-then-else using user-defined condition and computation
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apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | isfinite | def isfinite(data):
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input : NDArray
An N-D NDArray.
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apache/incubator-mxnet | example/speech_recognition/stt_layer_lstm.py | vanilla_lstm | def vanilla_lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, is_batchnorm=False, gamma=None, beta=None, name=None):
"""LSTM Cell symbol"""
i2h = mx.sym.FullyConnected(data=indata,
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... | python | def vanilla_lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, is_batchnorm=False, gamma=None, beta=None, name=None):
"""LSTM Cell symbol"""
i2h = mx.sym.FullyConnected(data=indata,
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apache/incubator-mxnet | example/speech_recognition/stt_layer_lstm.py | lstm | def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., num_hidden_proj=0, is_batchnorm=False,
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gamma=None, beta=None, name=None):
"""LSTM Cell symbol"""
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apache/incubator-mxnet | example/rcnn/symdata/image.py | get_image | def get_image(roi_rec, short, max_size, mean, std):
"""
read, resize, transform image, return im_tensor, im_info, gt_boxes
roi_rec should have keys: ["image", "boxes", "gt_classes", "flipped"]
0 --- x (width, second dim of im)
|
y (height, first dim of im)
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"""
read, resize, transform image, return im_tensor, im_info, gt_boxes
roi_rec should have keys: ["image", "boxes", "gt_classes", "flipped"]
0 --- x (width, second dim of im)
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"""
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apache/incubator-mxnet | example/rcnn/symdata/image.py | imdecode | def imdecode(image_path):
"""Return BGR image read by opencv"""
import os
assert os.path.exists(image_path), image_path + ' not found'
im = cv2.imread(image_path)
return im | python | def imdecode(image_path):
"""Return BGR image read by opencv"""
import os
assert os.path.exists(image_path), image_path + ' not found'
im = cv2.imread(image_path)
return im | [
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apache/incubator-mxnet | example/rcnn/symdata/image.py | resize | def resize(im, short, max_size):
"""
only resize input image to target size and return scale
:param im: BGR image input by opencv
:param short: one dimensional size (the short side)
:param max_size: one dimensional max size (the long side)
:return: resized image (NDArray) and scale (float)
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"""
only resize input image to target size and return scale
:param im: BGR image input by opencv
:param short: one dimensional size (the short side)
:param max_size: one dimensional max size (the long side)
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apache/incubator-mxnet | example/rcnn/symdata/image.py | transform | def transform(im, mean, std):
"""
transform into mxnet tensor,
subtract pixel size and transform to correct format
:param im: [height, width, channel] in BGR
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: [batch, channel, height, width]
"""
im_tensor = np.zero... | python | def transform(im, mean, std):
"""
transform into mxnet tensor,
subtract pixel size and transform to correct format
:param im: [height, width, channel] in BGR
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: [batch, channel, height, width]
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apache/incubator-mxnet | example/rcnn/symdata/image.py | transform_inverse | def transform_inverse(im_tensor, mean, std):
"""
transform from mxnet im_tensor to ordinary RGB image
im_tensor is limited to one image
:param im_tensor: [batch, channel, height, width]
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: im [height, width, channel(RGB)]
... | python | def transform_inverse(im_tensor, mean, std):
"""
transform from mxnet im_tensor to ordinary RGB image
im_tensor is limited to one image
:param im_tensor: [batch, channel, height, width]
:param mean: [RGB pixel mean]
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apache/incubator-mxnet | example/rcnn/symdata/image.py | tensor_vstack | def tensor_vstack(tensor_list, pad=0):
"""
vertically stack tensors by adding a new axis
expand dims if only 1 tensor
:param tensor_list: list of tensor to be stacked vertically
:param pad: label to pad with
:return: tensor with max shape
"""
if len(tensor_list) == 1:
return tens... | python | def tensor_vstack(tensor_list, pad=0):
"""
vertically stack tensors by adding a new axis
expand dims if only 1 tensor
:param tensor_list: list of tensor to be stacked vertically
:param pad: label to pad with
:return: tensor with max shape
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apache/incubator-mxnet | example/gluon/embedding_learning/train.py | get_distance_matrix | def get_distance_matrix(x):
"""Get distance matrix given a matrix. Used in testing."""
square = nd.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * nd.dot(x, x.transpose()))
return nd.sqrt(distance_square) | python | def get_distance_matrix(x):
"""Get distance matrix given a matrix. Used in testing."""
square = nd.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * nd.dot(x, x.transpose()))
return nd.sqrt(distance_square) | [
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apache/incubator-mxnet | example/gluon/embedding_learning/train.py | evaluate_emb | def evaluate_emb(emb, labels):
"""Evaluate embeddings based on Recall@k."""
d_mat = get_distance_matrix(emb)
d_mat = d_mat.asnumpy()
labels = labels.asnumpy()
names = []
accs = []
for k in [1, 2, 4, 8, 16]:
names.append('Recall@%d' % k)
correct, cnt = 0.0, 0.0
for i ... | python | def evaluate_emb(emb, labels):
"""Evaluate embeddings based on Recall@k."""
d_mat = get_distance_matrix(emb)
d_mat = d_mat.asnumpy()
labels = labels.asnumpy()
names = []
accs = []
for k in [1, 2, 4, 8, 16]:
names.append('Recall@%d' % k)
correct, cnt = 0.0, 0.0
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apache/incubator-mxnet | example/gluon/embedding_learning/train.py | get_lr | def get_lr(lr, epoch, steps, factor):
"""Get learning rate based on schedule."""
for s in steps:
if epoch >= s:
lr *= factor
return lr | python | def get_lr(lr, epoch, steps, factor):
"""Get learning rate based on schedule."""
for s in steps:
if epoch >= s:
lr *= factor
return lr | [
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apache/incubator-mxnet | example/gluon/embedding_learning/train.py | train | def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
if opt.optimizer == 'a... | python | def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
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apache/incubator-mxnet | example/ctc/lstm.py | _lstm_unroll_base | def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden):
""" Returns symbol for LSTM model up to loss/softmax"""
param_cells = []
last_states = []
for i in range(num_lstm_layer):
param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i),
... | python | def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden):
""" Returns symbol for LSTM model up to loss/softmax"""
param_cells = []
last_states = []
for i in range(num_lstm_layer):
param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i),
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apache/incubator-mxnet | example/ctc/lstm.py | _add_warp_ctc_loss | def _add_warp_ctc_loss(pred, seq_len, num_label, label):
""" Adds Symbol.contrib.ctc_loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Reshape(data=label, shape=(-1,))
label = mx.sym.Cast(data=label, dtype='int32')
return mx.sym.WarpCTC(data=pred, label=label, label_length=n... | python | def _add_warp_ctc_loss(pred, seq_len, num_label, label):
""" Adds Symbol.contrib.ctc_loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Reshape(data=label, shape=(-1,))
label = mx.sym.Cast(data=label, dtype='int32')
return mx.sym.WarpCTC(data=pred, label=label, label_length=n... | [
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apache/incubator-mxnet | example/ctc/lstm.py | _add_mxnet_ctc_loss | def _add_mxnet_ctc_loss(pred, seq_len, label):
""" Adds Symbol.WapCTC on top of pred symbol and returns the resulting symbol """
pred_ctc = mx.sym.Reshape(data=pred, shape=(-4, seq_len, -1, 0))
loss = mx.sym.contrib.ctc_loss(data=pred_ctc, label=label)
ctc_loss = mx.sym.MakeLoss(loss)
softmax_clas... | python | def _add_mxnet_ctc_loss(pred, seq_len, label):
""" Adds Symbol.WapCTC on top of pred symbol and returns the resulting symbol """
pred_ctc = mx.sym.Reshape(data=pred, shape=(-4, seq_len, -1, 0))
loss = mx.sym.contrib.ctc_loss(data=pred_ctc, label=label)
ctc_loss = mx.sym.MakeLoss(loss)
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apache/incubator-mxnet | example/ctc/lstm.py | _add_ctc_loss | def _add_ctc_loss(pred, seq_len, num_label, loss_type):
""" Adds CTC loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Variable('label')
if loss_type == 'warpctc':
print("Using WarpCTC Loss")
sm = _add_warp_ctc_loss(pred, seq_len, num_label, label)
else:
... | python | def _add_ctc_loss(pred, seq_len, num_label, loss_type):
""" Adds CTC loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Variable('label')
if loss_type == 'warpctc':
print("Using WarpCTC Loss")
sm = _add_warp_ctc_loss(pred, seq_len, num_label, label)
else:
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apache/incubator-mxnet | example/ctc/lstm.py | lstm_unroll | def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None):
"""
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if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'
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----------
num_lstm_layer: int
... | python | def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None):
"""
Creates an unrolled LSTM symbol for inference if loss_type is not specified, and for training
if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'
Parameters
----------
num_lstm_layer: int
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apache/incubator-mxnet | example/ctc/lstm.py | init_states | def init_states(batch_size, num_lstm_layer, num_hidden):
"""
Returns name and shape of init states of LSTM network
Parameters
----------
batch_size: list of tuple of str and tuple of int and int
num_lstm_layer: int
num_hidden: int
Returns
-------
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"""
Returns name and shape of init states of LSTM network
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----------
batch_size: list of tuple of str and tuple of int and int
num_lstm_layer: int
num_hidden: int
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apache/incubator-mxnet | python/mxnet/_ctypes/ndarray.py | _imperative_invoke | def _imperative_invoke(handle, ndargs, keys, vals, out):
"""ctypes implementation of imperative invoke wrapper"""
if out is not None:
original_output = out
if isinstance(out, NDArrayBase):
out = (out,)
num_output = ctypes.c_int(len(out))
output_vars = c_handle_array(o... | python | def _imperative_invoke(handle, ndargs, keys, vals, out):
"""ctypes implementation of imperative invoke wrapper"""
if out is not None:
original_output = out
if isinstance(out, NDArrayBase):
out = (out,)
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apache/incubator-mxnet | python/mxnet/contrib/autograd.py | set_is_training | def set_is_training(is_train):
"""Set status to training/not training. When training, graph will be constructed
for gradient computation. Operators will also run with ctx.is_train=True. For example,
Dropout will drop inputs randomly when is_train=True while simply passing through
if is_train=False.
... | python | def set_is_training(is_train):
"""Set status to training/not training. When training, graph will be constructed
for gradient computation. Operators will also run with ctx.is_train=True. For example,
Dropout will drop inputs randomly when is_train=True while simply passing through
if is_train=False.
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apache/incubator-mxnet | python/mxnet/contrib/autograd.py | backward | def backward(outputs, out_grads=None, retain_graph=False):
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Parameters
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outputs: list of NDArray
out_grads: list of NDArray or None
"""
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outputs: list of NDArray
out_grads: list of NDArray or None
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apache/incubator-mxnet | python/mxnet/contrib/autograd.py | grad_and_loss | def grad_and_loss(func, argnum=None):
"""Return function that computes both gradient of arguments and loss value.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
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"""Return function that computes both gradient of arguments and loss value.
Parameters
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func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
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func: a python function
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argnum: an int or a list of int
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | split_data | def split_data(data, num_slice, batch_axis=0, even_split=True):
"""Splits an NDArray into `num_slice` slices along `batch_axis`.
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).
Parameters
----------
data : NDArray
A batch of data.
num_slice : in... | python | def split_data(data, num_slice, batch_axis=0, even_split=True):
"""Splits an NDArray into `num_slice` slices along `batch_axis`.
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).
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----------
data : NDArray
A batch of data.
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | split_and_load | def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
"""Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
each slice to one context in `ctx_list`.
Parameters
----------
data : NDArray
A batch of data.
ctx_list : list of Context
A list of Co... | python | def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
"""Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | clip_global_norm | def clip_global_norm(arrays, max_norm, check_isfinite=True):
"""Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`.
Parameters
----------
arrays : list of NDArray
max_norm : float
check_isfinite : bool, default True
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"""Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`.
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arrays : list of NDArray
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | _indent | def _indent(s_, numSpaces):
"""Indent string
"""
s = s_.split('\n')
if len(s) == 1:
return s_
first = s.pop(0)
s = [first] + [(numSpaces * ' ') + line for line in s]
s = '\n'.join(s)
return s | python | def _indent(s_, numSpaces):
"""Indent string
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s = s_.split('\n')
if len(s) == 1:
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | check_sha1 | def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
----------
filename : str
Path to the file.
sha1_hash : str
Expected sha1 hash in hexadecimal digits.
Returns
-------
bool
Whether the f... | python | def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
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filename : str
Path to the file.
sha1_hash : str
Expected sha1 hash in hexadecimal digits.
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | download | def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_ssl=True):
"""Download an given URL
Parameters
----------
url : str
URL to download
path : str, optional
Destination path to store downloaded file. By default stores to the
current directory with... | python | def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_ssl=True):
"""Download an given URL
Parameters
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url : str
URL to download
path : str, optional
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | _get_repo_url | def _get_repo_url():
"""Return the base URL for Gluon dataset and model repository."""
default_repo = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
repo_url = os.environ.get('MXNET_GLUON_REPO', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url+'/'
return repo_url | python | def _get_repo_url():
"""Return the base URL for Gluon dataset and model repository."""
default_repo = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
repo_url = os.environ.get('MXNET_GLUON_REPO', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url+'/'
return repo_url | [
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | _get_repo_file_url | def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository.
Parameters
----------
namespace : str
Namespace of the file.
filename : str
Name of the file
"""
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
... | python | def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository.
Parameters
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namespace : str
Namespace of the file.
filename : str
Name of the file
"""
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
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apache/incubator-mxnet | python/mxnet/gluon/utils.py | _brief_print_list | def _brief_print_list(lst, limit=7):
"""Print at most `limit` elements of list."""
lst = list(lst)
if len(lst) > limit:
return _brief_print_list(lst[:limit//2], limit) + ', ..., ' + \
_brief_print_list(lst[-limit//2:], limit)
return ', '.join(["'%s'"%str(i) for i in lst]) | python | def _brief_print_list(lst, limit=7):
"""Print at most `limit` elements of list."""
lst = list(lst)
if len(lst) > limit:
return _brief_print_list(lst[:limit//2], limit) + ', ..., ' + \
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apache/incubator-mxnet | python/mxnet/symbol/register.py | _make_symbol_function | def _make_symbol_function(handle, name, func_name):
"""Create a symbol function by handle and function name."""
code, doc_str = _generate_symbol_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
symbol_function = local[func_name]
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"""Create a symbol function by handle and function name."""
code, doc_str = _generate_symbol_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
symbol_function = local[func_name]
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apache/incubator-mxnet | example/sparse/matrix_factorization/train.py | batch_row_ids | def batch_row_ids(data_batch):
""" Generate row ids based on the current mini-batch """
item = data_batch.data[0]
user = data_batch.data[1]
return {'user_weight': user.astype(np.int64),
'item_weight': item.astype(np.int64)} | python | def batch_row_ids(data_batch):
""" Generate row ids based on the current mini-batch """
item = data_batch.data[0]
user = data_batch.data[1]
return {'user_weight': user.astype(np.int64),
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apache/incubator-mxnet | example/sparse/matrix_factorization/train.py | all_row_ids | def all_row_ids(data_batch):
""" Generate row ids for all rows """
all_users = mx.nd.arange(0, MOVIELENS['max_user'], dtype='int64')
all_movies = mx.nd.arange(0, MOVIELENS['max_movie'], dtype='int64')
return {'user_weight': all_users, 'item_weight': all_movies} | python | def all_row_ids(data_batch):
""" Generate row ids for all rows """
all_users = mx.nd.arange(0, MOVIELENS['max_user'], dtype='int64')
all_movies = mx.nd.arange(0, MOVIELENS['max_movie'], dtype='int64')
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"""Convert caffe model
Parameters
----------
prototxt_fname : str
Filename of the prototxt model definition
caffemodel_fname : str
Filename of the binary caffe model
output_prefix : str, optinoal
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"""Convert caffe model
Parameters
----------
prototxt_fname : str
Filename of the prototxt model definition
caffemodel_fname : str
Filename of the binary caffe model
output_prefix : str, optinoal
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proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
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"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
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----------
prototxt_fname : str
Filename of the prototxt file
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-------
Symbol
Converted Symbol
tuple
Input shape
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Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
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apache/incubator-mxnet | python/mxnet/gluon/model_zoo/vision/vgg.py | get_vgg | def get_vgg(num_layers, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
<https://arxiv.org/abs/1409.1556>`_ paper.
Parameters
----------
num_layers : int
... | python | def get_vgg(num_layers, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
<https://arxiv.org/abs/1409.1556>`_ paper.
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num_layers : int
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apache/incubator-mxnet | example/profiler/profiler_ndarray.py | check_with_uniform | def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
"""check function consistency with uniform random numbers"""
if isinstance(arg_shapes, int):
assert dim
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
arg_shapes = [shape] ... | python | def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
"""check function consistency with uniform random numbers"""
if isinstance(arg_shapes, int):
assert dim
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apache/incubator-mxnet | example/rcnn/symimdb/imdb.py | IMDB.filter_roidb | def filter_roidb(self):
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num_roidb = len(self._roidb)
self._roidb = [roi_rec for roi_rec in self._roidb if len(roi_rec['gt_classes'])]
num_after = len(self._roidb)
logger.info('filter roidb: {} -> {}'.format(num_roidb, num_after)) | python | def filter_roidb(self):
"""Remove images without usable rois"""
num_roidb = len(self._roidb)
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apache/incubator-mxnet | example/rcnn/symimdb/imdb.py | IMDB.append_flipped_images | def append_flipped_images(self):
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roidb_flipped = []
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oldx1 ... | python | def append_flipped_images(self):
"""Only flip boxes coordinates, images will be flipped when loading into network"""
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roidb_flipped = []
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apache/incubator-mxnet | python/mxnet/gluon/model_zoo/model_store.py | get_model_file | def get_model_file(name, root=os.path.join(base.data_dir(), 'models')):
r"""Return location for the pretrained on local file system.
This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
Parameters
----... | python | def get_model_file(name, root=os.path.join(base.data_dir(), 'models')):
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This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
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"... | r"""Return location for the pretrained on local file system.
This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
Parameters
----------
name : str
Name of the model.
root : str, default $MX... | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/gluon/model_zoo/model_store.py#L73-L120 | train |
apache/incubator-mxnet | python/mxnet/gluon/model_zoo/model_store.py | purge | def purge(root=os.path.join(base.data_dir(), 'models')):
r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
root = os.path.expanduser(root)
files = os.listdir(root)
... | python | def purge(root=os.path.join(base.data_dir(), 'models')):
r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
root = os.path.expanduser(root)
files = os.listdir(root)
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"... | r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters. | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/gluon/model_zoo/model_store.py#L122-L134 | train |
apache/incubator-mxnet | example/ssd/dataset/mscoco.py | Coco.image_path_from_index | def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
assert self.image_set_index is not No... | python | def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
assert self.image_set_index is not No... | [
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Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/ssd/dataset/mscoco.py#L52-L68 | train |
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