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23,500 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule.init_optimizer | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),), force_init=False):
"""Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is
passed in and a special AssignmentOptimizer to... | python | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),), force_init=False):
"""Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is
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23,501 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule.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 for both two modules. This is necessary before one
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... | 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'):
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23,502 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule.forward | def forward(self, data_batch, is_train=None):
"""Forward computation for both two modules. It supports data batches with different shapes, such as
different batch sizes or different image sizes.
If reshaping of data batch relates to modification of symbol or module, such as
changing imag... | python | def forward(self, data_batch, is_train=None):
"""Forward computation for both two modules. It supports data batches with different shapes, such as
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23,503 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule.update_full_grads | def update_full_grads(self, train_data):
"""Computes the gradients over all data w.r.t weights of past
m epochs. For distributed env, it will accumulate full grads in the kvstore.
Parameters
----------
train_data: DataIter
Train data iterator
"""
para... | python | def update_full_grads(self, train_data):
"""Computes the gradients over all data w.r.t weights of past
m epochs. For distributed env, it will accumulate full grads in the kvstore.
Parameters
----------
train_data: DataIter
Train data iterator
"""
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23,504 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule._accumulate_kvstore | def _accumulate_kvstore(self, key, value):
"""Accumulate gradients over all data in the KVStore. In distributed setting, each worker sees a portion of
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Parameters
----------
key: int or str
... | python | def _accumulate_kvstore(self, key, value):
"""Accumulate gradients over all data in the KVStore. In distributed setting, each worker sees a portion of
data. The full gradients will be aggregated from each worker in the KVStore.
Parameters
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key: int or str
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23,505 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule._allocate_gradients | def _allocate_gradients(self, key, value):
"""Allocate average of full gradients accumulated in the KVStore to each device.
Parameters
----------
key: int or str
Key in the kvstore.
value: List of NDArray, List of RowSparseNDArray
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"""Allocate average of full gradients accumulated in the KVStore to each device.
Parameters
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key: int or str
Key in the kvstore.
value: List of NDArray, List of RowSparseNDArray
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23,506 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule._update_svrg_gradients | def _update_svrg_gradients(self):
"""Calculates gradients based on the SVRG update rule.
"""
param_names = self._exec_group.param_names
for ctx in range(self._ctx_len):
for index, name in enumerate(param_names):
g_curr_batch_reg = self._exec_group.grad_arrays[... | python | def _update_svrg_gradients(self):
"""Calculates gradients based on the SVRG update rule.
"""
param_names = self._exec_group.param_names
for ctx in range(self._ctx_len):
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23,507 | apache/incubator-mxnet | python/mxnet/contrib/svrg_optimization/svrg_module.py | SVRGModule.prepare | def prepare(self, data_batch, sparse_row_id_fn=None):
"""Prepares two modules for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
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"""Prepares two modules for processing a data batch.
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23,508 | apache/incubator-mxnet | python/mxnet/registry.py | get_register_func | def get_register_func(base_class, nickname):
"""Get registrator function.
Parameters
----------
base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
-------
a registrator function
"""
if base_... | python | def get_register_func(base_class, nickname):
"""Get registrator function.
Parameters
----------
base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
-------
a registrator function
"""
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23,509 | apache/incubator-mxnet | python/mxnet/registry.py | get_alias_func | def get_alias_func(base_class, nickname):
"""Get registrator function that allow aliases.
Parameters
----------
base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
-------
a registrator function
... | python | def get_alias_func(base_class, nickname):
"""Get registrator function that allow aliases.
Parameters
----------
base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
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23,510 | apache/incubator-mxnet | example/cnn_text_classification/data_helpers.py | pad_sentences | def pad_sentences(sentences, padding_word="</s>"):
"""Pads all sentences to the same length. The length is defined by the longest sentence.
Returns padded sentences.
"""
sequence_length = max(len(x) for x in sentences)
padded_sentences = []
for i, sentence in enumerate(sentences):
num_pa... | python | def pad_sentences(sentences, padding_word="</s>"):
"""Pads all sentences to the same length. The length is defined by the longest sentence.
Returns padded sentences.
"""
sequence_length = max(len(x) for x in sentences)
padded_sentences = []
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23,511 | apache/incubator-mxnet | example/cnn_text_classification/data_helpers.py | build_input_data | def build_input_data(sentences, labels, vocabulary):
"""Maps sentencs and labels to vectors based on a vocabulary."""
x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])
y = np.array(labels)
return [x, y] | python | def build_input_data(sentences, labels, vocabulary):
"""Maps sentencs and labels to vectors based on a vocabulary."""
x = np.array([[vocabulary[word] for word in sentence] for sentence in sentences])
y = np.array(labels)
return [x, y] | [
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23,512 | apache/incubator-mxnet | example/cnn_text_classification/data_helpers.py | build_input_data_with_word2vec | def build_input_data_with_word2vec(sentences, labels, word2vec_list):
"""
Map sentences and labels to vectors based on a pretrained word2vec
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x_vec = []
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vec = []
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"""
Map sentences and labels to vectors based on a pretrained word2vec
"""
x_vec = []
for sent in sentences:
vec = []
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23,513 | apache/incubator-mxnet | example/cnn_text_classification/data_helpers.py | batch_iter | def batch_iter(data, batch_size, num_epochs):
"""Generates a batch iterator for a dataset."""
data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int(len(data)/batch_size) + 1
for epoch in range(num_epochs):
# Shuffle the data at each epoch
shuffle_indices = np.random... | python | def batch_iter(data, batch_size, num_epochs):
"""Generates a batch iterator for a dataset."""
data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int(len(data)/batch_size) + 1
for epoch in range(num_epochs):
# Shuffle the data at each epoch
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23,514 | apache/incubator-mxnet | example/cnn_text_classification/data_helpers.py | load_pretrained_word2vec | def load_pretrained_word2vec(infile):
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if isinstance(infile, str):
infile = open(infile)
word2vec_list = {}
for idx, line in enumerate(infile):
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vocab_size, dim = line.strip().split()
else:
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"""Load the pre-trained word2vec from file."""
if isinstance(infile, str):
infile = open(infile)
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for idx, line in enumerate(infile):
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23,515 | apache/incubator-mxnet | example/caffe/caffe_net.py | get_mlp | def get_mlp():
"""Get multi-layer perceptron"""
data = mx.symbol.Variable('data')
fc1 = mx.symbol.CaffeOp(data_0=data, num_weight=2, name='fc1',
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act1 = mx.symbol.CaffeOp(data_0=fc1, prototxt="layer... | python | def get_mlp():
"""Get multi-layer perceptron"""
data = mx.symbol.Variable('data')
fc1 = mx.symbol.CaffeOp(data_0=data, num_weight=2, name='fc1',
prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 128} }")
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23,516 | apache/incubator-mxnet | example/sparse/linear_classification/weighted_softmax_ce.py | WeightedSoftmaxCrossEntropyLoss.forward | def forward(self, is_train, req, in_data, out_data, aux):
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is_train : bool, whether forwarding for training or testing.
req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
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"""Implements forward computation.
is_train : bool, whether forwarding for training or testing.
req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.
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23,517 | apache/incubator-mxnet | example/sparse/linear_classification/weighted_softmax_ce.py | WeightedSoftmaxCrossEntropyLoss.backward | def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
"""Implements backward computation
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out_grad : list of NDArray, gradient w.r.t. output data.
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"""Implements backward computation
req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
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23,518 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule._reset_bind | def _reset_bind(self):
"""Internal utility function to reset binding."""
self.binded = False
self._buckets = {}
self._curr_module = None
self._curr_bucket_key = None | python | def _reset_bind(self):
"""Internal utility function to reset binding."""
self.binded = False
self._buckets = {}
self._curr_module = None
self._curr_bucket_key = None | [
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23,519 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.data_names | def data_names(self):
"""A list of names for data required by this module."""
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return self._curr_module.data_names
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"""A list of names for data required by this module."""
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23,520 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.output_names | def output_names(self):
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"""A list of names for the outputs of this module."""
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23,521 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.set_states | def set_states(self, states=None, value=None):
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Parameters
----------
states : list of list of NDArrays
Source states arrays formatted like ``[[state1_dev1, state1_dev2],
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"""Sets value for states. Only one of states & values can be specified.
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----------
states : list of list of NDArrays
Source states arrays formatted like ``[[state1_dev1, state1_dev2],
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23,522 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.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'):
"""Binding for a `BucketingModule` means setting up the buckets and binding the
executor for the default bucket key. Executors co... | 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'):
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23,523 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.switch_bucket | def switch_bucket(self, bucket_key, data_shapes, label_shapes=None):
"""Switches to a different bucket. This will change ``self.curr_module``.
Parameters
----------
bucket_key : str (or any python object)
The key of the target bucket.
data_shapes : list of (str, tupl... | python | def switch_bucket(self, bucket_key, data_shapes, label_shapes=None):
"""Switches to a different bucket. This will change ``self.curr_module``.
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----------
bucket_key : str (or any python object)
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23,524 | apache/incubator-mxnet | python/mxnet/module/bucketing_module.py | BucketingModule.install_monitor | def install_monitor(self, mon):
"""Installs monitor on all executors """
assert self.binded
self._monitor = mon
for mod in self._buckets.values():
mod.install_monitor(mon) | python | def install_monitor(self, mon):
"""Installs monitor on all executors """
assert self.binded
self._monitor = mon
for mod in self._buckets.values():
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23,525 | apache/incubator-mxnet | python/mxnet/autograd.py | mark_variables | def mark_variables(variables, gradients, grad_reqs='write'):
"""Mark NDArrays as variables to compute gradient for autograd.
Parameters
----------
variables: NDArray or list of NDArray
gradients: NDArray or list of NDArray
grad_reqs: str or list of str
"""
if isinstance(variables, NDArr... | python | def mark_variables(variables, gradients, grad_reqs='write'):
"""Mark NDArrays as variables to compute gradient for autograd.
Parameters
----------
variables: NDArray or list of NDArray
gradients: NDArray or list of NDArray
grad_reqs: str or list of str
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23,526 | apache/incubator-mxnet | python/mxnet/autograd.py | _parse_head | def _parse_head(heads, head_grads):
"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
if isinstance(head_grads, NDArray):
head_grads = [head_grads]
head_handles = c_handle_array(heads)
if head_grads is None:
hgrad_handles = ctyp... | python | def _parse_head(heads, head_grads):
"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
if isinstance(head_grads, NDArray):
head_grads = [head_grads]
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23,527 | apache/incubator-mxnet | python/mxnet/autograd.py | backward | def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
heads: NDArray or list of NDArray
Output NDArray(s)
head_grads: NDArray or list of NDAr... | python | def backward(heads, head_grads=None, retain_graph=False, train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
heads: NDArray or list of NDArray
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23,528 | apache/incubator-mxnet | python/mxnet/autograd.py | get_symbol | def get_symbol(x):
"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
check_call(_LIB.MXAutogradGetSymbol... | python | def get_symbol(x):
"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
check_call(_LIB.MXAutogradGetSymbol... | [
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23,529 | apache/incubator-mxnet | example/recommenders/movielens_data.py | load_mldataset | def load_mldataset(filename):
"""Not particularly fast code to parse the text file and load it into three NDArray's
and product an NDArrayIter
"""
user = []
item = []
score = []
with open(filename) as f:
for line in f:
tks = line.strip().split('\t')
if len(tks... | python | def load_mldataset(filename):
"""Not particularly fast code to parse the text file and load it into three NDArray's
and product an NDArrayIter
"""
user = []
item = []
score = []
with open(filename) as f:
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tks = line.strip().split('\t')
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23,530 | apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | main | def main():
"""Read .caffemodel path and .params path as input from command line
and use CaffeModelConverter to do the conversion"""
parser = argparse.ArgumentParser(description='.caffemodel to MXNet .params converter.')
parser.add_argument('caffemodel', help='Path to the .caffemodel file to convert.')
... | python | def main():
"""Read .caffemodel path and .params path as input from command line
and use CaffeModelConverter to do the conversion"""
parser = argparse.ArgumentParser(description='.caffemodel to MXNet .params converter.')
parser.add_argument('caffemodel', help='Path to the .caffemodel file to convert.')
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23,531 | apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_param | def add_param(self, param_name, layer_index, blob_index):
"""Add a param to the .params file"""
blobs = self.layers[layer_index].blobs
self.dict_param[param_name] = mx.nd.array(caffe.io.blobproto_to_array(blobs[blob_index])) | python | def add_param(self, param_name, layer_index, blob_index):
"""Add a param to the .params file"""
blobs = self.layers[layer_index].blobs
self.dict_param[param_name] = mx.nd.array(caffe.io.blobproto_to_array(blobs[blob_index])) | [
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23,532 | apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.add_optional_arg_param | def add_optional_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param. If there is no such param in .caffemodel fie, silently ignore it."""
blobs = self.layers[layer_index].blobs
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self.add_arg_param(param_name, layer_index, blob_index) | python | def add_optional_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param. If there is no such param in .caffemodel fie, silently ignore it."""
blobs = self.layers[layer_index].blobs
if blob_index < len(blobs):
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23,533 | apache/incubator-mxnet | tools/caffe_translator/scripts/convert_caffe_model.py | CaffeModelConverter.convert | def convert(self, caffemodel_path, outmodel_path):
"""Convert a Caffe .caffemodel file to MXNet .params file"""
net_param = caffe_pb2.NetParameter()
with open(caffemodel_path, 'rb') as caffe_model_file:
net_param.ParseFromString(caffe_model_file.read())
layers = net_param.la... | python | def convert(self, caffemodel_path, outmodel_path):
"""Convert a Caffe .caffemodel file to MXNet .params file"""
net_param = caffe_pb2.NetParameter()
with open(caffemodel_path, 'rb') as caffe_model_file:
net_param.ParseFromString(caffe_model_file.read())
layers = net_param.la... | [
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23,534 | apache/incubator-mxnet | python/mxnet/operator.py | CustomOp.assign | def assign(self, dst, req, src):
"""Helper function for assigning into dst depending on requirements."""
if req == 'null':
return
elif req in ('write', 'inplace'):
dst[:] = src
elif req == 'add':
dst[:] += src | python | def assign(self, dst, req, src):
"""Helper function for assigning into dst depending on requirements."""
if req == 'null':
return
elif req in ('write', 'inplace'):
dst[:] = src
elif req == 'add':
dst[:] += src | [
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23,535 | apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_type | def infer_type(self, in_type):
"""infer_type interface. override to create new operators
Parameters
----------
in_type : list of np.dtype
list of argument types in the same order as
declared in list_arguments.
Returns
-------
in_type : li... | python | def infer_type(self, in_type):
"""infer_type interface. override to create new operators
Parameters
----------
in_type : list of np.dtype
list of argument types in the same order as
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Returns
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23,536 | apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_storage_type | def infer_storage_type(self, in_stype):
"""infer_storage_type interface. Used to infer storage type of
inputs and outputs in the forward pass. When this interface is not implemented,
all stypes will be inferred as default.
Parameters
----------
in_stype : list of stypes,... | python | def infer_storage_type(self, in_stype):
"""infer_storage_type interface. Used to infer storage type of
inputs and outputs in the forward pass. When this interface is not implemented,
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----------
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23,537 | apache/incubator-mxnet | python/mxnet/operator.py | CustomOpProp.infer_storage_type_backward | def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_stype, aux_stype):
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23,538 | apache/incubator-mxnet | python/mxnet/operator.py | _Registry.inc | def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
return cur | python | def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
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23,539 | apache/incubator-mxnet | tools/rec2idx.py | IndexCreator.close | def close(self):
"""Closes the record and index files."""
if not self.is_open:
return
super(IndexCreator, self).close()
self.fidx.close() | python | def close(self):
"""Closes the record and index files."""
if not self.is_open:
return
super(IndexCreator, self).close()
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23,540 | apache/incubator-mxnet | tools/rec2idx.py | IndexCreator.tell | def tell(self):
"""Returns the current position of read head.
"""
pos = ctypes.c_size_t()
check_call(_LIB.MXRecordIOReaderTell(self.handle, ctypes.byref(pos)))
return pos.value | python | def tell(self):
"""Returns the current position of read head.
"""
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23,541 | apache/incubator-mxnet | tools/rec2idx.py | IndexCreator.create_index | def create_index(self):
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print('time:', cur_time - pre_time, ' count:'... | python | def create_index(self):
"""Creates the index file from open record file
"""
self.reset()
counter = 0
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/tools/rec2idx.py#L72-L88 |
23,542 | apache/incubator-mxnet | docs/mxdoc.py | _run_cmd | def _run_cmd(cmds):
"""Run commands, raise exception if failed"""
if not isinstance(cmds, str):
cmds = "".join(cmds)
print("Execute \"%s\"" % cmds)
try:
subprocess.check_call(cmds, shell=True)
except subprocess.CalledProcessError as err:
print(err)
raise err | python | def _run_cmd(cmds):
"""Run commands, raise exception if failed"""
if not isinstance(cmds, str):
cmds = "".join(cmds)
print("Execute \"%s\"" % cmds)
try:
subprocess.check_call(cmds, shell=True)
except subprocess.CalledProcessError as err:
print(err)
raise err | [
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23,543 | apache/incubator-mxnet | docs/mxdoc.py | generate_doxygen | def generate_doxygen(app):
"""Run the doxygen make commands"""
_run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir)
_run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir) | python | def generate_doxygen(app):
"""Run the doxygen make commands"""
_run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir)
_run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir) | [
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23,544 | apache/incubator-mxnet | docs/mxdoc.py | build_mxnet | def build_mxnet(app):
"""Build mxnet .so lib"""
if not os.path.exists(os.path.join(app.builder.srcdir, '..', 'config.mk')):
_run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
app.builder.srcdir)
else:
_run_cmd("cd %s/.. && ... | python | def build_mxnet(app):
"""Build mxnet .so lib"""
if not os.path.exists(os.path.join(app.builder.srcdir, '..', 'config.mk')):
_run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) USE_MKLDNN=0 USE_CPP_PACKAGE=1 " %
app.builder.srcdir)
else:
_run_cmd("cd %s/.. && ... | [
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23,545 | apache/incubator-mxnet | docs/mxdoc.py | build_r_docs | def build_r_docs(app):
"""build r pdf"""
r_root = app.builder.srcdir + '/../R-package'
pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
_run_cmd('cd ' + r_root +
'; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path)
dest_path = app.builder.o... | python | def build_r_docs(app):
"""build r pdf"""
r_root = app.builder.srcdir + '/../R-package'
pdf_path = app.builder.srcdir + '/api/r/mxnet-r-reference-manual.pdf'
_run_cmd('cd ' + r_root +
'; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path)
dest_path = app.builder.o... | [
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23,546 | apache/incubator-mxnet | docs/mxdoc.py | build_scala | def build_scala(app):
"""build scala for scala docs, java docs, and clojure docs to use"""
if any(v in _BUILD_VER for v in ['1.2.', '1.3.', '1.4.']):
_run_cmd("cd %s/.. && make scalapkg" % app.builder.srcdir)
_run_cmd("cd %s/.. && make scalainstall" % app.builder.srcdir)
else:
_run_c... | python | def build_scala(app):
"""build scala for scala docs, java docs, and clojure docs to use"""
if any(v in _BUILD_VER for v in ['1.2.', '1.3.', '1.4.']):
_run_cmd("cd %s/.. && make scalapkg" % app.builder.srcdir)
_run_cmd("cd %s/.. && make scalainstall" % app.builder.srcdir)
else:
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23,547 | apache/incubator-mxnet | docs/mxdoc.py | build_scala_docs | def build_scala_docs(app):
"""build scala doc and then move the outdir"""
scala_path = app.builder.srcdir + '/../scala-package'
scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"\/javaapi\" | egrep -v \"Suite\"'
scala_doc_classpath = ':'.join([
'`fi... | python | def build_scala_docs(app):
"""build scala doc and then move the outdir"""
scala_path = app.builder.srcdir + '/../scala-package'
scala_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep -v \"\/javaapi\" | egrep -v \"Suite\"'
scala_doc_classpath = ':'.join([
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23,548 | apache/incubator-mxnet | docs/mxdoc.py | build_java_docs | def build_java_docs(app):
"""build java docs and then move the outdir"""
java_path = app.builder.srcdir + '/../scala-package'
java_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep \"\/javaapi\" | egrep -v \"Suite\"'
java_doc_classpath = ':'.join([
'`find nativ... | python | def build_java_docs(app):
"""build java docs and then move the outdir"""
java_path = app.builder.srcdir + '/../scala-package'
java_doc_sources = 'find . -type f -name "*.scala" | egrep \"\.\/core|\.\/infer\" | egrep \"\/javaapi\" | egrep -v \"Suite\"'
java_doc_classpath = ':'.join([
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23,549 | apache/incubator-mxnet | docs/mxdoc.py | build_clojure_docs | def build_clojure_docs(app):
"""build clojure doc and then move the outdir"""
clojure_path = app.builder.srcdir + '/../contrib/clojure-package'
_run_cmd('cd ' + clojure_path + '; lein codox')
dest_path = app.builder.outdir + '/api/clojure/docs'
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ... | python | def build_clojure_docs(app):
"""build clojure doc and then move the outdir"""
clojure_path = app.builder.srcdir + '/../contrib/clojure-package'
_run_cmd('cd ' + clojure_path + '; lein codox')
dest_path = app.builder.outdir + '/api/clojure/docs'
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ... | [
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23,550 | apache/incubator-mxnet | docs/mxdoc.py | _convert_md_table_to_rst | def _convert_md_table_to_rst(table):
"""Convert a markdown table to rst format"""
if len(table) < 3:
return ''
out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n'
for i,l in enumerate(table):
cols = l.split('|')[1:-1]
if i == 0:
ncol = len(cols)
else:... | python | def _convert_md_table_to_rst(table):
"""Convert a markdown table to rst format"""
if len(table) < 3:
return ''
out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n'
for i,l in enumerate(table):
cols = l.split('|')[1:-1]
if i == 0:
ncol = len(cols)
else:... | [
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23,551 | apache/incubator-mxnet | docs/mxdoc.py | convert_table | def convert_table(app, docname, source):
"""Find tables in a markdown and then convert them into the rst format"""
num_tables = 0
for i,j in enumerate(source):
table = []
output = ''
in_table = False
for l in j.split('\n'):
r = l.strip()
if r.startswit... | python | def convert_table(app, docname, source):
"""Find tables in a markdown and then convert them into the rst format"""
num_tables = 0
for i,j in enumerate(source):
table = []
output = ''
in_table = False
for l in j.split('\n'):
r = l.strip()
if r.startswit... | [
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23,552 | apache/incubator-mxnet | docs/mxdoc.py | _parse_code_lines | def _parse_code_lines(lines):
"""A iterator that returns if a line is within a code block
Returns
-------
iterator of (str, bool, str, int)
- line: the line
- in_code: if this line is in a code block
- lang: the code block langunage
- indent: the code indent
"""
... | python | def _parse_code_lines(lines):
"""A iterator that returns if a line is within a code block
Returns
-------
iterator of (str, bool, str, int)
- line: the line
- in_code: if this line is in a code block
- lang: the code block langunage
- indent: the code indent
"""
... | [
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23,553 | apache/incubator-mxnet | docs/mxdoc.py | _get_blocks | def _get_blocks(lines):
"""split lines into code and non-code blocks
Returns
-------
iterator of (bool, str, list of str)
- if it is a code block
- source language
- lines of source
"""
cur_block = []
pre_lang = None
pre_in_code = None
for (l, in_code, cur_lang, _)... | python | def _get_blocks(lines):
"""split lines into code and non-code blocks
Returns
-------
iterator of (bool, str, list of str)
- if it is a code block
- source language
- lines of source
"""
cur_block = []
pre_lang = None
pre_in_code = None
for (l, in_code, cur_lang, _)... | [
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23,554 | apache/incubator-mxnet | docs/mxdoc.py | _get_python_block_output | def _get_python_block_output(src, global_dict, local_dict):
"""Evaluate python source codes
Returns
(bool, str):
- True if success
- output
"""
src = '\n'.join([l for l in src.split('\n')
if not l.startswith('%') and not 'plt.show()' in l])
ret_status = True
... | python | def _get_python_block_output(src, global_dict, local_dict):
"""Evaluate python source codes
Returns
(bool, str):
- True if success
- output
"""
src = '\n'.join([l for l in src.split('\n')
if not l.startswith('%') and not 'plt.show()' in l])
ret_status = True
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23,555 | apache/incubator-mxnet | docs/mxdoc.py | copy_artifacts | def copy_artifacts(app):
"""Copies artifacts needed for website presentation"""
dest_path = app.builder.outdir + '/error'
source_path = app.builder.srcdir + '/build_version_doc/artifacts'
_run_cmd('cd ' + app.builder.srcdir)
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ' + dest_path)
_... | python | def copy_artifacts(app):
"""Copies artifacts needed for website presentation"""
dest_path = app.builder.outdir + '/error'
source_path = app.builder.srcdir + '/build_version_doc/artifacts'
_run_cmd('cd ' + app.builder.srcdir)
_run_cmd('rm -rf ' + dest_path)
_run_cmd('mkdir -p ' + dest_path)
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23,556 | apache/incubator-mxnet | tools/caffe_converter/convert_caffe_modelzoo.py | download_caffe_model | def download_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download caffe model into disk by the given meta info """
if not os.path.isdir(dst_dir):
os.mkdir(dst_dir)
model_name = os.path.join(dst_dir, model_name)
assert 'prototxt' in meta_info, "missing prototxt url"
proto_url, ... | python | def download_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download caffe model into disk by the given meta info """
if not os.path.isdir(dst_dir):
os.mkdir(dst_dir)
model_name = os.path.join(dst_dir, model_name)
assert 'prototxt' in meta_info, "missing prototxt url"
proto_url, ... | [
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23,557 | apache/incubator-mxnet | tools/caffe_converter/convert_caffe_modelzoo.py | convert_caffe_model | def convert_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download, convert and save a caffe model"""
(prototxt, caffemodel, mean) = download_caffe_model(model_name, meta_info, dst_dir)
model_name = os.path.join(dst_dir, model_name)
convert_model(prototxt, caffemodel, model_name)
if isi... | python | def convert_caffe_model(model_name, meta_info, dst_dir='./model'):
"""Download, convert and save a caffe model"""
(prototxt, caffemodel, mean) = download_caffe_model(model_name, meta_info, dst_dir)
model_name = os.path.join(dst_dir, model_name)
convert_model(prototxt, caffemodel, model_name)
if isi... | [
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23,558 | apache/incubator-mxnet | example/gluon/lipnet/utils/multi.py | multi_p_run | def multi_p_run(tot_num, _func, worker, params, n_process):
"""
Run _func with multi-process using params.
"""
from multiprocessing import Process, Queue
out_q = Queue()
procs = []
split_num = split_seq(list(range(0, tot_num)), n_process)
print(tot_num, ">>", split_num)
split_len ... | python | def multi_p_run(tot_num, _func, worker, params, n_process):
"""
Run _func with multi-process using params.
"""
from multiprocessing import Process, Queue
out_q = Queue()
procs = []
split_num = split_seq(list(range(0, tot_num)), n_process)
print(tot_num, ">>", split_num)
split_len ... | [
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23,559 | apache/incubator-mxnet | example/ssd/config/utils.py | namedtuple_with_defaults | def namedtuple_with_defaults(typename, field_names, default_values=()):
""" create a namedtuple with default values """
T = collections.namedtuple(typename, field_names)
T.__new__.__defaults__ = (None, ) * len(T._fields)
if isinstance(default_values, collections.Mapping):
prototype = T(**default... | python | def namedtuple_with_defaults(typename, field_names, default_values=()):
""" create a namedtuple with default values """
T = collections.namedtuple(typename, field_names)
T.__new__.__defaults__ = (None, ) * len(T._fields)
if isinstance(default_values, collections.Mapping):
prototype = T(**default... | [
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23,560 | apache/incubator-mxnet | example/ssd/config/utils.py | merge_dict | def merge_dict(a, b):
""" merge dict a, b, with b overriding keys in a """
c = a.copy()
c.update(b)
return c | python | def merge_dict(a, b):
""" merge dict a, b, with b overriding keys in a """
c = a.copy()
c.update(b)
return c | [
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23,561 | apache/incubator-mxnet | example/ssd/config/utils.py | zip_namedtuple | def zip_namedtuple(nt_list):
""" accept list of namedtuple, return a dict of zipped fields """
if not nt_list:
return dict()
if not isinstance(nt_list, list):
nt_list = [nt_list]
for nt in nt_list:
assert type(nt) == type(nt_list[0])
ret = {k : [v] for k, v in nt_list[0]._asd... | python | def zip_namedtuple(nt_list):
""" accept list of namedtuple, return a dict of zipped fields """
if not nt_list:
return dict()
if not isinstance(nt_list, list):
nt_list = [nt_list]
for nt in nt_list:
assert type(nt) == type(nt_list[0])
ret = {k : [v] for k, v in nt_list[0]._asd... | [
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23,562 | apache/incubator-mxnet | example/ssd/config/utils.py | config_as_dict | def config_as_dict(cfg):
""" convert raw configuration to unified dictionary """
ret = cfg.__dict__.copy()
# random cropping params
del ret['rand_crop_samplers']
assert isinstance(cfg.rand_crop_samplers, list)
ret = merge_dict(ret, zip_namedtuple(cfg.rand_crop_samplers))
num_crop_sampler = l... | python | def config_as_dict(cfg):
""" convert raw configuration to unified dictionary """
ret = cfg.__dict__.copy()
# random cropping params
del ret['rand_crop_samplers']
assert isinstance(cfg.rand_crop_samplers, list)
ret = merge_dict(ret, zip_namedtuple(cfg.rand_crop_samplers))
num_crop_sampler = l... | [
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23,563 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/import_model.py | get_model_metadata | def get_model_metadata(model_file):
"""
Returns the name and shape information of input and output tensors of the given ONNX model file.
Notes
-----
This method is available when you ``import mxnet.contrib.onnx``
Parameters
----------
model_file : str
ONNX model file name
... | python | def get_model_metadata(model_file):
"""
Returns the name and shape information of input and output tensors of the given ONNX model file.
Notes
-----
This method is available when you ``import mxnet.contrib.onnx``
Parameters
----------
model_file : str
ONNX model file name
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model_file : str
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23,564 | apache/incubator-mxnet | example/ssd/symbol/common.py | multi_layer_feature | def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra l... | python | def multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
"""Wrapper function to extract features from base network, attaching extra
layers and SSD specific layers
Parameters
----------
from_layers : list of str
feature extraction layers, use '' for add extra l... | [
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23,565 | apache/incubator-mxnet | python/mxnet/gluon/loss.py | _apply_weighting | def _apply_weighting(F, loss, weight=None, sample_weight=None):
"""Apply weighting to loss.
Parameters
----------
loss : Symbol
The loss to be weighted.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be bro... | python | def _apply_weighting(F, loss, weight=None, sample_weight=None):
"""Apply weighting to loss.
Parameters
----------
loss : Symbol
The loss to be weighted.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be bro... | [
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23,566 | apache/incubator-mxnet | python/mxnet/gluon/loss.py | _reshape_like | def _reshape_like(F, x, y):
"""Reshapes x to the same shape as y."""
return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y) | python | def _reshape_like(F, x, y):
"""Reshapes x to the same shape as y."""
return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y) | [
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23,567 | apache/incubator-mxnet | example/neural-style/nstyle.py | get_tv_grad_executor | def get_tv_grad_executor(img, ctx, tv_weight):
"""create TV gradient executor with input binded on img
"""
if tv_weight <= 0.0:
return None
nchannel = img.shape[1]
simg = mx.sym.Variable("img")
skernel = mx.sym.Variable("kernel")
channels = mx.sym.SliceChannel(simg, num_outputs=nchan... | python | def get_tv_grad_executor(img, ctx, tv_weight):
"""create TV gradient executor with input binded on img
"""
if tv_weight <= 0.0:
return None
nchannel = img.shape[1]
simg = mx.sym.Variable("img")
skernel = mx.sym.Variable("kernel")
channels = mx.sym.SliceChannel(simg, num_outputs=nchan... | [
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23,568 | apache/incubator-mxnet | example/deep-embedded-clustering/data.py | get_mnist | def get_mnist():
""" Gets MNIST dataset """
np.random.seed(1234) # set seed for deterministic ordering
mnist_data = mx.test_utils.get_mnist()
X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']])
Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']])
p = np.... | python | def get_mnist():
""" Gets MNIST dataset """
np.random.seed(1234) # set seed for deterministic ordering
mnist_data = mx.test_utils.get_mnist()
X = np.concatenate([mnist_data['train_data'], mnist_data['test_data']])
Y = np.concatenate([mnist_data['train_label'], mnist_data['test_label']])
p = np.... | [
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23,569 | apache/incubator-mxnet | python/mxnet/executor_manager.py | _split_input_slice | def _split_input_slice(batch_size, work_load_list):
"""Get input slice from the input shape.
Parameters
----------
batch_size : int
The number of samples in a mini-batch.
work_load_list : list of float or int, optional
The list of work load for different devices,
in the same... | python | def _split_input_slice(batch_size, work_load_list):
"""Get input slice from the input shape.
Parameters
----------
batch_size : int
The number of samples in a mini-batch.
work_load_list : list of float or int, optional
The list of work load for different devices,
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23,570 | apache/incubator-mxnet | python/mxnet/executor_manager.py | _check_arguments | def _check_arguments(symbol):
"""Check the argument names of symbol.
This function checks the duplication of arguments in Symbol.
The check is done for feedforward net for now.
Parameters
----------
symbol : Symbol
The network configuration.
"""
arg_set = set()
arg_names = s... | python | def _check_arguments(symbol):
"""Check the argument names of symbol.
This function checks the duplication of arguments in Symbol.
The check is done for feedforward net for now.
Parameters
----------
symbol : Symbol
The network configuration.
"""
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23,571 | apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorGroup.forward | def forward(self, is_train=False):
"""Perform a forward pass on each executor."""
for texec in self.train_execs:
texec.forward(is_train=is_train) | python | def forward(self, is_train=False):
"""Perform a forward pass on each executor."""
for texec in self.train_execs:
texec.forward(is_train=is_train) | [
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23,572 | apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorGroup.update_metric | def update_metric(self, metric, labels, pre_sliced=False):
"""Update evaluation metric with label and current outputs."""
for current_exec, (texec, islice) in enumerate(zip(self.train_execs, self.slices)):
if not pre_sliced:
labels_slice = [label[islice] for label in labels]
... | python | def update_metric(self, metric, labels, pre_sliced=False):
"""Update evaluation metric with label and current outputs."""
for current_exec, (texec, islice) in enumerate(zip(self.train_execs, self.slices)):
if not pre_sliced:
labels_slice = [label[islice] for label in labels]
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23,573 | apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.install_monitor | def install_monitor(self, monitor):
"""Install monitor on all executors."""
if self.sym_gen is not None:
raise NotImplementedError("Monitoring is not implemented for bucketing")
for train_exec in self.execgrp.train_execs:
monitor.install(train_exec) | python | def install_monitor(self, monitor):
"""Install monitor on all executors."""
if self.sym_gen is not None:
raise NotImplementedError("Monitoring is not implemented for bucketing")
for train_exec in self.execgrp.train_execs:
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23,574 | apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.set_params | def set_params(self, arg_params, aux_params):
"""Set parameter and aux values.
Parameters
----------
arg_params : list of NDArray
Source parameter arrays
aux_params : list of NDArray
Source aux arrays.
"""
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"""Set parameter and aux values.
Parameters
----------
arg_params : list of NDArray
Source parameter arrays
aux_params : list of NDArray
Source aux arrays.
"""
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23,575 | apache/incubator-mxnet | python/mxnet/executor_manager.py | DataParallelExecutorManager.update_metric | def update_metric(self, metric, labels, pre_sliced=False):
"""Update metric with the current executor."""
self.curr_execgrp.update_metric(metric, labels, pre_sliced) | python | def update_metric(self, metric, labels, pre_sliced=False):
"""Update metric with the current executor."""
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23,576 | apache/incubator-mxnet | example/reinforcement-learning/dqn/replay_memory.py | ReplayMemory.clear | def clear(self):
"""
Clear all contents in the relay memory
"""
self.states[:] = 0
self.actions[:] = 0
self.rewards[:] = 0
self.terminate_flags[:] = 0
self.top = 0
self.size = 0 | python | def clear(self):
"""
Clear all contents in the relay memory
"""
self.states[:] = 0
self.actions[:] = 0
self.rewards[:] = 0
self.terminate_flags[:] = 0
self.top = 0
self.size = 0 | [
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23,577 | apache/incubator-mxnet | cpp-package/scripts/lint.py | process | def process(fname, allow_type):
"""Process a file."""
fname = str(fname)
# HACK: ignore op.h which is automatically generated
if fname.endswith('op.h'):
return
arr = fname.rsplit('.', 1)
if fname.find('#') != -1 or arr[-1] not in allow_type:
return
if arr[-1] in CXX_SUFFIX:
... | python | def process(fname, allow_type):
"""Process a file."""
fname = str(fname)
# HACK: ignore op.h which is automatically generated
if fname.endswith('op.h'):
return
arr = fname.rsplit('.', 1)
if fname.find('#') != -1 or arr[-1] not in allow_type:
return
if arr[-1] in CXX_SUFFIX:
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23,578 | apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper._print_summary_map | def _print_summary_map(strm, result_map, ftype):
"""Print summary of certain result map."""
if len(result_map) == 0:
return 0
npass = len([x for k, x in result_map.iteritems() if len(x) == 0])
strm.write('=====%d/%d %s files passed check=====\n' % (npass, len(result_map), fty... | python | def _print_summary_map(strm, result_map, ftype):
"""Print summary of certain result map."""
if len(result_map) == 0:
return 0
npass = len([x for k, x in result_map.iteritems() if len(x) == 0])
strm.write('=====%d/%d %s files passed check=====\n' % (npass, len(result_map), fty... | [
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23,579 | apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.process_cpp | def process_cpp(self, path, suffix):
"""Process a cpp file."""
_cpplint_state.ResetErrorCounts()
cpplint.ProcessFile(str(path), _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
errors = _cpplint_state.errors_by_category.copy()
if suffix == 'h':
... | python | def process_cpp(self, path, suffix):
"""Process a cpp file."""
_cpplint_state.ResetErrorCounts()
cpplint.ProcessFile(str(path), _cpplint_state.verbose_level)
_cpplint_state.PrintErrorCounts()
errors = _cpplint_state.errors_by_category.copy()
if suffix == 'h':
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23,580 | apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.process_python | def process_python(self, path):
"""Process a python file."""
(pylint_stdout, pylint_stderr) = epylint.py_run(
' '.join([str(path)] + self.pylint_opts), return_std=True)
emap = {}
print(pylint_stderr.read())
for line in pylint_stdout:
sys.stderr.write(line)... | python | def process_python(self, path):
"""Process a python file."""
(pylint_stdout, pylint_stderr) = epylint.py_run(
' '.join([str(path)] + self.pylint_opts), return_std=True)
emap = {}
print(pylint_stderr.read())
for line in pylint_stdout:
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23,581 | apache/incubator-mxnet | cpp-package/scripts/lint.py | LintHelper.print_summary | def print_summary(self, strm):
"""Print summary of lint."""
nerr = 0
nerr += LintHelper._print_summary_map(strm, self.cpp_header_map, 'cpp-header')
nerr += LintHelper._print_summary_map(strm, self.cpp_src_map, 'cpp-soruce')
nerr += LintHelper._print_summary_map(strm, self.python_... | python | def print_summary(self, strm):
"""Print summary of lint."""
nerr = 0
nerr += LintHelper._print_summary_map(strm, self.cpp_header_map, 'cpp-header')
nerr += LintHelper._print_summary_map(strm, self.cpp_src_map, 'cpp-soruce')
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23,582 | apache/incubator-mxnet | python/mxnet/kvstore_server.py | KVStoreServer._controller | def _controller(self):
"""Return the server controller."""
def server_controller(cmd_id, cmd_body, _):
"""Server controler."""
if not self.init_logginig:
# the reason put the codes here is because we cannot get
# kvstore.rank earlier
... | python | def _controller(self):
"""Return the server controller."""
def server_controller(cmd_id, cmd_body, _):
"""Server controler."""
if not self.init_logginig:
# the reason put the codes here is because we cannot get
# kvstore.rank earlier
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23,583 | apache/incubator-mxnet | python/mxnet/kvstore_server.py | KVStoreServer.run | def run(self):
"""Run the server, whose behavior is like.
>>> while receive(x):
... if is_command x: controller(x)
... else if is_key_value x: updater(x)
"""
_ctrl_proto = ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_char_p, ctypes.c_void_p)
check_call(... | python | def run(self):
"""Run the server, whose behavior is like.
>>> while receive(x):
... if is_command x: controller(x)
... else if is_key_value x: updater(x)
"""
_ctrl_proto = ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.c_char_p, ctypes.c_void_p)
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23,584 | apache/incubator-mxnet | python/mxnet/ndarray/register.py | _make_ndarray_function | def _make_ndarray_function(handle, name, func_name):
"""Create a NDArray function from the FunctionHandle."""
code, doc_str = _generate_ndarray_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
ndarray_function = local[func_name]
ndarray_func... | python | def _make_ndarray_function(handle, name, func_name):
"""Create a NDArray function from the FunctionHandle."""
code, doc_str = _generate_ndarray_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
ndarray_function = local[func_name]
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23,585 | apache/incubator-mxnet | python/mxnet/contrib/text/utils.py | count_tokens_from_str | def count_tokens_from_str(source_str, token_delim=' ', seq_delim='\n',
to_lower=False, counter_to_update=None):
"""Counts tokens in the specified string.
For token_delim=\'<td>\' and seq_delim=\'<sd>\', a specified string of two sequences of
tokens may look like::
<td>token1<... | python | def count_tokens_from_str(source_str, token_delim=' ', seq_delim='\n',
to_lower=False, counter_to_update=None):
"""Counts tokens in the specified string.
For token_delim=\'<td>\' and seq_delim=\'<sd>\', a specified string of two sequences of
tokens may look like::
<td>token1<... | [
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23,586 | apache/incubator-mxnet | python/mxnet/ndarray/utils.py | load | def load(fname):
"""Loads an array from file.
See more details in ``save``.
Parameters
----------
fname : str
The filename.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict of str to NDArray, RowSparseNDArray or CSRNDArray
Loaded data.
... | python | def load(fname):
"""Loads an array from file.
See more details in ``save``.
Parameters
----------
fname : str
The filename.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict of str to NDArray, RowSparseNDArray or CSRNDArray
Loaded data.
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See more details in ``save``.
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fname : str
The filename.
Returns
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list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict of str to NDArray, RowSparseNDArray or CSRNDArray
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23,587 | apache/incubator-mxnet | python/mxnet/ndarray/utils.py | load_frombuffer | def load_frombuffer(buf):
"""Loads an array dictionary or list from a buffer
See more details in ``save``.
Parameters
----------
buf : str
Buffer containing contents of a file as a string or bytes.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict ... | python | def load_frombuffer(buf):
"""Loads an array dictionary or list from a buffer
See more details in ``save``.
Parameters
----------
buf : str
Buffer containing contents of a file as a string or bytes.
Returns
-------
list of NDArray, RowSparseNDArray or CSRNDArray, or \
dict ... | [
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23,588 | apache/incubator-mxnet | python/mxnet/ndarray/utils.py | save | def save(fname, data):
"""Saves a list of arrays or a dict of str->array to file.
Examples of filenames:
- ``/path/to/file``
- ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)
- ``hdfs://path/to/file`` (if compiled with HDFS supports)
Parameters
----------
fname : st... | python | def save(fname, data):
"""Saves a list of arrays or a dict of str->array to file.
Examples of filenames:
- ``/path/to/file``
- ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)
- ``hdfs://path/to/file`` (if compiled with HDFS supports)
Parameters
----------
fname : st... | [
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23,589 | apache/incubator-mxnet | python/mxnet/gluon/block.py | _common_prefix | def _common_prefix(names):
"""Get the common prefix for all names"""
if not names:
return ''
prefix = names[0]
for name in names:
i = 0
while i < len(prefix) and i < len(name) and prefix[i] == name[i]:
i += 1
prefix = prefix[:i]
return prefix | python | def _common_prefix(names):
"""Get the common prefix for all names"""
if not names:
return ''
prefix = names[0]
for name in names:
i = 0
while i < len(prefix) and i < len(name) and prefix[i] == name[i]:
i += 1
prefix = prefix[:i]
return prefix | [
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23,590 | apache/incubator-mxnet | python/mxnet/gluon/block.py | _infer_param_types | def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t):
"""Utility function that helps in inferring DType of args and auxs params
from given input param.
Parameters
----------
in_params: List of Symbol
List of input symbol variables.
out_params: S... | python | def _infer_param_types(in_params, out_params, arg_params, aux_params, default_dtype=mx_real_t):
"""Utility function that helps in inferring DType of args and auxs params
from given input param.
Parameters
----------
in_params: List of Symbol
List of input symbol variables.
out_params: S... | [
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out_params: Symbol
Output symbol variable.
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23,591 | apache/incubator-mxnet | python/mxnet/gluon/block.py | _BlockScope.create | def create(prefix, params, hint):
"""Creates prefix and params for new `Block`."""
current = getattr(_BlockScope._current, "value", None)
if current is None:
if prefix is None:
if not hasattr(_name.NameManager._current, "value"):
_name.NameManager.... | python | def create(prefix, params, hint):
"""Creates prefix and params for new `Block`."""
current = getattr(_BlockScope._current, "value", None)
if current is None:
if prefix is None:
if not hasattr(_name.NameManager._current, "value"):
_name.NameManager.... | [
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23,592 | apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.load_parameters | def load_parameters(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""Load parameters from file previously saved by `save_parameters`.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of C... | python | def load_parameters(self, filename, ctx=None, allow_missing=False,
ignore_extra=False):
"""Load parameters from file previously saved by `save_parameters`.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of C... | [
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filename : str
Path to parameter file.
ctx : Context or list of Context, default cpu()
Context(s) to initialize loaded parameters on.
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23,593 | apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.register_forward_pre_hook | def register_forward_pre_hook(self, hook):
r"""Registers a forward pre-hook on the block.
The hook function is called immediately before :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook functio... | python | def register_forward_pre_hook(self, hook):
r"""Registers a forward pre-hook on the block.
The hook function is called immediately before :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook functio... | [
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The hook function is called immediately before :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form `hook(block, input) -> None`.
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23,594 | apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.register_forward_hook | def register_forward_hook(self, hook):
r"""Registers a forward hook on the block.
The hook function is called immediately after :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form... | python | def register_forward_hook(self, hook):
r"""Registers a forward hook on the block.
The hook function is called immediately after :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form... | [
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The hook function is called immediately after :func:`forward`.
It should not modify the input or output.
Parameters
----------
hook : callable
The forward hook function of form `hook(block, input, output) -> None`.
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23,595 | apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.apply | def apply(self, fn):
r"""Applies ``fn`` recursively to every child block as well as self.
Parameters
----------
fn : callable
Function to be applied to each submodule, of form `fn(block)`.
Returns
-------
this block
"""
for cld in sel... | python | def apply(self, fn):
r"""Applies ``fn`` recursively to every child block as well as self.
Parameters
----------
fn : callable
Function to be applied to each submodule, of form `fn(block)`.
Returns
-------
this block
"""
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] | r"""Applies ``fn`` recursively to every child block as well as self.
Parameters
----------
fn : callable
Function to be applied to each submodule, of form `fn(block)`.
Returns
-------
this block | [
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23,596 | apache/incubator-mxnet | python/mxnet/gluon/block.py | Block.cast | def cast(self, dtype):
"""Cast this Block to use another data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
for child in self._children.values():
child.cast(dtype)
for _, param in self.params.items():
... | python | def cast(self, dtype):
"""Cast this Block to use another data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
for child in self._children.values():
child.cast(dtype)
for _, param in self.params.items():
... | [
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The new data type. | [
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23,597 | apache/incubator-mxnet | python/mxnet/gluon/block.py | HybridBlock._infer_attrs | def _infer_attrs(self, infer_fn, attr, *args):
"""Generic infer attributes."""
inputs, out = self._get_graph(*args)
args, _ = _flatten(args, "input")
with warnings.catch_warnings(record=True) as w:
arg_attrs, _, aux_attrs = getattr(out, infer_fn)(
**{i.name: g... | python | def _infer_attrs(self, infer_fn, attr, *args):
"""Generic infer attributes."""
inputs, out = self._get_graph(*args)
args, _ = _flatten(args, "input")
with warnings.catch_warnings(record=True) as w:
arg_attrs, _, aux_attrs = getattr(out, infer_fn)(
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23,598 | apache/incubator-mxnet | python/mxnet/gluon/block.py | HybridBlock.export | def export(self, path, epoch=0):
"""Export HybridBlock to json format that can be loaded by
`SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
.. note:: When there are only one input, it will have name `data`. When there
Are more than one inputs, they will be name... | python | def export(self, path, epoch=0):
"""Export HybridBlock to json format that can be loaded by
`SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.
.. note:: When there are only one input, it will have name `data`. When there
Are more than one inputs, they will be name... | [
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.. note:: When there are only one input, it will have name `data`. When there
Are more than one inputs, they will be named as `data0`, `data1`, etc.
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23,599 | apache/incubator-mxnet | python/mxnet/gluon/block.py | SymbolBlock.imports | def imports(symbol_file, input_names, param_file=None, ctx=None):
"""Import model previously saved by `HybridBlock.export` or
`Module.save_checkpoint` as a SymbolBlock for use in Gluon.
Parameters
----------
symbol_file : str
Path to symbol file.
input_names ... | python | def imports(symbol_file, input_names, param_file=None, ctx=None):
"""Import model previously saved by `HybridBlock.export` or
`Module.save_checkpoint` as a SymbolBlock for use in Gluon.
Parameters
----------
symbol_file : str
Path to symbol file.
input_names ... | [
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Path to symbol file.
input_names : list of str
List of input variable names
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