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train | Train.load_dataloader | Description : Setup the dataloader | example/gluon/lipnet/trainer.py | def load_dataloader(self):
"""
Description : Setup the dataloader
"""
input_transform = transforms.Compose([transforms.ToTensor(), \
transforms.Normalize((0.7136, 0.4906, 0.3283), \
... | def load_dataloader(self):
"""
Description : Setup the dataloader
"""
input_transform = transforms.Compose([transforms.ToTensor(), \
transforms.Normalize((0.7136, 0.4906, 0.3283), \
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train | Train.train | Description : training for LipNet | example/gluon/lipnet/trainer.py | def train(self, data, label, batch_size):
"""
Description : training for LipNet
"""
# pylint: disable=no-member
sum_losses = 0
len_losses = 0
with autograd.record():
losses = [self.loss_fn(self.net(X), Y) for X, Y in zip(data, label)]
for loss ... | def train(self, data, label, batch_size):
"""
Description : training for LipNet
"""
# pylint: disable=no-member
sum_losses = 0
len_losses = 0
with autograd.record():
losses = [self.loss_fn(self.net(X), Y) for X, Y in zip(data, label)]
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train | Train.infer | Description : Print sentence for prediction result | example/gluon/lipnet/trainer.py | def infer(self, input_data, input_label):
"""
Description : Print sentence for prediction result
"""
sum_losses = 0
len_losses = 0
for data, label in zip(input_data, input_label):
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sum_losses += mx.nd.array(self.loss_fn(pred, l... | def infer(self, input_data, input_label):
"""
Description : Print sentence for prediction result
"""
sum_losses = 0
len_losses = 0
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train | Train.train_batch | Description : training for LipNet | example/gluon/lipnet/trainer.py | def train_batch(self, dataloader):
"""
Description : training for LipNet
"""
sum_losses = 0
len_losses = 0
for input_data, input_label in tqdm(dataloader):
data = gluon.utils.split_and_load(input_data, self.ctx, even_split=False)
label = gluon.util... | def train_batch(self, dataloader):
"""
Description : training for LipNet
"""
sum_losses = 0
len_losses = 0
for input_data, input_label in tqdm(dataloader):
data = gluon.utils.split_and_load(input_data, self.ctx, even_split=False)
label = gluon.util... | [
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train | Train.infer_batch | Description : inference for LipNet | example/gluon/lipnet/trainer.py | def infer_batch(self, dataloader):
"""
Description : inference for LipNet
"""
sum_losses = 0
len_losses = 0
for input_data, input_label in dataloader:
data = gluon.utils.split_and_load(input_data, self.ctx, even_split=False)
label = gluon.utils.spl... | def infer_batch(self, dataloader):
"""
Description : inference for LipNet
"""
sum_losses = 0
len_losses = 0
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train | Train.run | Description : Run training for LipNet | example/gluon/lipnet/trainer.py | def run(self, epochs):
"""
Description : Run training for LipNet
"""
best_loss = sys.maxsize
for epoch in trange(epochs):
iter_no = 0
## train
sum_losses, len_losses = self.train_batch(self.train_dataloader)
if iter_no % 20 == 0:
... | def run(self, epochs):
"""
Description : Run training for LipNet
"""
best_loss = sys.maxsize
for epoch in trange(epochs):
iter_no = 0
## train
sum_losses, len_losses = self.train_batch(self.train_dataloader)
if iter_no % 20 == 0:
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train | sample_categorical | Sample from independent categorical distributions
Each batch is an independent categorical distribution.
Parameters
----------
prob : numpy.ndarray
Probability of the categorical distribution. Shape --> (batch_num, category_num)
rng : numpy.random.RandomState
Returns
-------
ret... | example/reinforcement-learning/dqn/utils.py | def sample_categorical(prob, rng):
"""Sample from independent categorical distributions
Each batch is an independent categorical distribution.
Parameters
----------
prob : numpy.ndarray
Probability of the categorical distribution. Shape --> (batch_num, category_num)
rng : numpy.random.Ra... | def sample_categorical(prob, rng):
"""Sample from independent categorical distributions
Each batch is an independent categorical distribution.
Parameters
----------
prob : numpy.ndarray
Probability of the categorical distribution. Shape --> (batch_num, category_num)
rng : numpy.random.Ra... | [
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train | sample_normal | Sample from independent normal distributions
Each element is an independent normal distribution.
Parameters
----------
mean : numpy.ndarray
Means of the normal distribution. Shape --> (batch_num, sample_dim)
var : numpy.ndarray
Variance of the normal distribution. Shape --> (batch_num,... | example/reinforcement-learning/dqn/utils.py | def sample_normal(mean, var, rng):
"""Sample from independent normal distributions
Each element is an independent normal distribution.
Parameters
----------
mean : numpy.ndarray
Means of the normal distribution. Shape --> (batch_num, sample_dim)
var : numpy.ndarray
Variance of the ... | def sample_normal(mean, var, rng):
"""Sample from independent normal distributions
Each element is an independent normal distribution.
Parameters
----------
mean : numpy.ndarray
Means of the normal distribution. Shape --> (batch_num, sample_dim)
var : numpy.ndarray
Variance of the ... | [
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train | sample_mog | Sample from independent mixture of gaussian (MoG) distributions
Each batch is an independent MoG distribution.
Parameters
----------
prob : numpy.ndarray
mixture probability of each gaussian. Shape --> (batch_num, center_num)
mean : numpy.ndarray
mean of each gaussian. Shape --> (batch... | example/reinforcement-learning/dqn/utils.py | def sample_mog(prob, mean, var, rng):
"""Sample from independent mixture of gaussian (MoG) distributions
Each batch is an independent MoG distribution.
Parameters
----------
prob : numpy.ndarray
mixture probability of each gaussian. Shape --> (batch_num, center_num)
mean : numpy.ndarray
... | def sample_mog(prob, mean, var, rng):
"""Sample from independent mixture of gaussian (MoG) distributions
Each batch is an independent MoG distribution.
Parameters
----------
prob : numpy.ndarray
mixture probability of each gaussian. Shape --> (batch_num, center_num)
mean : numpy.ndarray
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train | nce_loss_subwords | NCE-Loss layer under subword-units input. | example/nce-loss/nce.py | def nce_loss_subwords(
data, label, label_mask, label_weight, embed_weight, vocab_size, num_hidden):
"""NCE-Loss layer under subword-units input.
"""
# get subword-units embedding.
label_units_embed = mx.sym.Embedding(data=label,
input_dim=vocab_size,
... | def nce_loss_subwords(
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"""
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train | get_dataset | Download the BSDS500 dataset and return train and test iters. | example/gluon/super_resolution/super_resolution.py | def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
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"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
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train | evaluate | Run evaluation on cpu. | example/rnn/large_word_lm/run_utils.py | def evaluate(mod, data_iter, epoch, log_interval):
""" Run evaluation on cpu. """
start = time.time()
total_L = 0.0
nbatch = 0
density = 0
mod.set_states(value=0)
for batch in data_iter:
mod.forward(batch, is_train=False)
outputs = mod.get_outputs(merge_multi_context=False)
... | def evaluate(mod, data_iter, epoch, log_interval):
""" Run evaluation on cpu. """
start = time.time()
total_L = 0.0
nbatch = 0
density = 0
mod.set_states(value=0)
for batch in data_iter:
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outputs = mod.get_outputs(merge_multi_context=False)
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train | FileIter._read | get two list, each list contains two elements: name and nd.array value | example/fcn-xs/data.py | def _read(self):
"""get two list, each list contains two elements: name and nd.array value"""
_, data_img_name, label_img_name = self.f.readline().strip('\n').split("\t")
data = {}
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data[self.data_name], label[self.label_name] = self._read_img(data_img_name, label_img_n... | def _read(self):
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train | FileIter.next | return one dict which contains "data" and "label" | example/fcn-xs/data.py | def next(self):
"""return one dict which contains "data" and "label" """
if self.iter_next():
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if self.iter_next():
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train | GraphProto._convert_operator | Convert from onnx operator to mxnet operator.
The converter must specify conversions explicitly for incompatible name, and
apply handlers to operator attributes.
Parameters
----------
:param node_name : str
name of the node to be translated.
:param op_name : ... | python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | def _convert_operator(self, node_name, op_name, attrs, inputs):
"""Convert from onnx operator to mxnet operator.
The converter must specify conversions explicitly for incompatible name, and
apply handlers to operator attributes.
Parameters
----------
:param node_name : s... | def _convert_operator(self, node_name, op_name, attrs, inputs):
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The converter must specify conversions explicitly for incompatible name, and
apply handlers to operator attributes.
Parameters
----------
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The returned mxnet symbol
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graph : onnx protobuf object
The loaded onnx graph
Returns
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The returned mxnet symbol
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graph : onnx protobuf object
The loaded onnx graph
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The returned mxnet symbol
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train | GraphProto.get_graph_metadata | Get the model metadata from a given onnx graph. | python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | def get_graph_metadata(self, graph):
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The loaded onnx graph
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Loads the model into one or many context(s).
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graph : onnx protobuf object
The loaded onnx graph
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Loads the model into one or many context(s).
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train | GraphProto._parse_array | Grab data in TensorProto and convert to numpy array. | python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | def _parse_array(self, tensor_proto):
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try:
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train | GraphProto._parse_attr | Convert a list of AttributeProto to a dict, with names as keys. | python/mxnet/contrib/onnx/onnx2mx/import_onnx.py | def _parse_attr(self, attr_proto):
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train | SVRGModule.reshape | Reshapes both modules for new input shapes.
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data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tuple)
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Parameters
----------
data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tuple)
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train | SVRGModule.init_optimizer | Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is
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train | SVRGModule._create_optimizer | Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and
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Parameters
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kvstore : str or KVStore
Default `'local'`.
optimizer: str
Name for SVRGOpti... | python/mxnet/contrib/svrg_optimization/svrg_module.py | def _create_optimizer(self, optimizer, default_opt, kvstore, optimizer_params):
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Parameters
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train | SVRGModule.bind | Binds the symbols to construct executors for both two modules. This is necessary before one
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Parameters
----------
data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tup... | python/mxnet/contrib/svrg_optimization/svrg_module.py | def bind(self, data_shapes, label_shapes=None, for_training=True,
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If reshaping of data batch relates to modification of symbol or module, such as
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"""Forward computation for both two modules. It supports data batches with different shapes, such as
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If reshaping of data batch relates to modification of symbol or module, such as
changing imag... | def forward(self, data_batch, is_train=None):
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out_grads : NDArray or list of NDArray, optional
Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
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Gradient on the outputs to be propagated back.
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train | SVRGModule.update_full_grads | Computes the gradients over all data w.r.t weights of past
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Parameters
----------
train_data: DataIter
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Parameters
----------
train_data: DataIter
Train data iterator
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train | SVRGModule._accumulate_kvstore | Accumulate gradients over all data in the KVStore. In distributed setting, each worker sees a portion of
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key: int or str
Key in the KVStore.
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Parameters
----------
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train | SVRGModule._allocate_gradients | 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
----------
key: int or str
Key in the kvstore.
value: List of NDArray, List of RowSparseNDArray
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train | SVRGModule._svrg_grads_update_rule | Calculates the gradient based on the SVRG update rule.
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gradients of current weight of self.mod w.r.t current batch of data
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train | SVRGModule._update_svrg_gradients | Calculates gradients based on the SVRG update rule. | python/mxnet/contrib/svrg_optimization/svrg_module.py | def _update_svrg_gradients(self):
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train | SVRGModule.fit | Trains the module parameters.
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eval_data : DataIter
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whether to shuffle the image list
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whether to shuffle the image list
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index of a specific image
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index of a specific image
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given image index, find out annotation path
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full path of annotation file
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train | YoloFormat._load_image_labels | preprocess all ground-truths
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preprocess all ground-truths
Returns:
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labels packed in [num_images x max_num_objects x 5] tensor
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preprocess all ground-truths
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train | get_register_func | Get registrator function.
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base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
-------
a registrator function | python/mxnet/registry.py | def get_register_func(base_class, nickname):
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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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base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
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a registrator function
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train | get_alias_func | Get registrator function that allow aliases.
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base class for classes that will be reigstered
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nickname of base_class for logging
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base class for classes that will be reigstered
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nickname of base_class for logging
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base class for classes that will be reigstered
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nickname of base_class for logging
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train | get_create_func | Get creator function
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base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
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base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
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a creator function
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base class for classes that will be reigstered
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nickname of base_class for logging
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a creator function
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train | parse_args | Parse arguments. | tools/diagnose.py | def parse_args():
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train | clean_str | Tokenization/string cleaning for all datasets except for SST.
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train | load_data_and_labels | Loads MR polarity data from files, splits the data into words and generates labels.
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train | pad_sentences | Pads all sentences to the same length. The length is defined by the longest sentence.
Returns padded sentences. | example/cnn_text_classification/data_helpers.py | 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... | 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)
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train | build_input_data | Maps sentencs and labels to vectors based on a vocabulary. | example/cnn_text_classification/data_helpers.py | 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])
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return [x, y] | def build_input_data(sentences, labels, vocabulary):
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train | build_input_data_with_word2vec | Map sentences and labels to vectors based on a pretrained word2vec | example/cnn_text_classification/data_helpers.py | def build_input_data_with_word2vec(sentences, labels, word2vec_list):
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train | load_data_with_word2vec | Loads and preprocessed data for the MR dataset.
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train | load_data | Loads and preprocessed data for the MR dataset.
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train | batch_iter | Generates a batch iterator for a dataset. | example/cnn_text_classification/data_helpers.py | def batch_iter(data, batch_size, num_epochs):
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data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int(len(data)/batch_size) + 1
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train | load_pretrained_word2vec | Load the pre-trained word2vec from file. | example/cnn_text_classification/data_helpers.py | def load_pretrained_word2vec(infile):
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word2vec_list = {}
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infile = open(infile)
word2vec_list = {}
for idx, line in enumerate(infile):
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train | generate_batch | return batch | example/rcnn/symdata/loader.py | def generate_batch(im_tensor, im_info):
"""return batch"""
data = [im_tensor, im_info]
data_shapes = [('data', im_tensor.shape), ('im_info', im_info.shape)]
data_batch = mx.io.DataBatch(data=data, label=None, provide_data=data_shapes, provide_label=None)
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"""return batch"""
data = [im_tensor, im_info]
data_shapes = [('data', im_tensor.shape), ('im_info', im_info.shape)]
data_batch = mx.io.DataBatch(data=data, label=None, provide_data=data_shapes, provide_label=None)
return data_batch | [
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train | get_symbol | VGG 16 layers network
This is a modified version, with fc6/fc7 layers replaced by conv layers
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"""
VGG 16 layers network
This is a modified version, with fc6/fc7 layers replaced by conv layers
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data = mx.symbol.Variable(name="data")
label = mx.symbol.Variable(name="label")
... | def get_symbol(num_classes=1000, **kwargs):
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data = mx.symbol.Variable(name="data")
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train | get_mlp | Get multi-layer perceptron | example/caffe/caffe_net.py | def get_mlp():
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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... | def get_mlp():
"""Get multi-layer perceptron"""
data = mx.symbol.Variable('data')
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train | get_lenet | LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
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Proceedings of the IEEE (1998) | example/caffe/caffe_net.py | def get_lenet():
"""LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
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Proceedings of the IEEE (1998)
"""
data = mx.symbol.Variable('data')
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conv1 = mx.symbol.CaffeOp(data_0=data, num_weight=2,
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data = mx.symbol.Variable('data')
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train | parse_args | Parse the arguments | example/caffe/caffe_net.py | def parse_args():
"""Parse the arguments"""
parser = argparse.ArgumentParser(description='train an image classifier on mnist')
parser.add_argument('--network', type=str, default='lenet',
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"""Parse the arguments"""
parser = argparse.ArgumentParser(description='train an image classifier on mnist')
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train | WeightedSoftmaxCrossEntropyLoss.forward | Implements forward computation.
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in_data : list of NDArray, input data.
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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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train | WeightedSoftmaxCrossEntropyLoss.backward | Implements backward computation
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out_grad : list of NDArray, gradient w.r.t. output data.
in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer. | example/sparse/linear_classification/weighted_softmax_ce.py | def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
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out_grad : list of NDArray, gradient w.r.t. output data.
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train | BucketingModule._reset_bind | Internal utility function to reset binding. | python/mxnet/module/bucketing_module.py | def _reset_bind(self):
"""Internal utility function to reset binding."""
self.binded = False
self._buckets = {}
self._curr_module = None
self._curr_bucket_key = None | 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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train | BucketingModule.data_names | A list of names for data required by this module. | python/mxnet/module/bucketing_module.py | def data_names(self):
"""A list of names for data required by this module."""
if self.binded:
return self._curr_module.data_names
else:
_, data_names, _ = self._call_sym_gen(self._default_bucket_key)
return data_names | def data_names(self):
"""A list of names for data required by this module."""
if self.binded:
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_, data_names, _ = self._call_sym_gen(self._default_bucket_key)
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train | BucketingModule.output_names | A list of names for the outputs of this module. | python/mxnet/module/bucketing_module.py | def output_names(self):
"""A list of names for the outputs of this module."""
if self.binded:
return self._curr_module.output_names
else:
symbol, _, _ = self._call_sym_gen(self._default_bucket_key)
return symbol.list_outputs() | def output_names(self):
"""A list of names for the outputs of this module."""
if self.binded:
return self._curr_module.output_names
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symbol, _, _ = self._call_sym_gen(self._default_bucket_key)
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train | BucketingModule.get_params | Gets current parameters.
Returns
-------
`(arg_params, aux_params)`
A pair of dictionaries each mapping parameter names to NDArray values. | python/mxnet/module/bucketing_module.py | def get_params(self):
"""Gets current parameters.
Returns
-------
`(arg_params, aux_params)`
A pair of dictionaries each mapping parameter names to NDArray values.
"""
assert self.binded and self.params_initialized
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"""Gets current parameters.
Returns
-------
`(arg_params, aux_params)`
A pair of dictionaries each mapping parameter names to NDArray values.
"""
assert self.binded and self.params_initialized
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train | BucketingModule.init_params | Initializes parameters.
Parameters
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initializer : Initializer
arg_params : dict
Defaults to ``None``. Existing parameters. This has higher priority
than `initializer`.
aux_params : dict
Defaults to ``None``. Existing auxiliary states... | python/mxnet/module/bucketing_module.py | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
allow_missing=False, force_init=False, allow_extra=False):
"""Initializes parameters.
Parameters
----------
initializer : Initializer
arg_params : dict
Defaults to ... | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
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"""Initializes parameters.
Parameters
----------
initializer : Initializer
arg_params : dict
Defaults to ... | [
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train | BucketingModule.get_states | Gets states from all devices.
Parameters
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merge_multi_context : bool
Default is `True`. In the case when data-parallelism is used, the states
will be collected from multiple devices. A `True` value indicate that we
should merge the collected results... | python/mxnet/module/bucketing_module.py | def get_states(self, merge_multi_context=True):
"""Gets states from all devices.
Parameters
----------
merge_multi_context : bool
Default is `True`. In the case when data-parallelism is used, the states
will be collected from multiple devices. A `True` value indi... | def get_states(self, merge_multi_context=True):
"""Gets states from all devices.
Parameters
----------
merge_multi_context : bool
Default is `True`. In the case when data-parallelism is used, the states
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train | BucketingModule.set_states | Sets value for states. Only one of states & values can be specified.
Parameters
----------
states : list of list of NDArrays
Source states arrays formatted like ``[[state1_dev1, state1_dev2],
[state2_dev1, state2_dev2]]``.
value : number
A single scal... | python/mxnet/module/bucketing_module.py | def set_states(self, states=None, value=None):
"""Sets value for states. Only one of states & values can be specified.
Parameters
----------
states : list of list of NDArrays
Source states arrays formatted like ``[[state1_dev1, state1_dev2],
[state2_dev1, state2_... | def set_states(self, states=None, value=None):
"""Sets value for states. Only one of states & values can be specified.
Parameters
----------
states : list of list of NDArrays
Source states arrays formatted like ``[[state1_dev1, state1_dev2],
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train | BucketingModule.bind | Binding for a `BucketingModule` means setting up the buckets and binding the
executor for the default bucket key. Executors corresponding to other keys are
bound afterwards with `switch_bucket`.
Parameters
----------
data_shapes : list of (str, tuple)
This should cor... | python/mxnet/module/bucketing_module.py | def bind(self, data_shapes, label_shapes=None, for_training=True,
inputs_need_grad=False, force_rebind=False, shared_module=None,
grad_req='write'):
"""Binding for a `BucketingModule` means setting up the buckets and binding the
executor for the default bucket key. Executors co... | 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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train | BucketingModule.switch_bucket | 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, tuple)
Typically ``data_batch.provide_data``.
label_shapes : lis... | python/mxnet/module/bucketing_module.py | 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... | 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.
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train | BucketingModule.init_optimizer | Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Defaults to `'local'`.
optimizer : str or Optimizer
Defaults to `'sgd'`
optimizer_params : dict
Defaults to `(('learning_rate', 0.01),)`. The default value is ... | python/mxnet/module/bucketing_module.py | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Defaults to `'local... | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
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train | BucketingModule.prepare | Prepares the module for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.
Parameters
----------
data_batch : DataB... | python/mxnet/module/bucketing_module.py | def prepare(self, data_batch, sparse_row_id_fn=None):
'''Prepares the module for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.
... | def prepare(self, data_batch, sparse_row_id_fn=None):
'''Prepares the module for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.
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train | BucketingModule.forward | Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Defaults to ``None``, in which case `is_train` is take as ``self.for_training``. | python/mxnet/module/bucketing_module.py | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Defaults to ``None``, in which case `is_train` is take as ``self.for_training``.
"""
assert self.binded and self.params_... | def forward(self, data_batch, is_train=None):
"""Forward computation.
Parameters
----------
data_batch : DataBatch
is_train : bool
Defaults to ``None``, in which case `is_train` is take as ``self.for_training``.
"""
assert self.binded and self.params_... | [
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train | BucketingModule.backward | Backward computation. | python/mxnet/module/bucketing_module.py | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
self._curr_module.backward(out_grads=out_grads) | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
self._curr_module.backward(out_grads=out_grads) | [
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train | BucketingModule.update | Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
When KVStore is used to update parameters for multi-device or multi-machine training,
a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters,
... | python/mxnet/module/bucketing_module.py | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
in the previous forward-backward cycle.
When KVStore is used to update parameters for multi-device or multi-machine training,
a copy of the parameters are stored in KVStore. Note that for ... | def update(self):
"""Updates parameters according to installed optimizer and the gradient computed
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When KVStore is used to update parameters for multi-device or multi-machine training,
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train | BucketingModule.get_outputs | Gets outputs from a previous forward computation.
Parameters
----------
merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
will be collected from multiple devices. A ``True`` value indicate that we
should m... | python/mxnet/module/bucketing_module.py | def get_outputs(self, merge_multi_context=True):
"""Gets outputs from a previous forward computation.
Parameters
----------
merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
will be collected from multiple dev... | def get_outputs(self, merge_multi_context=True):
"""Gets outputs from a previous forward computation.
Parameters
----------
merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
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train | BucketingModule.get_input_grads | Gets the gradients with respect to the inputs of the module.
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Defaults to ``True``. In the case when data-parallelism is used, the outputs
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... | python/mxnet/module/bucketing_module.py | def get_input_grads(self, merge_multi_context=True):
"""Gets the gradients with respect to the inputs of the module.
Parameters
----------
merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
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merge_multi_context : bool
Defaults to ``True``. In the case when data-parallelism is used, the outputs
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train | BucketingModule.update_metric | Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically ``data_batch.label``. | python/mxnet/module/bucketing_module.py | def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically ``data_batch.label``.
... | def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
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----------
eval_metric : EvalMetric
labels : list of NDArray
Typically ``data_batch.label``.
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train | BucketingModule.install_monitor | Installs monitor on all executors | python/mxnet/module/bucketing_module.py | 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) | def install_monitor(self, mon):
"""Installs monitor on all executors """
assert self.binded
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train | set_recording | Set status to recording/not recording. When recording, graph will be constructed
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Parameters
----------
is_recording: bool
Returns
-------
previous state before this set. | python/mxnet/autograd.py | def set_recording(is_recording): #pylint: disable=redefined-outer-name
"""Set status to recording/not recording. When recording, graph will be constructed
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Parameters
----------
is_recording: bool
Returns
-------
previous state before this set.
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prev... | def set_recording(is_recording): #pylint: disable=redefined-outer-name
"""Set status to recording/not recording. When recording, graph will be constructed
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Parameters
----------
is_recording: bool
Returns
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previous state before this set.
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train | set_training | Set status to training/predicting. This affects ctx.is_train in operator
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"""Set status to training/predicting. This affects ctx.is_train in operator
running context. For example, Dropout will drop inputs randomly when
train_mode=True while simply passing through if train_mode=False.
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"""Set status to training/predicting. This affects ctx.is_train in operator
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train | is_recording | Get status on recording/not recording.
Returns
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"""Get status on recording/not recording.
Returns
-------
Current state of recording.
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Current state of recording.
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train | is_training | Get status on training/predicting.
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"""Get status on training/predicting.
Returns
-------
Current state of training/predicting.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsTraining(ctypes.byref(curr)))
return curr.value | def is_training():
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Current state of training/predicting.
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train | mark_variables | 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 | python/mxnet/autograd.py | 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... | 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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train | _parse_head | parse head gradient for backward and grad. | python/mxnet/autograd.py | 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:
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"""parse head gradient for backward and grad."""
if isinstance(heads, NDArray):
heads = [heads]
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head_grads = [head_grads]
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train | backward | 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 NDArray or None
Gradients with respect to heads.
train_mode: bool, optional
Whether to do backward for t... | python/mxnet/autograd.py | 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... | 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)
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train | grad | Compute the gradients of heads w.r.t variables. Gradients will be
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Supports recording gradient graph for computing higher order gradients.
.. note::
Currently only a very limited set of operators support higher order \
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train | get_symbol | Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
Returns
-------
Symbol
The retrieved Symbol. | python/mxnet/autograd.py | 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()
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"""Retrieve recorded computation history as `Symbol`.
Parameters
----------
x : NDArray
Array representing the head of computation graph.
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-------
Symbol
The retrieved Symbol.
"""
hdl = SymbolHandle()
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train | load_mldataset | Not particularly fast code to parse the text file and load it into three NDArray's
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"""Not particularly fast code to parse the text file and load it into three NDArray's
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"""
user = []
item = []
score = []
with open(filename) as f:
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tks = line.strip().split('\t')
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user = []
item = []
score = []
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train | ParseAllOps | MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
const char **name,
... | cpp-package/scripts/OpWrapperGenerator.py | def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
const char **nam... | def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
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MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
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train | main | Read .caffemodel path and .params path as input from command line
and use CaffeModelConverter to do the conversion | tools/caffe_translator/scripts/convert_caffe_model.py | 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.')
... | def main():
"""Read .caffemodel path and .params path as input from command line
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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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train | CaffeModelConverter.add_param | Add a param to the .params file | tools/caffe_translator/scripts/convert_caffe_model.py | def add_param(self, param_name, layer_index, blob_index):
"""Add a param to the .params file"""
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train | CaffeModelConverter.add_arg_param | Add an arg param to .params file. Example: weights of a fully connected layer. | tools/caffe_translator/scripts/convert_caffe_model.py | def add_arg_param(self, param_name, layer_index, blob_index):
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train | CaffeModelConverter.add_aux_param | Add an aux param to .params file. Example: moving_mean in BatchNorm layer | tools/caffe_translator/scripts/convert_caffe_model.py | def add_aux_param(self, param_name, layer_index, blob_index):
"""Add an aux param to .params file. Example: moving_mean in BatchNorm layer """
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train | CaffeModelConverter.add_optional_arg_param | Add an arg param. If there is no such param in .caffemodel fie, silently ignore it. | tools/caffe_translator/scripts/convert_caffe_model.py | def add_optional_arg_param(self, param_name, layer_index, blob_index):
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train | CaffeModelConverter.convert | Convert a Caffe .caffemodel file to MXNet .params file | tools/caffe_translator/scripts/convert_caffe_model.py | def convert(self, caffemodel_path, outmodel_path):
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train | sample_rois | generate random sample of ROIs comprising foreground and background examples
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train | register | Register a subclass of CustomOpProp to the registry with name reg_name. | python/mxnet/operator.py | def register(reg_name):
"""Register a subclass of CustomOpProp to the registry with name reg_name."""
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train | NDArrayOp.declare_backward_dependency | Declare dependencies of this operator for backward pass.
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out_grad : list of int
ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
out_data: list of int
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"""Declare dependencies of this operator for backward pass.
Parameters
----------
out_grad : list of int
ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
out_dat... | def declare_backward_dependency(self, out_grad, in_data, out_data):
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ids of out_grad blobs.
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train | CustomOp.assign | Helper function for assigning into dst depending on requirements. | python/mxnet/operator.py | def assign(self, dst, req, src):
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list of argument types in the same order as
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list of argument types in the same order as
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train | CustomOpProp.infer_storage_type | infer_storage_type interface. Used to infer storage type of
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train | CustomOpProp.declare_backward_dependency | Declare dependencies of this operator for backward pass.
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out_grad : list of int
ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
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----... | python/mxnet/operator.py | def declare_backward_dependency(self, out_grad, in_data, out_data):
"""Declare dependencies of this operator for backward pass.
Parameters
----------
out_grad : list of int
ids of out_grad blobs.
in_data : list of int
ids of in_data blobs.
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ids of out_grad blobs.
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train | _Registry.inc | Get index for new entry. | python/mxnet/operator.py | def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
return cur | def inc(self):
"""Get index for new entry."""
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