INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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Description: Setup the dataloader | def load_dataloader(self):
"""
Description : Setup the dataloader
"""
input_transform = transforms.Compose([transforms.ToTensor(), \
transforms.Normalize((0.7136, 0.4906, 0.3283), \
... |
Description: training for LipNet | 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 ... |
Description: Print sentence for prediction result | 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):
pred = self.net(data)
sum_losses += mx.nd.array(self.loss_fn(pred, l... |
Description: training for LipNet | 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... |
Description: inference for LipNet | 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... |
Description: Run training for LipNet | 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:
... |
Sample from independent categorical distributions | 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... |
Sample from independent normal distributions | 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 ... |
Sample from independent mixture of gaussian ( MoG ) distributions | 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
... |
NCE - Loss layer under subword - units input. | 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,
... |
Download the BSDS500 dataset and return train and test iters. | def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
print(
"Directory {} already exists, skipping.\n"
"To force download and extraction, delete the directory and re-run."
"".format(data_dir),
... |
Run evaluation on cpu. | 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)
... |
get two list each list contains two elements: name and nd. array value | 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 = {}
label = {}
data[self.data_name], label[self.label_name] = self._read_img(data_img_name, label_img_n... |
return one dict which contains data and label | def next(self):
"""return one dict which contains "data" and "label" """
if self.iter_next():
self.data, self.label = self._read()
return {self.data_name : self.data[0][1],
self.label_name : self.label[0][1]}
else:
raise StopIteration |
Convert from onnx operator to mxnet operator. The converter must specify conversions explicitly for incompatible name and apply handlers to operator attributes. | 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... |
Construct symbol from onnx graph. | def from_onnx(self, graph):
"""Construct symbol from onnx graph.
Parameters
----------
graph : onnx protobuf object
The loaded onnx graph
Returns
-------
sym :symbol.Symbol
The returned mxnet symbol
params : dict
A dic... |
Get the model metadata from a given onnx graph. | def get_graph_metadata(self, graph):
"""
Get the model metadata from a given onnx graph.
"""
_params = set()
for tensor_vals in graph.initializer:
_params.add(tensor_vals.name)
input_data = []
for graph_input in graph.input:
if graph_input... |
Construct SymbolBlock from onnx graph. | def graph_to_gluon(self, graph, ctx):
"""Construct SymbolBlock from onnx graph.
Parameters
----------
graph : onnx protobuf object
The loaded onnx graph
ctx : Context or list of Context
Loads the model into one or many context(s).
Returns
... |
Grab data in TensorProto and convert to numpy array. | def _parse_array(self, tensor_proto):
"""Grab data in TensorProto and convert to numpy array."""
try:
from onnx.numpy_helper import to_array
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to i... |
Convert a list of AttributeProto to a dict with names as keys. | def _parse_attr(self, attr_proto):
"""Convert a list of AttributeProto to a dict, with names as keys."""
attrs = {}
for a in attr_proto:
for f in ['f', 'i', 's']:
if a.HasField(f):
attrs[a.name] = getattr(a, f)
# Needed for supp... |
Reshapes both modules for new input shapes. | def reshape(self, data_shapes, label_shapes=None):
"""Reshapes both modules for new input shapes.
Parameters
----------
data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tuple)
Typically is ``data_ite... |
Installs and initializes SVRGOptimizer. The SVRGOptimizer is a wrapper class for a regular optimizer that is passed in and a special AssignmentOptimizer to accumulate the full gradients. If KVStore is local or None the full gradients will be accumulated locally without pushing to the KVStore. Otherwise additional keys ... | 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... |
Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and will redirect update () to the correct optimizer based on the key. | def _create_optimizer(self, optimizer, default_opt, kvstore, optimizer_params):
"""Helper function to create a svrg optimizer. SVRG optimizer encapsulates two optimizers and
will redirect update() to the correct optimizer based on the key.
Parameters
----------
kvstore : str or ... |
Binds the symbols to construct executors for both two modules. This is necessary before one can perform computation with the SVRGModule. | 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
can perform computation with the SVRGModule.
... |
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 image layout ordering or switching from training to predicting module rebinding is requ... | 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... |
Backward computation. | def backward(self, out_grads=None):
"""Backward computation.
See Also
----------
:meth:`BaseModule.backward`.
Parameters
----------
out_grads : NDArray or list of NDArray, optional
Gradient on the outputs to be propagated back.
This param... |
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. | 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... |
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. | 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
----------
key: int or str
... |
Allocate average of full gradients accumulated in the KVStore to each device. | 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
A list of average of th... |
Calculates the gradient based on the SVRG update rule. Parameters ---------- g_curr_batch_curr_weight: NDArray gradients of current weight of self. mod w. r. t current batch of data g_curr_batch_special_weight: NDArray gradients of the weight of past m epochs of self. _mod_special w. r. t current batch of data g_specia... | def _svrg_grads_update_rule(self, g_curr_batch_curr_weight, g_curr_batch_special_weight,
g_special_weight_all_batch):
"""Calculates the gradient based on the SVRG update rule.
Parameters
----------
g_curr_batch_curr_weight : NDArray
gradients o... |
Calculates gradients based on the SVRG update rule. | 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[... |
Trains the module parameters. | def fit(self, train_data, eval_data=None, eval_metric='acc',
epoch_end_callback=None, batch_end_callback=None, kvstore='local',
optimizer='sgd', optimizer_params=(('learning_rate', 0.01),),
eval_end_callback=None,
eval_batch_end_callback=None, initializer=mx.init.Uniform(... |
Prepares two modules for processing a data batch. | 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,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.... |
find out which indexes correspond to given image set ( train or val ) | def _load_image_set_index(self, shuffle):
"""
find out which indexes correspond to given image set (train or val)
Parameters:
----------
shuffle : boolean
whether to shuffle the image list
Returns:
----------
entire list of images specified in... |
given image index find out annotation path | def _label_path_from_index(self, index):
"""
given image index, find out annotation path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of annotation file
"""
label_file = os.path.joi... |
preprocess all ground - truths | def _load_image_labels(self):
"""
preprocess all ground-truths
Returns:
----------
labels packed in [num_images x max_num_objects x 5] tensor
"""
temp = []
# load ground-truths
for idx in self.image_set_index:
label_file = self._label... |
Get registrator function. | 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_... |
Get registrator function that allow aliases. | 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
... |
Get creator function | def get_create_func(base_class, nickname):
"""Get creator function
Parameters
----------
base_class : type
base class for classes that will be reigstered
nickname : str
nickname of base_class for logging
Returns
-------
a creator function
"""
if base_class not i... |
Parse arguments. | def parse_args():
"""Parse arguments."""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Diagnose script for checking the current system.')
choices = ['python', 'pip', 'mxnet', 'os', 'hardware', 'network']
for choice in choices:
... |
Tokenization/ string cleaning for all datasets except for SST. Original taken from https:// github. com/ yoonkim/ CNN_sentence/ blob/ master/ process_data. py | def clean_str(string):
"""Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'v... |
Loads MR polarity data from files splits the data into words and generates labels. Returns split sentences and labels. | def load_data_and_labels():
"""Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# Load data from files
pos_path = "./data/rt-polaritydata/rt-polarity.pos"
neg_path = "./data/rt-polaritydata/rt-polarity.neg"
if not os.... |
Pads all sentences to the same length. The length is defined by the longest sentence. Returns padded 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... |
Maps sentencs and labels to vectors based on a vocabulary. | 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] |
Map sentences and labels to vectors based on a pretrained word2vec | def build_input_data_with_word2vec(sentences, labels, word2vec_list):
"""
Map sentences and labels to vectors based on a pretrained word2vec
"""
x_vec = []
for sent in sentences:
vec = []
for word in sent:
if word in word2vec_list:
vec.append(word2vec_list... |
Loads and preprocessed data for the MR dataset. Returns input vectors labels vocabulary and inverse vocabulary. | def load_data_with_word2vec(word2vec_list):
"""Loads and preprocessed data for the MR dataset.
Returns input vectors, labels, vocabulary, and inverse vocabulary.
"""
# Load and preprocess data
sentences, labels = load_data_and_labels()
sentences_padded = pad_sentences(sentences)
# vocabulary... |
Loads and preprocessed data for the MR dataset. Returns input vectors labels vocabulary and inverse vocabulary. | def load_data():
"""Loads and preprocessed data for the MR dataset.
Returns input vectors, labels, vocabulary, and inverse vocabulary.
"""
# Load and preprocess data
sentences, labels = load_data_and_labels()
sentences_padded = pad_sentences(sentences)
vocabulary, vocabulary_inv = build_voca... |
Generates a batch iterator for a dataset. | 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... |
Load the pre - trained word2vec from file. | def load_pretrained_word2vec(infile):
"""Load the pre-trained word2vec from file."""
if isinstance(infile, str):
infile = open(infile)
word2vec_list = {}
for idx, line in enumerate(infile):
if idx == 0:
vocab_size, dim = line.strip().split()
else:
tks = l... |
return batch | 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)
return data_batch |
VGG 16 layers network This is a modified version with fc6/ fc7 layers replaced by conv layers And the network is slightly smaller than original VGG 16 network | def get_symbol(num_classes=1000, **kwargs):
"""
VGG 16 layers network
This is a modified version, with fc6/fc7 layers replaced by conv layers
And the network is slightly smaller than original VGG 16 network
"""
data = mx.symbol.Variable(name="data")
label = mx.symbol.Variable(name="label")
... |
Get multi - layer perceptron | 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} }")
act1 = mx.symbol.CaffeOp(data_0=fc1, prototxt="layer... |
LeCun Yann Leon Bottou Yoshua Bengio and Patrick Haffner. Gradient - based learning applied to document recognition. Proceedings of the IEEE ( 1998 ) | def get_lenet():
"""LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
Haffner. "Gradient-based learning applied to document recognition."
Proceedings of the IEEE (1998)
"""
data = mx.symbol.Variable('data')
# first conv
conv1 = mx.symbol.CaffeOp(data_0=data, num_weight=2,
... |
Parse the arguments | def parse_args():
"""Parse the arguments"""
parser = argparse.ArgumentParser(description='train an image classifier on mnist')
parser.add_argument('--network', type=str, default='lenet',
help='the cnn to use (mlp | lenet | <path to network json file>')
parser.add_argument('--caff... |
Implements forward computation. | def forward(self, is_train, req, in_data, out_data, aux):
"""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.
in_data : list of ND... |
Implements backward computation | def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
"""Implements backward computation
req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad
out_grad : list of NDArray, gradient w.r.t. output data.
in_grad : list of NDArray, gradient w.r.t. input da... |
Internal utility function to reset binding. | def _reset_bind(self):
"""Internal utility function to reset binding."""
self.binded = False
self._buckets = {}
self._curr_module = None
self._curr_bucket_key = None |
A list of names for data required by this module. | 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 |
A list of names for the outputs of this module. | 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() |
Gets current parameters. | 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
self._curr_module._params_dirty = se... |
Initializes parameters. | 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 ... |
Gets states from all devices. | 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... |
Sets value for states. Only one of states & values can be specified. | 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_... |
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. | 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... |
Switches to a different bucket. This will change self. curr_module. | 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... |
Installs and initializes optimizers. | 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... |
Prepares the module for processing a data batch. | 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.
... |
Forward computation. | 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_... |
Backward computation. | def backward(self, out_grads=None):
"""Backward computation."""
assert self.binded and self.params_initialized
self._curr_module.backward(out_grads=out_grads) |
Updates parameters according to installed optimizer and the gradient computed in the previous forward - backward cycle. | 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 ... |
Gets outputs from a previous forward computation. | 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... |
Gets the gradients with respect to the inputs of the module. | 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
will be collected fr... |
Evaluates and accumulates evaluation metric on outputs of the last forward computation. | 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``.
... |
Installs monitor on all executors | 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) |
Set status to recording/ not recording. When recording graph will be constructed for gradient computation. | def set_recording(is_recording): #pylint: disable=redefined-outer-name
"""Set status to recording/not recording. When recording, graph will be constructed
for gradient computation.
Parameters
----------
is_recording: bool
Returns
-------
previous state before this set.
"""
prev... |
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. | def set_training(train_mode): #pylint: disable=redefined-outer-name
"""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.
Parameters
---------... |
Get status on recording/ not recording. | def is_recording():
"""Get status on recording/not recording.
Returns
-------
Current state of recording.
"""
curr = ctypes.c_bool()
check_call(_LIB.MXAutogradIsRecording(ctypes.byref(curr)))
return curr.value |
Get status on training/ predicting. | def is_training():
"""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 |
Mark NDArrays as variables to compute gradient for autograd. | 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... |
parse head gradient for backward and grad. | 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... |
Compute the gradients of heads w. r. t previously marked variables. | 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... |
Compute the gradients of heads w. r. t variables. Gradients will be returned as new NDArrays instead of stored into variable. grad. Supports recording gradient graph for computing higher order gradients. | def grad(heads, variables, head_grads=None, retain_graph=None, create_graph=False,
train_mode=True): #pylint: disable=redefined-outer-name
"""Compute the gradients of heads w.r.t variables. Gradients will be
returned as new NDArrays instead of stored into `variable.grad`.
Supports recording gradie... |
Retrieve recorded computation history as 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... |
Not particularly fast code to parse the text file and load it into three NDArray s and product an NDArrayIter | 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... |
MXNET_DLL int MXSymbolListAtomicSymbolCreators ( mx_uint * out_size AtomicSymbolCreator ** out_array ) ; | def ParseAllOps():
"""
MXNET_DLL int MXSymbolListAtomicSymbolCreators(mx_uint *out_size,
AtomicSymbolCreator **out_array);
MXNET_DLL int MXSymbolGetAtomicSymbolInfo(AtomicSymbolCreator creator,
const char **nam... |
Read. caffemodel path and. params path as input from command line and use CaffeModelConverter to do the conversion | 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.')
... |
Add a param to the. params file | 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])) |
Add an arg param to. params file. Example: weights of a fully connected layer. | def add_arg_param(self, param_name, layer_index, blob_index):
"""Add an arg param to .params file. Example: weights of a fully connected layer."""
self.add_param('arg:%s' % param_name, layer_index, blob_index) |
Add an aux param to. params file. Example: moving_mean in BatchNorm layer | def add_aux_param(self, param_name, layer_index, blob_index):
"""Add an aux param to .params file. Example: moving_mean in BatchNorm layer """
self.add_param('aux:%s' % param_name, layer_index, blob_index) |
Add an arg param. If there is no such param in. caffemodel fie silently ignore it. | 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):
self.add_arg_param(param_name, layer_index, blob_index) |
Convert a Caffe. caffemodel file to MXNet. params file | 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... |
generate random sample of ROIs comprising foreground and background examples: param rois: [ n 5 ] ( batch_index x1 y1 x2 y2 ): param gt_boxes: [ n 5 ] ( x1 y1 x2 y2 cls ): param num_classes: number of classes: param rois_per_image: total roi number: param fg_rois_per_image: foreground roi number: param fg_overlap: over... | def sample_rois(rois, gt_boxes, num_classes, rois_per_image, fg_rois_per_image, fg_overlap, box_stds):
"""
generate random sample of ROIs comprising foreground and background examples
:param rois: [n, 5] (batch_index, x1, y1, x2, y2)
:param gt_boxes: [n, 5] (x1, y1, x2, y2, cls)
:param num_classes: ... |
Register a subclass of CustomOpProp to the registry with name reg_name. | def register(reg_name):
"""Register a subclass of CustomOpProp to the registry with name reg_name."""
def do_register(prop_cls):
"""Register a subclass of CustomOpProp to the registry."""
fb_functype = CFUNCTYPE(c_int, c_int, POINTER(c_void_p), POINTER(c_int),
POI... |
Declare dependencies of this operator for backward pass. | 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.
out_dat... |
Helper function for assigning into dst depending on requirements. | 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 |
infer_type interface. override to create new operators | 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... |
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. | 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,... |
infer_storage_type_backward interface. Used to infer storage type of inputs and outputs in the backward pass. | def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_stype, aux_stype):
"""infer_storage_type_backward interface. Used to infer storage
type of inputs and outputs in the backward pass.
Will raise an error if undefined storage type is returned.
Returned lists hav... |
Declare dependencies of this operator for backward pass. | 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.
out_dat... |
Get index for new entry. | def inc(self):
"""Get index for new entry."""
self.lock.acquire()
cur = self.counter
self.counter += 1
self.lock.release()
return cur |
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