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23,700 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | get_mnist_sym | def get_mnist_sym(output_op=None, num_hidden=400):
"""Get symbol of mnist"""
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='mnist_fc1', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu1', act_type="relu")
net = mx.symbol.FullyConnected(dat... | python | def get_mnist_sym(output_op=None, num_hidden=400):
"""Get symbol of mnist"""
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='mnist_fc1', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu1', act_type="relu")
net = mx.symbol.FullyConnected(dat... | [
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23,701 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | synthetic_grad | def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
"""Get synthetic gradient value"""
if grad is None:
grad = nd.empty(theta.shape, theta.context)
theta1 = theta.asnumpy()[0]
theta2 = theta.asnumpy()[1]
v1 = sigma1 ** 2
v2 = sigma2 ** 2
vx = sigmax ** ... | python | def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
"""Get synthetic gradient value"""
if grad is None:
grad = nd.empty(theta.shape, theta.context)
theta1 = theta.asnumpy()[0]
theta2 = theta.asnumpy()[1]
v1 = sigma1 ** 2
v2 = sigma2 ** 2
vx = sigmax ** ... | [
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23,702 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | get_toy_sym | def get_toy_sym(teacher=True, teacher_noise_precision=None):
"""Get toy symbol"""
if teacher:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='teacher_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='teacher_relu1', act_type="relu")
... | python | def get_toy_sym(teacher=True, teacher_noise_precision=None):
"""Get toy symbol"""
if teacher:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='teacher_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='teacher_relu1', act_type="relu")
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23,703 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_mnist_DistilledSGLD | def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
"""Run DistilledSGLD on mnist dataset"""
X, Y, X_test, Y_test = load_mnist(num_training)
minibatch_size = 100
if num_training >= 10000:
num_hidden = 800
total_iter_num = 1000000
teacher_learning_rate = 1E-6
stu... | python | def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
"""Run DistilledSGLD on mnist dataset"""
X, Y, X_test, Y_test = load_mnist(num_training)
minibatch_size = 100
if num_training >= 10000:
num_hidden = 800
total_iter_num = 1000000
teacher_learning_rate = 1E-6
stu... | [
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23,704 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_SGLD | def run_toy_SGLD(gpu_id=None):
"""Run SGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx... | python | def run_toy_SGLD(gpu_id=None):
"""Run SGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx... | [
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23,705 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_DistilledSGLD | def run_toy_DistilledSGLD(gpu_id):
"""Run DistilledSGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]... | python | def run_toy_DistilledSGLD(gpu_id):
"""Run DistilledSGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]... | [
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23,706 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_HMC | def run_toy_HMC(gpu_id=None):
"""Run HMC on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id... | python | def run_toy_HMC(gpu_id=None):
"""Run HMC on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id... | [
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23,707 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_synthetic_SGLD | def run_synthetic_SGLD():
"""Run synthetic SGLD"""
theta1 = 0
theta2 = 1
sigma1 = numpy.sqrt(10)
sigma2 = 1
sigmax = numpy.sqrt(2)
X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
minibatch_size = 1
total_iter_num = 1000000
lr_scheduler = SGLDScheduler(beg... | python | def run_synthetic_SGLD():
"""Run synthetic SGLD"""
theta1 = 0
theta2 = 1
sigma1 = numpy.sqrt(10)
sigma2 = 1
sigmax = numpy.sqrt(2)
X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
minibatch_size = 1
total_iter_num = 1000000
lr_scheduler = SGLDScheduler(beg... | [
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23,708 | apache/incubator-mxnet | example/ssd/tools/prepare_dataset.py | load_pascal | def load_pascal(image_set, year, devkit_path, shuffle=False):
"""
wrapper function for loading pascal voc dataset
Parameters:
----------
image_set : str
train, trainval...
year : str
2007, 2012 or combinations splitted by comma
devkit_path : str
root directory of dat... | python | def load_pascal(image_set, year, devkit_path, shuffle=False):
"""
wrapper function for loading pascal voc dataset
Parameters:
----------
image_set : str
train, trainval...
year : str
2007, 2012 or combinations splitted by comma
devkit_path : str
root directory of dat... | [
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23,709 | apache/incubator-mxnet | example/ssd/tools/prepare_dataset.py | load_coco | def load_coco(image_set, dirname, shuffle=False):
"""
wrapper function for loading ms coco dataset
Parameters:
----------
image_set : str
train2014, val2014, valminusminival2014, minival2014
dirname: str
root dir for coco
shuffle: boolean
initial shuffle
"""
... | python | def load_coco(image_set, dirname, shuffle=False):
"""
wrapper function for loading ms coco dataset
Parameters:
----------
image_set : str
train2014, val2014, valminusminival2014, minival2014
dirname: str
root dir for coco
shuffle: boolean
initial shuffle
"""
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23,710 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_transpose | def convert_transpose(net, node, module, builder):
"""Convert a transpose layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A... | python | def convert_transpose(net, node, module, builder):
"""Convert a transpose layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,711 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_flatten | def convert_flatten(net, node, module, builder):
"""Convert a flatten layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neu... | python | def convert_flatten(net, node, module, builder):
"""Convert a flatten layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,712 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_activation | def convert_activation(net, node, module, builder):
"""Convert an activation layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
... | python | def convert_activation(net, node, module, builder):
"""Convert an activation layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,713 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_leakyrelu | def convert_leakyrelu(net, node, module, builder):
"""Convert a leakyrelu layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A... | python | def convert_leakyrelu(net, node, module, builder):
"""Convert a leakyrelu layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,714 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_elementwise_add | def convert_elementwise_add(net, node, module, builder):
"""Convert an elementwise add layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuil... | python | def convert_elementwise_add(net, node, module, builder):
"""Convert an elementwise add layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuil... | [
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23,715 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_convolution | def convert_convolution(net, node, module, builder):
"""Convert a convolution layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
... | python | def convert_convolution(net, node, module, builder):
"""Convert a convolution layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,716 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_pooling | def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neu... | python | def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,717 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_batchnorm | def convert_batchnorm(net, node, module, builder):
"""Convert a batchnorm layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A... | python | def convert_batchnorm(net, node, module, builder):
"""Convert a batchnorm layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,718 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_concat | def convert_concat(net, node, module, builder):
"""Convert concat layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural ... | python | def convert_concat(net, node, module, builder):
"""Convert concat layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
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23,719 | apache/incubator-mxnet | tools/launch.py | dmlc_opts | def dmlc_opts(opts):
"""convert from mxnet's opts to dmlc's opts
"""
args = ['--num-workers', str(opts.num_workers),
'--num-servers', str(opts.num_servers),
'--cluster', opts.launcher,
'--host-file', opts.hostfile,
'--sync-dst-dir', opts.sync_dst_dir]
# c... | python | def dmlc_opts(opts):
"""convert from mxnet's opts to dmlc's opts
"""
args = ['--num-workers', str(opts.num_workers),
'--num-servers', str(opts.num_servers),
'--cluster', opts.launcher,
'--host-file', opts.hostfile,
'--sync-dst-dir', opts.sync_dst_dir]
# c... | [
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23,720 | apache/incubator-mxnet | python/mxnet/gluon/rnn/rnn_layer.py | _RNNLayer._unfuse | def _unfuse(self):
"""Unfuses the fused RNN in to a stack of rnn cells."""
assert not self._projection_size, "_unfuse does not support projection layer yet!"
assert not self._lstm_state_clip_min and not self._lstm_state_clip_max, \
"_unfuse does not support state clipping yet!"
... | python | def _unfuse(self):
"""Unfuses the fused RNN in to a stack of rnn cells."""
assert not self._projection_size, "_unfuse does not support projection layer yet!"
assert not self._lstm_state_clip_min and not self._lstm_state_clip_max, \
"_unfuse does not support state clipping yet!"
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23,721 | apache/incubator-mxnet | python/mxnet/gluon/rnn/rnn_layer.py | _RNNLayer._forward_kernel | def _forward_kernel(self, F, inputs, states, **kwargs):
""" forward using CUDNN or CPU kenrel"""
if self._layout == 'NTC':
inputs = F.swapaxes(inputs, dim1=0, dim2=1)
if self._projection_size is None:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
... | python | def _forward_kernel(self, F, inputs, states, **kwargs):
""" forward using CUDNN or CPU kenrel"""
if self._layout == 'NTC':
inputs = F.swapaxes(inputs, dim1=0, dim2=1)
if self._projection_size is None:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
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23,722 | apache/incubator-mxnet | example/distributed_training/cifar10_dist.py | evaluate_accuracy | def evaluate_accuracy(data_iterator, network):
""" Measure the accuracy of ResNet
Parameters
----------
data_iterator: Iter
examples of dataset
network:
ResNet
Returns
----------
tuple of array element
"""
acc = mx.metric.Accuracy()
# Iterate through data and l... | python | def evaluate_accuracy(data_iterator, network):
""" Measure the accuracy of ResNet
Parameters
----------
data_iterator: Iter
examples of dataset
network:
ResNet
Returns
----------
tuple of array element
"""
acc = mx.metric.Accuracy()
# Iterate through data and l... | [
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23,723 | apache/incubator-mxnet | example/distributed_training/cifar10_dist.py | train_batch | def train_batch(batch_list, context, network, gluon_trainer):
""" Training with multiple GPUs
Parameters
----------
batch_list: List
list of dataset
context: List
a list of all GPUs to be used for training
network:
ResNet
gluon_trainer:
rain module of gluon
"""
... | python | def train_batch(batch_list, context, network, gluon_trainer):
""" Training with multiple GPUs
Parameters
----------
batch_list: List
list of dataset
context: List
a list of all GPUs to be used for training
network:
ResNet
gluon_trainer:
rain module of gluon
"""
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23,724 | apache/incubator-mxnet | python/mxnet/contrib/tensorrt.py | get_optimized_symbol | def get_optimized_symbol(executor):
"""
Take an executor's underlying symbol graph and return its generated optimized version.
Parameters
----------
executor :
An executor for which you want to see an optimized symbol. Getting an optimized symbol
is useful to compare and verify the ... | python | def get_optimized_symbol(executor):
"""
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23,725 | apache/incubator-mxnet | python/mxnet/contrib/tensorrt.py | tensorrt_bind | def tensorrt_bind(symbol, ctx, all_params, type_dict=None, stype_dict=None, group2ctx=None,
**kwargs):
"""Bind current symbol to get an optimized trt executor.
Parameters
----------
symbol : Symbol
The symbol you wish to bind, and optimize with TensorRT.
ctx : Context
... | python | def tensorrt_bind(symbol, ctx, all_params, type_dict=None, stype_dict=None, group2ctx=None,
**kwargs):
"""Bind current symbol to get an optimized trt executor.
Parameters
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symbol : Symbol
The symbol you wish to bind, and optimize with TensorRT.
ctx : Context
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23,726 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.detect_iter | def detect_iter(self, det_iter, show_timer=False):
"""
detect all images in iterator
Parameters:
----------
det_iter : DetIter
iterator for all testing images
show_timer : Boolean
whether to print out detection exec time
Returns:
... | python | def detect_iter(self, det_iter, show_timer=False):
"""
detect all images in iterator
Parameters:
----------
det_iter : DetIter
iterator for all testing images
show_timer : Boolean
whether to print out detection exec time
Returns:
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23,727 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.im_detect | def im_detect(self, im_list, root_dir=None, extension=None, show_timer=False):
"""
wrapper for detecting multiple images
Parameters:
----------
im_list : list of str
image path or list of image paths
root_dir : str
directory of input images, optio... | python | def im_detect(self, im_list, root_dir=None, extension=None, show_timer=False):
"""
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im_list : list of str
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23,728 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.visualize_detection | def visualize_detection(self, img, dets, classes=[], thresh=0.6):
"""
visualize detections in one image
Parameters:
----------
img : numpy.array
image, in bgr format
dets : numpy.array
ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])
... | python | def visualize_detection(self, img, dets, classes=[], thresh=0.6):
"""
visualize detections in one image
Parameters:
----------
img : numpy.array
image, in bgr format
dets : numpy.array
ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])
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23,729 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.detect_and_visualize | def detect_and_visualize(self, im_list, root_dir=None, extension=None,
classes=[], thresh=0.6, show_timer=False):
"""
wrapper for im_detect and visualize_detection
Parameters:
----------
im_list : list of str or str
image path or list of ... | python | def detect_and_visualize(self, im_list, root_dir=None, extension=None,
classes=[], thresh=0.6, show_timer=False):
"""
wrapper for im_detect and visualize_detection
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----------
im_list : list of str or str
image path or list of ... | [
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23,730 | apache/incubator-mxnet | tools/caffe_converter/caffe_proto_utils.py | process_network_proto | def process_network_proto(caffe_root, deploy_proto):
"""
Runs the caffe upgrade tool on the prototxt to create a prototxt in the latest format.
This enable us to work just with latest structures, instead of supporting all the variants
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"""
Runs the caffe upgrade tool on the prototxt to create a prototxt in the latest format.
This enable us to work just with latest structures, instead of supporting all the variants
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23,731 | apache/incubator-mxnet | example/gluon/embedding_learning/model.py | get_distance | def get_distance(F, x):
"""Helper function for margin-based loss. Return a distance matrix given a matrix."""
n = x.shape[0]
square = F.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose()))
# Adding identity to make sqrt work.
retu... | python | def get_distance(F, x):
"""Helper function for margin-based loss. Return a distance matrix given a matrix."""
n = x.shape[0]
square = F.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose()))
# Adding identity to make sqrt work.
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23,732 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | cross_entropy_loss | def cross_entropy_loss(inputs, labels, rescale_loss=1):
""" cross entropy loss with a mask """
criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss)
loss = criterion(inputs, labels)
mask = S.var('mask')
loss = loss * S.reshape(mask, shape=(-1,))
return S.make_loss(loss.mean()) | python | def cross_entropy_loss(inputs, labels, rescale_loss=1):
""" cross entropy loss with a mask """
criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss)
loss = criterion(inputs, labels)
mask = S.var('mask')
loss = loss * S.reshape(mask, shape=(-1,))
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23,733 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | rnn | def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size):
""" word embedding + LSTM Projected """
state_names = []
data = S.var('data')
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embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size,
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""" word embedding + LSTM Projected """
state_names = []
data = S.var('data')
weight = S.var("encoder_weight", stype='row_sparse')
embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size,
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23,734 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | sampled_softmax | def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias,
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""" Sampled softmax via importance sampling.
This under-estimates the full softmax and is only used for training.
"""
# inputs = (n, in_dim)
... | python | def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias,
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""" Sampled softmax via importance sampling.
This under-estimates the full softmax and is only used for training.
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23,735 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | generate_samples | def generate_samples(label, num_splits, sampler):
""" Split labels into `num_splits` and
generate candidates based on log-uniform distribution.
"""
def listify(x):
return x if isinstance(x, list) else [x]
label_splits = listify(label.split(num_splits, axis=0))
prob_samples = []
p... | python | def generate_samples(label, num_splits, sampler):
""" Split labels into `num_splits` and
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"""
def listify(x):
return x if isinstance(x, list) else [x]
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23,736 | apache/incubator-mxnet | python/mxnet/gluon/model_zoo/vision/__init__.py | get_model | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default ... | python | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default ... | [
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23,737 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _new_alloc_handle | def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None):
"""Return a new handle with specified storage type, shape, dtype and context.
Empty handle is only used to hold results
Returns
-------
handle
A new empty ndarray handle
"""
hdl = NDArrayHandl... | python | def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None):
"""Return a new handle with specified storage type, shape, dtype and context.
Empty handle is only used to hold results
Returns
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handle
A new empty ndarray handle
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23,738 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _prepare_src_array | def _prepare_src_array(source_array, dtype):
"""Prepare `source_array` so that it can be used to construct NDArray.
`source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \
nor an `np.ndarray`.
"""
if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarr... | python | def _prepare_src_array(source_array, dtype):
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`source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \
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23,739 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _prepare_default_dtype | def _prepare_default_dtype(src_array, dtype):
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23,740 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _check_shape | def _check_shape(s1, s2):
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23,741 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _csr_matrix_from_definition | def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None,
dtype=None, indices_type=None, indptr_type=None):
"""Create a `CSRNDArray` based on data, indices and indptr"""
# pylint: disable= no-member, protected-access
storage_type = 'csr'
# context
c... | python | def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None,
dtype=None, indices_type=None, indptr_type=None):
"""Create a `CSRNDArray` based on data, indices and indptr"""
# pylint: disable= no-member, protected-access
storage_type = 'csr'
# context
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23,742 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | row_sparse_array | def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
to construct a RowSparseNDArray wi... | python | def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
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23,743 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _row_sparse_ndarray_from_definition | def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None,
dtype=None, indices_type=None):
"""Create a `RowSparseNDArray` based on data and indices"""
storage_type = 'row_sparse'
# context
ctx = current_context() if ctx is None else ctx
# typ... | python | def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None,
dtype=None, indices_type=None):
"""Create a `RowSparseNDArray` based on data and indices"""
storage_type = 'row_sparse'
# context
ctx = current_context() if ctx is None else ctx
# typ... | [
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23,744 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | array | def array(source_array, ctx=None, dtype=None):
"""Creates a sparse array from any object exposing the array interface.
Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
ctx : Context, optional
The default context... | python | def array(source_array, ctx=None, dtype=None):
"""Creates a sparse array from any object exposing the array interface.
Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
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23,745 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_type | def _aux_type(self, i):
"""Data-type of the array's ith aux data.
Returns
-------
numpy.dtype
This BaseSparseNDArray's aux data type.
"""
aux_type = ctypes.c_int()
check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type)))
re... | python | def _aux_type(self, i):
"""Data-type of the array's ith aux data.
Returns
-------
numpy.dtype
This BaseSparseNDArray's aux data type.
"""
aux_type = ctypes.c_int()
check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type)))
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23,746 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_types | def _aux_types(self):
"""The data types of the aux data for the BaseSparseNDArray.
"""
aux_types = []
num_aux = self._num_aux
for i in range(num_aux):
aux_types.append(self._aux_type(i))
return aux_types | python | def _aux_types(self):
"""The data types of the aux data for the BaseSparseNDArray.
"""
aux_types = []
num_aux = self._num_aux
for i in range(num_aux):
aux_types.append(self._aux_type(i))
return aux_types | [
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23,747 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray.astype | def astype(self, dtype, copy=True):
"""Return a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
... | python | def astype(self, dtype, copy=True):
"""Return a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
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23,748 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray.check_format | def check_format(self, full_check=True):
"""Check whether the NDArray format is valid.
Parameters
----------
full_check : bool, optional
If `True`, rigorous check, O(N) operations. Otherwise
basic check, O(1) operations (default True).
"""
check_c... | python | def check_format(self, full_check=True):
"""Check whether the NDArray format is valid.
Parameters
----------
full_check : bool, optional
If `True`, rigorous check, O(N) operations. Otherwise
basic check, O(1) operations (default True).
"""
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23,749 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._data | def _data(self):
"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.by... | python | def _data(self):
"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
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23,750 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_data | def _aux_data(self, i):
""" Get a deep copy NDArray of the i-th aux data array associated with the
BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetAuxND... | python | def _aux_data(self, i):
""" Get a deep copy NDArray of the i-th aux data array associated with the
BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
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23,751 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | CSRNDArray.asscipy | def asscipy(self):
"""Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array
Examples
--------
>>> x = mx.nd.sparse.zeros('csr', (2,3))
>>> y = x.asscipy()
>>> type(y)
<type 'scipy.sparse.csr.csr_matrix'>
>>> y
<2x3 spa... | python | def asscipy(self):
"""Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array
Examples
--------
>>> x = mx.nd.sparse.zeros('csr', (2,3))
>>> y = x.asscipy()
>>> type(y)
<type 'scipy.sparse.csr.csr_matrix'>
>>> y
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23,752 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | RowSparseNDArray.tostype | def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or RowSparseNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'csr':
... | python | def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or RowSparseNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'csr':
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23,753 | apache/incubator-mxnet | benchmark/python/sparse/memory_benchmark.py | bench_dot | def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
rhs_density, dot_func, trans_lhs, lhs_stype,
rhs_stype, only_storage, distribution="uniform"):
""" Benchmarking both storage and dot
"""
lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distributio... | python | def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
rhs_density, dot_func, trans_lhs, lhs_stype,
rhs_stype, only_storage, distribution="uniform"):
""" Benchmarking both storage and dot
"""
lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distributio... | [
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23,754 | apache/incubator-mxnet | tools/caffe_converter/convert_mean.py | convert_mean | def convert_mean(binaryproto_fname, output=None):
"""Convert caffe mean
Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray
"""
mean_blob = ca... | python | def convert_mean(binaryproto_fname, output=None):
"""Convert caffe mean
Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray
"""
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Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray | [
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23,755 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | import_module | def import_module(module_name):
"""Helper function to import module"""
import sys, os
import importlib
sys.path.append(os.path.dirname(__file__))
return importlib.import_module(module_name) | python | def import_module(module_name):
"""Helper function to import module"""
import sys, os
import importlib
sys.path.append(os.path.dirname(__file__))
return importlib.import_module(module_name) | [
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23,756 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | get_symbol_train | def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads,
sizes, ratios, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network symbol for training SSD
Parameters
------... | python | def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads,
sizes, ratios, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
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Parameters
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23,757 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | get_symbol | def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios,
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nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network for testing SSD
Parameters
----------
network : str
... | python | def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios,
strides, pads, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
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Parameters
----------
network : str
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23,758 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_conv_out_grad | def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None):
"""Get the output and gradients of output of a convolutional layer.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: i... | python | def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None):
"""Get the output and gradients of output of a convolutional layer.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
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23,759 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_image_grad | def get_image_grad(net, image, class_id=None):
"""Get the gradients of the image.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provi... | python | def get_image_grad(net, image, class_id=None):
"""Get the gradients of the image.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provi... | [
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Preprocessed image to use for visualization.
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23,760 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | grad_to_image | def grad_to_image(gradient):
"""Convert gradients of image obtained using `get_image_grad`
into image. This shows parts of the image that is most strongly activating
the output neurons."""
gradient = gradient - gradient.min()
gradient /= gradient.max()
gradient = np.uint8(gradient * 255).transpo... | python | def grad_to_image(gradient):
"""Convert gradients of image obtained using `get_image_grad`
into image. This shows parts of the image that is most strongly activating
the output neurons."""
gradient = gradient - gradient.min()
gradient /= gradient.max()
gradient = np.uint8(gradient * 255).transpo... | [
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23,761 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_img_heatmap | def get_img_heatmap(orig_img, activation_map):
"""Draw a heatmap on top of the original image using intensities from activation_map"""
heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
img_heatmap = np.float32(heatmap) + np.float32(orig_img... | python | def get_img_heatmap(orig_img, activation_map):
"""Draw a heatmap on top of the original image using intensities from activation_map"""
heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
img_heatmap = np.float32(heatmap) + np.float32(orig_img... | [
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23,762 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | to_grayscale | def to_grayscale(cv2im):
"""Convert gradients to grayscale. This gives a saliency map."""
# How strongly does each position activate the output
grayscale_im = np.sum(np.abs(cv2im), axis=0)
# Normalize between min and 99th percentile
im_max = np.percentile(grayscale_im, 99)
im_min = np.min(grays... | python | def to_grayscale(cv2im):
"""Convert gradients to grayscale. This gives a saliency map."""
# How strongly does each position activate the output
grayscale_im = np.sum(np.abs(cv2im), axis=0)
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im_max = np.percentile(grayscale_im, 99)
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23,763 | apache/incubator-mxnet | python/mxnet/metric.py | check_label_shapes | def check_label_shapes(labels, preds, wrap=False, shape=False):
"""Helper function for checking shape of label and prediction
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
wrap : boolean
If True, wr... | python | def check_label_shapes(labels, preds, wrap=False, shape=False):
"""Helper function for checking shape of label and prediction
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
wrap : boolean
If True, wr... | [
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23,764 | apache/incubator-mxnet | python/mxnet/metric.py | create | def create(metric, *args, **kwargs):
"""Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
- Name of a met... | python | def create(metric, *args, **kwargs):
"""Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
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23,765 | apache/incubator-mxnet | python/mxnet/metric.py | np | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy a... | python | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
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23,766 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.update_dict | def update_dict(self, label, pred):
"""Update the internal evaluation with named label and pred
Parameters
----------
labels : OrderedDict of str -> NDArray
name to array mapping for labels.
preds : OrderedDict of str -> NDArray
name to array mapping of ... | python | def update_dict(self, label, pred):
"""Update the internal evaluation with named label and pred
Parameters
----------
labels : OrderedDict of str -> NDArray
name to array mapping for labels.
preds : OrderedDict of str -> NDArray
name to array mapping of ... | [
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23,767 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.reset | def reset(self):
"""Resets the internal evaluation result to initial state."""
self.num_inst = 0
self.sum_metric = 0.0
self.global_num_inst = 0
self.global_sum_metric = 0.0 | python | def reset(self):
"""Resets the internal evaluation result to initial state."""
self.num_inst = 0
self.sum_metric = 0.0
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self.global_sum_metric = 0.0 | [
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23,768 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get | def get(self):
"""Gets the current evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self.num_inst == 0:
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"""Gets the current evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self.num_inst == 0:
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23,769 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_global | def get_global(self):
"""Gets the current global evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
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"""Gets the current global evaluation result.
Returns
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Name of the metrics.
values : list of float
Value of the evaluations.
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23,770 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_name_value | def get_name_value(self):
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A (name, value) tuple list.
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23,771 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_global_name_value | def get_global_name_value(self):
"""Returns zipped name and value pairs for global results.
Returns
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A (name, value) tuple list.
"""
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name, value = self.get_global()
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"""Returns zipped name and value pairs for global results.
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23,772 | apache/incubator-mxnet | python/mxnet/metric.py | _BinaryClassificationMetrics.matthewscc | def matthewscc(self, use_global=False):
"""
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"""
if use_global:
if not self.global_total_examples:
return 0.
true_pos = float(self.global_true_positives)
false_pos = float(self.global_false... | python | def matthewscc(self, use_global=False):
"""
Calculate the Matthew's Correlation Coefficent
"""
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if not self.global_total_examples:
return 0.
true_pos = float(self.global_true_positives)
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23,773 | apache/incubator-mxnet | python/mxnet/gluon/data/dataset.py | Dataset.transform | def transform(self, fn, lazy=True):
"""Returns a new dataset with each sample transformed by the
transformer function `fn`.
Parameters
----------
fn : callable
A transformer function that takes a sample as input and
returns the transformed sample.
... | python | def transform(self, fn, lazy=True):
"""Returns a new dataset with each sample transformed by the
transformer function `fn`.
Parameters
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fn : callable
A transformer function that takes a sample as input and
returns the transformed sample.
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23,774 | apache/incubator-mxnet | example/ctc/ocr_predict.py | lstm_ocr_model.forward_ocr | def forward_ocr(self, img_):
"""Forward the image through the LSTM network model
Parameters
----------
img_: int of array
Returns
----------
label_list: string of list
"""
img_ = cv2.resize(img_, (80, 30))
img_ = img_.transpose(1, 0)
... | python | def forward_ocr(self, img_):
"""Forward the image through the LSTM network model
Parameters
----------
img_: int of array
Returns
----------
label_list: string of list
"""
img_ = cv2.resize(img_, (80, 30))
img_ = img_.transpose(1, 0)
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23,775 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | read_prototxt | def read_prototxt(fname):
"""Return a caffe_pb2.NetParameter object that defined in a prototxt file
"""
proto = caffe_pb2.NetParameter()
with open(fname, 'r') as f:
text_format.Merge(str(f.read()), proto)
return proto | python | def read_prototxt(fname):
"""Return a caffe_pb2.NetParameter object that defined in a prototxt file
"""
proto = caffe_pb2.NetParameter()
with open(fname, 'r') as f:
text_format.Merge(str(f.read()), proto)
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23,776 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | get_layers | def get_layers(proto):
"""Returns layers in a caffe_pb2.NetParameter object
"""
if len(proto.layer):
return proto.layer
elif len(proto.layers):
return proto.layers
else:
raise ValueError('Invalid proto file.') | python | def get_layers(proto):
"""Returns layers in a caffe_pb2.NetParameter object
"""
if len(proto.layer):
return proto.layer
elif len(proto.layers):
return proto.layers
else:
raise ValueError('Invalid proto file.') | [
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23,777 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | read_caffemodel | def read_caffemodel(prototxt_fname, caffemodel_fname):
"""Return a caffe_pb2.NetParameter object that defined in a binary
caffemodel file
"""
if use_caffe:
caffe.set_mode_cpu()
net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST)
layer_names = net._layer_names
la... | python | def read_caffemodel(prototxt_fname, caffemodel_fname):
"""Return a caffe_pb2.NetParameter object that defined in a binary
caffemodel file
"""
if use_caffe:
caffe.set_mode_cpu()
net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST)
layer_names = net._layer_names
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23,778 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | layer_iter | def layer_iter(layers, layer_names):
"""Iterate over all layers"""
if use_caffe:
for layer_idx, layer in enumerate(layers):
layer_name = re.sub('[-/]', '_', layer_names[layer_idx])
layer_type = layer.type
layer_blobs = layer.blobs
yield (layer_name, layer_... | python | def layer_iter(layers, layer_names):
"""Iterate over all layers"""
if use_caffe:
for layer_idx, layer in enumerate(layers):
layer_name = re.sub('[-/]', '_', layer_names[layer_idx])
layer_type = layer.type
layer_blobs = layer.blobs
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23,779 | apache/incubator-mxnet | python/mxnet/profiler.py | set_state | def set_state(state='stop', profile_process='worker'):
"""Set up the profiler state to 'run' or 'stop'.
Parameters
----------
state : string, optional
Indicates whether to run the profiler, can
be 'stop' or 'run'. Default is `stop`.
profile_process : string
whether to profil... | python | def set_state(state='stop', profile_process='worker'):
"""Set up the profiler state to 'run' or 'stop'.
Parameters
----------
state : string, optional
Indicates whether to run the profiler, can
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23,780 | apache/incubator-mxnet | python/mxnet/profiler.py | dump | def dump(finished=True, profile_process='worker'):
"""Dump profile and stop profiler. Use this to save profile
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Parameters
----------
finished : boolean
Indicates whether to stop statistic output (dumping) after this dump.
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"""Dump profile and stop profiler. Use this to save profile
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finished : boolean
Indicates whether to stop statistic output (dumping) after this dump.
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23,781 | apache/incubator-mxnet | python/mxnet/profiler.py | dumps | def dumps(reset=False):
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reset: boolean
Indicates whether to clean aggeregate statistical data collected up to this point
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"""Return a printable string of aggregate profile stats.
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reset: boolean
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23,782 | apache/incubator-mxnet | python/mxnet/profiler.py | pause | def pause(profile_process='worker'):
"""Pause profiling.
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profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
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profile_proc... | python | def pause(profile_process='worker'):
"""Pause profiling.
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profile_process : string
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23,783 | apache/incubator-mxnet | python/mxnet/profiler.py | resume | def resume(profile_process='worker'):
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server can only be profiled when kvstore is of type dist.
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23,784 | apache/incubator-mxnet | python/mxnet/profiler.py | Counter.set_value | def set_value(self, value):
"""Set counter value.
Parameters
----------
value : int
Value for the counter
"""
check_call(_LIB.MXProfileSetCounter(self.handle, int(value))) | python | def set_value(self, value):
"""Set counter value.
Parameters
----------
value : int
Value for the counter
"""
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23,785 | apache/incubator-mxnet | python/mxnet/profiler.py | Counter.decrement | def decrement(self, delta=1):
"""Decrement counter value.
Parameters
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Amount by which to subtract from the counter
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check_call(_LIB.MXProfileAdjustCounter(self.handle, -int(delta))) | python | def decrement(self, delta=1):
"""Decrement counter value.
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value_change : int
Amount by which to subtract from the counter
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23,786 | apache/incubator-mxnet | python/mxnet/profiler.py | Marker.mark | def mark(self, scope='process'):
"""Set up the profiler state to record operator.
Parameters
----------
scope : string, optional
Indicates what scope the marker should refer to.
Can be 'global', 'process', thread', task', and 'marker'
Default is `proc... | python | def mark(self, scope='process'):
"""Set up the profiler state to record operator.
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Indicates what scope the marker should refer to.
Can be 'global', 'process', thread', task', and 'marker'
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23,787 | apache/incubator-mxnet | python/mxnet/rtc.py | CudaModule.get_kernel | def get_kernel(self, name, signature):
r"""Get CUDA kernel from compiled module.
Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
... | python | def get_kernel(self, name, signature):
r"""Get CUDA kernel from compiled module.
Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
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23,788 | apache/incubator-mxnet | python/mxnet/rtc.py | CudaKernel.launch | def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0):
"""Launch cuda kernel.
Parameters
----------
args : tuple of NDArray or numbers
List of arguments for kernel. NDArrays are expected for pointer
types (e.g. `float*`, `double*`) while numbers are ex... | python | def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0):
"""Launch cuda kernel.
Parameters
----------
args : tuple of NDArray or numbers
List of arguments for kernel. NDArrays are expected for pointer
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23,789 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric.reset | def reset(self):
"""Clear the internal statistics to initial state."""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num
self.records = dic... | python | def reset(self):
"""Clear the internal statistics to initial state."""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num
self.records = dic... | [
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23,790 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._update | def _update(self):
""" update num_inst and sum_metric """
aps = []
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recall, prec = self._recall_prec(v, self.counts[k])
ap = self._average_precision(recall, prec)
aps.append(ap)
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""" update num_inst and sum_metric """
aps = []
for k, v in self.records.items():
recall, prec = self._recall_prec(v, self.counts[k])
ap = self._average_precision(recall, prec)
aps.append(ap)
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23,791 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._recall_prec | def _recall_prec(self, record, count):
""" get recall and precision from internal records """
record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0)
sorted_records = record[record[:,0].argsort()[::-1]]
tp = np.cumsum(sorted_records[:, 1].astype(int) == 1)
... | python | def _recall_prec(self, record, count):
""" get recall and precision from internal records """
record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0)
sorted_records = record[record[:,0].argsort()[::-1]]
tp = np.cumsum(sorted_records[:, 1].astype(int) == 1)
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23,792 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._average_precision | def _average_precision(self, rec, prec):
"""
calculate average precision
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
# app... | python | def _average_precision(self, rec, prec):
"""
calculate average precision
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
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ap as float
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23,793 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._insert | def _insert(self, key, records, count):
""" Insert records according to key """
if key not in self.records:
assert key not in self.counts
self.records[key] = records
self.counts[key] = count
else:
self.records[key] = np.vstack((self.records[key], r... | python | def _insert(self, key, records, count):
""" Insert records according to key """
if key not in self.records:
assert key not in self.counts
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self.counts[key] = count
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23,794 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | VOC07MApMetric._average_precision | def _average_precision(self, rec, prec):
"""
calculate average precision, override the default one,
special 11-point metric
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
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rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
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23,795 | apache/incubator-mxnet | python/mxnet/initializer.py | Initializer._verbose_print | def _verbose_print(self, desc, init, arr):
"""Internal verbose print function
Parameters
----------
desc : InitDesc or str
name of the array
init : str
initializer pattern
arr : NDArray
initialized array
"""
if self._ve... | python | def _verbose_print(self, desc, init, arr):
"""Internal verbose print function
Parameters
----------
desc : InitDesc or str
name of the array
init : str
initializer pattern
arr : NDArray
initialized array
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23,796 | apache/incubator-mxnet | python/mxnet/initializer.py | Initializer._legacy_init | def _legacy_init(self, name, arr):
"""Legacy initialization method.
Parameters
----------
name : str
Name of corresponding NDArray.
arr : NDArray
NDArray to be initialized.
"""
warnings.warn(
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"""Legacy initialization method.
Parameters
----------
name : str
Name of corresponding NDArray.
arr : NDArray
NDArray to be initialized.
"""
warnings.warn(
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23,797 | apache/incubator-mxnet | example/ssd/dataset/imdb.py | Imdb.save_imglist | def save_imglist(self, fname=None, root=None, shuffle=False):
"""
save imglist to disk
Parameters:
----------
fname : str
saved filename
"""
def progress_bar(count, total, suffix=''):
import sys
bar_len = 24
filled_... | python | def save_imglist(self, fname=None, root=None, shuffle=False):
"""
save imglist to disk
Parameters:
----------
fname : str
saved filename
"""
def progress_bar(count, total, suffix=''):
import sys
bar_len = 24
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23,798 | apache/incubator-mxnet | example/ssd/dataset/imdb.py | Imdb._load_class_names | def _load_class_names(self, filename, dirname):
"""
load class names from text file
Parameters:
----------
filename: str
file stores class names
dirname: str
file directory
"""
full_path = osp.join(dirname, filename)
classe... | python | def _load_class_names(self, filename, dirname):
"""
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Parameters:
----------
filename: str
file stores class names
dirname: str
file directory
"""
full_path = osp.join(dirname, filename)
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23,799 | apache/incubator-mxnet | example/image-classification/train_mnist.py | read_data | def read_data(label, image):
"""
download and read data into numpy
"""
base_url = 'http://yann.lecun.com/exdb/mnist/'
with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl:
magic, num = struct.unpack(">II", flbl.read(8))
label = np.fromstring(flbl.read(), ... | python | def read_data(label, image):
"""
download and read data into numpy
"""
base_url = 'http://yann.lecun.com/exdb/mnist/'
with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl:
magic, num = struct.unpack(">II", flbl.read(8))
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