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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.hard_reset | def hard_reset(self):
"""Ignore roll over data and set to start."""
if self.shuffle:
self._shuffle_data()
self.cursor = -self.batch_size
self._cache_data = None
self._cache_label = None | python | def hard_reset(self):
"""Ignore roll over data and set to start."""
if self.shuffle:
self._shuffle_data()
self.cursor = -self.batch_size
self._cache_data = None
self._cache_label = None | [
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.reset | def reset(self):
"""Resets the iterator to the beginning of the data."""
if self.shuffle:
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# the range below indicate the last batch
if self.last_batch_handle == 'roll_over' and \
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... | python | def reset(self):
"""Resets the iterator to the beginning of the data."""
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self._shuffle_data()
# the range below indicate the last batch
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.iter_next | def iter_next(self):
"""Increments the coursor by batch_size for next batch
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self.cursor += self.batch_size
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"""Increments the coursor by batch_size for next batch
and check current cursor if it exceed the number of data points."""
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.next | def next(self):
"""Returns the next batch of data."""
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label = self.getlabel()
# iter should stop when last batch is not complete
if data[0].shape[0] != self.batch_size:
# in this case, ... | python | def next(self):
"""Returns the next batch of data."""
if not self.iter_next():
raise StopIteration
data = self.getdata()
label = self.getlabel()
# iter should stop when last batch is not complete
if data[0].shape[0] != self.batch_size:
# in this case, ... | [
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._getdata | def _getdata(self, data_source, start=None, end=None):
"""Load data from underlying arrays."""
assert start is not None or end is not None, 'should at least specify start or end'
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"""Load data from underlying arrays."""
assert start is not None or end is not None, 'should at least specify start or end'
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._concat | def _concat(self, first_data, second_data):
"""Helper function to concat two NDArrays."""
assert len(first_data) == len(
second_data), 'data source should contain the same size'
if first_data and second_data:
return [
concat(
first_data... | python | def _concat(self, first_data, second_data):
"""Helper function to concat two NDArrays."""
assert len(first_data) == len(
second_data), 'data source should contain the same size'
if first_data and second_data:
return [
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._batchify | def _batchify(self, data_source):
"""Load data from underlying arrays, internal use only."""
assert self.cursor < self.num_data, 'DataIter needs reset.'
# first batch of next epoch with 'roll_over'
if self.last_batch_handle == 'roll_over' and \
-self.batch_size < self.cursor ... | python | def _batchify(self, data_source):
"""Load data from underlying arrays, internal use only."""
assert self.cursor < self.num_data, 'DataIter needs reset.'
# first batch of next epoch with 'roll_over'
if self.last_batch_handle == 'roll_over' and \
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter.getpad | def getpad(self):
"""Get pad value of DataBatch."""
if self.last_batch_handle == 'pad' and \
self.cursor + self.batch_size > self.num_data:
return self.cursor + self.batch_size - self.num_data
# check the first batch
elif self.last_batch_handle == 'roll_over' and \... | python | def getpad(self):
"""Get pad value of DataBatch."""
if self.last_batch_handle == 'pad' and \
self.cursor + self.batch_size > self.num_data:
return self.cursor + self.batch_size - self.num_data
# check the first batch
elif self.last_batch_handle == 'roll_over' and \... | [
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apache/incubator-mxnet | python/mxnet/io/io.py | NDArrayIter._shuffle_data | def _shuffle_data(self):
"""Shuffle the data."""
# shuffle index
np.random.shuffle(self.idx)
# get the data by corresponding index
self.data = _getdata_by_idx(self.data, self.idx)
self.label = _getdata_by_idx(self.label, self.idx) | python | def _shuffle_data(self):
"""Shuffle the data."""
# shuffle index
np.random.shuffle(self.idx)
# get the data by corresponding index
self.data = _getdata_by_idx(self.data, self.idx)
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _quantize_params | def _quantize_params(qsym, params, th_dict):
"""Given a quantized symbol and a dict of params that have not been quantized,
generate quantized params. Currently only supports quantizing the arg_params
with names of `weight` or `bias`, not aux_params. If `qsym` contains symbols
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _quantize_symbol | def _quantize_symbol(sym, excluded_symbols=None, offline_params=None, quantized_dtype='int8'):
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quantize it into a INT8 network.
Parameters
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sym : Symbol
FP32 neural network symbol.
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Parameters
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sym : Symbol
FP32 neural network symbol.
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _calibrate_quantized_sym | def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
return qsym
num_layer_outputs = len(th_dict... | python | def _calibrate_quantized_sym(qsym, th_dict):
"""Given a dictionary containing the thresholds for quantizing the layers,
set the thresholds into the quantized symbol as the params of requantize operators.
"""
if th_dict is None or len(th_dict) == 0:
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _collect_layer_output_min_max | def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
"""Collect min and max values from layer outputs and save them in
a dictionary mapped by layer names.
"""
collector = _LayerOutputMinMaxCollector(include_layer=include_... | python | def _collect_layer_output_min_max(mod, data, include_layer=None,
max_num_examples=None, logger=None):
"""Collect min and max values from layer outputs and save them in
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"""
collector = _LayerOutputMinMaxCollector(include_layer=include_... | [
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _collect_layer_outputs | def _collect_layer_outputs(mod, data, include_layer=None, max_num_examples=None, logger=None):
"""Collect layer outputs and save them in a dictionary mapped by layer names."""
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
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"""Collect layer outputs and save them in a dictionary mapped by layer names."""
collector = _LayerOutputCollector(include_layer=include_layer, logger=logger)
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _smooth_distribution | def _smooth_distribution(p, eps=0.0001):
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Ref: http://web.engr.illinois.edu/~hanj/cs412/bk3/KL-divergence... | python | def _smooth_distribution(p, eps=0.0001):
"""Given a discrete distribution (may have not been normalized to 1),
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _get_optimal_threshold | def _get_optimal_threshold(arr, quantized_dtype, num_bins=8001, num_quantized_bins=255):
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _get_optimal_thresholds | def _get_optimal_thresholds(nd_dict, quantized_dtype, num_bins=8001, num_quantized_bins=255, logger=None):
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _load_sym | def _load_sym(sym, logger=logging):
"""Given a str as a path the symbol .json file or a symbol, returns a Symbol object."""
if isinstance(sym, str): # sym is a symbol file path
cur_path = os.path.dirname(os.path.realpath(__file__))
symbol_file_path = os.path.join(cur_path, sym)
logger.i... | python | def _load_sym(sym, logger=logging):
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if isinstance(sym, str): # sym is a symbol file path
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _load_params | def _load_params(params, logger=logging):
"""Given a str as a path to the .params file or a pair of params,
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"""
if isinstance(params, str):
cur_path = os.path.dirname(os.path.realpath(__file__))
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"""Given a str as a path to the .params file or a pair of params,
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | quantize_model | def quantize_model(sym, arg_params, aux_params,
data_names=('data',), label_names=('softmax_label',),
ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
calib_data=None, num_calib_examples=None, calib_layer=None,
quantized_dtype='int8', ... | python | def quantize_model(sym, arg_params, aux_params,
data_names=('data',), label_names=('softmax_label',),
ctx=cpu(), excluded_sym_names=None, calib_mode='entropy',
calib_data=None, num_calib_examples=None, calib_layer=None,
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _LayerOutputCollector.collect | def collect(self, name, arr):
"""Callback function for collecting layer output NDArrays."""
name = py_str(name)
if self.include_layer is not None and not self.include_layer(name):
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handle = ctypes.cast(arr, NDArrayHandle)
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name = py_str(name)
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apache/incubator-mxnet | python/mxnet/contrib/quantization.py | _LayerOutputMinMaxCollector.collect | def collect(self, name, arr):
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name = py_str(name)
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handle = ctypes.cast(arr, NDArrayHandle)
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | encoder | def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''The encoder is a CNN which takes 32x32 image as input
generates the 100 dimensional shape embedding as a sample from normal distribution
using predicted meand and variance
'''
BatchNorm = mx.sym.BatchNorm
da... | python | def encoder(nef, z_dim, batch_size, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
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generates the 100 dimensional shape embedding as a sample from normal distribution
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BatchNorm = mx.sym.BatchNorm
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | generator | def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
'''The genrator is a CNN which takes 100 dimensional embedding as input
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BatchNorm = mx.sym.BatchNorm
rand = mx.sym.Variable('rand')
... | python | def generator(ngf, nc, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12, z_dim=100, activation='sigmoid'):
'''The genrator is a CNN which takes 100 dimensional embedding as input
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BatchNorm = mx.sym.BatchNorm
rand = mx.sym.Variable('rand')
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | discriminator1 | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
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BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
d1 ... | python | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | discriminator2 | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
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... | python | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | GaussianLogDensity | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
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'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
c = mx.symbol.log(c)
var = mx.sym.exp(log_var)
x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di... | python | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
'''GaussianLogDensity loss calculation for layer wise loss
'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
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var = mx.sym.exp(log_var)
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | DiscriminatorLayerLoss | def DiscriminatorLayerLoss():
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'''
data = mx.sym.Variable('data')
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data = mx.sym.Flatten(data)
label = mx.sym.Flatten(label)
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output = -Gaussi... | python | def DiscriminatorLayerLoss():
'''Calculate the discriminator layer loss
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di... | python | def KLDivergenceLoss():
'''KLDivergenceLoss loss
'''
data = mx.sym.Variable('data')
mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0)
mu2 = mx.sym.zeros_like(mu1)
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | get_data | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
data = np... | python | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | fill_buf | def fill_buf(buf, i, img, shape):
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buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image'''
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | visual | def visual(title, X, activation):
'''create a grid of images and save it as a final image
title : grid image name
X : array of images
'''
assert len(X.shape) == 4
X = X.transpose((0, 2, 3, 1))
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X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
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'''create a grid of images and save it as a final image
title : grid image name
X : array of images
'''
assert len(X.shape) == 4
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | train | def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every):
'''adversarial training of the VAE
'''
#encoder
z_mu, z_lv, z = encoder(nef,... | python | def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every):
'''adversarial training of the VAE
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#encoder
z_mu, z_lv, z = encoder(nef,... | [
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | create_and_validate_dir | def create_and_validate_dir(data_dir):
'''Creates/Validates dir
'''
if data_dir != "":
if not os.path.exists(data_dir):
try:
logging.info('create directory %s', data_dir)
os.makedirs(data_dir)
except OSError as exc:
if exc.errno... | python | def create_and_validate_dir(data_dir):
'''Creates/Validates dir
'''
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apache/incubator-mxnet | example/vae-gan/vaegan_mxnet.py | parse_args | def parse_args():
'''Parse args
'''
parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder')
parser.add_argument('--train', help='train the network', action='store_true')
parser.add_argument('--test', help='test the network', action='store_true')
parser... | python | def parse_args():
'''Parse args
'''
parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder')
parser.add_argument('--train', help='train the network', action='store_true')
parser.add_argument('--test', help='test the network', action='store_true')
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apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | get_rmse_log | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu... | python | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
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apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | get_net | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
return net | python | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
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apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | train | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
shuffle... | python | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
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apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | k_fold_cross_valid | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
learning_rate, weight_decay, batch_size):
"""Conducts k-fold cross validation for the model."""
assert k > 1
fold_size = X_train.shape[0] // k
train_loss_sum = 0.0
test_loss_sum = 0.0
for test_idx in range... | python | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
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"""Conducts k-fold cross validation for the model."""
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fold_size = X_train.shape[0] // k
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apache/incubator-mxnet | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | learn | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
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net = get_net()
_ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
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preds =... | python | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
weight_decay, batch_size):
"""Trains the model and predicts on the test data set."""
net = get_net()
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apache/incubator-mxnet | example/capsnet/capsulenet.py | capsnet | def capsnet(batch_size, n_class, num_routing, recon_loss_weight):
"""Create CapsNet"""
# data.shape = [batch_size, 1, 28, 28]
data = mx.sym.Variable('data')
input_shape = (1, 28, 28)
# Conv2D layer
# net.shape = [batch_size, 256, 20, 20]
conv1 = mx.sym.Convolution(data=data,
... | python | def capsnet(batch_size, n_class, num_routing, recon_loss_weight):
"""Create CapsNet"""
# data.shape = [batch_size, 1, 28, 28]
data = mx.sym.Variable('data')
input_shape = (1, 28, 28)
# Conv2D layer
# net.shape = [batch_size, 256, 20, 20]
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apache/incubator-mxnet | example/capsnet/capsulenet.py | do_training | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
module.init_opt... | python | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
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apache/incubator-mxnet | example/capsnet/capsulenet.py | _shuffle | def _shuffle(data, idx):
"""Shuffle the data."""
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for idx_k, idx_v in data:
shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context)))
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"""Shuffle the data."""
shuffle_data = []
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apache/incubator-mxnet | example/capsnet/capsulenet.py | LossMetric.update | def update(self, labels, preds):
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apache/incubator-mxnet | example/capsnet/capsulenet.py | MNISTCustomIter.reset | def reset(self):
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"""Reset class MNISTCustomIter(mx.io.NDArrayIter):"""
# shuffle data
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apache/incubator-mxnet | python/mxnet/model.py | _create_sparse_kvstore | def _create_sparse_kvstore(kvstore):
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kvstore : KVStore or str
The kvstore.
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kvstore : KVStore
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The kvstore.
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apache/incubator-mxnet | python/mxnet/model.py | _create_kvstore | def _create_kvstore(kvstore, num_device, arg_params):
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Parameters
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kvstore : KVStore or str
The kvstore.
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The number of devices
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apache/incubator-mxnet | python/mxnet/model.py | _initialize_kvstore | def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_on_kvstore):
"""Initialize kvstore"""
for idx, param_on_devs in enumerate(param_arrays):
name = param_names[idx]
kvstore.init(name, arg_params[name])
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"""Initialize kvstore"""
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name = param_names[idx]
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apache/incubator-mxnet | python/mxnet/model.py | _update_params_on_kvstore_nccl | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
"""Perform update of param_arrays from grad_arrays on NCCL kvstore."""
valid_indices = [index for index, grad_list in
enumerate(grad_arrays) if grad_list[0] is not None]
valid_grad_arrays = [grad_arrays... | python | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
"""Perform update of param_arrays from grad_arrays on NCCL kvstore."""
valid_indices = [index for index, grad_list in
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apache/incubator-mxnet | python/mxnet/model.py | _update_params_on_kvstore | def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names):
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apache/incubator-mxnet | python/mxnet/model.py | _update_params | def _update_params(param_arrays, grad_arrays, updater, num_device,
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updates = [[] for _ in range(num_device)]
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apache/incubator-mxnet | python/mxnet/model.py | _multiple_callbacks | def _multiple_callbacks(callbacks, *args, **kwargs):
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apache/incubator-mxnet | python/mxnet/model.py | _train_multi_device | def _train_multi_device(symbol, ctx, arg_names, param_names, aux_names,
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apache/incubator-mxnet | python/mxnet/model.py | save_checkpoint | def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params):
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Parameters
----------
prefix : str
Prefix of model name.
epoch : int
The epoch number of the model.
symbol : Symbol
The input Symbol.
arg_params : dict of str ... | python | def save_checkpoint(prefix, epoch, symbol, arg_params, aux_params):
"""Checkpoint the model data into file.
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prefix : str
Prefix of model name.
epoch : int
The epoch number of the model.
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The input Symbol.
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apache/incubator-mxnet | python/mxnet/model.py | load_checkpoint | def load_checkpoint(prefix, epoch):
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Prefix of model name.
epoch : int
Epoch number of model we would like to load.
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apache/incubator-mxnet | python/mxnet/model.py | FeedForward._init_predictor | def _init_predictor(self, input_shapes, type_dict=None):
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apache/incubator-mxnet | python/mxnet/model.py | FeedForward.fit | def fit(self, X, y=None, eval_data=None, eval_metric='acc',
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apache/incubator-mxnet | ci/docker_cache.py | _upload_image | def _upload_image(registry, docker_tag, image_id) -> None:
"""
Upload the passed image by id, tag it with docker tag and upload to S3 bucket
:param registry: Docker registry name
:param docker_tag: Docker tag
:param image_id: Image id
:return: None
"""
# We don't have to retag the image ... | python | def _upload_image(registry, docker_tag, image_id) -> None:
"""
Upload the passed image by id, tag it with docker tag and upload to S3 bucket
:param registry: Docker registry name
:param docker_tag: Docker tag
:param image_id: Image id
:return: None
"""
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apache/incubator-mxnet | ci/docker_cache.py | _login_dockerhub | def _login_dockerhub():
"""
Login to the Docker Hub account
:return: None
"""
dockerhub_credentials = _get_dockerhub_credentials()
logging.info('Logging in to DockerHub')
# We use password-stdin instead of --password to avoid leaking passwords in case of an error.
# This method will pro... | python | def _login_dockerhub():
"""
Login to the Docker Hub account
:return: None
"""
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logging.info('Logging in to DockerHub')
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apache/incubator-mxnet | ci/docker_cache.py | load_docker_cache | def load_docker_cache(registry, docker_tag) -> None:
"""
Load the precompiled docker cache from the registry
:param registry: Docker registry name
:param docker_tag: Docker tag to load
:return: None
"""
# We don't have to retag the image since it's already in the right format
if not regi... | python | def load_docker_cache(registry, docker_tag) -> None:
"""
Load the precompiled docker cache from the registry
:param registry: Docker registry name
:param docker_tag: Docker tag to load
:return: None
"""
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apache/incubator-mxnet | ci/docker_cache.py | delete_local_docker_cache | def delete_local_docker_cache(docker_tag):
"""
Delete the local docker cache for the entire docker image chain
:param docker_tag: Docker tag
:return: None
"""
history_cmd = ['docker', 'history', '-q', docker_tag]
try:
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image_... | python | def delete_local_docker_cache(docker_tag):
"""
Delete the local docker cache for the entire docker image chain
:param docker_tag: Docker tag
:return: None
"""
history_cmd = ['docker', 'history', '-q', docker_tag]
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apache/incubator-mxnet | ci/docker_cache.py | main | def main() -> int:
"""
Utility to create and publish the Docker cache to Docker Hub
:return:
"""
# We need to be in the same directory than the script so the commands in the dockerfiles work as
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base = os.path.split(os.path.realp... | python | def main() -> int:
"""
Utility to create and publish the Docker cache to Docker Hub
:return:
"""
# We need to be in the same directory than the script so the commands in the dockerfiles work as
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apache/incubator-mxnet | example/cnn_chinese_text_classification/data_helpers.py | get_chinese_text | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
(not os.path.exists('data/neg')):
os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | python | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
(not os.path.exists('data/neg')):
os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | [
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apache/incubator-mxnet | example/cnn_chinese_text_classification/data_helpers.py | load_data_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.
"""
# download dataset
get_chinese_text()
# Load data from files
positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli... | python | def load_data_and_labels():
"""Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# download dataset
get_chinese_text()
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positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli... | [
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apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.reset | def reset(self):
"""
override reset behavior
"""
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 | python | def reset(self):
"""
override reset behavior
"""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
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apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.reset_local | def reset_local(self):
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override reset behavior
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self.sum_metric = [0.0] * self.num | python | def reset_local(self):
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override reset behavior
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apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.update | def update(self, labels, preds):
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cls_prob = preds[0].asnumpy()
loc_loss = preds[1].asnumpy()
cls_label = preds[2].asnumpy()
valid_count = np.sum(cls_label >= 0)
# o... | python | def update(self, labels, preds):
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Implementation of updating metrics
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# get generated multi label from network
cls_prob = preds[0].asnumpy()
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apache/incubator-mxnet | example/ssd/train/metric.py | MultiBoxMetric.get | def get(self):
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name : str
Name of the metric.
value : float
Value of the evaluation.
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Override the default behavior
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name : str
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value : float
Value of the evaluation.
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apache/incubator-mxnet | example/reinforcement-learning/dqn/operators.py | dqn_sym_nips | def dqn_sym_nips(action_num, data=None, name='dqn'):
"""Structure of the Deep Q Network in the NIPS 2013 workshop paper:
Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)
Parameters
----------
action_num : int
data : mxnet.sym.Symbol, optional
n... | python | def dqn_sym_nips(action_num, data=None, name='dqn'):
"""Structure of the Deep Q Network in the NIPS 2013 workshop paper:
Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)
Parameters
----------
action_num : int
data : mxnet.sym.Symbol, optional
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apache/incubator-mxnet | python/mxnet/executor.py | _monitor_callback_wrapper | def _monitor_callback_wrapper(callback):
"""A wrapper for the user-defined handle."""
def callback_handle(name, array, _):
""" ctypes function """
callback(name, array)
return callback_handle | python | def _monitor_callback_wrapper(callback):
"""A wrapper for the user-defined handle."""
def callback_handle(name, array, _):
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callback(name, array)
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apache/incubator-mxnet | python/mxnet/executor.py | Executor._get_dict | def _get_dict(names, ndarrays):
"""Get the dictionary given name and ndarray pairs."""
nset = set()
for nm in names:
if nm in nset:
raise ValueError('Duplicate names detected, %s' % str(names))
nset.add(nm)
return dict(zip(names, ndarrays)) | python | def _get_dict(names, ndarrays):
"""Get the dictionary given name and ndarray pairs."""
nset = set()
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apache/incubator-mxnet | python/mxnet/executor.py | Executor._get_outputs | def _get_outputs(self):
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Returns
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A list of ndarray bound to the heads of executor.
"""
out_size = mx_uint()
handles = ctypes.POINTER(NDArrayHandle)()
check_call(_LIB.MXExecutorOutputs(self.handle,
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"""List all the output NDArray.
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A list of ndarray bound to the heads of executor.
"""
out_size = mx_uint()
handles = ctypes.POINTER(NDArrayHandle)()
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.forward | def forward(self, is_train=False, **kwargs):
"""Calculate the outputs specified by the bound symbol.
Parameters
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is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
a backward call is expected to follow.
**kwargs
... | python | def forward(self, is_train=False, **kwargs):
"""Calculate the outputs specified by the bound symbol.
Parameters
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is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.backward | def backward(self, out_grads=None, is_train=True):
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.set_monitor_callback | def set_monitor_callback(self, callback, monitor_all=False):
"""Install callback for monitor.
Parameters
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callback : function
Takes a string and an NDArrayHandle.
monitor_all : bool, default False
If true, monitor both input and output, otherwis... | python | def set_monitor_callback(self, callback, monitor_all=False):
"""Install callback for monitor.
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callback : function
Takes a string and an NDArrayHandle.
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.arg_dict | def arg_dict(self):
"""Get dictionary representation of argument arrrays.
Returns
-------
arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
Raises
------
ValueError : if there are duplicated names in the a... | python | def arg_dict(self):
"""Get dictionary representation of argument arrrays.
Returns
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arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.grad_dict | def grad_dict(self):
"""Get dictionary representation of gradient arrays.
Returns
-------
grad_dict : dict of str to NDArray
The dictionary that maps name of arguments to gradient arrays.
"""
if self._grad_dict is None:
self._grad_dict = Executor.... | python | def grad_dict(self):
"""Get dictionary representation of gradient arrays.
Returns
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grad_dict : dict of str to NDArray
The dictionary that maps name of arguments to gradient arrays.
"""
if self._grad_dict is None:
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.aux_dict | def aux_dict(self):
"""Get dictionary representation of auxiliary states arrays.
Returns
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aux_dict : dict of str to NDArray
The dictionary that maps name of auxiliary states to NDArrays.
Raises
------
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.output_dict | def output_dict(self):
"""Get dictionary representation of output arrays.
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-------
output_dict : dict of str to NDArray
The dictionary that maps name of output names to NDArrays.
Raises
------
ValueError : if there are duplicated names in the ... | python | def output_dict(self):
"""Get dictionary representation of output arrays.
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.copy_params_from | def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False):
"""Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
----------
arg_params : dict of str to NDArray
Parameters, dict of name to NDArray of arguments.
... | python | def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False):
"""Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
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arg_params : dict of str to NDArray
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.reshape | def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs):
"""Return a new executor with the same symbol and shared memory,
but different input/output shapes.
For runtime reshaping, variable length sequences, etc.
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... | python | def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs):
"""Return a new executor with the same symbol and shared memory,
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For runtime reshaping, variable length sequences, etc.
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apache/incubator-mxnet | python/mxnet/executor.py | Executor.debug_str | def debug_str(self):
"""Get a debug string about internal execution plan.
Returns
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debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
... | python | def debug_str(self):
"""Get a debug string about internal execution plan.
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-------
debug_str : string
Debug string of the executor.
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>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
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apache/incubator-mxnet | example/ssd/evaluate/eval_voc.py | parse_voc_rec | def parse_voc_rec(filename):
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"""
import xml.etree.ElementTree as ET
tree = ET.parse(filename)
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obj_dict = dict()
obj_dict[... | python | def parse_voc_rec(filename):
"""
parse pascal voc record into a dictionary
:param filename: xml file path
:return: list of dict
"""
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tree = ET.parse(filename)
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apache/incubator-mxnet | example/ssd/evaluate/eval_voc.py | voc_eval | def voc_eval(detpath, annopath, imageset_file, classname, cache_dir, ovthresh=0.5, use_07_metric=False):
"""
pascal voc evaluation
:param detpath: detection results detpath.format(classname)
:param annopath: annotations annopath.format(classname)
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apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.register | def register(op_name):
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"""Register operators"""
def wrapper(func):
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except ImportError:
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apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.convert_layer | def convert_layer(node, **kwargs):
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py#L86-L92 | train |
apache/incubator-mxnet | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | MXNetGraph.split_params | def split_params(sym, params):
"""Helper function to split params dictionary into args and aux params
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
Dict of convert... | python | def split_params(sym, params):
"""Helper function to split params dictionary into args and aux params
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
Dict of convert... | [
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"(... | Helper function to split params dictionary into args and aux params
Parameters
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sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/contrib/onnx/mx2onnx/export_onnx.py#L95-L120 | train |
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