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23,900 | apache/incubator-mxnet | example/gluon/lipnet/BeamSearch.py | BeamState.sort | def sort(self):
"""
return beam-labelings, sorted by probability
"""
beams = [v for (_, v) in self.entries.items()]
sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText)
return [x.labeling for x in sortedBeams] | python | def sort(self):
"""
return beam-labelings, sorted by probability
"""
beams = [v for (_, v) in self.entries.items()]
sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText)
return [x.labeling for x in sortedBeams] | [
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23,901 | apache/incubator-mxnet | example/image-classification/symbols/lenet.py | get_loc | def get_loc(data, attr={'lr_mult':'0.01'}):
"""
the localisation network in lenet-stn, it will increase acc about more than 1%,
when num-epoch >=15
"""
loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2))
loc = mx.symbol.Activation(data = loc, act_type='relu')
l... | python | def get_loc(data, attr={'lr_mult':'0.01'}):
"""
the localisation network in lenet-stn, it will increase acc about more than 1%,
when num-epoch >=15
"""
loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2))
loc = mx.symbol.Activation(data = loc, act_type='relu')
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23,902 | apache/incubator-mxnet | example/ssd/demo.py | get_detector | def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class,
nms_thresh=0.5, force_nms=True, nms_topk=400):
"""
wrapper for initialize a detector
Parameters:
----------
net : str
test network name
prefix : str
load model prefix
epoch : int
... | python | def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class,
nms_thresh=0.5, force_nms=True, nms_topk=400):
"""
wrapper for initialize a detector
Parameters:
----------
net : str
test network name
prefix : str
load model prefix
epoch : int
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net : str
test network name
prefix : str
load model prefix
epoch : int
load model epoch
data_shape : int
resize image shape
mean_pixels : tuple (float, float, float)
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23,903 | apache/incubator-mxnet | example/ssd/demo.py | parse_data_shape | def parse_data_shape(data_shape_str):
"""Parse string to tuple or int"""
ds = data_shape_str.strip().split(',')
if len(ds) == 1:
data_shape = (int(ds[0]), int(ds[0]))
elif len(ds) == 2:
data_shape = (int(ds[0]), int(ds[1]))
else:
raise ValueError("Unexpected data_shape: %s", ... | python | def parse_data_shape(data_shape_str):
"""Parse string to tuple or int"""
ds = data_shape_str.strip().split(',')
if len(ds) == 1:
data_shape = (int(ds[0]), int(ds[0]))
elif len(ds) == 2:
data_shape = (int(ds[0]), int(ds[1]))
else:
raise ValueError("Unexpected data_shape: %s", ... | [
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23,904 | apache/incubator-mxnet | example/kaggle-ndsb2/Train.py | get_lenet | def get_lenet():
""" A lenet style net, takes difference of each frame as input.
"""
source = mx.sym.Variable("data")
source = (source - 128) * (1.0/128)
frames = mx.sym.SliceChannel(source, num_outputs=30)
diffs = [frames[i+1] - frames[i] for i in range(29)]
source = mx.sym.Concat(*diffs)
... | python | def get_lenet():
""" A lenet style net, takes difference of each frame as input.
"""
source = mx.sym.Variable("data")
source = (source - 128) * (1.0/128)
frames = mx.sym.SliceChannel(source, num_outputs=30)
diffs = [frames[i+1] - frames[i] for i in range(29)]
source = mx.sym.Concat(*diffs)
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23,905 | apache/incubator-mxnet | example/kaggle-ndsb2/Train.py | CRPS | def CRPS(label, pred):
""" Custom evaluation metric on CRPS.
"""
for i in range(pred.shape[0]):
for j in range(pred.shape[1] - 1):
if pred[i, j] > pred[i, j + 1]:
pred[i, j + 1] = pred[i, j]
return np.sum(np.square(label - pred)) / label.size | python | def CRPS(label, pred):
""" Custom evaluation metric on CRPS.
"""
for i in range(pred.shape[0]):
for j in range(pred.shape[1] - 1):
if pred[i, j] > pred[i, j + 1]:
pred[i, j + 1] = pred[i, j]
return np.sum(np.square(label - pred)) / label.size | [
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23,906 | apache/incubator-mxnet | example/kaggle-ndsb2/Train.py | encode_label | def encode_label(label_data):
"""Run encoding to encode the label into the CDF target.
"""
systole = label_data[:, 1]
diastole = label_data[:, 2]
systole_encode = np.array([
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diastole_encode = np.array([
(x <... | python | def encode_label(label_data):
"""Run encoding to encode the label into the CDF target.
"""
systole = label_data[:, 1]
diastole = label_data[:, 2]
systole_encode = np.array([
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23,907 | apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | foreach | def foreach(body, data, init_states):
"""Run a for loop with user-defined computation over NDArrays on dimension 0.
This operator simulates a for loop and body has the computation for an iteration
of the for loop. It runs the computation in body on each slice from the input
NDArrays.
body takes tw... | python | def foreach(body, data, init_states):
"""Run a for loop with user-defined computation over NDArrays on dimension 0.
This operator simulates a for loop and body has the computation for an iteration
of the for loop. It runs the computation in body on each slice from the input
NDArrays.
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23,908 | apache/incubator-mxnet | python/mxnet/ndarray/contrib.py | isfinite | def isfinite(data):
"""Performs an element-wise check to determine if the NDArray contains an infinite element
or not.
Parameters
----------
input : NDArray
An N-D NDArray.
Returns
-------
output: NDArray
The output NDarray, with same shape as input, where 1 indicates ... | python | def isfinite(data):
"""Performs an element-wise check to determine if the NDArray contains an infinite element
or not.
Parameters
----------
input : NDArray
An N-D NDArray.
Returns
-------
output: NDArray
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23,909 | apache/incubator-mxnet | example/rcnn/symdata/image.py | imdecode | def imdecode(image_path):
"""Return BGR image read by opencv"""
import os
assert os.path.exists(image_path), image_path + ' not found'
im = cv2.imread(image_path)
return im | python | def imdecode(image_path):
"""Return BGR image read by opencv"""
import os
assert os.path.exists(image_path), image_path + ' not found'
im = cv2.imread(image_path)
return im | [
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23,910 | apache/incubator-mxnet | example/gluon/embedding_learning/train.py | get_distance_matrix | def get_distance_matrix(x):
"""Get distance matrix given a matrix. Used in testing."""
square = nd.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * nd.dot(x, x.transpose()))
return nd.sqrt(distance_square) | python | def get_distance_matrix(x):
"""Get distance matrix given a matrix. Used in testing."""
square = nd.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * nd.dot(x, x.transpose()))
return nd.sqrt(distance_square) | [
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23,911 | apache/incubator-mxnet | example/gluon/embedding_learning/train.py | evaluate_emb | def evaluate_emb(emb, labels):
"""Evaluate embeddings based on Recall@k."""
d_mat = get_distance_matrix(emb)
d_mat = d_mat.asnumpy()
labels = labels.asnumpy()
names = []
accs = []
for k in [1, 2, 4, 8, 16]:
names.append('Recall@%d' % k)
correct, cnt = 0.0, 0.0
for i ... | python | def evaluate_emb(emb, labels):
"""Evaluate embeddings based on Recall@k."""
d_mat = get_distance_matrix(emb)
d_mat = d_mat.asnumpy()
labels = labels.asnumpy()
names = []
accs = []
for k in [1, 2, 4, 8, 16]:
names.append('Recall@%d' % k)
correct, cnt = 0.0, 0.0
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23,912 | apache/incubator-mxnet | example/gluon/embedding_learning/train.py | get_lr | def get_lr(lr, epoch, steps, factor):
"""Get learning rate based on schedule."""
for s in steps:
if epoch >= s:
lr *= factor
return lr | python | def get_lr(lr, epoch, steps, factor):
"""Get learning rate based on schedule."""
for s in steps:
if epoch >= s:
lr *= factor
return lr | [
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23,913 | apache/incubator-mxnet | example/ctc/lstm.py | _add_warp_ctc_loss | def _add_warp_ctc_loss(pred, seq_len, num_label, label):
""" Adds Symbol.contrib.ctc_loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Reshape(data=label, shape=(-1,))
label = mx.sym.Cast(data=label, dtype='int32')
return mx.sym.WarpCTC(data=pred, label=label, label_length=n... | python | def _add_warp_ctc_loss(pred, seq_len, num_label, label):
""" Adds Symbol.contrib.ctc_loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Reshape(data=label, shape=(-1,))
label = mx.sym.Cast(data=label, dtype='int32')
return mx.sym.WarpCTC(data=pred, label=label, label_length=n... | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/ctc/lstm.py#L96-L100 |
23,914 | apache/incubator-mxnet | example/ctc/lstm.py | _add_mxnet_ctc_loss | def _add_mxnet_ctc_loss(pred, seq_len, label):
""" Adds Symbol.WapCTC on top of pred symbol and returns the resulting symbol """
pred_ctc = mx.sym.Reshape(data=pred, shape=(-4, seq_len, -1, 0))
loss = mx.sym.contrib.ctc_loss(data=pred_ctc, label=label)
ctc_loss = mx.sym.MakeLoss(loss)
softmax_clas... | python | def _add_mxnet_ctc_loss(pred, seq_len, label):
""" Adds Symbol.WapCTC on top of pred symbol and returns the resulting symbol """
pred_ctc = mx.sym.Reshape(data=pred, shape=(-4, seq_len, -1, 0))
loss = mx.sym.contrib.ctc_loss(data=pred_ctc, label=label)
ctc_loss = mx.sym.MakeLoss(loss)
softmax_clas... | [
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23,915 | apache/incubator-mxnet | example/ctc/lstm.py | _add_ctc_loss | def _add_ctc_loss(pred, seq_len, num_label, loss_type):
""" Adds CTC loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Variable('label')
if loss_type == 'warpctc':
print("Using WarpCTC Loss")
sm = _add_warp_ctc_loss(pred, seq_len, num_label, label)
else:
... | python | def _add_ctc_loss(pred, seq_len, num_label, loss_type):
""" Adds CTC loss on top of pred symbol and returns the resulting symbol """
label = mx.sym.Variable('label')
if loss_type == 'warpctc':
print("Using WarpCTC Loss")
sm = _add_warp_ctc_loss(pred, seq_len, num_label, label)
else:
... | [
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23,916 | apache/incubator-mxnet | example/ctc/lstm.py | lstm_unroll | def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None):
"""
Creates an unrolled LSTM symbol for inference if loss_type is not specified, and for training
if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'
Parameters
----------
num_lstm_layer: int
... | python | def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None):
"""
Creates an unrolled LSTM symbol for inference if loss_type is not specified, and for training
if loss_type is specified. loss_type must be one of 'ctc' or 'warpctc'
Parameters
----------
num_lstm_layer: int
... | [
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23,917 | apache/incubator-mxnet | example/ctc/lstm.py | init_states | def init_states(batch_size, num_lstm_layer, num_hidden):
"""
Returns name and shape of init states of LSTM network
Parameters
----------
batch_size: list of tuple of str and tuple of int and int
num_lstm_layer: int
num_hidden: int
Returns
-------
list of tuple of str and tuple ... | python | def init_states(batch_size, num_lstm_layer, num_hidden):
"""
Returns name and shape of init states of LSTM network
Parameters
----------
batch_size: list of tuple of str and tuple of int and int
num_lstm_layer: int
num_hidden: int
Returns
-------
list of tuple of str and tuple ... | [
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23,918 | apache/incubator-mxnet | python/mxnet/_ctypes/ndarray.py | _imperative_invoke | def _imperative_invoke(handle, ndargs, keys, vals, out):
"""ctypes implementation of imperative invoke wrapper"""
if out is not None:
original_output = out
if isinstance(out, NDArrayBase):
out = (out,)
num_output = ctypes.c_int(len(out))
output_vars = c_handle_array(o... | python | def _imperative_invoke(handle, ndargs, keys, vals, out):
"""ctypes implementation of imperative invoke wrapper"""
if out is not None:
original_output = out
if isinstance(out, NDArrayBase):
out = (out,)
num_output = ctypes.c_int(len(out))
output_vars = c_handle_array(o... | [
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23,919 | apache/incubator-mxnet | python/mxnet/contrib/autograd.py | backward | def backward(outputs, out_grads=None, retain_graph=False):
"""Compute the gradients of outputs w.r.t variables.
Parameters
----------
outputs: list of NDArray
out_grads: list of NDArray or None
"""
assert isinstance(outputs, (list, tuple)), \
"outputs must be a list or tuple of NDAr... | python | def backward(outputs, out_grads=None, retain_graph=False):
"""Compute the gradients of outputs w.r.t variables.
Parameters
----------
outputs: list of NDArray
out_grads: list of NDArray or None
"""
assert isinstance(outputs, (list, tuple)), \
"outputs must be a list or tuple of NDAr... | [
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23,920 | apache/incubator-mxnet | python/mxnet/contrib/autograd.py | grad_and_loss | def grad_and_loss(func, argnum=None):
"""Return function that computes both gradient of arguments and loss value.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
... | python | def grad_and_loss(func, argnum=None):
"""Return function that computes both gradient of arguments and loss value.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
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23,921 | apache/incubator-mxnet | python/mxnet/contrib/autograd.py | grad | def grad(func, argnum=None):
"""Return function that computes gradient of arguments.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
-------
grad_func: a ... | python | def grad(func, argnum=None):
"""Return function that computes gradient of arguments.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
-------
grad_func: a ... | [
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23,922 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | clip_global_norm | def clip_global_norm(arrays, max_norm, check_isfinite=True):
"""Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`.
Parameters
----------
arrays : list of NDArray
max_norm : float
check_isfinite : bool, default True
If True, check that the total_norm is finite... | python | def clip_global_norm(arrays, max_norm, check_isfinite=True):
"""Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`.
Parameters
----------
arrays : list of NDArray
max_norm : float
check_isfinite : bool, default True
If True, check that the total_norm is finite... | [
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23,923 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | check_sha1 | def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
----------
filename : str
Path to the file.
sha1_hash : str
Expected sha1 hash in hexadecimal digits.
Returns
-------
bool
Whether the f... | python | def check_sha1(filename, sha1_hash):
"""Check whether the sha1 hash of the file content matches the expected hash.
Parameters
----------
filename : str
Path to the file.
sha1_hash : str
Expected sha1 hash in hexadecimal digits.
Returns
-------
bool
Whether the f... | [
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filename : str
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sha1_hash : str
Expected sha1 hash in hexadecimal digits.
Returns
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23,924 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | download | def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_ssl=True):
"""Download an given URL
Parameters
----------
url : str
URL to download
path : str, optional
Destination path to store downloaded file. By default stores to the
current directory with... | python | def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_ssl=True):
"""Download an given URL
Parameters
----------
url : str
URL to download
path : str, optional
Destination path to store downloaded file. By default stores to the
current directory with... | [
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url : str
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Whether to overwrite destination file... | [
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23,925 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | _get_repo_url | def _get_repo_url():
"""Return the base URL for Gluon dataset and model repository."""
default_repo = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
repo_url = os.environ.get('MXNET_GLUON_REPO', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url+'/'
return repo_url | python | def _get_repo_url():
"""Return the base URL for Gluon dataset and model repository."""
default_repo = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'
repo_url = os.environ.get('MXNET_GLUON_REPO', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url+'/'
return repo_url | [
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23,926 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | _get_repo_file_url | def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository.
Parameters
----------
namespace : str
Namespace of the file.
filename : str
Name of the file
"""
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
... | python | def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository.
Parameters
----------
namespace : str
Namespace of the file.
filename : str
Name of the file
"""
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
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23,927 | apache/incubator-mxnet | python/mxnet/gluon/utils.py | _brief_print_list | def _brief_print_list(lst, limit=7):
"""Print at most `limit` elements of list."""
lst = list(lst)
if len(lst) > limit:
return _brief_print_list(lst[:limit//2], limit) + ', ..., ' + \
_brief_print_list(lst[-limit//2:], limit)
return ', '.join(["'%s'"%str(i) for i in lst]) | python | def _brief_print_list(lst, limit=7):
"""Print at most `limit` elements of list."""
lst = list(lst)
if len(lst) > limit:
return _brief_print_list(lst[:limit//2], limit) + ', ..., ' + \
_brief_print_list(lst[-limit//2:], limit)
return ', '.join(["'%s'"%str(i) for i in lst]) | [
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23,928 | apache/incubator-mxnet | python/mxnet/symbol/register.py | _make_symbol_function | def _make_symbol_function(handle, name, func_name):
"""Create a symbol function by handle and function name."""
code, doc_str = _generate_symbol_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
symbol_function = local[func_name]
symbol_funct... | python | def _make_symbol_function(handle, name, func_name):
"""Create a symbol function by handle and function name."""
code, doc_str = _generate_symbol_function_code(handle, name, func_name)
local = {}
exec(code, None, local) # pylint: disable=exec-used
symbol_function = local[func_name]
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23,929 | apache/incubator-mxnet | example/sparse/matrix_factorization/train.py | batch_row_ids | def batch_row_ids(data_batch):
""" Generate row ids based on the current mini-batch """
item = data_batch.data[0]
user = data_batch.data[1]
return {'user_weight': user.astype(np.int64),
'item_weight': item.astype(np.int64)} | python | def batch_row_ids(data_batch):
""" Generate row ids based on the current mini-batch """
item = data_batch.data[0]
user = data_batch.data[1]
return {'user_weight': user.astype(np.int64),
'item_weight': item.astype(np.int64)} | [
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23,930 | apache/incubator-mxnet | example/sparse/matrix_factorization/train.py | all_row_ids | def all_row_ids(data_batch):
""" Generate row ids for all rows """
all_users = mx.nd.arange(0, MOVIELENS['max_user'], dtype='int64')
all_movies = mx.nd.arange(0, MOVIELENS['max_movie'], dtype='int64')
return {'user_weight': all_users, 'item_weight': all_movies} | python | def all_row_ids(data_batch):
""" Generate row ids for all rows """
all_users = mx.nd.arange(0, MOVIELENS['max_user'], dtype='int64')
all_movies = mx.nd.arange(0, MOVIELENS['max_movie'], dtype='int64')
return {'user_weight': all_users, 'item_weight': all_movies} | [
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23,931 | apache/incubator-mxnet | example/profiler/profiler_ndarray.py | check_with_uniform | def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
"""check function consistency with uniform random numbers"""
if isinstance(arg_shapes, int):
assert dim
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
arg_shapes = [shape] ... | python | def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
"""check function consistency with uniform random numbers"""
if isinstance(arg_shapes, int):
assert dim
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
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23,932 | apache/incubator-mxnet | example/rcnn/symimdb/imdb.py | IMDB.filter_roidb | def filter_roidb(self):
"""Remove images without usable rois"""
num_roidb = len(self._roidb)
self._roidb = [roi_rec for roi_rec in self._roidb if len(roi_rec['gt_classes'])]
num_after = len(self._roidb)
logger.info('filter roidb: {} -> {}'.format(num_roidb, num_after)) | python | def filter_roidb(self):
"""Remove images without usable rois"""
num_roidb = len(self._roidb)
self._roidb = [roi_rec for roi_rec in self._roidb if len(roi_rec['gt_classes'])]
num_after = len(self._roidb)
logger.info('filter roidb: {} -> {}'.format(num_roidb, num_after)) | [
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23,933 | apache/incubator-mxnet | example/rcnn/symimdb/imdb.py | IMDB.append_flipped_images | def append_flipped_images(self):
"""Only flip boxes coordinates, images will be flipped when loading into network"""
logger.info('%s append flipped images to roidb' % self._name)
roidb_flipped = []
for roi_rec in self._roidb:
boxes = roi_rec['boxes'].copy()
oldx1 ... | python | def append_flipped_images(self):
"""Only flip boxes coordinates, images will be flipped when loading into network"""
logger.info('%s append flipped images to roidb' % self._name)
roidb_flipped = []
for roi_rec in self._roidb:
boxes = roi_rec['boxes'].copy()
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23,934 | apache/incubator-mxnet | python/mxnet/gluon/model_zoo/model_store.py | get_model_file | def get_model_file(name, root=os.path.join(base.data_dir(), 'models')):
r"""Return location for the pretrained on local file system.
This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
Parameters
----... | python | def get_model_file(name, root=os.path.join(base.data_dir(), 'models')):
r"""Return location for the pretrained on local file system.
This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
Parameters
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This function will download from online model zoo when model cannot be found or has mismatch.
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name : str
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23,935 | apache/incubator-mxnet | python/mxnet/gluon/model_zoo/model_store.py | purge | def purge(root=os.path.join(base.data_dir(), 'models')):
r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
root = os.path.expanduser(root)
files = os.listdir(root)
... | python | def purge(root=os.path.join(base.data_dir(), 'models')):
r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
"""
root = os.path.expanduser(root)
files = os.listdir(root)
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23,936 | apache/incubator-mxnet | example/ssd/dataset/mscoco.py | Coco._load_all | def _load_all(self, anno_file, shuffle):
"""
initialize all entries given annotation json file
Parameters:
----------
anno_file: str
annotation json file
shuffle: bool
whether to shuffle image list
"""
image_set_index = []
... | python | def _load_all(self, anno_file, shuffle):
"""
initialize all entries given annotation json file
Parameters:
----------
anno_file: str
annotation json file
shuffle: bool
whether to shuffle image list
"""
image_set_index = []
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23,937 | apache/incubator-mxnet | example/rnn/word_lm/module.py | CustomStatefulModule.forward | def forward(self, data_batch, is_train=None, carry_state=True):
"""Forward computation. States from previous forward computation are carried
to the current iteration if `carry_state` is set to `True`.
"""
# propagate states from the previous iteration
if carry_state:
... | python | def forward(self, data_batch, is_train=None, carry_state=True):
"""Forward computation. States from previous forward computation are carried
to the current iteration if `carry_state` is set to `True`.
"""
# propagate states from the previous iteration
if carry_state:
... | [
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23,938 | apache/incubator-mxnet | example/rnn/word_lm/module.py | CustomStatefulModule.update | def update(self, max_norm=None):
"""Updates parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch. Gradients are clipped by their global norm
if `max_norm` is set.
Parameters
----------
max_norm: float, optional... | python | def update(self, max_norm=None):
"""Updates parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch. Gradients are clipped by their global norm
if `max_norm` is set.
Parameters
----------
max_norm: float, optional... | [
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23,939 | apache/incubator-mxnet | example/gluon/dc_gan/dcgan.py | transformer | def transformer(data, label):
"""Get the translation of images"""
# resize to 64x64
data = mx.image.imresize(data, 64, 64)
# transpose from (64, 64, 3) to (3, 64, 64)
data = mx.nd.transpose(data, (2, 0, 1))
# normalize to [-1, 1]
data = data.astype(np.float32)/128 - 1
# if image is greys... | python | def transformer(data, label):
"""Get the translation of images"""
# resize to 64x64
data = mx.image.imresize(data, 64, 64)
# transpose from (64, 64, 3) to (3, 64, 64)
data = mx.nd.transpose(data, (2, 0, 1))
# normalize to [-1, 1]
data = data.astype(np.float32)/128 - 1
# if image is greys... | [
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23,940 | apache/incubator-mxnet | example/gluon/dc_gan/dcgan.py | get_netG | def get_netG():
"""Get net G"""
# build the generator
netG = nn.Sequential()
with netG.name_scope():
# input is Z, going into a convolution
netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0, use_bias=False))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
# st... | python | def get_netG():
"""Get net G"""
# build the generator
netG = nn.Sequential()
with netG.name_scope():
# input is Z, going into a convolution
netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0, use_bias=False))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
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23,941 | apache/incubator-mxnet | example/gluon/dc_gan/dcgan.py | get_netD | def get_netD():
"""Get the netD"""
# build the discriminator
netD = nn.Sequential()
with netD.name_scope():
# input is (nc) x 64 x 64
netD.add(nn.Conv2D(ndf, 4, 2, 1, use_bias=False))
netD.add(nn.LeakyReLU(0.2))
# state size. (ndf) x 32 x 32
netD.add(nn.Conv2D(ndf... | python | def get_netD():
"""Get the netD"""
# build the discriminator
netD = nn.Sequential()
with netD.name_scope():
# input is (nc) x 64 x 64
netD.add(nn.Conv2D(ndf, 4, 2, 1, use_bias=False))
netD.add(nn.LeakyReLU(0.2))
# state size. (ndf) x 32 x 32
netD.add(nn.Conv2D(ndf... | [
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23,942 | apache/incubator-mxnet | example/gluon/dc_gan/dcgan.py | get_configurations | def get_configurations(netG, netD):
"""Get configurations for net"""
# loss
loss = gluon.loss.SoftmaxCrossEntropyLoss()
# initialize the generator and the discriminator
netG.initialize(mx.init.Normal(0.02), ctx=ctx)
netD.initialize(mx.init.Normal(0.02), ctx=ctx)
# trainer for the generator... | python | def get_configurations(netG, netD):
"""Get configurations for net"""
# loss
loss = gluon.loss.SoftmaxCrossEntropyLoss()
# initialize the generator and the discriminator
netG.initialize(mx.init.Normal(0.02), ctx=ctx)
netD.initialize(mx.init.Normal(0.02), ctx=ctx)
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23,943 | apache/incubator-mxnet | example/gluon/sn_gan/data.py | get_training_data | def get_training_data(batch_size):
""" helper function to get dataloader"""
return gluon.data.DataLoader(
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batch_size=batch_size, shuffle=True, last_batch='discard') | python | def get_training_data(batch_size):
""" helper function to get dataloader"""
return gluon.data.DataLoader(
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23,944 | apache/incubator-mxnet | python/mxnet/symbol/random.py | poisson | def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
"""Draw random samples from a Poisson distribution.
Samples are distributed according to a Poisson distribution parametrized
by *lambda* (rate). Samples will always be returned as a floating point data type.
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----------
lam : fl... | python | def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
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Samples are distributed according to a Poisson distribution parametrized
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23,945 | apache/incubator-mxnet | python/mxnet/symbol/random.py | generalized_negative_binomial | def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, **kwargs):
"""Draw random samples from a generalized negative binomial distribution.
Samples are distributed according to a generalized negative binomial
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"""Draw random samples from a generalized negative binomial distribution.
Samples are distributed according to a generalized negative binomial
distribution parametrized by *mu* (mean) and *alpha* (dispersion).
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23,946 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.load | def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Creates a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
o... | python | def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Creates a model from previously saved checkpoint.
Parameters
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prefix : str
path prefix of saved model files. You should have
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23,947 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.save_checkpoint | def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
"""Saves current progress to checkpoint.
Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.
Parameters
----------
prefix : str
The file prefix to checkpoint to.
... | python | def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
"""Saves current progress to checkpoint.
Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.
Parameters
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prefix : str
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23,948 | apache/incubator-mxnet | python/mxnet/module/module.py | Module._reset_bind | def _reset_bind(self):
"""Internal function to reset binded state."""
self.binded = False
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self._data_shapes = None
self._label_shapes = None | python | def _reset_bind(self):
"""Internal function to reset binded state."""
self.binded = False
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23,949 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.reshape | def reshape(self, data_shapes, label_shapes=None):
"""Reshapes the module for new input shapes.
Parameters
----------
data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tuple)
Typically is ``data_iter.... | python | def reshape(self, data_shapes, label_shapes=None):
"""Reshapes the module for new input shapes.
Parameters
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data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
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23,950 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.forward | def forward(self, data_batch, is_train=None):
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If reshaping of data batch relates to modification of symbol or module, such as
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23,951 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.update | def update(self):
"""Updates parameters according to the installed optimizer and the gradients computed
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23,952 | apache/incubator-mxnet | python/mxnet/module/module.py | Module.get_outputs | def get_outputs(self, merge_multi_context=True):
"""Gets outputs of the previous forward computation.
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is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output
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"""Gets outputs of the previous forward computation.
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23,953 | apache/incubator-mxnet | python/mxnet/module/module.py | Module._sync_params_from_devices | def _sync_params_from_devices(self):
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latest parameters from ``self._arg_params`` and ``self._aux_params``.
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"""Synchronizes parameters from devices to CPU. This function should be called after
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23,954 | apache/incubator-mxnet | python/mxnet/ndarray/random.py | uniform | def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
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Samples are uniformly distributed over the half-open interval *[low, high)*
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Parameters
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23,955 | apache/incubator-mxnet | python/mxnet/ndarray/random.py | exponential | def exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
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r"""Draw samples from an exponential distribution.
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23,956 | apache/incubator-mxnet | python/mxnet/ndarray/random.py | gamma | def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a gamma distribution.
Samples are distributed according to a gamma distribution parametrized
by *alpha* (shape) and *beta* (scale).
Parameters
----------
alpha : float or NDArray, op... | python | def gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a gamma distribution.
Samples are distributed according to a gamma distribution parametrized
by *alpha* (shape) and *beta* (scale).
Parameters
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alpha : float or NDArray, op... | [
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23,957 | apache/incubator-mxnet | python/mxnet/ndarray/random.py | negative_binomial | def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None,
out=None, **kwargs):
"""Draw random samples from a negative binomial distribution.
Samples are distributed according to a negative binomial distribution
parametrized by *k* (limit of unsuccessful experiments) and *p* ... | python | def negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None,
out=None, **kwargs):
"""Draw random samples from a negative binomial distribution.
Samples are distributed according to a negative binomial distribution
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23,958 | apache/incubator-mxnet | python/mxnet/ndarray/random.py | randint | def randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a discrete uniform distribution.
Samples are uniformly distributed over the half-open interval *[low, high)*
(includes *low*, but excludes *high*).
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low : int, requi... | python | def randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a discrete uniform distribution.
Samples are uniformly distributed over the half-open interval *[low, high)*
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23,959 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer._init_params | def _init_params(self):
"""Initialize parameters in the KVStore.
Parameters with incomplete initialization are ignored.
"""
assert self._kv_initialized, "Cannot initialize parameters in KVStore " \
"when KVStore is not initialized."
params_t... | python | def _init_params(self):
"""Initialize parameters in the KVStore.
Parameters with incomplete initialization are ignored.
"""
assert self._kv_initialized, "Cannot initialize parameters in KVStore " \
"when KVStore is not initialized."
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23,960 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer._reset_kvstore | def _reset_kvstore(self):
"""Reset kvstore."""
if self._kvstore and 'dist' in self._kvstore.type:
raise RuntimeError("Cannot reset distributed KVStore.")
self._kv_initialized = False
self._kvstore = None
self._distributed = None
self._update_on_kvstore = None
... | python | def _reset_kvstore(self):
"""Reset kvstore."""
if self._kvstore and 'dist' in self._kvstore.type:
raise RuntimeError("Cannot reset distributed KVStore.")
self._kv_initialized = False
self._kvstore = None
self._distributed = None
self._update_on_kvstore = None
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23,961 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer._init_kvstore | def _init_kvstore(self):
"""Create kvstore."""
config = self._kvstore_params
# configure kvstore, update_on_kvstore and self._distributed on three cases:
if self._contains_sparse_weight:
# If weight is sparse, kvstore must be present and the weight must be updated on kvstore.... | python | def _init_kvstore(self):
"""Create kvstore."""
config = self._kvstore_params
# configure kvstore, update_on_kvstore and self._distributed on three cases:
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23,962 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer.set_learning_rate | def set_learning_rate(self, lr):
"""Sets a new learning rate of the optimizer.
Parameters
----------
lr : float
The new learning rate of the optimizer.
"""
if not isinstance(self._optimizer, opt.Optimizer):
raise UserWarning("Optimizer has to be d... | python | def set_learning_rate(self, lr):
"""Sets a new learning rate of the optimizer.
Parameters
----------
lr : float
The new learning rate of the optimizer.
"""
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23,963 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer._row_sparse_pull | def _row_sparse_pull(self, parameter, out, row_id, full_idx=False):
"""Internal method to invoke pull operations on KVStore. If `full_idx` is set to True,
`kv.pull` is preferred instead of `kv.row_sparse_pull`.
"""
# initialize kv and params if not already
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"""Internal method to invoke pull operations on KVStore. If `full_idx` is set to True,
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23,964 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer.allreduce_grads | def allreduce_grads(self):
"""For each parameter, reduce the gradients from different contexts.
Should be called after `autograd.backward()`, outside of `record()` scope,
and before `trainer.update()`.
For normal parameter updates, `step()` should be used, which internally calls
... | python | def allreduce_grads(self):
"""For each parameter, reduce the gradients from different contexts.
Should be called after `autograd.backward()`, outside of `record()` scope,
and before `trainer.update()`.
For normal parameter updates, `step()` should be used, which internally calls
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23,965 | apache/incubator-mxnet | python/mxnet/gluon/trainer.py | Trainer.update | def update(self, batch_size, ignore_stale_grad=False):
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Should be called after `autograd.backward()` and outside of `record()` scope,
and after `trainer.update()`.
For normal parameter updates, `step()` should be used, which internally calls
... | python | def update(self, batch_size, ignore_stale_grad=False):
"""Makes one step of parameter update.
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23,966 | apache/incubator-mxnet | benchmark/python/sparse/util.py | estimate_density | def estimate_density(DATA_PATH, feature_size):
"""sample 10 times of a size of 1000 for estimating the density of the sparse dataset"""
if not os.path.exists(DATA_PATH):
raise Exception("Data is not there!")
density = []
P = 0.01
for _ in range(10):
num_non_zero = 0
num_sampl... | python | def estimate_density(DATA_PATH, feature_size):
"""sample 10 times of a size of 1000 for estimating the density of the sparse dataset"""
if not os.path.exists(DATA_PATH):
raise Exception("Data is not there!")
density = []
P = 0.01
for _ in range(10):
num_non_zero = 0
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23,967 | apache/incubator-mxnet | example/reinforcement-learning/a3c/launcher.py | exec_cmd | def exec_cmd(cmd, role, taskid, pass_env):
"""Execute the command line command."""
if cmd[0].find('/') == -1 and os.path.exists(cmd[0]) and os.name != 'nt':
cmd[0] = './' + cmd[0]
cmd = ' '.join(cmd)
env = os.environ.copy()
for k, v in pass_env.items():
env[k] = str(v)
env['DMLC... | python | def exec_cmd(cmd, role, taskid, pass_env):
"""Execute the command line command."""
if cmd[0].find('/') == -1 and os.path.exists(cmd[0]) and os.name != 'nt':
cmd[0] = './' + cmd[0]
cmd = ' '.join(cmd)
env = os.environ.copy()
for k, v in pass_env.items():
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env['DMLC... | [
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23,968 | apache/incubator-mxnet | example/reinforcement-learning/a3c/launcher.py | submit | def submit(args):
gpus = args.gpus.strip().split(',')
"""Submit function of local jobs."""
def mthread_submit(nworker, nserver, envs):
"""
customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional p... | python | def submit(args):
gpus = args.gpus.strip().split(',')
"""Submit function of local jobs."""
def mthread_submit(nworker, nserver, envs):
"""
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23,969 | apache/incubator-mxnet | example/ctc/ctc_metrics.py | CtcMetrics._remove_blank | def _remove_blank(l):
""" Removes trailing zeros in the list of integers and returns a new list of integers"""
ret = []
for i, _ in enumerate(l):
if l[i] == 0:
break
ret.append(l[i])
return ret | python | def _remove_blank(l):
""" Removes trailing zeros in the list of integers and returns a new list of integers"""
ret = []
for i, _ in enumerate(l):
if l[i] == 0:
break
ret.append(l[i])
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23,970 | apache/incubator-mxnet | example/sparse/matrix_factorization/data.py | get_movielens_iter | def get_movielens_iter(filename, batch_size):
"""Not particularly fast code to parse the text file and load into NDArrays.
return two data iters, one for train, the other for validation.
"""
logging.info("Preparing data iterators for " + filename + " ... ")
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item = []
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"""Not particularly fast code to parse the text file and load into NDArrays.
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"""
logging.info("Preparing data iterators for " + filename + " ... ")
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23,971 | apache/incubator-mxnet | plugin/opencv/opencv.py | imdecode | def imdecode(str_img, flag=1):
"""Decode image from str buffer.
Wrapper for cv2.imdecode that uses mx.nd.NDArray
Parameters
----------
str_img : str
str buffer read from image file
flag : int
same as flag for cv2.imdecode
Returns
-------
img : NDArray
decoded... | python | def imdecode(str_img, flag=1):
"""Decode image from str buffer.
Wrapper for cv2.imdecode that uses mx.nd.NDArray
Parameters
----------
str_img : str
str buffer read from image file
flag : int
same as flag for cv2.imdecode
Returns
-------
img : NDArray
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23,972 | apache/incubator-mxnet | plugin/opencv/opencv.py | resize | def resize(src, size, interpolation=cv2.INTER_LINEAR):
"""Decode image from str buffer.
Wrapper for cv2.imresize that uses mx.nd.NDArray
Parameters
----------
src : NDArray
image in (width, height, channels)
size : tuple
target size in (width, height)
interpolation : int
... | python | def resize(src, size, interpolation=cv2.INTER_LINEAR):
"""Decode image from str buffer.
Wrapper for cv2.imresize that uses mx.nd.NDArray
Parameters
----------
src : NDArray
image in (width, height, channels)
size : tuple
target size in (width, height)
interpolation : int
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23,973 | apache/incubator-mxnet | plugin/opencv/opencv.py | copyMakeBorder | def copyMakeBorder(src, top, bot, left, right, border_type=cv2.BORDER_CONSTANT, value=0):
"""Pad image border
Wrapper for cv2.copyMakeBorder that uses mx.nd.NDArray
Parameters
----------
src : NDArray
Image in (width, height, channels).
Others are the same with cv2.copyMakeBorder
... | python | def copyMakeBorder(src, top, bot, left, right, border_type=cv2.BORDER_CONSTANT, value=0):
"""Pad image border
Wrapper for cv2.copyMakeBorder that uses mx.nd.NDArray
Parameters
----------
src : NDArray
Image in (width, height, channels).
Others are the same with cv2.copyMakeBorder
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23,974 | apache/incubator-mxnet | plugin/opencv/opencv.py | random_crop | def random_crop(src, size):
"""Randomly crop src with size. Upsample result if src is smaller than size"""
h, w, _ = src.shape
new_w, new_h = scale_down((w, h), size)
x0 = random.randint(0, w - new_w)
y0 = random.randint(0, h - new_h)
out = fixed_crop(src, x0, y0, new_w, new_h, size)
retur... | python | def random_crop(src, size):
"""Randomly crop src with size. Upsample result if src is smaller than size"""
h, w, _ = src.shape
new_w, new_h = scale_down((w, h), size)
x0 = random.randint(0, w - new_w)
y0 = random.randint(0, h - new_h)
out = fixed_crop(src, x0, y0, new_w, new_h, size)
retur... | [
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23,975 | apache/incubator-mxnet | plugin/opencv/opencv.py | random_size_crop | def random_size_crop(src, size, min_area=0.25, ratio=(3.0/4.0, 4.0/3.0)):
"""Randomly crop src with size. Randomize area and aspect ratio"""
h, w, _ = src.shape
area = w*h
for _ in range(10):
new_area = random.uniform(min_area, 1.0) * area
new_ratio = random.uniform(*ratio)
new_w... | python | def random_size_crop(src, size, min_area=0.25, ratio=(3.0/4.0, 4.0/3.0)):
"""Randomly crop src with size. Randomize area and aspect ratio"""
h, w, _ = src.shape
area = w*h
for _ in range(10):
new_area = random.uniform(min_area, 1.0) * area
new_ratio = random.uniform(*ratio)
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23,976 | apache/incubator-mxnet | plugin/opencv/opencv.py | ImageListIter.next | def next(self):
"""Move iterator position forward"""
batch = mx.nd.zeros((self.batch_size, self.size[1], self.size[0], 3))
i = self.cur
for i in range(self.cur, min(len(self.list), self.cur+self.batch_size)):
str_img = open(self.root+self.list[i]+'.jpg').read()
im... | python | def next(self):
"""Move iterator position forward"""
batch = mx.nd.zeros((self.batch_size, self.size[1], self.size[0], 3))
i = self.cur
for i in range(self.cur, min(len(self.list), self.cur+self.batch_size)):
str_img = open(self.root+self.list[i]+'.jpg').read()
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23,977 | apache/incubator-mxnet | example/speech_recognition/stt_metric.py | check_label_shapes | def check_label_shapes(labels, preds, shape=0):
"""Check to see if the two arrays are the same size."""
if shape == 0:
label_shape, pred_shape = len(labels), len(preds)
else:
label_shape, pred_shape = labels.shape, preds.shape
if label_shape != pred_shape:
raise ValueError("Sha... | python | def check_label_shapes(labels, preds, shape=0):
"""Check to see if the two arrays are the same size."""
if shape == 0:
label_shape, pred_shape = len(labels), len(preds)
else:
label_shape, pred_shape = labels.shape, preds.shape
if label_shape != pred_shape:
raise ValueError("Sha... | [
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23,978 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/import_to_gluon.py | import_to_gluon | def import_to_gluon(model_file, ctx):
"""
Imports the ONNX model files, passed as a parameter, into Gluon SymbolBlock object.
Parameters
----------
model_file : str
ONNX model file name
ctx : Context or list of Context
Loads the model into one or many context(s).
Returns
... | python | def import_to_gluon(model_file, ctx):
"""
Imports the ONNX model files, passed as a parameter, into Gluon SymbolBlock object.
Parameters
----------
model_file : str
ONNX model file name
ctx : Context or list of Context
Loads the model into one or many context(s).
Returns
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23,979 | apache/incubator-mxnet | example/gluon/image_classification.py | get_model | def get_model(model, ctx, opt):
"""Model initialization."""
kwargs = {'ctx': ctx, 'pretrained': opt.use_pretrained, 'classes': classes}
if model.startswith('resnet'):
kwargs['thumbnail'] = opt.use_thumbnail
elif model.startswith('vgg'):
kwargs['batch_norm'] = opt.batch_norm
net = mo... | python | def get_model(model, ctx, opt):
"""Model initialization."""
kwargs = {'ctx': ctx, 'pretrained': opt.use_pretrained, 'classes': classes}
if model.startswith('resnet'):
kwargs['thumbnail'] = opt.use_thumbnail
elif model.startswith('vgg'):
kwargs['batch_norm'] = opt.batch_norm
net = mo... | [
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23,980 | apache/incubator-mxnet | example/gluon/image_classification.py | get_data_iters | def get_data_iters(dataset, batch_size, opt):
"""get dataset iterators"""
if dataset == 'mnist':
train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28),
num_parts=kv.num_workers, part_index=kv.rank)
elif dataset == 'cifar10':
train... | python | def get_data_iters(dataset, batch_size, opt):
"""get dataset iterators"""
if dataset == 'mnist':
train_data, val_data = get_mnist_iterator(batch_size, (1, 28, 28),
num_parts=kv.num_workers, part_index=kv.rank)
elif dataset == 'cifar10':
train... | [
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23,981 | apache/incubator-mxnet | example/gluon/image_classification.py | update_learning_rate | def update_learning_rate(lr, trainer, epoch, ratio, steps):
"""Set the learning rate to the initial value decayed by ratio every N epochs."""
new_lr = lr * (ratio ** int(np.sum(np.array(steps) < epoch)))
trainer.set_learning_rate(new_lr)
return trainer | python | def update_learning_rate(lr, trainer, epoch, ratio, steps):
"""Set the learning rate to the initial value decayed by ratio every N epochs."""
new_lr = lr * (ratio ** int(np.sum(np.array(steps) < epoch)))
trainer.set_learning_rate(new_lr)
return trainer | [
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23,982 | apache/incubator-mxnet | python/mxnet/random.py | seed | def seed(seed_state, ctx="all"):
"""Seeds the random number generators in MXNet.
This affects the behavior of modules in MXNet that uses random number generators,
like the dropout operator and `NDArray`'s random sampling operators.
Parameters
----------
seed_state : int
The random numb... | python | def seed(seed_state, ctx="all"):
"""Seeds the random number generators in MXNet.
This affects the behavior of modules in MXNet that uses random number generators,
like the dropout operator and `NDArray`'s random sampling operators.
Parameters
----------
seed_state : int
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23,983 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | random_uniform | def random_uniform(attrs, inputs, proto_obj):
"""Draw random samples from a uniform distribtuion."""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
"Instructions to install - http... | python | def random_uniform(attrs, inputs, proto_obj):
"""Draw random samples from a uniform distribtuion."""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
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23,984 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | random_normal | def random_normal(attrs, inputs, proto_obj):
"""Draw random samples from a Gaussian distribution."""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
"Instructions to install - http... | python | def random_normal(attrs, inputs, proto_obj):
"""Draw random samples from a Gaussian distribution."""
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
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23,985 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | add | def add(attrs, inputs, proto_obj):
"""Adding two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
broadcast_axis = attrs['axis']
op_value = translation_utils._fix_broadcast('broadcast_add', inputs,
broadcast_ax... | python | def add(attrs, inputs, proto_obj):
"""Adding two tensors"""
new_attr = {}
if 'broadcast' in attrs and attrs['broadcast'] == 1:
broadcast_axis = attrs['axis']
op_value = translation_utils._fix_broadcast('broadcast_add', inputs,
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23,986 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | mean | def mean(attrs, inputs, proto_obj):
"""Mean of all the input tensors."""
concat_input = [symbol.expand_dims(op_input, axis=0) for op_input in inputs]
concat_sym = symbol.concat(*concat_input, dim=0)
mean_sym = symbol.mean(concat_sym, axis=0)
return mean_sym, attrs, inputs | python | def mean(attrs, inputs, proto_obj):
"""Mean of all the input tensors."""
concat_input = [symbol.expand_dims(op_input, axis=0) for op_input in inputs]
concat_sym = symbol.concat(*concat_input, dim=0)
mean_sym = symbol.mean(concat_sym, axis=0)
return mean_sym, attrs, inputs | [
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23,987 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | argmax | def argmax(attrs, inputs, proto_obj):
"""Returns indices of the maximum values along an axis"""
axis = attrs.get('axis', 0)
keepdims = attrs.get('keepdims', 1)
argmax_op = symbol.argmax(inputs[0], axis=axis, keepdims=keepdims)
# onnx argmax operator always expects int64 as output type
cast_attrs... | python | def argmax(attrs, inputs, proto_obj):
"""Returns indices of the maximum values along an axis"""
axis = attrs.get('axis', 0)
keepdims = attrs.get('keepdims', 1)
argmax_op = symbol.argmax(inputs[0], axis=axis, keepdims=keepdims)
# onnx argmax operator always expects int64 as output type
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23,988 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | argmin | def argmin(attrs, inputs, proto_obj):
"""Returns indices of the minimum values along an axis."""
axis = attrs.get('axis', 0)
keepdims = attrs.get('keepdims', 1)
argmin_op = symbol.argmin(inputs[0], axis=axis, keepdims=keepdims)
# onnx argmax operator always expects int64 as output type
cast_attr... | python | def argmin(attrs, inputs, proto_obj):
"""Returns indices of the minimum values along an axis."""
axis = attrs.get('axis', 0)
keepdims = attrs.get('keepdims', 1)
argmin_op = symbol.argmin(inputs[0], axis=axis, keepdims=keepdims)
# onnx argmax operator always expects int64 as output type
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23,989 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | maximum | def maximum(attrs, inputs, proto_obj):
"""
Elementwise maximum of arrays.
MXNet maximum compares only two symbols at a time.
ONNX can send more than two to compare.
Breaking into multiple mxnet ops to compare two symbols at a time
"""
if len(inputs) > 1:
mxnet_op = symbol.maximum(inp... | python | def maximum(attrs, inputs, proto_obj):
"""
Elementwise maximum of arrays.
MXNet maximum compares only two symbols at a time.
ONNX can send more than two to compare.
Breaking into multiple mxnet ops to compare two symbols at a time
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23,990 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | minimum | def minimum(attrs, inputs, proto_obj):
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# ONNX can send more than two to compare.
# Breaking into multiple mxnet ops to compare two symbols at a time
if len(inputs) > 1:
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"""Elementwise minimum of arrays."""
# MXNet minimum compares only two symbols at a time.
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23,991 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | concat | def concat(attrs, inputs, proto_obj):
""" Joins input arrays along a given axis. """
new_attrs = translation_utils._fix_attribute_names(attrs, {'axis': 'dim'})
return 'concat', new_attrs, inputs | python | def concat(attrs, inputs, proto_obj):
""" Joins input arrays along a given axis. """
new_attrs = translation_utils._fix_attribute_names(attrs, {'axis': 'dim'})
return 'concat', new_attrs, inputs | [
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23,992 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | pad | def pad(attrs, inputs, proto_obj):
""" Add padding to input tensor"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'pads' : 'pad_width',
'value' : 'constant_value'
})
n... | python | def pad(attrs, inputs, proto_obj):
""" Add padding to input tensor"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'pads' : 'pad_width',
'value' : 'constant_value'
})
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23,993 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | instance_norm | def instance_norm(attrs, inputs, proto_obj):
"""Instance Normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps'})
new_attrs['eps'] = attrs.get('epsilon', 1e-5)
return 'InstanceNorm', new_attrs, inputs | python | def instance_norm(attrs, inputs, proto_obj):
"""Instance Normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'epsilon' : 'eps'})
new_attrs['eps'] = attrs.get('epsilon', 1e-5)
return 'InstanceNorm', new_attrs, inputs | [
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23,994 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | leaky_relu | def leaky_relu(attrs, inputs, proto_obj):
"""Leaky Relu function"""
if 'alpha' in attrs:
new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'})
else:
new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 0.01})
return 'LeakyReLU', new_attrs, inputs | python | def leaky_relu(attrs, inputs, proto_obj):
"""Leaky Relu function"""
if 'alpha' in attrs:
new_attrs = translation_utils._fix_attribute_names(attrs, {'alpha' : 'slope'})
else:
new_attrs = translation_utils._add_extra_attributes(attrs, {'slope': 0.01})
return 'LeakyReLU', new_attrs, inputs | [
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23,995 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | softmax | def softmax(attrs, inputs, proto_obj):
"""Softmax function."""
if 'axis' not in attrs:
attrs = translation_utils._add_extra_attributes(attrs, {'axis': 1})
return 'softmax', attrs, inputs | python | def softmax(attrs, inputs, proto_obj):
"""Softmax function."""
if 'axis' not in attrs:
attrs = translation_utils._add_extra_attributes(attrs, {'axis': 1})
return 'softmax', attrs, inputs | [
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23,996 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | softplus | def softplus(attrs, inputs, proto_obj):
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new_attrs = translation_utils._add_extra_attributes(attrs, {'act_type' : 'softrelu'})
return 'Activation', new_attrs, inputs | python | def softplus(attrs, inputs, proto_obj):
"""Applies the sofplus activation function element-wise to the input."""
new_attrs = translation_utils._add_extra_attributes(attrs, {'act_type' : 'softrelu'})
return 'Activation', new_attrs, inputs | [
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23,997 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | deconv | def deconv(attrs, inputs, proto_obj):
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new_attrs = translation_utils._fix_attribute_names(attrs, {'kernel_shape' : 'kernel',
'strides' : 'stride',
... | python | def deconv(attrs, inputs, proto_obj):
"""Computes transposed convolution of the input tensor."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'kernel_shape' : 'kernel',
'strides' : 'stride',
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23,998 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | global_maxpooling | def global_maxpooling(attrs, inputs, proto_obj):
"""Performs max pooling on the input."""
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
... | python | def global_maxpooling(attrs, inputs, proto_obj):
"""Performs max pooling on the input."""
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
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23,999 | apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_op_translations.py | global_avgpooling | def global_avgpooling(attrs, inputs, proto_obj):
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new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
... | python | def global_avgpooling(attrs, inputs, proto_obj):
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/contrib/onnx/onnx2mx/_op_translations.py#L398-L403 |
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