INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Run encoding to encode the label into the CDF target. | 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([
(x < np.arange(600)) for x in systole
], dtype=np.uint8)
diastole_encode = np.array([
(x <... |
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
iscrowd:
crowd instances are handled by marking their overlaps with all categories to -1
and later excluded in training
bbox:
[x1, y1, w, h]
:param index: coco image ... | def _load_annotation(self, _coco, coco_ind_to_class_ind, index):
"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
iscrowd:
crowd instances are handled by marking their overlaps with all categories to -1
and later excluded i... |
example results
[{"image_id": 42,
"category_id": 18,
"bbox": [258.15,41.29,348.26,243.78],
"score": 0.236}, ...] | def _write_coco_results(self, _coco, detections):
""" example results
[{"image_id": 42,
"category_id": 18,
"bbox": [258.15,41.29,348.26,243.78],
"score": 0.236}, ...]
"""
cats = [cat['name'] for cat in _coco.loadCats(_coco.getCatIds())]
class_to_coco... |
Draw random samples from an approximately log-uniform or Zipfian distribution.
This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).
The elements of sampled_candidates are drawn with replacement from the base distribution.
The base distribution for this operator is... | def rand_zipfian(true_classes, num_sampled, range_max, ctx=None):
"""Draw random samples from an approximately log-uniform or Zipfian distribution.
This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).
The elements of sampled_candidates are drawn with replacement fr... |
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 two arguments as input and outputs a tuple of t... | 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... |
Run a while loop with user-defined computation and loop condition.
This operator simulates a while loop which iterately does customized computation
as long as the condition is satisfied.
`loop_vars` is a list of NDArrays on which the computation uses.
`cond` is a user-defined function, used as the lo... | def while_loop(cond, func, loop_vars, max_iterations=None):
"""Run a while loop with user-defined computation and loop condition.
This operator simulates a while loop which iterately does customized computation
as long as the condition is satisfied.
`loop_vars` is a list of NDArrays on which the compu... |
Run an if-then-else using user-defined condition and computation
This operator simulates a if-like branch which chooses to do one of
the two customized computations according to the specified condition.
`pred` is a scalar MXNet NDArray,
indicating which branch of computation should be used.
`then... | def cond(pred, then_func, else_func):
"""Run an if-then-else using user-defined condition and computation
This operator simulates a if-like branch which chooses to do one of
the two customized computations according to the specified condition.
`pred` is a scalar MXNet NDArray,
indicating which bra... |
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 the array element is
... | 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 ... |
LSTM Cell symbol | def vanilla_lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, is_batchnorm=False, gamma=None, beta=None, name=None):
"""LSTM Cell symbol"""
i2h = mx.sym.FullyConnected(data=indata,
weight=param.i2h_weight,
bias=param.i2h_bias,
... |
LSTM Cell symbol | def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., num_hidden_proj=0, is_batchnorm=False,
gamma=None, beta=None, name=None):
"""LSTM Cell symbol"""
# dropout input
if dropout > 0.:
indata = mx.sym.Dropout(data=indata, p=dropout)
i2h = mx.sym.FullyConnected(da... |
read, resize, transform image, return im_tensor, im_info, gt_boxes
roi_rec should have keys: ["image", "boxes", "gt_classes", "flipped"]
0 --- x (width, second dim of im)
|
y (height, first dim of im) | def get_image(roi_rec, short, max_size, mean, std):
"""
read, resize, transform image, return im_tensor, im_info, gt_boxes
roi_rec should have keys: ["image", "boxes", "gt_classes", "flipped"]
0 --- x (width, second dim of im)
|
y (height, first dim of im)
"""
im = imdecode(roi_rec['imag... |
Return BGR image read by opencv | 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 |
only resize input image to target size and return scale
:param im: BGR image input by opencv
:param short: one dimensional size (the short side)
:param max_size: one dimensional max size (the long side)
:return: resized image (NDArray) and scale (float) | def resize(im, short, max_size):
"""
only resize input image to target size and return scale
:param im: BGR image input by opencv
:param short: one dimensional size (the short side)
:param max_size: one dimensional max size (the long side)
:return: resized image (NDArray) and scale (float)
"... |
transform into mxnet tensor,
subtract pixel size and transform to correct format
:param im: [height, width, channel] in BGR
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: [batch, channel, height, width] | def transform(im, mean, std):
"""
transform into mxnet tensor,
subtract pixel size and transform to correct format
:param im: [height, width, channel] in BGR
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: [batch, channel, height, width]
"""
im_tensor = np.zero... |
transform from mxnet im_tensor to ordinary RGB image
im_tensor is limited to one image
:param im_tensor: [batch, channel, height, width]
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: im [height, width, channel(RGB)] | def transform_inverse(im_tensor, mean, std):
"""
transform from mxnet im_tensor to ordinary RGB image
im_tensor is limited to one image
:param im_tensor: [batch, channel, height, width]
:param mean: [RGB pixel mean]
:param std: [RGB pixel std var]
:return: im [height, width, channel(RGB)]
... |
vertically stack tensors by adding a new axis
expand dims if only 1 tensor
:param tensor_list: list of tensor to be stacked vertically
:param pad: label to pad with
:return: tensor with max shape | def tensor_vstack(tensor_list, pad=0):
"""
vertically stack tensors by adding a new axis
expand dims if only 1 tensor
:param tensor_list: list of tensor to be stacked vertically
:param pad: label to pad with
:return: tensor with max shape
"""
if len(tensor_list) == 1:
return tens... |
Get distance matrix given a matrix. Used in testing. | 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) |
Evaluate embeddings based on Recall@k. | 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 ... |
Get learning rate based on schedule. | def get_lr(lr, epoch, steps, factor):
"""Get learning rate based on schedule."""
for s in steps:
if epoch >= s:
lr *= factor
return lr |
Training function. | def train(epochs, ctx):
"""Training function."""
if isinstance(ctx, mx.Context):
ctx = [ctx]
net.initialize(mx.init.Xavier(magnitude=2), ctx=ctx)
opt_options = {'learning_rate': opt.lr, 'wd': opt.wd}
if opt.optimizer == 'sgd':
opt_options['momentum'] = 0.9
if opt.optimizer == 'a... |
Returns symbol for LSTM model up to loss/softmax | def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden):
""" Returns symbol for LSTM model up to loss/softmax"""
param_cells = []
last_states = []
for i in range(num_lstm_layer):
param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable("l%d_i2h_weight" % i),
... |
Adds Symbol.contrib.ctc_loss on top of pred symbol and returns the resulting symbol | 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... |
Adds Symbol.WapCTC on top of pred symbol and returns the resulting symbol | 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... |
Adds CTC loss on top of pred symbol and returns the resulting symbol | 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:
... |
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
seq_len: int
num_hidden: int
num_label: int
loss_type: str
'ctc' or 'war... | 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
... |
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 of int and int | 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 ... |
ctypes implementation of imperative invoke wrapper | 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... |
Set status to training/not training. When training, graph will be constructed
for gradient computation. Operators will also run with ctx.is_train=True. For example,
Dropout will drop inputs randomly when is_train=True while simply passing through
if is_train=False.
Parameters
----------
is_trai... | def set_is_training(is_train):
"""Set status to training/not training. When training, graph will be constructed
for gradient computation. Operators will also run with ctx.is_train=True. For example,
Dropout will drop inputs randomly when is_train=True while simply passing through
if is_train=False.
... |
Compute the gradients of outputs w.r.t variables.
Parameters
----------
outputs: list of NDArray
out_grads: list of NDArray or None | 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... |
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
-------
grad_and_loss_func: a 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
... |
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 function
A function t... | 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 ... |
Splits an NDArray into `num_slice` slices along `batch_axis`.
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).
Parameters
----------
data : NDArray
A batch of data.
num_slice : int
Number of desired slices.
batch_axis : int, default 0
... | def split_data(data, num_slice, batch_axis=0, even_split=True):
"""Splits an NDArray into `num_slice` slices along `batch_axis`.
Usually used for data parallelism where each slices is sent
to one device (i.e. GPU).
Parameters
----------
data : NDArray
A batch of data.
num_slice : in... |
Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
each slice to one context in `ctx_list`.
Parameters
----------
data : NDArray
A batch of data.
ctx_list : list of Context
A list of Contexts.
batch_axis : int, default 0
The axis along which to sl... | def split_and_load(data, ctx_list, batch_axis=0, even_split=True):
"""Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads
each slice to one context in `ctx_list`.
Parameters
----------
data : NDArray
A batch of data.
ctx_list : list of Context
A list of Co... |
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 (not nan or inf). This
requires a blocking .asscalar() cal... | 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... |
Indent string | def _indent(s_, numSpaces):
"""Indent string
"""
s = s_.split('\n')
if len(s) == 1:
return s_
first = s.pop(0)
s = [first] + [(numSpaces * ' ') + line for line in s]
s = '\n'.join(s)
return s |
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 file content matches the expected hash. | 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... |
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 same name as in url.
overwrite : bool, optional
Whether to overwrite destination file... | 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... |
Return the base URL for Gluon dataset and model repository. | 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 |
Return the URL for hosted file in Gluon repository.
Parameters
----------
namespace : str
Namespace of the file.
filename : str
Name of the file | 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(),
... |
Print at most `limit` elements of 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]) |
Create a symbol function by handle and function name. | 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... |
Generate row ids based on the current mini-batch | 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)} |
Generate row ids for all rows | 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} |
Convert caffe model
Parameters
----------
prototxt_fname : str
Filename of the prototxt model definition
caffemodel_fname : str
Filename of the binary caffe model
output_prefix : str, optinoal
If given, then save the converted MXNet into output_prefx+'.json' and
... | def convert_model(prototxt_fname, caffemodel_fname, output_prefix=None):
"""Convert caffe model
Parameters
----------
prototxt_fname : str
Filename of the prototxt model definition
caffemodel_fname : str
Filename of the binary caffe model
output_prefix : str, optinoal
... |
Parse Caffe prototxt into symbol string | def _parse_proto(prototxt_fname):
"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
# only support single input, so always use `data` as the input data
mapping = {input_nam... |
Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape | def convert_symbol(prototxt_fname):
"""Convert caffe model definition into Symbol
Parameters
----------
prototxt_fname : str
Filename of the prototxt file
Returns
-------
Symbol
Converted Symbol
tuple
Input shape
"""
sym, output_name, input_dim = _parse_... |
r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
<https://arxiv.org/abs/1409.1556>`_ paper.
Parameters
----------
num_layers : int
Number of layers for the variant of densenet. Options are 11, 13, 16, 19.
pretrained : bool, default False
W... | def get_vgg(num_layers, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
<https://arxiv.org/abs/1409.1556>`_ paper.
Parameters
----------
num_layers : int
... |
check function consistency with uniform random numbers | 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] ... |
Remove images without usable rois | 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)) |
Only flip boxes coordinates, images will be flipped when loading into network | 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 ... |
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
----------
name : str
Name of the model.
root : str, default $MX... | def get_model_file(name, root=os.path.join(base.data_dir(), 'models')):
r"""Return location for the pretrained on local file system.
This function will download from online model zoo when model cannot be found or has mismatch.
The root directory will be created if it doesn't exist.
Parameters
----... |
r"""Purge all pretrained model files in local file store.
Parameters
----------
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters. | 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)
... |
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image | def image_path_from_index(self, index):
"""
given image index, find out full path
Parameters:
----------
index: int
index of a specific image
Returns:
----------
full path of this image
"""
assert self.image_set_index is not No... |
initialize all entries given annotation json file
Parameters:
----------
anno_file: str
annotation json file
shuffle: bool
whether to shuffle image list | 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 = []
... |
Initializes the parameters and auxiliary states. | def init_params(self, initializer=mx.init.Uniform(0.01), **kwargs):
"""Initializes the parameters and auxiliary states.
"""
self._module.init_params(initializer=initializer, **kwargs) |
Forward computation. States from previous forward computation are carried
to the current iteration if `carry_state` is set to `True`. | 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:
... |
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
If set, clip values of all grad... | 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... |
Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Parameters
------... | def _clip_by_global_norm(self, max_norm):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurren... |
Image visualization and preservation
:param title: title
:param X: images to visualized
:param name: saved picture`s name
:return: | def visual(title, X, name):
"""Image visualization and preservation
:param title: title
:param X: images to visualized
:param name: saved picture`s name
:return:
"""
assert len(X.shape) == 4
X = X.transpose((0, 2, 3, 1))
X = np.clip((X - np.min(X))*(255.0/(np.max(X) - np.min(X))), 0,... |
Get the translation of images | 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... |
Load the dataset and split it to train/valid data
:param dataset_name: string
Returns:
train_data: int array
training dataset
val_data: int array
valid dataset | def get_dataset(dataset_name):
"""Load the dataset and split it to train/valid data
:param dataset_name: string
Returns:
train_data: int array
training dataset
val_data: int array
valid dataset
"""
# mnist
if dataset == "mnist":
train_data = gluon.data.DataLoade... |
Get net G | 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... |
Get the 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... |
Get configurations for net | 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... |
Entry point to dcgan | def main():
"""Entry point to dcgan"""
print("|------- new changes!!!!!!!!!")
# to get the dataset and net configuration
train_data, val_data = get_dataset(dataset)
netG = get_netG()
netD = get_netD()
loss, trainerG, trainerD = get_configurations(netG, netD)
# set labels
real_label ... |
Gets a customized logger.
.. note:: `getLogger` is deprecated. Use `get_logger` instead. | def getLogger(name=None, filename=None, filemode=None, level=WARNING):
"""Gets a customized logger.
.. note:: `getLogger` is deprecated. Use `get_logger` instead.
"""
warnings.warn("getLogger is deprecated, Use get_logger instead.",
DeprecationWarning, stacklevel=2)
return get_lo... |
Gets a customized logger.
Parameters
----------
name: str, optional
Name of the logger.
filename: str, optional
The filename to which the logger's output will be sent.
filemode: str, optional
The file mode to open the file (corresponding to `filename`),
default is 'a... | def get_logger(name=None, filename=None, filemode=None, level=WARNING):
"""Gets a customized logger.
Parameters
----------
name: str, optional
Name of the logger.
filename: str, optional
The filename to which the logger's output will be sent.
filemode: str, optional
The ... |
data preparation | def transformer(data, label):
""" data preparation """
data = mx.image.imresize(data, IMAGE_SIZE, IMAGE_SIZE)
data = mx.nd.transpose(data, (2, 0, 1))
data = data.astype(np.float32) / 128.0 - 1
return data, label |
helper function to get dataloader | def get_training_data(batch_size):
""" helper function to get dataloader"""
return gluon.data.DataLoader(
CIFAR10(train=True, transform=transformer),
batch_size=batch_size, shuffle=True, last_batch='discard') |
r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
ResNet V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
Parameters
----------
version : int
Version of ResNet. Opti... | def get_resnet(version, num_layers, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
ResNet V2 model from `"Identity Mappings in Deep Residu... |
Helper function for random generators. | def _random_helper(random, sampler, params, shape, dtype, kwargs):
"""Helper function for random generators."""
if isinstance(params[0], Symbol):
for i in params[1:]:
assert isinstance(i, Symbol), \
"Distribution parameters must all have the same type, but got " \
... |
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.
Parameters
----------
lam : float or Symbol, optional
Expectation of interval, should... | 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.
Parameters
----------
lam : fl... |
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).
*alpha* is defined as *1/k* where *k* is the failure limit of the
number of unsuccessful experim... | 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
distribution parametrized by *mu* (mean) and *alpha* (dispersion).
*alpha*... |
Concurrent sampling from multiple multinomial distributions.
.. note:: The input distribution must be normalized, i.e. `data` must sum to
1 along its last dimension.
Parameters
----------
data : Symbol
An *n* dimensional array whose last dimension has length `k`, where
`k... | def multinomial(data, shape=_Null, get_prob=True, dtype='int32', **kwargs):
"""Concurrent sampling from multiple multinomial distributions.
.. note:: The input distribution must be normalized, i.e. `data` must sum to
1 along its last dimension.
Parameters
----------
data : Symbol
... |
Single-shot multi-box detection with VGG 16 layers ConvNet
This is a modified version, with fc6/fc7 layers replaced by conv layers
And the network is slightly smaller than original VGG 16 network
This is a training network with losses
Parameters:
----------
num_classes: int
number of ob... | def get_symbol_train(num_classes=20, nms_thresh=0.5, force_suppress=False,
nms_topk=400, **kwargs):
"""
Single-shot multi-box detection with VGG 16 layers ConvNet
This is a modified version, with fc6/fc7 layers replaced by conv layers
And the network is slightly smaller than origina... |
Single-shot multi-box detection with VGG 16 layers ConvNet
This is a modified version, with fc6/fc7 layers replaced by conv layers
And the network is slightly smaller than original VGG 16 network
This is the detection network
Parameters:
----------
num_classes: int
number of object clas... | def get_symbol(num_classes=20, nms_thresh=0.5, force_suppress=False,
nms_topk=400, **kwargs):
"""
Single-shot multi-box detection with VGG 16 layers ConvNet
This is a modified version, with fc6/fc7 layers replaced by conv layers
And the network is slightly smaller than original VGG 16 net... |
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
optionally "prefix-xxxx.states", where xxxx is the
epoch number.... | 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... |
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.
epoch : int
The current epoch number.
save_optimizer_st... | 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.
... |
Internal function to reset binded state. | def _reset_bind(self):
"""Internal function to reset binded state."""
self.binded = False
self._exec_group = None
self._data_shapes = None
self._label_shapes = None |
Gets current parameters.
Returns
-------
`(arg_params, aux_params)`
A pair of dictionaries each mapping parameter names to NDArray values. | def get_params(self):
"""Gets current parameters.
Returns
-------
`(arg_params, aux_params)`
A pair of dictionaries each mapping parameter names to NDArray values.
"""
assert self.binded and self.params_initialized
if self._params_dirty:
... |
Initializes the parameters and auxiliary states.
Parameters
----------
initializer : Initializer
Called to initialize parameters if needed.
arg_params : dict
If not ``None``, should be a dictionary of existing arg_params. Initialization
will be copied... | def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,
allow_missing=False, force_init=False, allow_extra=False):
"""Initializes the parameters and auxiliary states.
Parameters
----------
initializer : Initializer
Called to ini... |
Assigns parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
allow_missing : bool
If ``True``, params could contain missing values, and the ... | def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,
allow_extra=False):
"""Assigns parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
... |
Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.
Parameters
----------
data_shapes : list of (str, tuple)
Typically is ``data_iter.provide_data``.
label_shapes : list of (str, tuple)
Typically... | def bind(self, data_shapes, label_shapes=None, for_training=True,
inputs_need_grad=False, force_rebind=False, shared_module=None,
grad_req='write'):
"""Binds the symbols to construct executors. This is necessary before one
can perform computation with the module.
Param... |
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.provide_label``. | 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.... |
Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
optimizer : str or Optimizer
Default `'sgd'`
optimizer_params : dict
Default `(('learning_rate', 0.01),)`. The default value is not a dictio... | def init_optimizer(self, kvstore='local', optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),), force_init=False):
"""Installs and initializes optimizers.
Parameters
----------
kvstore : str or KVStore
Default `'local'`.
optimizer : str... |
Borrows optimizer from a shared module. Used in bucketing, where exactly the same
optimizer (esp. kvstore) is used.
Parameters
----------
shared_module : Module | def borrow_optimizer(self, shared_module):
"""Borrows optimizer from a shared module. Used in bucketing, where exactly the same
optimizer (esp. kvstore) is used.
Parameters
----------
shared_module : Module
"""
assert shared_module.optimizer_initialized
s... |
Forward computation. It supports data batches with different shapes, such as
different batch sizes or different image sizes.
If reshaping of data batch relates to modification of symbol or module, such as
changing image layout ordering or switching from training to predicting, module
reb... | def forward(self, data_batch, is_train=None):
"""Forward computation. It supports data batches with different shapes, such as
different batch sizes or different image sizes.
If reshaping of data batch relates to modification of symbol or module, such as
changing image layout ordering or ... |
Backward computation.
See Also
----------
:meth:`BaseModule.backward`.
Parameters
----------
out_grads : NDArray or list of NDArray, optional
Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
... | def backward(self, out_grads=None):
"""Backward computation.
See Also
----------
:meth:`BaseModule.backward`.
Parameters
----------
out_grads : NDArray or list of NDArray, optional
Gradient on the outputs to be propagated back.
This param... |
Updates parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch.
When KVStore is used to update parameters for multi-device or multi-machine training,
a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters,... | def update(self):
"""Updates parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch.
When KVStore is used to update parameters for multi-device or multi-machine training,
a copy of the parameters are stored in KVStore. Note that... |
Gets outputs of the previous forward computation.
If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it
is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output
elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray`
... | def get_outputs(self, merge_multi_context=True):
"""Gets outputs of the previous forward computation.
If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it
is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output
elements are `NDArray`. W... |
Gets the gradients with respect to the inputs of the module.
If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it
is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output
elements are `NDArray`.
Parameters
----------
... | def get_input_grads(self, merge_multi_context=True):
"""Gets the gradients with respect to the inputs of the module.
If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it
is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output
elem... |
Gets states from all devices.
If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it
is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output
elements are `NDArray`.
Parameters
----------
merge_multi_context : bool
... | def get_states(self, merge_multi_context=True):
"""Gets states from all devices.
If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it
is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output
elements are `NDArray`.
Parameters
... |
Evaluates and accumulates evaluation metric on outputs of the last forward computation.
See Also
----------
:meth:`BaseModule.update_metric`.
Parameters
----------
eval_metric : EvalMetric
Evaluation metric to use.
labels : list of NDArray if `pre_sl... | def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
See Also
----------
:meth:`BaseModule.update_metric`.
Parameters
----------
eval_metric : EvalMetric
... |
Synchronizes parameters from devices to CPU. This function should be called after
calling `update` that updates the parameters on the devices, before one can read the
latest parameters from ``self._arg_params`` and ``self._aux_params``.
For row_sparse parameters on devices, ther are pulled from... | def _sync_params_from_devices(self):
"""Synchronizes parameters from devices to CPU. This function should be called after
calling `update` that updates the parameters on the devices, before one can read the
latest parameters from ``self._arg_params`` and ``self._aux_params``.
For row_sp... |
Saves optimizer (updater) state to a file.
Parameters
----------
fname : str
Path to output states file. | def save_optimizer_states(self, fname):
"""Saves optimizer (updater) state to a file.
Parameters
----------
fname : str
Path to output states file.
"""
assert self.optimizer_initialized
if self._update_on_kvstore:
self._kvstore.save_optim... |
Loads optimizer (updater) state from a file.
Parameters
----------
fname : str
Path to input states file. | def load_optimizer_states(self, fname):
"""Loads optimizer (updater) state from a file.
Parameters
----------
fname : str
Path to input states file.
"""
assert self.optimizer_initialized
if self._update_on_kvstore:
self._kvstore.load_opti... |
Prepares the module for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.
When KVStore is used to update parameters for multi-devi... | def prepare(self, data_batch, sparse_row_id_fn=None):
'''Prepares the module for processing a data batch.
Usually involves switching bucket and reshaping.
For modules that contain `row_sparse` parameters in KVStore,
it prepares the `row_sparse` parameters based on the sparse_row_id_fn.
... |
Helper function for random generators. | def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
"""Helper function for random generators."""
if isinstance(params[0], NDArray):
for i in params[1:]:
assert isinstance(i, NDArray), \
"Distribution parameters must all have the same type, but got " \... |
Draw random samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval *[low, high)*
(includes *low*, but excludes *high*).
Parameters
----------
low : float or NDArray, optional
Lower boundary of the output interval. All values generated will be
... | def uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval *[low, high)*
(includes *low*, but excludes *high*).
Parameters
----------
low : float or NDArra... |
Draw random samples from a normal (Gaussian) distribution.
Samples are distributed according to a normal distribution parametrized
by *loc* (mean) and *scale* (standard deviation).
Parameters
----------
loc : float or NDArray, optional
Mean (centre) of the distribution.
scale : float ... | def normal(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a normal (Gaussian) distribution.
Samples are distributed according to a normal distribution parametrized
by *loc* (mean) and *scale* (standard deviation).
Parameters
----------
loc... |
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