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Description:
def imdecode(self, s):
"""Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.""" |
def locate():
"""Locate the image file/index if decode fails."""
if self.seq is not None:
idx = self.seq[(self.cur % self.num_image) - 1]
else:
idx = (self.cur % self.num_image) - 1
if self.imglist is not None:
_, f... |
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def word_to_vector(word):
""" Convert character vectors to integer vectors. """ |
vector = []
for char in list(word):
vector.append(char2int(char))
return vector |
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def vector_to_word(vector):
""" Convert integer vectors to character vectors. """ |
word = ""
for vec in vector:
word = word + int2char(vec)
return word |
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def char_conv(out):
""" Convert integer vectors to character vectors for batch. """ |
out_conv = list()
for i in range(out.shape[0]):
tmp_str = ''
for j in range(out.shape[1]):
if int(out[i][j]) >= 0:
tmp_char = int2char(int(out[i][j]))
if int(out[i][j]) == 27:
tmp_char = ''
tmp_str = tmp_str + tmp_c... |
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def get_frames(root_path):
"""Get path to all the frame in view SAX and contain complete frames""" |
ret = []
for root, _, files in os.walk(root_path):
root=root.replace('\\','/')
files=[s for s in files if ".dcm" in s]
if len(files) == 0 or not files[0].endswith(".dcm") or root.find("sax") == -1:
continue
prefix = files[0].rsplit('-', 1)[0]
fileset = set(files)
... |
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def write_data_csv(fname, frames, preproc):
"""Write data to csv file""" |
fdata = open(fname, "w")
dr = Parallel()(delayed(get_data)(lst,preproc) for lst in frames)
data,result = zip(*dr)
for entry in data:
fdata.write(','.join(entry)+'\r\n')
print("All finished, %d slices in total" % len(data))
fdata.close()
result = np.ravel(result)
return result |
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def crop_resize(img, size):
"""crop center and resize""" |
if img.shape[0] < img.shape[1]:
img = img.T
# we crop image from center
short_egde = min(img.shape[:2])
yy = int((img.shape[0] - short_egde) / 2)
xx = int((img.shape[1] - short_egde) / 2)
crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
# resize to 64, 64
resized_img = transfor... |
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def get_generator():
""" construct and return generator """ |
g_net = gluon.nn.Sequential()
with g_net.name_scope():
g_net.add(gluon.nn.Conv2DTranspose(
channels=512, kernel_size=4, strides=1, padding=0, use_bias=False))
g_net.add(gluon.nn.BatchNorm())
g_net.add(gluon.nn.LeakyReLU(0.2))
g_net.add(gluon.nn.Conv2DTranspose(
... |
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def get_descriptor(ctx):
""" construct and return descriptor """ |
d_net = gluon.nn.Sequential()
with d_net.name_scope():
d_net.add(SNConv2D(num_filter=64, kernel_size=4, strides=2, padding=1, in_channels=3, ctx=ctx))
d_net.add(gluon.nn.LeakyReLU(0.2))
d_net.add(SNConv2D(num_filter=128, kernel_size=4, strides=2, padding=1, in_channels=64, ctx=ctx))
... |
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def sample(self, label):
""" generate random cropping boxes according to parameters if satifactory crops generated, apply to ground-truth as well Parameters: lab... |
samples = []
count = 0
for trial in range(self.max_trials):
if count >= self.max_sample:
return samples
scale = np.random.uniform(self.min_scale, self.max_scale)
min_ratio = max(self.min_aspect_ratio, scale * scale)
max_ratio = min... |
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def _check_satisfy(self, rand_box, gt_boxes):
""" check if overlap with any gt box is larger than threshold """ |
l, t, r, b = rand_box
num_gt = gt_boxes.shape[0]
ls = np.ones(num_gt) * l
ts = np.ones(num_gt) * t
rs = np.ones(num_gt) * r
bs = np.ones(num_gt) * b
mask = np.where(ls < gt_boxes[:, 1])[0]
ls[mask] = gt_boxes[mask, 1]
mask = np.where(ts < gt_boxes... |
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def sample(self, label):
""" generate random padding boxes according to parameters if satifactory padding generated, apply to ground-truth as well Parameters: la... |
samples = []
count = 0
for trial in range(self.max_trials):
if count >= self.max_sample:
return samples
scale = np.random.uniform(self.min_scale, self.max_scale)
min_ratio = max(self.min_aspect_ratio, scale * scale)
max_ratio = min... |
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def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, *args, **kwargs):
"""Measure time cost of running a function """ |
mx.nd.waitall()
args_list = []
for arg in args:
args_list.append(arg)
start = time.time()
if scipy_trans_lhs:
args_list[0] = np.transpose(args_list[0]) if scipy_dns_lhs else sp.spmatrix.transpose(args_list[0])
for _ in range(repeat):
func_name(*args_list, **kwargs)
m... |
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def default_batchify_fn(data):
"""Collate data into batch.""" |
if isinstance(data[0], nd.NDArray):
return nd.stack(*data)
elif isinstance(data[0], tuple):
data = zip(*data)
return [default_batchify_fn(i) for i in data]
else:
data = np.asarray(data)
return nd.array(data, dtype=data.dtype) |
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def default_mp_batchify_fn(data):
"""Collate data into batch. Use shared memory for stacking.""" |
if isinstance(data[0], nd.NDArray):
out = nd.empty((len(data),) + data[0].shape, dtype=data[0].dtype,
ctx=context.Context('cpu_shared', 0))
return nd.stack(*data, out=out)
elif isinstance(data[0], tuple):
data = zip(*data)
return [default_mp_batchify_fn(i)... |
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def _as_in_context(data, ctx):
"""Move data into new context.""" |
if isinstance(data, nd.NDArray):
return data.as_in_context(ctx)
elif isinstance(data, (list, tuple)):
return [_as_in_context(d, ctx) for d in data]
return data |
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def worker_loop_v1(dataset, key_queue, data_queue, batchify_fn):
"""Worker loop for multiprocessing DataLoader.""" |
while True:
idx, samples = key_queue.get()
if idx is None:
break
batch = batchify_fn([dataset[i] for i in samples])
data_queue.put((idx, batch)) |
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def fetcher_loop_v1(data_queue, data_buffer, pin_memory=False, pin_device_id=0, data_buffer_lock=None):
"""Fetcher loop for fetching data from queue and put in r... |
while True:
idx, batch = data_queue.get()
if idx is None:
break
if pin_memory:
batch = _as_in_context(batch, context.cpu_pinned(pin_device_id))
else:
batch = _as_in_context(batch, context.cpu())
if data_buffer_lock is not None:
... |
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def shutdown(self):
"""Shutdown internal workers by pushing terminate signals.""" |
if not self._shutdown:
# send shutdown signal to the fetcher and join data queue first
# Remark: loop_fetcher need to be joined prior to the workers.
# otherwise, the the fetcher may fail at getting data
self._data_queue.put((None, None))
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def _ctype_key_value(keys, vals):
""" Returns ctype arrays for the key-value args, and the whether string keys are used. For internal use only. """ |
if isinstance(keys, (tuple, list)):
assert(len(keys) == len(vals))
c_keys = []
c_vals = []
use_str_keys = None
for key, val in zip(keys, vals):
c_key_i, c_val_i, str_keys_i = _ctype_key_value(key, val)
c_keys += c_key_i
c_vals += c_val_i
... |
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def create(name='local'):
"""Creates a new KVStore. For single machine training, there are two commonly used types: ``local``: Copies all gradients to CPU memory... |
if not isinstance(name, string_types):
raise TypeError('name must be a string')
handle = KVStoreHandle()
check_call(_LIB.MXKVStoreCreate(c_str(name),
ctypes.byref(handle)))
kv = KVStore(handle)
set_kvstore_handle(kv.handle)
return kv |
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def init(self, key, value):
""" Initializes a single or a sequence of key-value pairs into the store. For each key, one must `init` it before calling `push` or `... |
ckeys, cvals, use_str_keys = _ctype_key_value(key, value)
if use_str_keys:
check_call(_LIB.MXKVStoreInitEx(self.handle, mx_uint(len(ckeys)), ckeys, cvals))
else:
check_call(_LIB.MXKVStoreInit(self.handle, mx_uint(len(ckeys)), ckeys, cvals)) |
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def push(self, key, value, priority=0):
""" Pushes a single or a sequence of key-value pairs into the store. This function returns immediately after adding an op... |
ckeys, cvals, use_str_keys = _ctype_key_value(key, value)
if use_str_keys:
check_call(_LIB.MXKVStorePushEx(
self.handle, mx_uint(len(ckeys)), ckeys, cvals, ctypes.c_int(priority)))
else:
check_call(_LIB.MXKVStorePush(
self.handle, mx_uint(... |
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def pull(self, key, out=None, priority=0, ignore_sparse=True):
""" Pulls a single value or a sequence of values from the store. This function returns immediately... |
assert(out is not None)
ckeys, cvals, use_str_keys = _ctype_key_value(key, out)
if use_str_keys:
check_call(_LIB.MXKVStorePullWithSparseEx(self.handle, mx_uint(len(ckeys)), ckeys,
cvals, ctypes.c_int(priority),
... |
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def row_sparse_pull(self, key, out=None, priority=0, row_ids=None):
""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \ from the... |
assert(out is not None)
assert(row_ids is not None)
if isinstance(row_ids, NDArray):
row_ids = [row_ids]
assert(isinstance(row_ids, list)), \
"row_ids should be NDArray or list of NDArray"
first_out = out
# whether row_ids are the same
sin... |
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def set_gradient_compression(self, compression_params):
""" Specifies type of low-bit quantization for gradient compression \ and additional arguments depending ... |
if ('device' in self.type) or ('dist' in self.type): # pylint: disable=unsupported-membership-test
ckeys, cvals = _ctype_dict(compression_params)
check_call(_LIB.MXKVStoreSetGradientCompression(self.handle,
mx_uint(len(compress... |
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def set_optimizer(self, optimizer):
""" Registers an optimizer with the kvstore. When using a single machine, this function updates the local optimizer. If using... |
is_worker = ctypes.c_int()
check_call(_LIB.MXKVStoreIsWorkerNode(ctypes.byref(is_worker)))
# pylint: disable=invalid-name
if 'dist' in self.type and is_worker.value: # pylint: disable=unsupported-membership-test
# send the optimizer to server
try:
... |
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def type(self):
""" Returns the type of this kvstore. Returns ------- type : str the string type """ |
kv_type = ctypes.c_char_p()
check_call(_LIB.MXKVStoreGetType(self.handle, ctypes.byref(kv_type)))
return py_str(kv_type.value) |
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def rank(self):
""" Returns the rank of this worker node. Returns ------- rank : int The rank of this node, which is in range [0, num_workers()) """ |
rank = ctypes.c_int()
check_call(_LIB.MXKVStoreGetRank(self.handle, ctypes.byref(rank)))
return rank.value |
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def num_workers(self):
"""Returns the number of worker nodes. Returns ------- size :int The number of worker nodes. """ |
size = ctypes.c_int()
check_call(_LIB.MXKVStoreGetGroupSize(self.handle, ctypes.byref(size)))
return size.value |
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def _set_updater(self, updater):
"""Sets a push updater into the store. This function only changes the local store. When running on multiple machines one must us... |
self._updater = updater
# set updater with int keys
_updater_proto = ctypes.CFUNCTYPE(
None, ctypes.c_int, NDArrayHandle, NDArrayHandle, ctypes.c_void_p)
self._updater_func = _updater_proto(_updater_wrapper(updater))
# set updater with str keys
_str_updater_p... |
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def _send_command_to_servers(self, head, body):
"""Sends a command to all server nodes. Sending command to a server node will cause that server node to invoke ``... |
check_call(_LIB.MXKVStoreSendCommmandToServers(
self.handle, mx_uint(head), c_str(body))) |
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def add(self, module, **kwargs):
"""Add a module to the chain. Parameters module : BaseModule The new module to add. kwargs : ``**keywords`` All the keyword argu... |
self._modules.append(module)
# a sanity check to avoid typo
for key in kwargs:
assert key in self._meta_keys, ('Unknown meta "%s", a typo?' % key)
self._metas.append(kwargs)
# after adding new modules, we are reset back to raw states, needs
# to bind, init... |
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def install_monitor(self, mon):
"""Installs monitor on all executors.""" |
assert self.binded
for module in self._modules:
module.install_monitor(mon) |
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def get_iterator(data_shape, use_caffe_data):
"""Generate the iterator of mnist dataset""" |
def get_iterator_impl_mnist(args, kv):
"""return train and val iterators for mnist"""
# download data
get_mnist_ubyte()
flat = False if len(data_shape) != 1 else True
train = mx.io.MNISTIter(
image="data/train-images-idx3-ubyte",
label="data/train-la... |
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def predict(prediction_dir='./Test'):
"""The function is used to run predictions on the audio files in the directory `pred_directory`. Parameters net: The model ... |
if not os.path.exists(prediction_dir):
warnings.warn("The directory on which predictions are to be made is not found!")
return
if len(os.listdir(prediction_dir)) == 0:
warnings.warn("The directory on which predictions are to be made is empty! Exiting...")
return
# Loading... |
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def _proc_loop(proc_id, alive, queue, fn):
"""Thread loop for generating data Parameters proc_id: int Process id alive: multiprocessing.Value variable for signal... |
print("proc {} started".format(proc_id))
try:
while alive.value:
data = fn()
put_success = False
while alive.value and not put_success:
try:
queue.put(data, timeout=0.5)
put_s... |
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def _init_proc(self):
"""Start processes if not already started""" |
if not self.proc:
self.proc = [
mp.Process(target=self._proc_loop, args=(i, self.alive, self.queue, self.fn))
for i in range(self.num_proc)
]
self.alive.value = True
for p in self.proc:
p.start() |
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def reset(self):
"""Resets the generator by stopping all processes""" |
self.alive.value = False
qsize = 0
try:
while True:
self.queue.get(timeout=0.1)
qsize += 1
except QEmptyExcept:
pass
print("Queue size on reset: {}".format(qsize))
for i, p in enumerate(self.proc):
p.joi... |
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def _load_lib():
"""Load library by searching possible path.""" |
lib_path = libinfo.find_lib_path()
lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
# DMatrix functions
lib.MXGetLastError.restype = ctypes.c_char_p
return lib |
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def c_array(ctype, values):
"""Create ctypes array from a Python array. Parameters ctype : ctypes data type Data type of the array we want to convert to, such as... |
out = (ctype * len(values))()
out[:] = values
return out |
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def ctypes2numpy_shared(cptr, shape):
"""Convert a ctypes pointer to a numpy array. The resulting NumPy array shares the memory with the pointer. Parameters cptr... |
if not isinstance(cptr, ctypes.POINTER(mx_float)):
raise RuntimeError('expected float pointer')
size = 1
for s in shape:
size *= s
dbuffer = (mx_float * size).from_address(ctypes.addressof(cptr.contents))
return _np.frombuffer(dbuffer, dtype=_np.float32).reshape(shape) |
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def build_param_doc(arg_names, arg_types, arg_descs, remove_dup=True):
"""Build argument docs in python style. arg_names : list of str Argument names. arg_types ... |
param_keys = set()
param_str = []
for key, type_info, desc in zip(arg_names, arg_types, arg_descs):
if key in param_keys and remove_dup:
continue
if key == 'num_args':
continue
param_keys.add(key)
ret = '%s : %s' % (key, type_info)
if len(desc... |
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def add_fileline_to_docstring(module, incursive=True):
"""Append the definition position to each function contained in module. Examples -------- # Put the follow... |
def _add_fileline(obj):
"""Add fileinto to a object.
"""
if obj.__doc__ is None or 'From:' in obj.__doc__:
return
fname = inspect.getsourcefile(obj)
if fname is None:
return
try:
line = inspect.getsourcelines(obj)[-1]
exce... |
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def is_np_compat():
""" Checks whether the NumPy compatibility is currently turned on. NumPy-compatibility is turned off by default in backend. Returns ------- A... |
curr = ctypes.c_bool()
check_call(_LIB.MXIsNumpyCompatible(ctypes.byref(curr)))
return curr.value |
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def use_np_compat(func):
"""Wraps a function with an activated NumPy-compatibility scope. This ensures that the execution of the function is guaranteed with NumP... |
@wraps(func)
def _with_np_compat(*args, **kwargs):
with np_compat(active=True):
return func(*args, **kwargs)
return _with_np_compat |
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def corr(label, pred):
"""computes the empirical correlation coefficient""" |
numerator1 = label - np.mean(label, axis=0)
numerator2 = pred - np.mean(pred, axis = 0)
numerator = np.mean(numerator1 * numerator2, axis=0)
denominator = np.std(label, axis=0) * np.std(pred, axis=0)
return np.mean(numerator / denominator) |
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def _get_input(proto):
"""Get input size """ |
layer = caffe_parser.get_layers(proto)
if len(proto.input_dim) > 0:
input_dim = proto.input_dim
elif len(proto.input_shape) > 0:
input_dim = proto.input_shape[0].dim
elif layer[0].type == "Input":
input_dim = layer[0].input_param.shape[0].dim
layer.pop(0)
else:
... |
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def _convert_conv_param(param):
""" Convert convolution layer parameter from Caffe to MXNet """ |
param_string = "num_filter=%d" % param.num_output
pad_w = 0
pad_h = 0
if isinstance(param.pad, int):
pad = param.pad
param_string += ", pad=(%d, %d)" % (pad, pad)
else:
if len(param.pad) > 0:
pad = param.pad[0]
param_string += ", pad=(%d, %d)" % (pad... |
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def _convert_pooling_param(param):
"""Convert the pooling layer parameter """ |
param_string = "pooling_convention='full', "
if param.global_pooling:
param_string += "global_pool=True, kernel=(1,1)"
else:
param_string += "pad=(%d,%d), kernel=(%d,%d), stride=(%d,%d)" % (
param.pad, param.pad, param.kernel_size, param.kernel_size,
param.stride, pa... |
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def parse_caffemodel(file_path):
""" parses the trained .caffemodel file filepath: /path/to/trained-model.caffemodel returns: layers """ |
f = open(file_path, 'rb')
contents = f.read()
net_param = caffe_pb2.NetParameter()
net_param.ParseFromString(contents)
layers = find_layers(net_param)
return layers |
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def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array.""" |
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, target_ht)
# Normalize in the same way as the pre-trained model.
data = data.astype(np.float32) / 255... |
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def cub200_iterator(data_path, batch_k, batch_size, data_shape):
"""Return training and testing iterator for the CUB200-2011 dataset.""" |
return (CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=True),
CUB200Iter(data_path, batch_k, batch_size, data_shape, is_train=False)) |
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def get_image(self, img, is_train):
"""Load and transform an image.""" |
img_arr = mx.image.imread(img)
img_arr = transform(img_arr, 256, 256, is_train, self.boxes[img])
return img_arr |
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def next(self):
"""Return a batch.""" |
if self.is_train:
data, labels = self.sample_train_batch()
else:
if self.test_count * self.batch_size < len(self.test_image_files):
data, labels = self.get_test_batch()
self.test_count += 1
else:
self.test_count = 0
... |
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def load_mnist(training_num=50000):
"""Load mnist dataset""" |
data_path = os.path.join(os.path.dirname(os.path.realpath('__file__')), 'mnist.npz')
if not os.path.isfile(data_path):
from six.moves import urllib
origin = (
'https://github.com/sxjscience/mxnet/raw/master/example/bayesian-methods/mnist.npz'
)
print('Downloading dat... |
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def feature_list():
""" Check the library for compile-time features. The list of features are maintained in libinfo.h and libinfo.cc Returns ------- list List of... |
lib_features_c_array = ctypes.POINTER(Feature)()
lib_features_size = ctypes.c_size_t()
check_call(_LIB.MXLibInfoFeatures(ctypes.byref(lib_features_c_array), ctypes.byref(lib_features_size)))
features = [lib_features_c_array[i] for i in range(lib_features_size.value)]
return features |
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def is_enabled(self, feature_name):
""" Check for a particular feature by name Parameters feature_name: str The name of a valid feature as string for example 'CU... |
feature_name = feature_name.upper()
if feature_name not in self:
raise RuntimeError("Feature '{}' is unknown, known features are: {}".format(
feature_name, list(self.keys())))
return self[feature_name].enabled |
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def cache_path(self):
""" make a directory to store all caches Returns: --------- cache path """ |
cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache')
if not os.path.exists(cache_path):
os.mkdir(cache_path)
return cache_path |
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def do_python_eval(self):
""" python evaluation wrapper Returns: None """ |
annopath = os.path.join(self.data_path, 'Annotations', '{:s}.xml')
imageset_file = os.path.join(self.data_path, 'ImageSets', 'Main', self.image_set + '.txt')
cache_dir = os.path.join(self.cache_path, self.name)
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metr... |
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def CreateMultiRandCropAugmenter(min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 1.0), min_eject_coverage=0.3, max_attempts=50, skip_pr... |
def align_parameters(params):
"""Align parameters as pairs"""
out_params = []
num = 1
for p in params:
if not isinstance(p, list):
p = [p]
out_params.append(p)
num = max(num, len(p))
# align for each param
for k, p ... |
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def CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0,... |
auglist = []
if resize > 0:
auglist.append(DetBorrowAug(ResizeAug(resize, inter_method)))
if rand_crop > 0:
crop_augs = CreateMultiRandCropAugmenter(min_object_covered, aspect_ratio_range,
area_range, min_eject_coverage,
... |
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def dumps(self):
"""Override default.""" |
return [self.__class__.__name__.lower(), [x.dumps() for x in self.aug_list]] |
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def _calculate_areas(self, label):
"""Calculate areas for multiple labels""" |
heights = np.maximum(0, label[:, 3] - label[:, 1])
widths = np.maximum(0, label[:, 2] - label[:, 0])
return heights * widths |
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def _intersect(self, label, xmin, ymin, xmax, ymax):
"""Calculate intersect areas, normalized.""" |
left = np.maximum(label[:, 0], xmin)
right = np.minimum(label[:, 2], xmax)
top = np.maximum(label[:, 1], ymin)
bot = np.minimum(label[:, 3], ymax)
invalid = np.where(np.logical_or(left >= right, top >= bot))[0]
out = label.copy()
out[:, 0] = left
out[:, 1... |
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def _check_satisfy_constraints(self, label, xmin, ymin, xmax, ymax, width, height):
"""Check if constrains are satisfied""" |
if (xmax - xmin) * (ymax - ymin) < 2:
return False # only 1 pixel
x1 = float(xmin) / width
y1 = float(ymin) / height
x2 = float(xmax) / width
y2 = float(ymax) / height
object_areas = self._calculate_areas(label[:, 1:])
valid_objects = np.where(object... |
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def _update_labels(self, label, crop_box, height, width):
"""Convert labels according to crop box""" |
xmin = float(crop_box[0]) / width
ymin = float(crop_box[1]) / height
w = float(crop_box[2]) / width
h = float(crop_box[3]) / height
out = label.copy()
out[:, (1, 3)] -= xmin
out[:, (2, 4)] -= ymin
out[:, (1, 3)] /= w
out[:, (2, 4)] /= h
ou... |
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def _random_crop_proposal(self, label, height, width):
"""Propose cropping areas""" |
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
for _ in range(self.max_attempts):
ratio = random.uniform(*self.aspect_ratio_r... |
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def _update_labels(self, label, pad_box, height, width):
"""Update label according to padding region""" |
out = label.copy()
out[:, (1, 3)] = (out[:, (1, 3)] * width + pad_box[0]) / pad_box[2]
out[:, (2, 4)] = (out[:, (2, 4)] * height + pad_box[1]) / pad_box[3]
return out |
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def _random_pad_proposal(self, label, height, width):
"""Generate random padding region""" |
from math import sqrt
if not self.enabled or height <= 0 or width <= 0:
return ()
min_area = self.area_range[0] * height * width
max_area = self.area_range[1] * height * width
for _ in range(self.max_attempts):
ratio = random.uniform(*self.aspect_ratio_ra... |
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def _check_valid_label(self, label):
"""Validate label and its shape.""" |
if len(label.shape) != 2 or label.shape[1] < 5:
msg = "Label with shape (1+, 5+) required, %s received." % str(label)
raise RuntimeError(msg)
valid_label = np.where(np.logical_and(label[:, 0] >= 0, label[:, 3] > label[:, 1],
label[:,... |
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def _estimate_label_shape(self):
"""Helper function to estimate label shape""" |
max_count = 0
self.reset()
try:
while True:
label, _ = self.next_sample()
label = self._parse_label(label)
max_count = max(max_count, label.shape[0])
except StopIteration:
pass
self.reset()
return (m... |
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def _parse_label(self, label):
"""Helper function to parse object detection label. Format for raw label: where n is the width of header, 2 or larger k is the wid... |
if isinstance(label, nd.NDArray):
label = label.asnumpy()
raw = label.ravel()
if raw.size < 7:
raise RuntimeError("Label shape is invalid: " + str(raw.shape))
header_width = int(raw[0])
obj_width = int(raw[1])
if (raw.size - header_width) % obj_wi... |
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def reshape(self, data_shape=None, label_shape=None):
"""Reshape iterator for data_shape or label_shape. Parameters data_shape : tuple or None Reshape the data_s... |
if data_shape is not None:
self.check_data_shape(data_shape)
self.provide_data = [(self.provide_data[0][0], (self.batch_size,) + data_shape)]
self.data_shape = data_shape
if label_shape is not None:
self.check_label_shape(label_shape)
self.pro... |
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def _batchify(self, batch_data, batch_label, start=0):
"""Override the helper function for batchifying data""" |
i = start
batch_size = self.batch_size
try:
while i < batch_size:
label, s = self.next_sample()
data = self.imdecode(s)
try:
self.check_valid_image([data])
label = self._parse_label(label)
... |
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def next(self):
"""Override the function for returning next batch.""" |
batch_size = self.batch_size
c, h, w = self.data_shape
# if last batch data is rolled over
if self._cache_data is not None:
# check both the data and label have values
assert self._cache_label is not None, "_cache_label didn't have values"
assert self... |
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def augmentation_transform(self, data, label):
# pylint: disable=arguments-differ """Override Transforms input data with specified augmentations.""" |
for aug in self.auglist:
data, label = aug(data, label)
return (data, label) |
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def check_label_shape(self, label_shape):
"""Checks if the new label shape is valid""" |
if not len(label_shape) == 2:
raise ValueError('label_shape should have length 2')
if label_shape[0] < self.label_shape[0]:
msg = 'Attempts to reduce label count from %d to %d, not allowed.' \
% (self.label_shape[0], label_shape[0])
raise ValueError(m... |
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def _ratio_enum(anchor, ratios):
""" Enumerate a set of anchors for each aspect ratio wrt an anchor. """ |
w, h, x_ctr, y_ctr = AnchorGenerator._whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = AnchorGenerator._mkanchors(ws, hs, x_ctr, y_ctr)
return anchors |
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def _scale_enum(anchor, scales):
""" Enumerate a set of anchors for each scale wrt an anchor. """ |
w, h, x_ctr, y_ctr = AnchorGenerator._whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = AnchorGenerator._mkanchors(ws, hs, x_ctr, y_ctr)
return anchors |
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def prepare_data(args):
""" set atual shape of data """ |
rnn_type = args.config.get("arch", "rnn_type")
num_rnn_layer = args.config.getint("arch", "num_rnn_layer")
num_hidden_rnn_list = json.loads(args.config.get("arch", "num_hidden_rnn_list"))
batch_size = args.config.getint("common", "batch_size")
if rnn_type == 'lstm':
init_c = [('l%d_init_c... |
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def check_error(model, path, shapes, output = 'softmax_output', verbose = True):
""" Check the difference between predictions from MXNet and CoreML. """ |
coreml_model = _coremltools.models.MLModel(path)
input_data = {}
input_data_copy = {}
for ip in shapes:
input_data[ip] = _np.random.rand(*shapes[ip]).astype('f')
input_data_copy[ip] = _np.copy(input_data[ip])
dataIter = _mxnet.io.NDArrayIter(input_data_copy)
mx_out = model.pred... |
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def sample_categorical(prob, rng):
"""Sample from independent categorical distributions Each batch is an independent categorical distribution. Parameters prob : ... |
ret = numpy.empty(prob.shape[0], dtype=numpy.float32)
for ind in range(prob.shape[0]):
ret[ind] = numpy.searchsorted(numpy.cumsum(prob[ind]), rng.rand()).clip(min=0.0,
max=prob.shape[
... |
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def sample_normal(mean, var, rng):
"""Sample from independent normal distributions Each element is an independent normal distribution. Parameters mean : numpy.nd... |
ret = numpy.sqrt(var) * rng.randn(*mean.shape) + mean
return ret |
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def nce_loss_subwords( data, label, label_mask, label_weight, embed_weight, vocab_size, num_hidden):
"""NCE-Loss layer under subword-units input. """ |
# get subword-units embedding.
label_units_embed = mx.sym.Embedding(data=label,
input_dim=vocab_size,
weight=embed_weight,
output_dim=num_hidden)
# get valid subword-units embedding wi... |
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def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters.""" |
if path.exists(data_dir):
print(
"Directory {} already exists, skipping.\n"
"To force download and extraction, delete the directory and re-run."
"".format(data_dir),
file=sys.stderr,
)
else:
print("Downloading dataset...", file=sys.stderr... |
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def evaluate(mod, data_iter, epoch, log_interval):
""" Run evaluation on cpu. """ |
start = time.time()
total_L = 0.0
nbatch = 0
density = 0
mod.set_states(value=0)
for batch in data_iter:
mod.forward(batch, is_train=False)
outputs = mod.get_outputs(merge_multi_context=False)
states = outputs[:-1]
total_L += outputs[-1][0]
mod.set_states... |
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def next(self):
"""return one dict which contains "data" and "label" """ |
if self.iter_next():
self.data, self.label = self._read()
return {self.data_name : self.data[0][1],
self.label_name : self.label[0][1]}
else:
raise StopIteration |
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def from_onnx(self, graph):
"""Construct symbol from onnx graph. Parameters graph : onnx protobuf object The loaded onnx graph Returns ------- sym :symbol.Symbol... |
# get input, output shapes
self.model_metadata = self.get_graph_metadata(graph)
# parse network inputs, aka parameters
for init_tensor in graph.initializer:
if not init_tensor.name.strip():
raise ValueError("Tensor's name is required.")
self._para... |
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def get_graph_metadata(self, graph):
""" Get the model metadata from a given onnx graph. """ |
_params = set()
for tensor_vals in graph.initializer:
_params.add(tensor_vals.name)
input_data = []
for graph_input in graph.input:
if graph_input.name not in _params:
shape = [val.dim_value for val in graph_input.type.tensor_type.shape.dim]
... |
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def graph_to_gluon(self, graph, ctx):
"""Construct SymbolBlock from onnx graph. Parameters graph : onnx protobuf object The loaded onnx graph ctx : Context or li... |
sym, arg_params, aux_params = self.from_onnx(graph)
metadata = self.get_graph_metadata(graph)
data_names = [input_tensor[0] for input_tensor in metadata['input_tensor_data']]
data_inputs = [symbol.var(data_name) for data_name in data_names]
from ....gluon import SymbolBlock
... |
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def reshape(self, data_shapes, label_shapes=None):
"""Reshapes both modules for new input shapes. Parameters data_shapes : list of (str, tuple) Typically is ``da... |
super(SVRGModule, self).reshape(data_shapes, label_shapes=label_shapes)
self._mod_aux.reshape(data_shapes, label_shapes=label_shapes) |
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def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False):
"""Installs and initializes SVRGOptimi... |
# Init dict for storing average of full gradients for each device
self._param_dict = [{key: mx.nd.zeros(shape=value.shape, ctx=self._context[i])
for key, value in self.get_params()[0].items()} for i in range(self._ctx_len)]
svrg_optimizer = self._create_optimizer(... |
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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... |
# force rebinding is typically used when one want to switch from
# training to prediction phase.
super(SVRGModule, self).bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind,
shared_module, grad_req)
if for_training:
... |
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def forward(self, data_batch, is_train=None):
"""Forward computation for both two modules. It supports data batches with different shapes, such as different batc... |
super(SVRGModule, self).forward(data_batch, is_train)
if is_train:
self._mod_aux.forward(data_batch, is_train) |
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def update_full_grads(self, train_data):
"""Computes the gradients over all data w.r.t weights of past m epochs. For distributed env, it will accumulate full gra... |
param_names = self._exec_group.param_names
arg, aux = self.get_params()
self._mod_aux.set_params(arg_params=arg, aux_params=aux)
train_data.reset()
nbatch = 0
padding = 0
for batch in train_data:
self._mod_aux.forward(batch, is_train=True)
... |
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def _accumulate_kvstore(self, key, value):
"""Accumulate gradients over all data in the KVStore. In distributed setting, each worker sees a portion of data. The ... |
# Accumulate full gradients for current epochs
self._kvstore.push(key + "_full", value)
self._kvstore._barrier()
self._kvstore.pull(key + "_full", value)
self._allocate_gradients(key, value) |
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def _allocate_gradients(self, key, value):
"""Allocate average of full gradients accumulated in the KVStore to each device. Parameters key: int or str Key in the... |
for i in range(self._ctx_len):
self._param_dict[i][key] = value[i] / self._ctx_len |
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def _update_svrg_gradients(self):
"""Calculates gradients based on the SVRG update rule. """ |
param_names = self._exec_group.param_names
for ctx in range(self._ctx_len):
for index, name in enumerate(param_names):
g_curr_batch_reg = self._exec_group.grad_arrays[index][ctx]
g_curr_batch_special = self._mod_aux._exec_group.grad_arrays[index][ctx]
... |
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def prepare(self, data_batch, sparse_row_id_fn=None):
"""Prepares two modules for processing a data batch. Usually involves switching bucket and reshaping. For m... |
super(SVRGModule, self).prepare(data_batch, sparse_row_id_fn=sparse_row_id_fn)
self._mod_aux.prepare(data_batch, sparse_row_id_fn=sparse_row_id_fn) |
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