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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.... |
is_data_not_nan = data == data
is_data_not_infinite = data.abs() != np.inf
return ndarray.logical_and(is_data_not_infinite, is_data_not_nan) |
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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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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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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 in range(emb.shape[0]):
d_mat[i, i] = 1e10
nns = argpar... |
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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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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=num_label, input_length=seq_len) |
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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_class = mx.symbol.SoftmaxActivation(data=pred)
softmax_loss = mx.sym.MakeLoss(softmax_class)
softmax_loss = mx.sym.BlockGrad(sof... |
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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:
print("Using MXNet CTC Loss")
assert loss_type == 'ctc'
sm = _add_mxnet_ctc_loss(pred, seq_len, label)
return sm |
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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,... |
# Create the base (shared between training and inference) and add loss to the end
pred = _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden)
if loss_type:
# Training mode, add loss
return _add_ctc_loss(pred, seq_len, num_label, loss_type)
else:
# Inference mode, add softmax
... |
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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 an... |
init_c = [('l%d_init_c' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
init_h = [('l%d_init_h' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
return init_c + init_h |
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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(out)
output_vars = ctypes.cast(output_vars, ctypes.POINTER(NDArrayHandle))
else:
original_output =... |
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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: li... |
assert isinstance(outputs, (list, tuple)), \
"outputs must be a list or tuple of NDArrays"
if out_grads is None:
check_call(_LIB.MXAutogradBackward(
len(outputs),
c_handle_array(outputs),
ctypes.c_void_p(0),
ctypes.c_int(retain_graph)))
r... |
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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 ... |
@functools.wraps(func)
def wrapped(*args):
"""Wrapped function."""
variables = args
if argnum is not None:
argnum_ = argnum if isinstance(argnum, list) else [argnum]
variables = [args[i] for i in argnum_]
for x in variables:
assert isinstance(... |
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def grad(func, argnum=None):
"""Return function that computes gradient of arguments. Parameters func: a python function The forward (loss) function. argnum: an i... |
grad_with_loss_func = grad_and_loss(func, argnum)
@functools.wraps(grad_with_loss_func)
def wrapped(*args):
return grad_with_loss_func(*args)[0]
return wrapped |
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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 :... |
def _norm(array):
if array.stype == 'default':
x = array.reshape((-1,))
return ndarray.dot(x, x)
return array.norm().square()
assert len(arrays) > 0
ctx = arrays[0].context
total_norm = ndarray.add_n(*[_norm(arr).as_in_context(ctx) for arr in arrays])
total_n... |
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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. sh... |
sha1 = hashlib.sha1()
with open(filename, 'rb') as f:
while True:
data = f.read(1048576)
if not data:
break
sha1.update(data)
return sha1.hexdigest() == sha1_hash |
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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 : s... |
if path is None:
fname = url.split('/')[-1]
# Empty filenames are invalid
assert fname, 'Can\'t construct file-name from this URL. ' \
'Please set the `path` option manually.'
else:
path = os.path.expanduser(path)
if os.path.isdir(path):
fname = o... |
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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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def _get_repo_file_url(namespace, filename):
"""Return the URL for hosted file in Gluon repository. Parameters namespace : str Namespace of the file. filename : ... |
return '{base_url}{namespace}/{filename}'.format(base_url=_get_repo_url(),
namespace=namespace,
filename=filename) |
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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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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_function.__name__ = func_name
symbol_function.__doc__ = doc_str
symbol_function.__module__ = 'mxnet.symbol'
... |
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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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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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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] * arg_shapes
for dtype in type_list:
ndarray_arg = []
numpy_arg = []
for s in arg_shapes:
npy = np.random.uniform(rmin,... |
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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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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 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = roi_rec['width'] - oldx2 - 1
... |
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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 ... |
file_name = '{name}-{short_hash}'.format(name=name,
short_hash=short_hash(name))
root = os.path.expanduser(root)
file_path = os.path.join(root, file_name+'.params')
sha1_hash = _model_sha1[name]
if os.path.exists(file_path):
if check_sha1(file_pa... |
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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/mo... |
root = os.path.expanduser(root)
files = os.listdir(root)
for f in files:
if f.endswith(".params"):
os.remove(os.path.join(root, f)) |
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def _load_all(self, anno_file, shuffle):
""" initialize all entries given annotation json file Parameters: anno_file: str annotation json file shuffle: bool whet... |
image_set_index = []
labels = []
coco = COCO(anno_file)
img_ids = coco.getImgIds()
# deal with class names
cats = [cat['name'] for cat in coco.loadCats(coco.getCatIds())]
class_to_coco_ind = dict(zip(cats, coco.getCatIds()))
class_to_ind = dict(zip(self.c... |
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def forward(self, data_batch, is_train=None, carry_state=True):
"""Forward computation. States from previous forward computation are carried to the current itera... |
# propagate states from the previous iteration
if carry_state:
if isinstance(self._next_states, (int, float)):
self._module.set_states(value=self._next_states)
else:
self._module.set_states(states=self._next_states)
self._module.forward(da... |
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def update(self, max_norm=None):
"""Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. Gr... |
if max_norm is not None:
self._clip_by_global_norm(max_norm)
self._module.update() |
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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 greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
... |
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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'))
# state size. (ngf*8) x 4 x 4
n... |
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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 * 2, 4, 2, 1, use_bias=False))
... |
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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 and the discriminator
trainerG = gluon.Trainer(netG.collect_params(... |
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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') |
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def poisson(lam=1, shape=_Null, dtype=_Null, **kwargs):
"""Draw random samples from a Poisson distribution. Samples are distributed according to a Poisson distri... |
return _random_helper(_internal._random_poisson, _internal._sample_poisson,
[lam], shape, dtype, kwargs) |
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def generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, **kwargs):
"""Draw random samples from a generalized negative binomial distribution. S... |
return _random_helper(_internal._random_generalized_negative_binomial,
_internal._sample_generalized_negative_binomial,
[mu, alpha], shape, dtype, kwargs) |
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def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Creates a model from previously saved checkpoint. Parameters prefix : str path prefix of saved... |
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return... |
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def save_checkpoint(self, prefix, epoch, save_optimizer_states=False):
"""Saves current progress to checkpoint. Use `mx.callback.module_checkpoint` as `epoch_end... |
self._symbol.save('%s-symbol.json'%prefix)
param_name = '%s-%04d.params' % (prefix, epoch)
self.save_params(param_name)
logging.info('Saved checkpoint to \"%s\"', param_name)
if save_optimizer_states:
state_name = '%s-%04d.states' % (prefix, epoch)
self.s... |
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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 |
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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... |
assert self.binded
self._data_shapes, self._label_shapes = _parse_data_desc(
self.data_names, self.label_names, data_shapes, label_shapes)
self._exec_group.reshape(self._data_shapes, self._label_shapes) |
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def forward(self, data_batch, is_train=None):
"""Forward computation. It supports data batches with different shapes, such as different batch sizes or different ... |
assert self.binded and self.params_initialized
curr_data_shapes = tuple(i.shape for i in self._data_shapes)
if isinstance(data_batch, list):
assert data_batch is not None, "Encountered empty data batch"
new_data_shapes = []
for i in range(len(data_batch[0].d... |
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def update(self):
"""Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. When KVStore is u... |
assert self.binded and self.params_initialized and self.optimizer_initialized
self._params_dirty = True
if self._update_on_kvstore:
_update_params_on_kvstore(self._exec_group.param_arrays,
self._exec_group.grad_arrays,
... |
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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,... |
assert self.binded and self.params_initialized
return self._exec_group.get_outputs(merge_multi_context=merge_multi_context) |
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def _sync_params_from_devices(self):
"""Synchronizes parameters from devices to CPU. This function should be called after calling `update` that updates the param... |
self._exec_group.get_params(self._arg_params, self._aux_params)
if self._kvstore and self._update_on_kvstore:
for param_name, param_val in sorted(self._arg_params.items()):
if param_val.stype == 'row_sparse':
row_ids = nd.arange(0, param_val.shape[0], dty... |
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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 dis... |
return _random_helper(_internal._random_uniform, _internal._sample_uniform,
[low, high], shape, dtype, ctx, out, kwargs) |
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def exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
r"""Draw samples from an exponential distribution. Its probability density func... |
return _random_helper(_internal._random_exponential, _internal._sample_exponential,
[1.0/scale], shape, dtype, ctx, out, kwargs) |
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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 acc... |
return _random_helper(_internal._random_gamma, _internal._sample_gamma,
[alpha, beta], shape, dtype, ctx, out, kwargs) |
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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 ar... |
return _random_helper(_internal._random_negative_binomial,
_internal._sample_negative_binomial,
[k, p], shape, dtype, ctx, out, kwargs) |
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def randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs):
"""Draw random samples from a discrete uniform distribution. Samples are uniforml... |
return _random_helper(_internal._random_randint, None,
[low, high], shape, dtype, ctx, out, kwargs) |
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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_to_init = []
if self._kvstore:
for param in self._params_to_init:
if param._deferred_init:
par... |
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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
self._params_to_init = [param for param in sel... |
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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.
# The training loop is the following:
... |
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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 defined before its learning "
"rate is mutated.")
else:
self._optimizer.set_learning_rate(lr) |
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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.pu... |
# initialize kv and params if not already
if not self._kv_initialized:
self._init_kvstore()
if self._params_to_init:
self._init_params()
idx = self._param2idx[parameter.name]
if full_idx and 'dist' not in self._kvstore.type:
assert row_id.size... |
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def allreduce_grads(self):
"""For each parameter, reduce the gradients from different contexts. Should be called after `autograd.backward()`, outside of `record(... |
if not self._kv_initialized:
self._init_kvstore()
if self._params_to_init:
self._init_params()
assert not (self._kvstore and self._update_on_kvstore), \
'allreduce_grads() when parameters are updated on kvstore ' \
'is not supported. Try s... |
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def update(self, batch_size, ignore_stale_grad=False):
"""Makes one step of parameter update. Should be called after `autograd.backward()` and outside of `record... |
if not self._kv_initialized:
self._init_kvstore()
if self._params_to_init:
self._init_params()
assert not (self._kvstore and self._update_on_kvstore), \
'update() when parameters are updated on kvstore ' \
'is not supported. Try setting `u... |
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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_sample = 0
with open(DATA_PATH) as f:
for line in f:
if (random.random() < P):
num_non_z... |
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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_TASK_ID'] = str(taskid)
env['DMLC_ROLE'] = role
env['DMLC_JOB_CLUSTER'] = 'lo... |
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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 parameters in input
Parameters
----------
nworker: number of slave process... |
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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 |
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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, ... |
logging.info("Preparing data iterators for " + filename + " ... ")
user = []
item = []
score = []
with open(filename, 'r') as f:
num_samples = 0
for line in f:
tks = line.strip().split('::')
if len(tks) != 4:
continue
num_samples +... |
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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 im... |
hdl = NDArrayHandle()
check_call(_LIB.MXCVImdecode(ctypes.c_char_p(str_img),
mx_uint(len(str_img)),
flag, ctypes.byref(hdl)))
return mx.nd.NDArray(hdl) |
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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... |
hdl = NDArrayHandle()
check_call(_LIB.MXCVResize(src.handle, mx_uint(size[0]), mx_uint(size[1]),
interpolation, ctypes.byref(hdl)))
return mx.nd.NDArray(hdl) |
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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.NDAr... |
hdl = NDArrayHandle()
check_call(_LIB.MXCVcopyMakeBorder(src.handle, ctypes.c_int(top), ctypes.c_int(bot),
ctypes.c_int(left), ctypes.c_int(right),
ctypes.c_int(border_type), ctypes.c_double(value),
... |
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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)
return out, (x0, y0, new_w, new_h) |
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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 = int(new_area*new_ratio)
new_h = int(new_area/new_ratio)
if random.uniform(0., 1.) < 0.5:
new_w, new_h = new_h, n... |
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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()
img = imdecode(str_img, 1)
img, _ = random_crop(im... |
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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("Shape of labels {} does not match shape of "
"predictions {}".format(label_shape, pre... |
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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 mo... |
graph = GraphProto()
try:
import onnx
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. Instructions to"
+ " install - https://github.com/onnx/onnx#installation")
model_proto = onnx.load_model(model_file)
net = graph.graph_to_gl... |
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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 = models.get_model(model, **kwargs)
if opt.resume:
net.... |
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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_data, val_data = get_cifar10_iterator(batch_size, (3, 32, 32),
... |
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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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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... |
if not isinstance(seed_state, integer_types):
raise ValueError('seed_state must be int')
seed_state = ctypes.c_int(int(seed_state))
if ctx == "all":
check_call(_LIB.MXRandomSeed(seed_state))
else:
ctx = Context(ctx)
check_call(_LIB.MXRandomSeedContext(seed_state, ctx.dev... |
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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 - https://github.com/onnx/onnx")
new_attrs = translation_utils._remove_attributes(attrs, ['seed'])
new... |
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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 - https://github.com/onnx/onnx")
new_attr = translation_utils._remove_attributes(attrs, ['seed'])
new_... |
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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_axis, proto_obj)
return op_value, new_attr, inputs
re... |
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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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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 = {'dtype': 'int64'}
return 'cast', cast_attrs, argmax_op |
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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_attrs = {'dtype': 'int64'}
return 'cast', cast_attrs, argmin_op |
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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 compa... |
if len(inputs) > 1:
mxnet_op = symbol.maximum(inputs[0], inputs[1])
for op_input in inputs[2:]:
mxnet_op = symbol.maximum(mxnet_op, op_input)
else:
mxnet_op = symbol.maximum(inputs[0], inputs[0])
return mxnet_op, attrs, inputs |
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def minimum(attrs, inputs, proto_obj):
"""Elementwise minimum of arrays.""" |
# MXNet minimum 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.minimum(inputs[0], inputs[1])
for op_input in inputs[2:]:
mxnet_op = symbo... |
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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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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'
})
new_attrs['pad_width'] = translation_utils._pad_sequence_fix(new_attrs.get... |
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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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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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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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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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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',
'pads': 'pad',
'di... |
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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),
'pool_type': 'max'})
return 'Pooling', new_attrs, inputs |
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def global_avgpooling(attrs, inputs, proto_obj):
"""Performs avg pooling on the input.""" |
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
'pool_type': 'avg'})
return 'Pooling', new_attrs, inputs |
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def global_lppooling(attrs, inputs, proto_obj):
"""Performs global lp pooling on the input.""" |
p_value = attrs.get('p', 2)
new_attrs = translation_utils._add_extra_attributes(attrs, {'global_pool': True,
'kernel': (1, 1),
'pool_type': 'lp',
... |
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def linalg_gemm(attrs, inputs, proto_obj):
"""Performs general matrix multiplication and accumulation""" |
trans_a = 0
trans_b = 0
alpha = 1
beta = 1
if 'transA' in attrs:
trans_a = attrs['transA']
if 'transB' in attrs:
trans_b = attrs['transB']
if 'alpha' in attrs:
alpha = attrs['alpha']
if 'beta' in attrs:
beta = attrs['beta']
flatten_a = symbol.flatten(... |
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def local_response_norm(attrs, inputs, proto_obj):
"""Local Response Normalization.""" |
new_attrs = translation_utils._fix_attribute_names(attrs,
{'bias': 'knorm',
'size' : 'nsize'})
return 'LRN', new_attrs, inputs |
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def dropout(attrs, inputs, proto_obj):
"""Dropout Regularization.""" |
mode = 'training'
if 'is_test' in attrs and attrs['is_test'] == 0:
mode = 'always'
new_attrs = translation_utils._fix_attribute_names(attrs,
{'ratio': 'p'})
new_attrs = translation_utils._remove_attributes(new_attrs, ['is_test'])
new_at... |
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def reshape(attrs, inputs, proto_obj):
"""Reshape the given array by the shape attribute.""" |
if len(inputs) == 1:
return 'reshape', attrs, inputs[0]
reshape_shape = list(proto_obj._params[inputs[1].name].asnumpy())
reshape_shape = [int(i) for i in reshape_shape]
new_attrs = {'shape': reshape_shape}
return 'reshape', new_attrs, inputs[:1] |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def cast(attrs, inputs, proto_obj):
""" Cast input to a given dtype""" |
try:
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
except ImportError:
raise ImportError("Onnx and protobuf need to be installed. "
+ "Instructions to install - https://github.com/onnx/onnx")
new_attrs = translation_utils._fix_attribute_names(attrs, {'to' : 'dtyp... |
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Description:
def split(attrs, inputs, proto_obj):
"""Splits an array along a particular axis into multiple sub-arrays.""" |
split_list = attrs.get('split') if 'split' in attrs else []
new_attrs = translation_utils._fix_attribute_names(attrs,
{'split' : 'num_outputs'})
if 'axis' not in attrs:
new_attrs = translation_utils._add_extra_attributes(new_attrs, {'axis': 0})... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def _slice(attrs, inputs, proto_obj):
"""Returns a slice of the input tensor along multiple axes.""" |
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis',
'ends' : 'end',
'starts' : 'begin'})
# onnx slice provides slicing o... |
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