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micropython/micropython | ports/stm32/boards/make-pins.py | AlternateFunction.print | def print(self):
"""Prints the C representation of this AF."""
cond_var = None
if self.supported:
cond_var = conditional_var('{}{}'.format(self.func, self.fn_num))
print_conditional_if(cond_var)
print(' AF', end='')
else:
print(' //', en... | python | def print(self):
"""Prints the C representation of this AF."""
cond_var = None
if self.supported:
cond_var = conditional_var('{}{}'.format(self.func, self.fn_num))
print_conditional_if(cond_var)
print(' AF', end='')
else:
print(' //', en... | [
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micropython/micropython | ports/cc3200/boards/make-pins.py | parse_port_pin | def parse_port_pin(name_str):
"""Parses a string and returns a (port, gpio_bit) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 characters")
if name_str[:2] != 'GP':
raise ValueError("Expecting pin name to start with GP")
if not name_str[2:].isdigit(... | python | def parse_port_pin(name_str):
"""Parses a string and returns a (port, gpio_bit) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 characters")
if name_str[:2] != 'GP':
raise ValueError("Expecting pin name to start with GP")
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onnx/onnx | onnx/backend/base.py | Backend.run_node | def run_node(cls,
node, # type: NodeProto
inputs, # type: Any
device='CPU', # type: Text
outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
**kwargs # type: Dict[Text, Any]
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onnx/onnx | onnx/external_data_helper.py | load_external_data_for_tensor | def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None
"""
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
if tensor.HasField("raw_data"): # already loaded
... | python | def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None
"""
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
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onnx/onnx | onnx/external_data_helper.py | load_external_data_for_model | def load_external_data_for_model(model, base_dir): # type: (ModelProto, Text) -> None
"""
Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data
"""
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Loads external tensors into model
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model: ModelProto to load external data to
base_dir: directory that contains external data
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onnx/onnx | onnx/external_data_helper.py | convert_model_to_external_data | def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None):
# type: (ModelProto, bool, Optional[Text]) -> None
"""
call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
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model: ModelProto to be... | python | def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None):
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call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
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onnx/onnx | onnx/external_data_helper.py | convert_model_from_external_data | def convert_model_from_external_data(model): # type: (ModelProto) -> None
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call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
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onnx/onnx | onnx/external_data_helper.py | save_external_data | def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None
"""
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
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"""
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
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onnx/onnx | onnx/external_data_helper.py | _get_attribute_tensors | def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto]
"""Create an iterator of tensors from node attributes of an ONNX model."""
for node in onnx_model_proto.graph.node:
for attribute in node.attribute:
if attribute.HasField("t"):
yield ... | python | def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto]
"""Create an iterator of tensors from node attributes of an ONNX model."""
for node in onnx_model_proto.graph.node:
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onnx/onnx | onnx/external_data_helper.py | remove_external_data_field | def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None
"""
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
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"""
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
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tensor: Tensor object from which value will be removed
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onnx/onnx | onnx/external_data_helper.py | write_external_data_tensors | def write_external_data_tensors(model, filepath): # type: (ModelProto, Text) -> ModelProto
"""
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Note: This function also strips basepath information from all tensors' external_data fields.
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onnx/onnx | tools/protoc-gen-mypy.py | PkgWriter._import | def _import(self, path, name):
# type: (Text, Text) -> Text
"""Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional"
"""
imp = path.replace('/', '.')
self.imports[imp].add(name)
return name | python | def _import(self, path, name):
# type: (Text, Text) -> Text
"""Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional"
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imp = path.replace('/', '.')
self.imports[imp].add(name)
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onnx/onnx | tools/protoc-gen-mypy.py | PkgWriter._import_message | def _import_message(self, type_name):
# type: (d.FieldDescriptorProto) -> Text
"""Import a referenced message and return a handle"""
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# Message defined in this file
return ... | python | def _import_message(self, type_name):
# type: (d.FieldDescriptorProto) -> Text
"""Import a referenced message and return a handle"""
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onnx/onnx | setup.py | mypy_type_check.run | def run(self):
"""Run command."""
onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
returncode = subprocess.call([sys.executable, onnx_script])
sys.exit(returncode) | python | def run(self):
"""Run command."""
onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
returncode = subprocess.call([sys.executable, onnx_script])
sys.exit(returncode) | [
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onnx/onnx | onnx/helper.py | make_node | def make_node(
op_type, # type: Text
inputs, # type: Sequence[Text]
outputs, # type: Sequence[Text]
name=None, # type: Optional[Text]
doc_string=None, # type: Optional[Text]
domain=None, # type: Optional[Text]
**kwargs # type: Any
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op_type, # type: Text
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onnx/onnx | onnx/helper.py | make_operatorsetid | def make_operatorsetid(
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version, # type: int
): # type: (...) -> OperatorSetIdProto
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Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
"""
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domain (string): The domain of the operator set id
version (integer): Version of operator set id
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onnx/onnx | onnx/helper.py | make_attribute | def make_attribute(
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onnx/onnx | onnx/helper.py | make_tensor_value_info | def make_tensor_value_info(
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shape, # type: Optional[Sequence[Union[Text, int]]]
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shape, # type: Optional[Sequence[Union[Text, int]]]
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onnx/onnx | onnx/helper.py | strip_doc_string | def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None
"""
Empties `doc_string` field on any nested protobuf messages
"""
assert isinstance(proto, google.protobuf.message.Message)
for descriptor in proto.DESCRIPTOR.fields:
if descriptor.name == 'doc_string':
... | python | def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None
"""
Empties `doc_string` field on any nested protobuf messages
"""
assert isinstance(proto, google.protobuf.message.Message)
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onnx/onnx | onnx/numpy_helper.py | to_array | def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any]
"""Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array.
"""
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"""Converts a tensor def object to a numpy array.
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tensor: a TensorProto object.
Returns:
arr: the converted array.
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onnx/onnx | onnx/numpy_helper.py | from_array | def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto
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Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def.
"""
tensor = TensorProto()... | python | def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto
"""Converts a numpy array to a tensor def.
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arr: a numpy array.
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onnx/onnx | onnx/__init__.py | _serialize | def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes
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Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes
'''
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Serialize a in-memory proto to bytes
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onnx/onnx | onnx/__init__.py | _deserialize | def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto
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s is bytes containing serialized proto
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onnx/onnx | onnx/__init__.py | load_model | def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto
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Loads a serialized ModelProto into memory
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f can be a file-like object (has "read" function) or a string containing a file name
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Loads a serialized ModelProto into memory
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onnx/onnx | onnx/__init__.py | load_tensor | def load_tensor(f, format=None): # type: (Union[IO[bytes], Text], Optional[Any]) -> TensorProto
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onnx/onnx | onnx/__init__.py | save_model | def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None
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@params
proto should be a in-memory ModelProto
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onnx/onnx | onnx/utils.py | polish_model | def polish_model(model): # type: (ModelProto) -> ModelProto
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This function combines several useful utility functions together.
'''
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This function combines several useful utility functions together.
'''
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apache/incubator-mxnet | python/mxnet/gluon/contrib/rnn/rnn_cell.py | dynamic_unroll | def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0,
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"""Unrolls an RNN cell across time steps.
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"""Unrolls an RNN cell across time steps.
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_attribute_names | def _fix_attribute_names(attrs, change_map):
"""
Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
Returns
-------
:retur... | python | def _fix_attribute_names(attrs, change_map):
"""
Change attribute names as per values in change_map dictionary.
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:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _remove_attributes | def _remove_attributes(attrs, remove_list):
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:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes.
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Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes.
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _add_extra_attributes | def _add_extra_attributes(attrs, extra_attr_map):
"""
:param attrs: Current Attribute list
:param extraAttrMap: Additional attributes to be added
:return: new_attr
"""
for attr in extra_attr_map:
if attr not in attrs:
attrs[attr] = extra_attr_map[attr]
return attrs | python | def _add_extra_attributes(attrs, extra_attr_map):
"""
:param attrs: Current Attribute list
:param extraAttrMap: Additional attributes to be added
:return: new_attr
"""
for attr in extra_attr_map:
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _pad_sequence_fix | def _pad_sequence_fix(attr, kernel_dim=None):
"""Changing onnx's pads sequence to match with mxnet's pad_width
mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
onnx: (x1_begin, x2_begin, ... , xn_end, xn_end)"""
new_attr = ()
if len(attr) % 2 == 0:
for index in range(int(len(attr) / 2)):
... | python | def _pad_sequence_fix(attr, kernel_dim=None):
"""Changing onnx's pads sequence to match with mxnet's pad_width
mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
onnx: (x1_begin, x2_begin, ... , xn_end, xn_end)"""
new_attr = ()
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_pooling | def _fix_pooling(pool_type, inputs, new_attr):
"""onnx pooling operator supports asymmetrical padding
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stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
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p_value = new_attr.get('p_value')
... | python | def _fix_pooling(pool_type, inputs, new_attr):
"""onnx pooling operator supports asymmetrical padding
Adding pad operator before pooling in mxnet to work with onnx"""
stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
padding = new_attr.get('pad')
p_value = new_attr.get('p_value')
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_bias | def _fix_bias(op_name, attrs, num_inputs):
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... | python | def _fix_bias(op_name, attrs, num_inputs):
"""A workaround for 'use_bias' attribute since onnx don't provide this attribute,
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if num_inputs == 3:
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_broadcast | def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj):
"""A workaround to reshape bias term to (1, num_channel)."""
if int(len(proto_obj._params)) > 0:
assert len(list(inputs)) == 2
input0_shape = get_input_shape(inputs[0], proto_obj)
#creating reshape shape
reshape_sha... | python | def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj):
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if int(len(proto_obj._params)) > 0:
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_channels | def _fix_channels(op_name, attrs, inputs, proto_obj):
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"""
weight_name = inputs[1].name
if not weight_name in proto_obj._params:
raise ValueEr... | python | def _fix_channels(op_name, attrs, inputs, proto_obj):
"""A workaround for getting 'channels' or 'units' since onnx don't provide
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | _fix_gemm | def _fix_gemm(op_name, inputs, old_attr, proto_obj):
"""Using FullyConnected operator in place of linalg_gemm to perform same operation"""
op_sym = getattr(symbol, op_name, None)
alpha = float(old_attr.get('alpha', 1.0))
beta = float(old_attr.get('beta', 1.0))
trans_a = int(old_attr.get('transA', 0)... | python | def _fix_gemm(op_name, inputs, old_attr, proto_obj):
"""Using FullyConnected operator in place of linalg_gemm to perform same operation"""
op_sym = getattr(symbol, op_name, None)
alpha = float(old_attr.get('alpha', 1.0))
beta = float(old_attr.get('beta', 1.0))
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apache/incubator-mxnet | python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py | get_input_shape | def get_input_shape(sym, proto_obj):
"""Helper function to obtain the shape of an array"""
arg_params = proto_obj.arg_dict
aux_params = proto_obj.aux_dict
model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')]
data_names = [data[0] for data in proto_obj.model_... | python | def get_input_shape(sym, proto_obj):
"""Helper function to obtain the shape of an array"""
arg_params = proto_obj.arg_dict
aux_params = proto_obj.aux_dict
model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')]
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apache/incubator-mxnet | python/mxnet/image/image.py | imresize | def imresize(src, w, h, *args, **kwargs):
r"""Resize image with OpenCV.
.. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built
with USE_OPENCV=1 for `imresize` to work.
Parameters
----------
src : NDArray
source image
w : int, required
... | python | def imresize(src, w, h, *args, **kwargs):
r"""Resize image with OpenCV.
.. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built
with USE_OPENCV=1 for `imresize` to work.
Parameters
----------
src : NDArray
source image
w : int, required
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Parameters
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src : NDArray
source image
w : int, required
Width of resized image.
h : int, required
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apache/incubator-mxnet | python/mxnet/image/image.py | imdecode | def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray.
.. note:: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes/bytearray or numpy.ndarray
Binary image dat... | python | def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray.
.. note:: `imdecode` uses OpenCV (not the CV2 Python library).
MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.
Parameters
----------
buf : str/bytes/bytearray or numpy.ndarray
Binary image dat... | [
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apache/incubator-mxnet | python/mxnet/image/image.py | scale_down | def scale_down(src_size, size):
"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
Parameters
----------
src_size : tuple of int
Size of the image in ... | python | def scale_down(src_size, size):
"""Scales down crop size if it's larger than image size.
If width/height of the crop is larger than the width/height of the image,
sets the width/height to the width/height of the image.
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src_size : tuple of int
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apache/incubator-mxnet | python/mxnet/image/image.py | copyMakeBorder | def copyMakeBorder(src, top, bot, left, right, *args, **kwargs):
"""Pad image border with OpenCV.
Parameters
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src : NDArray
source image
top : int, required
Top margin.
bot : int, required
Bottom margin.
left : int, required
Left margin.
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"""Pad image border with OpenCV.
Parameters
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src : NDArray
source image
top : int, required
Top margin.
bot : int, required
Bottom margin.
left : int, required
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apache/incubator-mxnet | python/mxnet/image/image.py | _get_interp_method | def _get_interp_method(interp, sizes=()):
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and a auto-estimation method.
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----------
interp : int
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"""Get the interpolation method for resize functions.
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apache/incubator-mxnet | python/mxnet/image/image.py | resize_short | def resize_short(src, size, interp=2):
"""Resizes shorter edge to size.
.. note:: `resize_short` uses OpenCV (not the CV2 Python library).
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"""Resizes shorter edge to size.
.. note:: `resize_short` uses OpenCV (not the CV2 Python library).
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apache/incubator-mxnet | python/mxnet/image/image.py | fixed_crop | def fixed_crop(src, x0, y0, w, h, size=None, interp=2):
"""Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
w : int
... | python | def fixed_crop(src, x0, y0, w, h, size=None, interp=2):
"""Crop src at fixed location, and (optionally) resize it to size.
Parameters
----------
src : NDArray
Input image
x0 : int
Left boundary of the cropping area
y0 : int
Top boundary of the cropping area
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apache/incubator-mxnet | python/mxnet/image/image.py | center_crop | def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
than `size`.
.. note:: This requires MXNet to be compiled with USE_OPENCV.
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----------
src : NDAr... | python | def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is smaller
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apache/incubator-mxnet | python/mxnet/image/image.py | color_normalize | def color_normalize(src, mean, std=None):
"""Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
Returns
-------
NDArray
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"""Normalize src with mean and std.
Parameters
----------
src : NDArray
Input image
mean : NDArray
RGB mean to be subtracted
std : NDArray
RGB standard deviation to be divided
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apache/incubator-mxnet | python/mxnet/image/image.py | random_size_crop | def random_size_crop(src, size, area, ratio, interp=2, **kwargs):
"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or ... | python | def random_size_crop(src, size, area, ratio, interp=2, **kwargs):
"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
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apache/incubator-mxnet | python/mxnet/image/image.py | CreateAugmenter | def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False,
mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0,
pca_noise=0, rand_gray=0, inter_method=2):
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Parameters
----------
dat... | python | def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False,
mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0,
pca_noise=0, rand_gray=0, inter_method=2):
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----------
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apache/incubator-mxnet | python/mxnet/image/image.py | Augmenter.dumps | def dumps(self):
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JSON formatted string that describes the Augmenter.
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str
JSON formatted string that describes the Augmenter.
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter.reset | def reset(self):
"""Resets the iterator to the beginning of the data."""
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"""Resets the iterator to the beginning of the data."""
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter.hard_reset | def hard_reset(self):
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter.next_sample | def next_sample(self):
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"""Helper function for reading in next sample."""
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter._batchify | def _batchify(self, batch_data, batch_label, start=0):
"""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:
... | python | def _batchify(self, batch_data, batch_label, start=0):
"""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:
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter.imdecode | 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]
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"""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:
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apache/incubator-mxnet | python/mxnet/image/image.py | ImageIter.read_image | def read_image(self, fname):
"""Reads an input image `fname` and returns the decoded raw bytes.
Examples
--------
>>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.
"""
with open(os.path.join(self.path_root, fname), 'rb') as fin:
img = fin.read()... | python | def read_image(self, fname):
"""Reads an input image `fname` and returns the decoded raw bytes.
Examples
--------
>>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.
"""
with open(os.path.join(self.path_root, fname), 'rb') as fin:
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apache/incubator-mxnet | example/gluon/sn_gan/train.py | facc | def facc(label, pred):
""" evaluate accuracy """
pred = pred.ravel()
label = label.ravel()
return ((pred > 0.5) == label).mean() | python | def facc(label, pred):
""" evaluate accuracy """
pred = pred.ravel()
label = label.ravel()
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apache/incubator-mxnet | example/gluon/lipnet/utils/common.py | word_to_vector | def word_to_vector(word):
"""
Convert character vectors to integer vectors.
"""
vector = []
for char in list(word):
vector.append(char2int(char))
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Convert character vectors to integer vectors.
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vector = []
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vector.append(char2int(char))
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apache/incubator-mxnet | example/gluon/lipnet/utils/common.py | vector_to_word | def vector_to_word(vector):
"""
Convert integer vectors to character vectors.
"""
word = ""
for vec in vector:
word = word + int2char(vec)
return word | python | def vector_to_word(vector):
"""
Convert integer vectors to character vectors.
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word = ""
for vec in vector:
word = word + int2char(vec)
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apache/incubator-mxnet | example/gluon/lipnet/utils/common.py | char_conv | def char_conv(out):
"""
Convert integer vectors to character vectors for batch.
"""
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for i in range(out.shape[0]):
tmp_str = ''
for j in range(out.shape[1]):
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tmp_char = int2char(int(out[i][j]))
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"""
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]):
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apache/incubator-mxnet | tools/coreml/converter/_add_pooling.py | add_pooling_with_padding_types | def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width,
layer_type, padding_type, input_name, output_name,
padding_top = 0, padding_bottom = 0, padding_left = 0, padding_right = 0,
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exclude_pad_a... | python | def add_pooling_with_padding_types(builder, name, height, width, stride_height, stride_width,
layer_type, padding_type, input_name, output_name,
padding_top = 0, padding_bottom = 0, padding_left = 0, padding_right = 0,
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apache/incubator-mxnet | example/kaggle-ndsb2/Preprocessing.py | get_frames | 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") ... | python | 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") ... | [
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apache/incubator-mxnet | example/kaggle-ndsb2/Preprocessing.py | write_data_csv | 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))
... | python | 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')
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apache/incubator-mxnet | example/kaggle-ndsb2/Preprocessing.py | crop_resize | 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 + ... | python | 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 + ... | [
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apache/incubator-mxnet | example/gluon/sn_gan/model.py | get_generator | 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.L... | python | 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())
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apache/incubator-mxnet | example/gluon/sn_gan/model.py | get_descriptor | 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))
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""" 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))
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apache/incubator-mxnet | example/gluon/sn_gan/model.py | SNConv2D._spectral_norm | def _spectral_norm(self):
""" spectral normalization """
w = self.params.get('weight').data(self.ctx)
w_mat = nd.reshape(w, [w.shape[0], -1])
_u = self.u.data(self.ctx)
_v = None
for _ in range(POWER_ITERATION):
_v = nd.L2Normalization(nd.dot(_u, w_mat))
... | python | def _spectral_norm(self):
""" spectral normalization """
w = self.params.get('weight').data(self.ctx)
w_mat = nd.reshape(w, [w.shape[0], -1])
_u = self.u.data(self.ctx)
_v = None
for _ in range(POWER_ITERATION):
_v = nd.L2Normalization(nd.dot(_u, w_mat))
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apache/incubator-mxnet | example/speech_recognition/stt_utils.py | conv_output_length | def conv_output_length(input_length, filter_size, border_mode, stride,
dilation=1):
""" Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
Params:
i... | python | def conv_output_length(input_length, filter_size, border_mode, stride,
dilation=1):
""" Compute the length of the output sequence after 1D convolution along
time. Note that this function is in line with the function used in
Convolution1D class from Keras.
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apache/incubator-mxnet | example/speech_recognition/stt_utils.py | spectrogram | def spectrogram(samples, fft_length=256, sample_rate=2, hop_length=128):
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"""
Compute the spectrogram for a real signal.
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apache/incubator-mxnet | example/speech_recognition/stt_utils.py | spectrogram_from_file | def spectrogram_from_file(filename, step=10, window=20, max_freq=None,
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label : numpy.array (n x 5 matrix)
ground-truths
Returns:
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... | python | def sample(self, label):
"""
generate random cropping boxes according to parameters
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label : numpy.array (n x 5 matrix)
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apache/incubator-mxnet | example/ssd/tools/rand_sampler.py | RandCropper._check_satisfy | def _check_satisfy(self, rand_box, gt_boxes):
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check if overlap with any gt box is larger than threshold
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l, t, r, b = rand_box
num_gt = gt_boxes.shape[0]
ls = np.ones(num_gt) * l
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check if overlap with any gt box is larger than threshold
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num_gt = gt_boxes.shape[0]
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apache/incubator-mxnet | example/ssd/tools/rand_sampler.py | RandPadder.sample | def sample(self, label):
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generate random padding boxes according to parameters
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Parameters:
----------
label : numpy.array (n x 5 matrix)
ground-truths
Returns:
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... | python | def sample(self, label):
"""
generate random padding boxes according to parameters
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label : numpy.array (n x 5 matrix)
ground-truths
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apache/incubator-mxnet | benchmark/python/sparse/dot.py | measure_cost | def measure_cost(repeat, scipy_trans_lhs, scipy_dns_lhs, func_name, *args, **kwargs):
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"""
mx.nd.waitall()
args_list = []
for arg in args:
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start = time.time()
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args_list[0] = np.transpose(args_... | python | 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 = []
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start = time.time()
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.info | def info(self):
"""
Print information about the annotation file.
:return:
"""
for key, value in self.dataset['info'].items():
print('{}: {}'.format(key, value)) | python | def info(self):
"""
Print information about the annotation file.
:return:
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.getCatIds | def getCatIds(self, catNms=[], supNms=[], catIds=[]):
"""
filtering parameters. default skips that filter.
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:param supNms (str array) : get cats for given supercategory names
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"""
filtering parameters. default skips that filter.
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.loadAnns | def loadAnns(self, ids=[]):
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:param ids (int array) : integer ids specifying anns
:return: anns (object array) : loaded ann objects
"""
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"""
Load anns with the specified ids.
:param ids (int array) : integer ids specifying anns
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.loadCats | def loadCats(self, ids=[]):
"""
Load cats with the specified ids.
:param ids (int array) : integer ids specifying cats
:return: cats (object array) : loaded cat objects
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"""
Load cats with the specified ids.
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.loadImgs | def loadImgs(self, ids=[]):
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Load anns with the specified ids.
:param ids (int array) : integer ids specifying img
:return: imgs (object array) : loaded img objects
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"""
Load anns with the specified ids.
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.showAnns | def showAnns(self, anns):
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Display the specified annotations.
:param anns (array of object): annotations to display
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if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
datasetType = 'inst... | python | def showAnns(self, anns):
"""
Display the specified annotations.
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.download | def download(self, tarDir = None, imgIds = [] ):
'''
Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return:
'''
if tarDir is None:
print('Please specify targe... | python | def download(self, tarDir = None, imgIds = [] ):
'''
Download COCO images from mscoco.org server.
:param tarDir (str): COCO results directory name
imgIds (list): images to be downloaded
:return:
'''
if tarDir is None:
print('Please specify targe... | [
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.loadNumpyAnnotations | def loadNumpyAnnotations(self, data):
"""
Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list)
"""
print('Converting ndarray to lists...')
assert... | python | def loadNumpyAnnotations(self, data):
"""
Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list)
"""
print('Converting ndarray to lists...')
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apache/incubator-mxnet | example/ssd/dataset/pycocotools/coco.py | COCO.annToRLE | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
if type(segm) == list:
... | python | def annToRLE(self, ann):
"""
Convert annotation which can be polygons, uncompressed RLE to RLE.
:return: binary mask (numpy 2D array)
"""
t = self.imgs[ann['image_id']]
h, w = t['height'], t['width']
segm = ann['segmentation']
if type(segm) == list:
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apache/incubator-mxnet | example/cnn_chinese_text_classification/text_cnn.py | save_model | def save_model():
"""Save cnn model
Returns
----------
callback: A callback function that can be passed as epoch_end_callback to fit
"""
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period) | python | def save_model():
"""Save cnn model
Returns
----------
callback: A callback function that can be passed as epoch_end_callback to fit
"""
if not os.path.exists("checkpoint"):
os.mkdir("checkpoint")
return mx.callback.do_checkpoint("checkpoint/checkpoint", args.save_period) | [
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apache/incubator-mxnet | example/cnn_chinese_text_classification/text_cnn.py | highway | def highway(data):
"""Construct highway net
Parameters
----------
data:
Returns
----------
Highway Networks
"""
_data = data
high_weight = mx.sym.Variable('high_weight')
high_bias = mx.sym.Variable('high_bias')
high_fc = mx.sym.FullyConnected(data=data, weight=high_weight... | python | def highway(data):
"""Construct highway net
Parameters
----------
data:
Returns
----------
Highway Networks
"""
_data = data
high_weight = mx.sym.Variable('high_weight')
high_bias = mx.sym.Variable('high_bias')
high_fc = mx.sym.FullyConnected(data=data, weight=high_weight... | [
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apache/incubator-mxnet | example/cnn_chinese_text_classification/text_cnn.py | train | def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names):
"""Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
data_column_names: lis... | python | def train(symbol_data, train_iterator, valid_iterator, data_column_names, target_names):
"""Train cnn model
Parameters
----------
symbol_data: symbol
train_iterator: DataIter
Train DataIter
valid_iterator: DataIter
Valid DataIter
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | default_batchify_fn | 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(... | python | 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(... | [
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | default_mp_batchify_fn | 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)
... | python | 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)
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | _as_in_context | 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 | python | 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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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | worker_loop_v1 | 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)) | python | 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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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | fetcher_loop_v1 | 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 reorder dict."""
while True:
idx, batch = data_queue.get()
if idx is None:
break
if pin_memory:
... | python | 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 reorder dict."""
while True:
idx, batch = data_queue.get()
if idx is None:
break
if pin_memory:
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | _worker_fn | def _worker_fn(samples, batchify_fn, dataset=None):
"""Function for processing data in worker process."""
# pylint: disable=unused-argument
# it is required that each worker process has to fork a new MXIndexedRecordIO handle
# preserving dataset as global variable can save tons of overhead and is safe i... | python | def _worker_fn(samples, batchify_fn, dataset=None):
"""Function for processing data in worker process."""
# pylint: disable=unused-argument
# it is required that each worker process has to fork a new MXIndexedRecordIO handle
# preserving dataset as global variable can save tons of overhead and is safe i... | [
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | ConnectionWrapper.send | def send(self, obj):
"""Send object"""
buf = io.BytesIO()
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj)
self.send_bytes(buf.getvalue()) | python | def send(self, obj):
"""Send object"""
buf = io.BytesIO()
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj)
self.send_bytes(buf.getvalue()) | [
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apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | _MultiWorkerIterV1._push_next | def _push_next(self):
"""Assign next batch workload to workers."""
r = next(self._iter, None)
if r is None:
return
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"""Assign next batch workload to workers."""
r = next(self._iter, None)
if r is None:
return
self._key_queue.put((self._sent_idx, r))
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/gluon/data/dataloader.py#L237-L243 | train |
apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | _MultiWorkerIterV1.shutdown | 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 fet... | python | 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 fet... | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/gluon/data/dataloader.py#L266-L281 | train |
apache/incubator-mxnet | python/mxnet/gluon/data/dataloader.py | _MultiWorkerIter._push_next | def _push_next(self):
"""Assign next batch workload to workers."""
r = next(self._iter, None)
if r is None:
return
async_ret = self._worker_pool.apply_async(
self._worker_fn, (r, self._batchify_fn, self._dataset))
self._data_buffer[self._sent_idx] = async_... | python | def _push_next(self):
"""Assign next batch workload to workers."""
r = next(self._iter, None)
if r is None:
return
async_ret = self._worker_pool.apply_async(
self._worker_fn, (r, self._batchify_fn, self._dataset))
self._data_buffer[self._sent_idx] = async_... | [
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apache/incubator-mxnet | python/mxnet/kvstore.py | _ctype_key_value | 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
f... | python | 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
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