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def transpose(attrs, inputs, proto_obj):
"""Transpose the input array.""" |
new_attrs = translation_utils._fix_attribute_names(attrs,
{'perm' : 'axes'})
return 'transpose', new_attrs, inputs |
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def squeeze(attrs, inputs, proto_obj):
"""Remove single-dimensional entries from the shape of a tensor.""" |
new_attrs = translation_utils._fix_attribute_names(attrs,
{'axes' : 'axis'})
return 'squeeze', new_attrs, inputs |
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def unsqueeze(attrs, inputs, cls):
"""Inserts a new axis of size 1 into the array shape""" |
# MXNet can only add one axis at a time.
mxnet_op = inputs[0]
for axis in attrs["axes"]:
mxnet_op = symbol.expand_dims(mxnet_op, axis=axis)
return mxnet_op, attrs, inputs |
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def flatten(attrs, inputs, proto_obj):
"""Flattens the input array into a 2-D array by collapsing the higher dimensions.""" |
#Mxnet does not have axis support. By default uses axis=1
if 'axis' in attrs and attrs['axis'] != 1:
raise RuntimeError("Flatten operator only supports axis=1")
new_attrs = translation_utils._remove_attributes(attrs, ['axis'])
return 'Flatten', new_attrs, inputs |
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def reduce_max(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by maximum value""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'max', new_attrs, inputs |
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def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs |
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def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs |
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def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs |
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def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs |
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def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value""" |
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs |
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def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value""" |
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
return log_sym, attrs, inputs |
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def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value""" |
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs |
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def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization.""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'ord' : 1})
return 'norm', new_attrs, inputs |
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def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization.""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs |
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def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling.""" |
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
})
return 'ROIPooling'... |
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def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data.""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs |
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def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth.""" |
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs |
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def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors.""" |
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_shape) - 1:
amax = symbol.argmax(inputs[0], axis=-1)
one_hot = sy... |
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def run_ut_python3_qemu_internal():
"""this runs inside the vm""" |
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt")
check_call(['sudo', 'pip3', 'install', '-r', 'mxnet/test_require... |
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def _new_empty_handle():
"""Returns a new empty handle. Empty handle can be used to hold a result. Returns ------- handle A new empty `NDArray` handle. """ |
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl |
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def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context. Empty handle is only used to hold resul... |
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(d... |
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def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions.""" |
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
for i in key:
if not isinstance(i, integer_types):
raise TypeError('Indexing NDArray only... |
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def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape. This basically implements the infer shape logic ... |
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
oshape.extend(dshape[ishape[0]:])
return tuple(oshape) |
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def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements of this slice.""" |
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1) // (-step) + 1
return dim_size |
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def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape that both input shapes can broadcast to.""" |
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
shape = list(shape2)
i = max(length1, length2) - 1
for a, b in zip(shape1[::-1], shape2[::-1]):
if a != 1 and b != 1 and a != b:
... |
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def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type. Parameters shape : int or tuple of i... |
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs) |
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def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position while leaving the other axes in their original order Parame... |
try:
source = np.core.numeric.normalize_axis_tuple(source, tensor.ndim)
except IndexError:
raise ValueError('Source should verify 0 <= source < tensor.ndim'
'Got %d' % source)
try:
destination = np.core.numeric.normalize_axis_tuple(destination, tensor.ndim)
... |
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def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation. The function will perform numpy-li... |
if isinstance(lhs, numeric_types):
if isinstance(rhs, numeric_types):
return fn_scalar(lhs, rhs)
else:
if rfn_scalar is None:
# commutative function
return lfn_scalar(rhs, float(lhs))
else:
return rfn_scalar(rhs, fl... |
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def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting. Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``. ..... |
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mod,
operator.mod,
_internal._mod_scalar,
_internal._rmod_scalar) |
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def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise with broadcasting. Equivalent to ``base ** exp``... |
# pylint: disable= no-member, protected-access
return _ufunc_helper(
base,
exp,
op.broadcast_power,
operator.pow,
_internal._power_scalar,
_internal._rpower_scalar) |
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def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``. .. note:: If t... |
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None) |
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def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting. Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``. .. note:: If t... |
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_minimum,
lambda x, y: x if x < y else y,
_internal._minimum_scalar,
None) |
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def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead Parameters arrays : list of `NDArray` Arrays to be concatenate. They mus... |
assert isinstance(arrays, list)
assert len(arrays) > 0
assert isinstance(arrays[0], NDArray)
if not always_copy and len(arrays) == 1:
return arrays[0]
shape_axis = arrays[0].shape[axis]
shape_rest1 = arrays[0].shape[0:axis]
shape_rest2 = arrays[0].shape[axis+1:]
dtype = arrays... |
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def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead Parameters str_img : str Binary image ... |
# pylint: disable= no-member, protected-access, too-many-arguments
if mean is None:
mean = NDArray(_new_empty_handle())
if out is None:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
... |
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def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type. Parameters shape : int or tuple of... |
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs) |
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def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array with ones on the diagonal and zeros elsewhere. Parameters N: int Number of rows in th... |
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._eye(N=N, M=M, k=k, ctx=ctx, dtype=dtype, **kwargs) |
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def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until all previous write operations on the current array ... |
data.wait_to_read()
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter) |
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def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor. Parameters dlpack: PyCapsule (the pointer of DLManagedTensor) input data Returns -------... |
handle = NDArrayHandle()
dlpack = ctypes.py_object(dlpack)
assert ctypes.pythonapi.PyCapsule_IsValid(dlpack, _c_str_dltensor), ValueError(
'Invalid DLPack Tensor. DLTensor capsules can be consumed only once.')
dlpack_handle = ctypes.c_void_p(ctypes.pythonapi.PyCapsule_GetPointer(dlpack, _c_str_... |
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def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray. Parameters ndarray: numpy.ndarray input data zero_copy: bool Wh... |
def _make_manager_ctx(obj):
pyobj = ctypes.py_object(obj)
void_p = ctypes.c_void_p.from_buffer(pyobj)
ctypes.pythonapi.Py_IncRef(pyobj)
return void_p
def _make_dl_tensor(array):
if str(array.dtype) not in DLDataType.TYPE_MAP:
raise ValueError(str(array.dtyp... |
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def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same context and dtype as the current one and broa... |
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
elif isinstance(value, NDArray):
value_nd = value.as_in_context(self.context)
if value_nd.dtype != self.dtype:
value_nd = value_nd.astype... |
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def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape. Broadcasting is only allowed on axes with size 1. The new shape cannot change the nu... |
cur_shape = self.shape
err_str = 'operands could not be broadcast together with remapped shapes' \
'[original->remapped]: {} and requested shape {}'.format(cur_shape, shape)
if len(shape) < len(cur_shape):
raise ValueError(err_str)
cur_shape = (1,) * (len(s... |
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def shape(self):
"""Tuple of array dimensions. Examples -------- (4L,) (2L, 3L, 4L) """ |
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)()
check_call(_LIB.MXNDArrayGetShapeEx(
self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
if ndim.value == -1:
return None
else:
return tuple(pdata[:ndim.value]) |
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def context(self):
"""Device context of the array. Examples -------- cpu(0) <class 'mxnet.context.Context'> gpu(0) """ |
dev_typeid = ctypes.c_int()
dev_id = ctypes.c_int()
check_call(_LIB.MXNDArrayGetContext(
self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
return Context(Context.devtype2str[dev_typeid.value], dev_id.value) |
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def dtype(self):
"""Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- <type 'numpy.float32'> <type 'nump... |
mx_dtype = ctypes.c_int()
check_call(_LIB.MXNDArrayGetDType(
self.handle, ctypes.byref(mx_dtype)))
return _DTYPE_MX_TO_NP[mx_dtype.value] |
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def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- <type 'numpy.ndarray'> array([[ 1., 1., 1.], [ 1., ... |
data = np.empty(self.shape, dtype=self.dtype)
check_call(_LIB.MXNDArraySyncCopyToCPU(
self.handle,
data.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(data.size)))
return data |
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def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type. Parameters dtype : numpy.dtype or str The type of the retur... |
if not copy and np.dtype(dtype) == self.dtype:
return self
res = empty(self.shape, ctx=self.context, dtype=dtype)
self.copyto(res)
return res |
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def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context... |
if self.context == context:
return self
return self.copyto(context) |
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def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Par... |
from . import zeros as _zeros
if stype is not None:
grad = _zeros(self.shape, stype=stype)
else:
grad = op.zeros_like(self) # pylint: disable=undefined-variable
grad_req = _GRAD_REQ_MAP[grad_req]
check_call(_LIB.MXAutogradMarkVariables(
1, ct... |
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def grad(self):
"""Returns gradient buffer attached to this NDArray.""" |
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl) |
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def detach(self):
"""Returns a new NDArray, detached from the current graph.""" |
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl) |
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def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables. Parameters out_grad : NDArray, ... |
if out_grad is None:
ograd_handles = [NDArrayHandle(0)]
else:
ograd_handles = [out_grad.handle]
check_call(_LIB.MXAutogradBackwardEx(
1, c_handle_array([self]),
c_array(NDArrayHandle, ograd_handles),
0,
ctypes.c_void_p(0),... |
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def build(self, align_path):
""" Build the align array """ |
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
if word not in Align.skip_list:
words.append((int(_op), int(_ed... |
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def word_frame_pos(self, _id):
""" Get the position of words """ |
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right) |
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| def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
if ... |
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def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """ |
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param_idx]
for grad in grad_vals:
grad[:] *= scale |
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def _build_doc(func_name, desc, arg_names, arg_types, arg_desc, key_var_num_args=None, ret_type=None):
"""Build docstring for symbolic functions.""" |
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_var_num_args:
desc += '\nThis function support variable length of positional input.'
doc_str = ('%s\n\n' +
'%s\n' +
'name : string, optional.\n' +
' Name of the resulting symbol.\n\n'... |
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def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes.""" |
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs)) |
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def num_gpus():
"""Query CUDA for the number of GPUs present. Raises ------ Will raise an exception on any CUDA error. Returns ------- count : int The number of ... |
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value |
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def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory. Parameters device_id : int, optional The device id of the GPU ... |
free = ctypes.c_uint64()
total = ctypes.c_uint64()
dev_id = ctypes.c_int(device_id)
check_call(_LIB.MXGetGPUMemoryInformation64(dev_id, ctypes.byref(free), ctypes.byref(total)))
return (free.value, total.value) |
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def current_context():
"""Returns the current context. By default, `mx.cpu()` is used for all the computations and it can be overridden by using `with mx.Context... |
if not hasattr(Context._default_ctx, "value"):
Context._default_ctx.value = Context('cpu', 0)
return Context._default_ctx.value |
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def config_cython():
"""Try to configure cython and return cython configuration""" |
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
try:
from Cython.Build import cythonize
# from setuptools.extension import Extensio... |
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def _compose(self, *args, **kwargs):
"""Compose symbol on inputs. This call mutates the current symbol. Parameters args: provide positional arguments kwargs: pro... |
name = kwargs.pop('name', None)
if name:
name = c_str(name)
if len(args) != 0 and len(kwargs) != 0:
raise TypeError('compose only accept input Symbols \
either as positional or keyword arguments, not both')
for arg in args:
if not is... |
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def _set_attr(self, **kwargs):
"""Set the attribute of the symbol. Parameters **kwargs The attributes to set """ |
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))
check_call(_LIB.MXSymbolSetAttrs(
self.handle, num_args, keys, vals)) |
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def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train Parameters network : str name for the base network symbol data_shape : i... |
if network.startswith('legacy'):
logging.warn('Using legacy model.')
return symbol_builder.import_module(network).get_symbol_train(**kwargs)
config = get_config(network, data_shape, **kwargs).copy()
config.update(kwargs)
return symbol_builder.get_symbol_train(**config) |
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def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """ |
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set the trainer for Parameter '%s' because it was already set. " \
"More than one trainer... |
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def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """ |
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id)))
if not self._trainer:
raise RuntimeError("Cannot get row_sparse data for Parameter '%s' when no " \
... |
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def _finish_deferred_init(self):
"""Finishes deferred initialization.""" |
if not self._deferred_init:
return
init, ctx, default_init, data = self._deferred_init
self._deferred_init = ()
assert self.shape is not None and np.prod(self.shape) > 0, \
"Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s. Pleas... |
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def _init_impl(self, data, ctx_list):
"""Sets data and grad.""" |
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
while len(dev_list) <= ctx.device_id:
dev_list.append(None)
dev_list[ctx.device_id] = i
se... |
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def _init_grad(self):
"""Initialize grad buffers.""" |
if self.grad_req == 'null':
self._grad = None
return
self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
stype=self._grad_stype) for i in self._data]
autograd.mark_variables(self._check_and_get(self._data, list),... |
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def _reduce(self):
"""Reduce data from multiple context to cpu.""" |
ctx = context.cpu()
if self._stype == 'default':
block = self.list_data()
data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
else:
# fetch all rows for 'row_sparse' param
all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64'... |
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def reset_ctx(self, ctx):
"""Re-assign Parameter to other contexts. Parameters ctx : Context or list of Context, default ``context.current_context()``. Assign Pa... |
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if self._data:
data = self._reduce()
with autograd.pause():
self._init_impl(data, ctx)
elif self._deferred_init:
init, _... |
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def set_data(self, data):
"""Sets this parameter's value on all contexts.""" |
self.shape = data.shape
if self._data is None:
assert self._deferred_init, \
"Parameter '%s' has not been initialized"%self.name
self._deferred_init = self._deferred_init[:3] + (data,)
return
# if update_on_kvstore, we need to make sure the ... |
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def row_sparse_data(self, row_id):
"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's. The copy only retains rows whose ids occur in... |
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
"because its storage type is %s. Please use data() instead." \
%(self.name, self._stype))
return self._get_row_spa... |
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def list_row_sparse_data(self, row_id):
"""Returns copies of the 'row_sparse' parameter on all contexts, in the same order as creation. The copy only retains row... |
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_row_sparse_data() because its storage type is %s. Please " \
"use data() instead." % (self.name, self._stype))
... |
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def grad(self, ctx=None):
"""Returns a gradient buffer for this parameter on one context. Parameters ctx : Context Desired context. """ |
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get(self._grad, ctx) |
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def list_ctx(self):
"""Returns a list of contexts this parameter is initialized on.""" |
if self._data is None:
if self._deferred_init:
return self._deferred_init[1]
raise RuntimeError("Parameter '%s' has not been initialized"%self.name)
return self._ctx_list |
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def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if parameter is uninitialized or doesn't require gradient.""" |
if self._grad is None:
return
for i in self._grad:
ndarray.zeros_like(i, out=i) |
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def var(self):
"""Returns a symbol representing this parameter.""" |
if self._var is None:
self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype,
lr_mult=self.lr_mult, wd_mult=self.wd_mult,
init=self.init, stype=self._stype)
return self._var |
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def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type. Parameters dtype : str or numpy.dtype The new data type. """ |
self.dtype = dtype
if self._data is None:
return
with autograd.pause():
self._data = [i.astype(dtype) for i in self._data]
if self._grad is None:
return
self._grad = [i.astype(dtype) for i in self._grad]
autograd.mark_v... |
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def update(self, other):
"""Copies all Parameters in ``other`` to self.""" |
for k, v in other.items():
if k in self._params:
assert self._params[k] is v, \
"Cannot update self with other because they have different " \
"Parameters with the same name '%s'"%k
for k, v in other.items():
self._params[... |
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def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters. For example, set grad_req to null if you don't need gradient w.r.t a model... |
for i in self.values():
setattr(i, name, value) |
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def save(self, filename, strip_prefix=''):
"""Save parameters to file. Parameters filename : str Path to parameter file. strip_prefix : str, default '' Strip pre... |
arg_dict = {}
for param in self.values():
weight = param._reduce()
if not param.name.startswith(strip_prefix):
raise ValueError(
"Prefix '%s' is to be striped before saving, but Parameter's "
"name '%s' does not start with ... |
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def _init_torch_module():
"""List and add all the torch backed ndarray functions to current module.""" |
plist = ctypes.POINTER(FunctionHandle)()
size = ctypes.c_uint()
check_call(_LIB.MXListFunctions(ctypes.byref(size),
ctypes.byref(plist)))
module_obj = sys.modules[__name__]
for i in range(size.value):
hdl = FunctionHandle(plist[i])
function = _ma... |
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def pack(header, s):
"""Pack a string into MXImageRecord. Parameters header : IRHeader Header of the image record. ``header.label`` can be a number or an array. ... |
header = IRHeader(*header)
if isinstance(header.label, numbers.Number):
header = header._replace(flag=0)
else:
label = np.asarray(header.label, dtype=np.float32)
header = header._replace(flag=label.size, label=0)
s = label.tostring() + s
s = struct.pack(_IR_FORMAT, *head... |
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def unpack(s):
"""Unpack a MXImageRecord to string. Parameters s : str String buffer from ``MXRecordIO.read``. Returns ------- header : IRHeader Header of the im... |
header = IRHeader(*struct.unpack(_IR_FORMAT, s[:_IR_SIZE]))
s = s[_IR_SIZE:]
if header.flag > 0:
header = header._replace(label=np.frombuffer(s, np.float32, header.flag))
s = s[header.flag*4:]
return header, s |
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def unpack_img(s, iscolor=-1):
"""Unpack a MXImageRecord to image. Parameters s : str String buffer from ``MXRecordIO.read``. iscolor : int Image format option f... |
header, s = unpack(s)
img = np.frombuffer(s, dtype=np.uint8)
assert cv2 is not None
img = cv2.imdecode(img, iscolor)
return header, img |
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def pack_img(header, img, quality=95, img_fmt='.jpg'):
"""Pack an image into ``MXImageRecord``. Parameters header : IRHeader Header of the image record. ``header... |
assert cv2 is not None
jpg_formats = ['.JPG', '.JPEG']
png_formats = ['.PNG']
encode_params = None
if img_fmt.upper() in jpg_formats:
encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
elif img_fmt.upper() in png_formats:
encode_params = [cv2.IMWRITE_PNG_COMPRESSION, quality]
... |
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def open(self):
"""Opens the record file.""" |
if self.flag == "w":
check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle)))
self.writable = True
elif self.flag == "r":
check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.byref(self.handle)))
self.writable = False
else:
... |
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def _check_pid(self, allow_reset=False):
"""Check process id to ensure integrity, reset if in new process.""" |
if not self.pid == current_process().pid:
if allow_reset:
self.reset()
else:
raise RuntimeError("Forbidden operation in multiple processes") |
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def write(self, buf):
"""Inserts a string buffer as a record. Examples --------- Parameters buf : string (python2), bytes (python3) Buffer to write. """ |
assert self.writable
self._check_pid(allow_reset=False)
check_call(_LIB.MXRecordIOWriterWriteRecord(self.handle,
ctypes.c_char_p(buf),
ctypes.c_size_t(len(buf)))) |
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def read(self):
"""Returns record as a string. Examples --------- record_0 record_1 record_2 record_3 record_4 Returns buf : string Buffer read. """ |
assert not self.writable
# trying to implicitly read from multiple processes is forbidden,
# there's no elegant way to handle unless lock is introduced
self._check_pid(allow_reset=False)
buf = ctypes.c_char_p()
size = ctypes.c_size_t()
check_call(_LIB.MXRecordIOR... |
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def seek(self, idx):
"""Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position... |
assert not self.writable
self._check_pid(allow_reset=True)
pos = ctypes.c_size_t(self.idx[idx])
check_call(_LIB.MXRecordIOReaderSeek(self.handle, pos)) |
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def tell(self):
"""Returns the current position of write head. Examples --------- 0 16 32 48 64 80 """ |
assert self.writable
pos = ctypes.c_size_t()
check_call(_LIB.MXRecordIOWriterTell(self.handle, ctypes.byref(pos)))
return pos.value |
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def write_idx(self, idx, buf):
"""Inserts input record at given index. Examples --------- Parameters idx : int Index of a file. buf : Record to write. """ |
key = self.key_type(idx)
pos = self.tell()
self.write(buf)
self.fidx.write('%s\t%d\n'%(str(key), pos))
self.idx[key] = pos
self.keys.append(key) |
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def _add_new_columns(dataframe, metrics):
"""Add new metrics as new columns to selected pandas dataframe. Parameters dataframe : pandas.DataFrame Selected datafr... |
#TODO(leodirac): we don't really need to do this on every update. Optimize
new_columns = set(metrics.keys()) - set(dataframe.columns)
for col in new_columns:
dataframe[col] = None |
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def append_metrics(self, metrics, df_name):
"""Append new metrics to selected dataframes. Parameters metrics : metric.EvalMetric New metrics to be added. df_name... |
dataframe = self._dataframes[df_name]
_add_new_columns(dataframe, metrics)
dataframe.loc[len(dataframe)] = metrics |
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def train_cb(self, param):
"""Callback funtion for training. """ |
if param.nbatch % self.frequent == 0:
self._process_batch(param, 'train') |
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def epoch_cb(self):
"""Callback function after each epoch. Now it records each epoch time and append it to epoch dataframe. """ |
metrics = {}
metrics['elapsed'] = self.elapsed()
now = datetime.datetime.now()
metrics['epoch_time'] = now - self.last_epoch_time
self.append_metrics(metrics, 'epoch')
self.last_epoch_time = now |
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def _push_render(self):
"""Render the plot with bokeh.io and push to notebook. """ |
bokeh.io.push_notebook(handle=self.handle)
self.last_update = time.time() |
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def to_indices(self, tokens):
"""Converts tokens to indices according to the vocabulary. Parameters tokens : str or list of strs A source token or tokens to be c... |
to_reduce = False
if not isinstance(tokens, list):
tokens = [tokens]
to_reduce = True
indices = [self.token_to_idx[token] if token in self.token_to_idx
else C.UNKNOWN_IDX for token in tokens]
return indices[0] if to_reduce else indices |
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