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def transform_padding(pad_width):
"""Helper function to convert padding format for pad operator. """ |
num_pad_values = len(pad_width)
onnx_pad_width = [0]*num_pad_values
start_index = 0
# num_pad_values will always be multiple of 2
end_index = int(num_pad_values/2)
for idx in range(0, num_pad_values):
if idx % 2 == 0:
onnx_pad_width[start_index] = pad_width[idx]
... |
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def convert_string_to_list(string_val):
"""Helper function to convert string to list. Used to convert shape attribute string to list format. """ |
result_list = []
list_string = string_val.split(',')
for val in list_string:
val = str(val.strip())
val = val.replace("(", "")
val = val.replace(")", "")
val = val.replace("L", "")
val = val.replace("[", "")
val = val.replace("]", "")
if val not in (... |
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def get_inputs(node, kwargs):
"""Helper function to get inputs""" |
name = node["name"]
proc_nodes = kwargs["proc_nodes"]
index_lookup = kwargs["index_lookup"]
inputs = node["inputs"]
attrs = node.get("attrs", {})
input_nodes = []
for ip in inputs:
input_node_id = index_lookup[ip[0]]
input_nodes.append(proc_nodes[input_node_id].name)
r... |
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def create_basic_op_node(op_name, node, kwargs):
"""Helper function to create a basic operator node that doesn't contain op specific attrs""" |
name, input_nodes, _ = get_inputs(node, kwargs)
node = onnx.helper.make_node(
op_name,
input_nodes,
[name],
name=name
)
return [node] |
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def convert_weights_and_inputs(node, **kwargs):
"""Helper function to convert weights and inputs. """ |
name, _, _ = get_inputs(node, kwargs)
if kwargs["is_input"] is False:
weights = kwargs["weights"]
initializer = kwargs["initializer"]
np_arr = weights[name]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
dims = np.shape(np_arr)
tensor_node = onnx... |
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def convert_convolution(node, **kwargs):
"""Map MXNet's convolution operator attributes to onnx's Conv operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "d... |
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def convert_deconvolution(node, **kwargs):
"""Map MXNet's deconvolution operator attributes to onnx's ConvTranspose operator and return the created node. """ |
name, inputs, attrs = get_inputs(node, kwargs)
kernel_dims = list(parse_helper(attrs, "kernel"))
stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
num_group = int(attrs.get("num_group", 1))
dilations = list(parse_helper(attrs, "dilate... |
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def convert_crop(node, **kwargs):
"""Map MXNet's crop operator attributes to onnx's Crop operator and return the created node. """ |
name, inputs, attrs = get_inputs(node, kwargs)
num_inputs = len(inputs)
y, x = list(parse_helper(attrs, "offset", [0, 0]))
h, w = list(parse_helper(attrs, "h_w", [0, 0]))
if num_inputs > 1:
h, w = kwargs["out_shape"][-2:]
border = [x, y, x + w, y + h]
crop_node = onnx.helper.make_... |
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def convert_fully_connected(node, **kwargs):
"""Map MXNet's FullyConnected operator attributes to onnx's Gemm operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
no_bias = get_boolean_attribute_value(attrs, "no_bias")
fcnode = []
op_name = "flatten_" + str(kwargs["idx"])
flatten_node = onnx.helper.make_node(
'Flatten',
inputs=[input_nodes[0]],
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def convert_batchnorm(node, **kwargs):
"""Map MXNet's BatchNorm operator attributes to onnx's BatchNormalization operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
momentum = float(attrs.get("momentum", 0.9))
eps = float(attrs.get("eps", 0.001))
bn_node = onnx.helper.make_node(
"BatchNormalization",
input_nodes,
[name],
name=name,
epsilon=eps,
momentum=momentum,
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def convert_pad(node, **kwargs):
"""Map MXNet's pad operator attributes to onnx's Pad operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
mxnet_pad_width = convert_string_to_list(attrs.get("pad_width"))
onnx_pad_width = transform_padding(mxnet_pad_width)
pad_mode = attrs.get("mode")
if pad_mode == "constant":
pad_value = float(attrs.get("constant_value")) \
if... |
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def create_helper_trans_node(op_name, input_node, node_name):
"""create extra transpose node for dot operator""" |
node_name = op_name + "_" + node_name
trans_node = onnx.helper.make_node(
'Transpose',
inputs=[input_node],
outputs=[node_name],
name=node_name
)
return trans_node |
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def convert_dot(node, **kwargs):
"""Map MXNet's dot operator attributes to onnx's MatMul and Transpose operators based on the values set for transpose_a, transpo... |
name, input_nodes, attrs = get_inputs(node, kwargs)
input_node_a = input_nodes[0]
input_node_b = input_nodes[1]
trans_a_node = None
trans_b_node = None
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "t... |
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def convert_linalg_gemm2(node, **kwargs):
"""Map MXNet's _linalg_gemm2 operator attributes to onnx's MatMul and Transpose operators based on the values set for t... |
name, input_nodes, attrs = get_inputs(node, kwargs)
# Getting the attributes and assigning default values.
alpha = float(attrs.get("alpha", 1.0))
trans_a = get_boolean_attribute_value(attrs, "transpose_a")
trans_b = get_boolean_attribute_value(attrs, "transpose_b")
op_name = "transpose" + str... |
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def convert_instancenorm(node, **kwargs):
"""Map MXNet's InstanceNorm operator attributes to onnx's InstanceNormalization operator based on the input node's attr... |
name, input_nodes, attrs = get_inputs(node, kwargs)
eps = float(attrs.get("eps", 0.001))
node = onnx.helper.make_node(
'InstanceNormalization',
inputs=input_nodes,
outputs=[name],
name=name,
epsilon=eps)
return [node] |
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def convert_softmax(node, **kwargs):
"""Map MXNet's softmax operator attributes to onnx's Softmax operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis", -1))
softmax_node = onnx.helper.make_node(
"Softmax",
input_nodes,
[name],
axis=axis,
name=name
)
return [softmax_node] |
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def convert_concat(node, **kwargs):
"""Map MXNet's Concat operator attributes to onnx's Concat operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("dim", 1))
concat_node = onnx.helper.make_node(
"Concat",
input_nodes,
[name],
axis=axis,
name=name
)
return [concat_node] |
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def convert_transpose(node, **kwargs):
"""Map MXNet's transpose operator attributes to onnx's Transpose operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = attrs.get("axes", ())
if axes:
axes = tuple(map(int, re.findall(r'\d+', axes)))
transpose_node = onnx.helper.make_node(
"Transpose",
input_nodes,
[name],
perm=axes,
name=... |
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def convert_lrn(node, **kwargs):
"""Map MXNet's LRN operator attributes to onnx's LRN operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
alpha = float(attrs.get("alpha", 0.0001))
beta = float(attrs.get("beta", 0.75))
bias = float(attrs.get("knorm", 1.0))
size = int(attrs.get("nsize"))
lrn_node = onnx.helper.make_node(
"LRN",
inputs=input_nodes,
outputs... |
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def convert_l2normalization(node, **kwargs):
"""Map MXNet's L2Normalization operator attributes to onnx's LpNormalization operator and return the created node. "... |
name, input_nodes, attrs = get_inputs(node, kwargs)
mode = attrs.get("mode", "instance")
if mode != "channel":
raise AttributeError("L2Normalization: ONNX currently supports channel mode only")
l2norm_node = onnx.helper.make_node(
"LpNormalization",
input_nodes,
[name... |
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def convert_dropout(node, **kwargs):
"""Map MXNet's Dropout operator attributes to onnx's Dropout operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
probability = float(attrs.get("p", 0.5))
dropout_node = onnx.helper.make_node(
"Dropout",
input_nodes,
[name],
ratio=probability,
name=name
)
return [dropout_node] |
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def convert_clip(node, **kwargs):
"""Map MXNet's Clip operator attributes to onnx's Clip operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
a_min = np.float(attrs.get('a_min', -np.inf))
a_max = np.float(attrs.get('a_max', np.inf))
clip_node = onnx.helper.make_node(
"Clip",
input_nodes,
[name],
name=name,
min=a_min,
max=a_max
)
retu... |
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def scalar_op_helper(node, op_name, **kwargs):
"""Helper function for scalar arithmetic operations""" |
name, input_nodes, attrs = get_inputs(node, kwargs)
from onnx import numpy_helper
input_type = kwargs["in_type"]
scalar_value = np.array([attrs.get("scalar", 1)],
dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[input_type])
initializer = kwargs["initializer"]
flag = True
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def convert_argmax(node, **kwargs):
"""Map MXNet's argmax operator attributes to onnx's ArgMax operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
keepdims = get_boolean_attribute_value(attrs, "keepdims")
node = onnx.helper.make_node(
'ArgMax',
inputs=input_nodes,
axis=axis,
keepdims=keepdims,
outputs=[name],
name=na... |
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def convert_reshape(node, **kwargs):
"""Map MXNet's Reshape operator attributes to onnx's Reshape operator. Converts output shape attribute to output shape tenso... |
name, input_nodes, attrs = get_inputs(node, kwargs)
output_shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(output_shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shap... |
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def convert_cast(node, **kwargs):
"""Map MXNet's Cast operator attributes to onnx's Cast operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = attrs["dtype"]
# dtype can be mapped only with types from TensorProto
# float32 is mapped to float and float64 to double in onnx
# following tensorproto mapping https://github.com/onnx/onnx/blob/master/onnx/mapping.py
if dtype == 'fl... |
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def convert_slice_axis(node, **kwargs):
"""Map MXNet's slice_axis operator attributes to onnx's Slice operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axes = int(attrs.get("axis"))
starts = int(attrs.get("begin"))
ends = int(attrs.get("end", None))
if not ends:
raise ValueError("Slice: ONNX doesnt't support 'None' in 'end' attribute")
node = onnx.helper.make_node(
"Slice",
... |
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def convert_slice_channel(node, **kwargs):
"""Map MXNet's SliceChannel operator attributes to onnx's Squeeze or Split operator based on squeeze_axis attribute an... |
name, input_nodes, attrs = get_inputs(node, kwargs)
num_outputs = int(attrs.get("num_outputs"))
axis = int(attrs.get("axis", 1))
squeeze_axis = int(attrs.get("squeeze_axis", 0))
if squeeze_axis == 1 and num_outputs == 1:
node = onnx.helper.make_node(
"Squeeze",
inp... |
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def convert_expand_dims(node, **kwargs):
"""Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
node = onnx.helper.make_node(
"Unsqueeze",
input_nodes,
[name],
axes=[axis],
name=name,
)
return [node] |
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def convert_squeeze(node, **kwargs):
"""Map MXNet's squeeze operator attributes to onnx's squeeze operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = attrs.get("axis", None)
if not axis:
raise AttributeError("Squeeze: Missing axis attribute: ONNX currently requires axis to "
"be specified for squeeze operator")
axis = convert_string_to_list(axis)
no... |
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def convert_depthtospace(node, **kwargs):
"""Map MXNet's depth_to_space operator attributes to onnx's DepthToSpace operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
blksize = int(attrs.get("block_size", 0))
node = onnx.helper.make_node(
"DepthToSpace",
input_nodes,
[name],
blocksize=blksize,
name=name,
)
return [node] |
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def convert_square(node, **kwargs):
"""Map MXNet's square operator attributes to onnx's Pow operator and return the created node. """ |
name, input_nodes, _ = get_inputs(node, kwargs)
initializer = kwargs["initializer"]
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
power2_name = "square_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(power2_name, data_type, (1,))
initializer.... |
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def convert_sum(node, **kwargs):
"""Map MXNet's sum operator attributes to onnx's ReduceSum operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
if axes:
node = onnx.helper.make_node(
'ReduceSum',
... |
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def convert_hardsigmoid(node, **kwargs):
"""Map MXNet's hard_sigmoid operator attributes to onnx's HardSigmoid operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
alpha = float(attrs.get("alpha", 0.2))
beta = float(attrs.get("beta", 0.5))
node = onnx.helper.make_node(
'HardSigmoid',
input_nodes,
[name],
alpha=alpha,
beta=beta,
name=na... |
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def convert_logsoftmax(node, **kwargs):
"""Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to int
axis = int(attrs.get("axis", -1))
temp = attrs.get("temperature", 'None')
if temp != 'None':
raise AttributeError("LogSoftMax: ONNX supports only temperature=None")
node = onnx.helper.make_node(
'LogSoftma... |
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def convert_norm(node, **kwargs):
"""Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
ord = int(attrs.get("ord", 2))
onnx_op_name = "ReduceL1" if ord == 1 else "ReduceL2... |
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def convert_multinomial(node, **kwargs):
"""Map MXNet's multinomial operator attributes to onnx's Multinomial operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
sample_size = convert_string_to_list(attrs.get("shape", '1'))
if len(sample_size) < 2:
sample_size = sample_size[-1]
else:
raise AttributeError("ONN... |
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def convert_random_uniform(node, **kwargs):
"""Map MXNet's random_uniform operator attributes to onnx's RandomUniform operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
low = float(attrs.get("low", 0))
high = float(attrs.get("high", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
n... |
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def convert_random_normal(node, **kwargs):
"""Map MXNet's random_normal operator attributes to onnx's RandomNormal operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
mean = float(attrs.get("loc", 0))
scale = float(attrs.get("scale", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
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def convert_roipooling(node, **kwargs):
"""Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
scale = float(attrs.get("spatial_scale"))
node = onnx.helper.make_node(
'MaxRoiPool',
input_nodes,
[name],
pooled_shape=pooled_shape,
spatial_sca... |
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def convert_tile(node, **kwargs):
"""Map MXNet's Tile operator attributes to onnx's Tile operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
reps_list = convert_string_to_list(attrs["reps"])
initializer = kwargs["initializer"]
reps_shape_np = np.array(reps_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[reps_shape_np.dtype]
dims = np.shape(reps_shape_np)
... |
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def convert_broadcast_to(node, **kwargs):
"""Map MXNet's broadcast_to operator attributes to onnx's Expand operator and return the created node. """ |
name, input_nodes, attrs = get_inputs(node, kwargs)
shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape... |
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def exe(self):
"""Get the current executor Returns ------- exe : mxnet.executor.Executor """ |
return self._buckets[self.curr_bucket_key]['exe'][tuple(self.data_shapes.items())] |
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def compute_internal(self, sym_name, bucket_kwargs=None, **arg_dict):
""" View the internal symbols using the forward function. :param sym_name: :param bucket_kw... |
data_shapes = {k: v.shape for k, v in arg_dict.items()}
self.switch_bucket(bucket_kwargs=bucket_kwargs,
data_shapes=data_shapes)
internal_sym = self.sym.get_internals()[sym_name]
data_inputs = {k: mx.nd.empty(v, ctx=self.ctx)
for k, v in... |
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def init_from_fcnxs(ctx, fcnxs_symbol, fcnxs_args_from, fcnxs_auxs_from):
""" use zero initialization for better convergence, because it tends to oputut 0, and t... |
fcnxs_args = fcnxs_args_from.copy()
fcnxs_auxs = fcnxs_auxs_from.copy()
for k,v in fcnxs_args.items():
if(v.context != ctx):
fcnxs_args[k] = mx.nd.zeros(v.shape, ctx)
v.copyto(fcnxs_args[k])
for k,v in fcnxs_auxs.items():
if(v.context != ctx):
fcnxs_a... |
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def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None, init=None, stype=None, **kwargs):
"""Creates a symbolic variable with specified name... |
if not isinstance(name, string_types):
raise TypeError('Expect a string for variable `name`')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateVariable(c_str(name), ctypes.byref(handle)))
ret = Symbol(handle)
if not hasattr(AttrScope._current, "value"):
AttrScope._current.value... |
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def Group(symbols):
"""Creates a symbol that contains a collection of other symbols, grouped together. Example ------- <Symbol Grouped> Parameters symbols : list... |
if not symbols or any(not isinstance(sym, Symbol) for sym in symbols):
raise TypeError('Expected a list of symbols as input')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateGroup(
mx_uint(len(symbols)),
c_handle_array(symbols), ctypes.byref(handle)))
return Symbol(handle) |
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def load(fname):
"""Loads symbol from a JSON file. You can also use pickle to do the job if you only work on python. The advantage of load/save is the file is la... |
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromFile(c_str(fname), ctypes.byref(handle)))
return Symbol(handle) |
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def load_json(json_str):
"""Loads symbol from json string. Parameters json_str : str A JSON string. Returns ------- sym : Symbol The loaded symbol. See Also ----... |
if not isinstance(json_str, string_types):
raise TypeError('fname required to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
return Symbol(handle) |
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def maximum(left, right):
"""Returns element-wise maximum of the input elements. Both inputs can be Symbol or scalar number. Broadcasting is not supported. Param... |
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Maximum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MaximumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _interna... |
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def minimum(left, right):
"""Returns element-wise minimum of the input elements. Both inputs can be Symbol or scalar number. Broadcasting is not supported. Param... |
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Minimum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MinimumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _interna... |
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def hypot(left, right):
"""Given the "legs" of a right triangle, returns its hypotenuse. Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise. Both input... |
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Hypot(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._HypotScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._H... |
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def eye(N, M=0, k=0, dtype=None, **kwargs):
"""Returns a new symbol of 2-D shpae, filled with ones on the diagonal and zeros elsewhere. Parameters N: int Number ... |
if dtype is None:
dtype = _numpy.float32
return _internal._eye(N, M, k, dtype=dtype, **kwargs) |
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def zeros(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with zeros. Parameters shape : int or sequence of ints Shape of t... |
if dtype is None:
dtype = _numpy.float32
return _internal._zeros(shape=shape, dtype=dtype, **kwargs) |
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def ones(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with ones. Parameters shape : int or sequence of ints Shape of the... |
if dtype is None:
dtype = _numpy.float32
return _internal._ones(shape=shape, dtype=dtype, **kwargs) |
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def name(self):
"""Gets name string from the symbol, this function only works for non-grouped symbol. Returns ------- value : str The name of this symbol, return... |
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetName(
self.handle, ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None |
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def attr(self, key):
"""Returns the attribute string for corresponding input key from the symbol. This function only works for non-grouped symbols. Example -----... |
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetAttr(
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None |
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def list_attr(self, recursive=False):
"""Gets all attributes from the symbol. Example ------- {'mood': 'angry'} Returns ------- ret : Dict of str to str A dictio... |
if recursive:
raise DeprecationWarning("Symbol.list_attr with recursive=True has been deprecated. "
"Please use attr_dict instead.")
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttrShallow
... |
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def attr_dict(self):
"""Recursively gets all attributes from the symbol and its children. Example ------- {'a': {'a1': 'a2'}, 'b': {'b1': 'b2'}} Returns ------- ... |
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttr
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
ret = {}
for i in range(size.value):
name, key = py_str(pairs[i * 2]).split('$')
... |
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def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol. For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"`` to the symbol's attribut... |
for key, value in kwargs.items():
if not isinstance(value, string_types):
raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value)))) |
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def get_internals(self):
"""Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of outputs of all of the internal nodes. Consider the following ... |
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetInternals(
self.handle, ctypes.byref(handle)))
return Symbol(handle=handle) |
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def get_children(self):
"""Gets a new grouped symbol whose output contains inputs to output nodes of the original symbol. Example ------- <Symbol Grouped> ['x', ... |
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetChildren(
self.handle, ctypes.byref(handle)))
ret = Symbol(handle=handle)
if len(ret.list_outputs()) == 0:
return None
return ret |
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def list_arguments(self):
"""Lists all the arguments in the symbol. Example ------- ['a', 'b'] Returns ------- args : list of string List containing the names of... |
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListArguments(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] |
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def list_outputs(self):
"""Lists all the outputs in the symbol. Example ------- ['_plus12_output'] Returns ------- list of str List of all the outputs. For most ... |
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListOutputs(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] |
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def list_auxiliary_states(self):
"""Lists all the auxiliary states in the symbol. Example ------- [] Example of auxiliary states in `BatchNorm`. ['batchnorm0_mov... |
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListAuxiliaryStates(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] |
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def list_inputs(self):
"""Lists all arguments and auxiliary states of this Symbol. Returns ------- inputs : list of str List of all inputs. Examples -------- ['b... |
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.NNSymbolListInputNames(
self.handle, 0, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)] |
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def infer_type(self, *args, **kwargs):
"""Infers the type of all arguments and all outputs, given the known types for some arguments. This function takes the kno... |
try:
res = self._infer_type_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_type_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, dtype in zip(arg_names, a... |
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def _infer_type_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling type inference API.""" |
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
types either by positional or kwargs way.')
sdata = []
if len(args) != 0:
keys = c_array(ctypes.c_char_p, [])
... |
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def infer_shape(self, *args, **kwargs):
"""Infers the shapes of all arguments and all outputs given the known shapes of some arguments. This function takes the k... |
try:
res = self._infer_shape_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_shape_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, shape in zip(arg_names,... |
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def save(self, fname):
"""Saves symbol to a file. You can also use pickle to do the job if you only work on python. The advantage of `load`/`save` functions is t... |
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
check_call(_LIB.MXSymbolSaveToFile(self.handle, c_str(fname))) |
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def tojson(self):
"""Saves symbol to a JSON string. See Also -------- symbol.load_json : Used to load symbol from JSON string. """ |
json_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolSaveToJSON(self.handle, ctypes.byref(json_str)))
return py_str(json_str.value) |
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def _get_ndarray_inputs(arg_key, args, arg_names, allow_missing):
"""Helper function to get NDArray lists handles from various inputs. Parameters arg_key : str T... |
# setup args
arg_handles = []
arg_arrays = []
if isinstance(args, list):
if len(args) != len(arg_names):
raise ValueError('Length of %s does not match the number of arguments' % arg_key)
for narr in args:
if narr is None and allow_... |
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def bind(self, ctx, args, args_grad=None, grad_req='write', aux_states=None, group2ctx=None, shared_exec=None):
"""Binds the current symbol to an executor and re... |
# pylint: disable=too-many-locals, too-many-branches
if not isinstance(ctx, Context):
raise TypeError("Context type error")
listed_arguments = self.list_arguments()
args_handle, args = self._get_ndarray_inputs('args', args, listed_arguments, False)
# setup args grad... |
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def gradient(self, wrt):
"""Gets the autodiff of current symbol. This function can only be used if current symbol is a loss function. .. note:: This function is ... |
handle = SymbolHandle()
c_wrt = c_str_array(wrt)
check_call(_LIB.MXSymbolGrad(self.handle,
mx_uint(len(wrt)),
c_wrt,
ctypes.byref(handle)))
return Symbol(handle) |
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def eval(self, ctx=None, **kwargs):
"""Evaluates a symbol given arguments. The `eval` method combines a call to `bind` (which returns an executor) with a call to... |
if ctx is None:
ctx = current_context()
return self.bind(ctx, kwargs).forward() |
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def get_backend_symbol(self, backend):
"""Return symbol for target backend. Parameters backend : str The backend names. Returns ------- out : Symbol The created ... |
out = SymbolHandle()
check_call(_LIB.MXGenBackendSubgraph(self.handle, c_str(backend), ctypes.byref(out)))
return Symbol(out) |
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def load_model(model_name, epoch_num, data_shapes, label_shapes, label_names, gpus=''):
"""Returns a module loaded with the provided model. Parameters model_name... |
sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, epoch_num)
mod = create_module(sym, data_shapes, label_shapes, label_names, gpus)
mod.set_params(
arg_params=arg_params,
aux_params=aux_params,
allow_missing=True
)
return mod |
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def create_module(sym, data_shapes, label_shapes, label_names, gpus=''):
"""Creates a new MXNet module. Parameters sym : Symbol An MXNet symbol. input_shape: tup... |
if gpus == '':
devices = mx.cpu()
else:
devices = [mx.gpu(int(i)) for i in gpus.split(',')]
data_names = [data_shape[0] for data_shape in data_shapes]
mod = mx.mod.Module(
symbol=sym,
data_names=data_names,
context=devices,
label_names=label_names
)... |
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def evaluate_net(net, path_imgrec, num_classes, num_batch, mean_pixels, data_shape, model_prefix, epoch, ctx=mx.cpu(), batch_size=32, path_imglist="", nms_thresh=... |
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# args
if isinstance(data_shape, int):
data_shape = (3, data_shape, data_shape)
assert len(data_shape) == 3 and data_shape[0] == 3
model_prefix += '_' + str(data_shape[1])
# it... |
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def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False):
"""Initializes the ... |
pass |
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def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation. Subcla... |
if self._label_shapes is None:
# since we do not need labels, we are probably not a module with a loss
# function or predictions, so just ignore this call
return
if pre_sliced:
raise RuntimeError("PythonModule does not support presliced labels")
... |
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def forward(self, data_batch, is_train=None):
"""Forward computation. Here we do nothing but to keep a reference to the scores and the labels so that we can do b... |
self._scores = data_batch.data[0]
if is_train is None:
is_train = self.for_training
if is_train:
self._labels = data_batch.label[0] |
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def _backward_impl(self):
"""Actual implementation of the backward computation. The computation should take ``self._scores`` and ``self._labels`` and then comput... |
if self._grad_func is not None:
grad = self._grad_func(self._scores, self._labels)
if not isinstance(grad, nd.NDArray):
grad = nd.array(grad)
self._scores_grad = grad
else:
raise NotImplementedError() |
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def get(self, name, **kwargs):
"""Get the variable given a name if one exists or create a new one if missing. Parameters name : str name of the variable **kwargs... |
name = self._prefix + name
if name not in self._params:
self._params[name] = symbol.Variable(name, **kwargs)
return self._params[name] |
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def unpack_weights(self, args):
"""Unpack fused weight matrices into separate weight matrices. For example, say you use a module object `mod` to run a network th... |
args = args.copy()
if not self._gate_names:
return args
h = self._num_hidden
for group_name in ['i2h', 'h2h']:
weight = args.pop('%s%s_weight'%(self._prefix, group_name))
bias = args.pop('%s%s_bias' % (self._prefix, group_name))
for j, gat... |
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def pack_weights(self, args):
"""Pack separate weight matrices into a single packed weight. Parameters args : dict of str -> NDArray Dictionary containing unpack... |
args = args.copy()
if not self._gate_names:
return args
for group_name in ['i2h', 'h2h']:
weight = []
bias = []
for gate in self._gate_names:
wname = '%s%s%s_weight'%(self._prefix, group_name, gate)
weight.append(ar... |
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def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
"""Unroll an RNN cell across time steps. Parameters length : int Number of ... |
self.reset()
inputs, _ = _normalize_sequence(length, inputs, layout, False)
if begin_state is None:
begin_state = self.begin_state()
states = begin_state
outputs = []
for i in range(length):
output, states = self(inputs[i], states)
o... |
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def _slice_weights(self, arr, li, lh):
"""slice fused rnn weights""" |
args = {}
gate_names = self._gate_names
directions = self._directions
b = len(directions)
p = 0
for layer in range(self._num_layers):
for direction in directions:
for gate in gate_names:
name = '%s%s%d_i2h%s_weight'%(self.... |
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def unfuse(self):
"""Unfuse the fused RNN in to a stack of rnn cells. Returns ------- cell : mxnet.rnn.SequentialRNNCell unfused cell that can be used for steppi... |
stack = SequentialRNNCell()
get_cell = {'rnn_relu': lambda cell_prefix: RNNCell(self._num_hidden,
activation='relu',
prefix=cell_prefix),
'rnn_tanh': lambda cell_p... |
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def add(self, cell):
"""Append a cell into the stack. Parameters cell : BaseRNNCell The cell to be appended. During unroll, previous cell's output (or raw inputs... |
self._cells.append(cell)
if self._override_cell_params:
assert cell._own_params, \
"Either specify params for SequentialRNNCell " \
"or child cells, not both."
cell.params._params.update(self.params._params)
self.params._params.update(cell... |
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def main():
"""Entrypoint for compare_layers""" |
parser = argparse.ArgumentParser(
description='Tool for testing caffe to mxnet conversion layer by layer')
parser.add_argument('--image_url', type=str,
default='https://github.com/dmlc/web-data/raw/master/mxnet/doc/'\
'tutorials/python/predict_im... |
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def copy_param(exe, new_param=None):
"""Create copy of parameters""" |
if new_param is None:
new_param = {k: nd.empty(v.shape, ctx=mx.cpu()) for k, v in exe.arg_dict.items()}
for k, v in new_param.items():
exe.arg_dict[k].copyto(v)
return new_param |
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def get_mnist_sym(output_op=None, num_hidden=400):
"""Get symbol of mnist""" |
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='mnist_fc1', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='mnist_fc2', num_hidden=num_hidden)
net = mx.symbol.Activation(d... |
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def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
"""Get synthetic gradient value""" |
if grad is None:
grad = nd.empty(theta.shape, theta.context)
theta1 = theta.asnumpy()[0]
theta2 = theta.asnumpy()[1]
v1 = sigma1 ** 2
v2 = sigma2 ** 2
vx = sigmax ** 2
denominator = numpy.exp(-(X - theta1) ** 2 / (2 * vx)) + numpy.exp(
-(X - theta1 - theta2) ** 2 / (2 * vx))... |
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def get_toy_sym(teacher=True, teacher_noise_precision=None):
"""Get toy symbol""" |
if teacher:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='teacher_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='teacher_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='teacher_fc2', num_hidden=1)
ne... |
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def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
"""Run DistilledSGLD on mnist dataset""" |
X, Y, X_test, Y_test = load_mnist(num_training)
minibatch_size = 100
if num_training >= 10000:
num_hidden = 800
total_iter_num = 1000000
teacher_learning_rate = 1E-6
student_learning_rate = 0.0001
teacher_prior = 1
student_prior = 0.1
perturb_deviatio... |
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def run_toy_SGLD(gpu_id=None):
"""Run SGLD on toy dataset""" |
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zer... |
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def run_toy_DistilledSGLD(gpu_id):
"""Run DistilledSGLD on toy dataset""" |
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
... |
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def run_toy_HMC(gpu_id=None):
"""Run HMC on toy dataset""" |
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minib... |
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def run_synthetic_SGLD():
"""Run synthetic SGLD""" |
theta1 = 0
theta2 = 1
sigma1 = numpy.sqrt(10)
sigma2 = 1
sigmax = numpy.sqrt(2)
X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
minibatch_size = 1
total_iter_num = 1000000
lr_scheduler = SGLDScheduler(begin_rate=0.01, end_rate=0.0001, total_iter_num=total_it... |
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