def extract(cls, node):
pb = node.parameters
read_learning_info(pb)
weights = read_binary_vector(pb)
mapping_rule = {}
embed_input(mapping_rule, 1, 'weights', weights)
ScaleShiftOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
pb = node.parameters
read_learning_info(pb)
biases = read_binary_vector(pb)
bias_term = True
mapping_rule = {'bias_term': bias_term}
embed_input(mapping_rule, 1, 'weights', np.ones(biases.shape))
embed_input(mapping_rule, 2, 'biases', biases)
ScaleShiftOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node: Node) -> bool:
"""
Extract conv parameters from node.parameters.
node.parameters like file descriptor object.
:param node: Convolution node
:return:
"""
pb = node.parameters
kernel = read_token_value(pb, b'<PatchDim>')
stride = read_token_value(pb, b'<PatchStep>')
patch_stride = read_token_value(pb, b'<PatchStride>')
read_learning_info(pb)
collect_until_whitespace(pb)
weights, weights_shape = read_binary_matrix(pb)
collect_until_whitespace(pb)
biases = read_binary_vector(pb)
if (patch_stride - kernel) % stride != 0:
raise Error(
'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' +
refer_to_faq_msg(93))
output = biases.shape[0]
if weights_shape[0] != output:
raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' +
refer_to_faq_msg(93))
mapping_rule = {
'output': output,
'patch_stride': patch_stride,
'bias_term': None,
'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64),
'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64),
'dilation': np.array([1, 1, 1, 1], dtype=np.int64),
'kernel': np.array([1, 1, 1, kernel], dtype=np.int64),
'stride': np.array([1, 1, 1, stride], dtype=np.int64),
'kernel_spatial': np.array([1, kernel], dtype=np.int64),
'input_feature_channel': 1,
'output_feature_channel': 0,
'kernel_spatial_idx': [2, 3],
'group': 1,
'reshape_kernel': True,
}
mapping_rule.update(layout_attrs())
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
mapping_rule['bias_addable'] = len(biases) > 0
Convolution.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
pb = node.parameters
read_learning_info(pb)
weights, weights_shape = read_binary_matrix(pb)
biases = read_binary_vector(pb)
mapping_rule = {
'out-size': weights_shape[0],
'transpose_weights': True,
}
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
FullyConnected.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node: Node):
read_learning_info(node.parameters)
return FixedAffineComponentFrontExtractor.extract(node)