def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Dim>')
dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<BlockDim>')
block_dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<Epsilon>')
eps = read_binary_float_token(pb)
collect_until_token(pb, b'<TargetRms>')
target_rms = read_binary_float_token(pb)
collect_until_token(pb, b'<StatsMean>')
mean = read_binary_vector(pb)
collect_until_token(pb, b'<StatsVar>')
var = read_binary_vector(pb)
scale = target_rms / np.sqrt(var + eps)
shift = -target_rms * mean / np.sqrt(var + eps)
scale = np.tile(scale, dim // block_dim)
shift = np.tile(shift, dim // block_dim)
attrs = {'out-size': dim}
embed_input(attrs, 1, 'weights', scale)
embed_input(attrs, 2, 'biases', shift)
ScaleShiftOp.update_node_stat(node, attrs)
return cls.enabled
def extract(node):
clip_value = 50
pb = node.parameters
res = collect_until_whitespace(pb)
if res == b'<CellClip>':
clip_value = get_uint32(pb.read(4))
collect_until_token(pb, b'FM')
gifo_x_weights, gifo_x_weights_shape = read_binary_matrix(pb, False)
gifo_r_weights, gifo_r_weights_shape = read_binary_matrix(pb)
gifo_biases = read_binary_vector(pb)
input_gate_weights = read_binary_vector(pb)
forget_gate_weights = read_binary_vector(pb)
output_gate_weights = read_binary_vector(pb)
projection_weights, projection_weights_shape = read_binary_matrix(pb)
mapping_rule = {'gifo_x_weights_shape': gifo_x_weights_shape,
'gifo_r_weights_shape': gifo_r_weights_shape,
'projection_weights_shape': projection_weights_shape,
'clip_value': clip_value
}
embed_input(mapping_rule, 1, 'gifo_x_weights', gifo_x_weights)
embed_input(mapping_rule, 2, 'gifo_r_weights', gifo_r_weights)
embed_input(mapping_rule, 3, 'gifo_biases', gifo_biases)
embed_input(mapping_rule, 4, 'input_gate_weights', input_gate_weights)
embed_input(mapping_rule, 5, 'forget_gate_weights', forget_gate_weights)
embed_input(mapping_rule, 6, 'output_gate_weights', output_gate_weights)
embed_input(mapping_rule, 7, 'projection_weights', projection_weights)
LSTMCell.update_node_stat(node, mapping_rule)
return __class__.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<ConvolutionModel>')
in_shape = read_token_value(pb, b'<NumFiltersIn>')
out_shape = read_token_value(pb, b'<NumFiltersOut>')
height_in = read_token_value(pb, b'<HeightIn>')
height_out = read_token_value(pb, b'<HeightOut>')
height_subsample = read_token_value(pb, b'<HeightSubsampleOut>')
collect_until_token(pb, b'<Offsets>')
offsets = read_binary_vector_of_pairs(pb,
read_token=False,
dtype=np.int32)
collect_until_token(pb, b'<RequiredTimeOffsets>')
time_offsets = read_binary_vector(pb, read_token=False, dtype=np.int32)
collect_until_token(pb, b'<LinearParams>')
weights, _ = read_binary_matrix(pb)
collect_until_token(pb, b'<BiasParams>')
biases = read_binary_vector(pb)
offsets = offsets.reshape([len(offsets) // 2, 2])
mapping_rule = { # stride for h axis
'height_subsample': height_subsample,
# input dimension for h axis
'height_in': height_in,
# output dimension for h axis
'height_out': height_out,
# input dimension for channel axis
'in_channels': in_shape,
# output dimension for channel axis
'out_channels': out_shape,
# array with pairs like the following
# [ (-1, -1) (-1, 0) (-1, 1)
# (0, -1) (0, 0) (0, 1)
# (1, -1) (1, 0) (1, 1)]
# it means that kernel 3x3 will be applied to calculate current value of output
'offsets': offsets,
# required time offsets to calculate current convolution
# time_offsets = [-1, 0, 1] for previous example means no padding for time axis and
# 3 values should be prepared
# time_offsets = [0] means zero padding [1, 1] for time axis
'time_offsets': time_offsets,
'out-size': out_shape * height_out
}
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
TimeHeightConvolutionComponent.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
pb = node.parameters
mapping_rule = {'context': list()}
tag = find_next_tag(pb)
if tag == '<LeftContext>':
read_placeholder(pb, 1)
l_context = read_binary_integer32_token(pb)
tag = find_next_tag(pb)
if tag != '<RightContext>':
raise Error(
'Unknown token {} in SpliceComponent node {}'.format(
tag, node.id))
read_placeholder(pb, 1)
r_context = read_binary_integer32_token(pb)
for i in range(-l_context, r_context + 1):
mapping_rule['context'].append(i)
elif tag == '<Context>':
collect_until_whitespace(pb)
mapping_rule['context'] = read_binary_vector(pb,
False,
dtype=np.int32)
else:
raise Error('Unknown token {} in SpliceComponent node {}'.format(
tag, node.id))
tag = find_next_tag(pb)
if tag == '<ConstComponentDim>':
read_placeholder(pb, 1)
const_dim = read_binary_integer32_token(pb)
mapping_rule['const_dim'] = const_dim
Splice.update_node_stat(node, mapping_rule)
return cls.enabled
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
collect_until_token(pb, b'<MaxChange>')
max_change = read_binary_float_token(pb)
collect_until_token(pb, b'<L2Regularize>')
collect_until_token(pb, b'<LearningRate>')
collect_until_token(pb, b'<TimeOffsets>')
time_offsets = read_binary_vector(pb, False, np.int32)
collect_until_token(pb, b'<LinearParams>')
weights, weights_shape = read_binary_matrix(pb)
collect_until_token(pb, b'<BiasParams>')
bias_params = read_binary_vector(pb)
collect_until_token(pb, b'<OrthonormalConstraint>')
orthonormal_constraint = read_binary_float_token(
pb) # used only on training
collect_until_token(pb, b'<UseNaturalGradient>')
use_natural_grad = read_binary_bool_token(pb) # used only on training
collect_until_token(pb, b'<NumSamplesHistory>')
num_samples_hist = read_binary_float_token(pb)
collect_until_token(pb, b'<AlphaInOut>')
alpha_in_out = read_binary_float_token(pb), read_binary_float_token(
pb) # for training, usually (4, 4)
# according to Kaldi documentation http://kaldi-asr.org/doc/classkaldi_1_1nnet3_1_1TdnnComponent.html#details
# it looks like it's used only during training (but not 100% sure)
collect_until_token(pb, b'<RankInOut>')
rank_in_out = read_binary_integer32_token(
pb), read_binary_integer32_token(pb)
biases = np.array(bias_params) if len(bias_params) != 0 else None
attrs = {
'weights': np.reshape(weights, weights_shape),
'biases': biases,
'time_offsets': time_offsets,
}
TdnnComponent.update_node_stat(node, attrs)
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)
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 load_priors(file_descr, graph):
try:
collect_until_token(file_descr, b'<Priors>')
except Error:
# just ignore if priors were not found
return
if graph.graph['cmd_params'].counts is not None:
graph.graph['priors'] = read_binary_vector(file_descr)
else:
log.error(
"Model contains Prior values, if you want to embed them into the generated IR add option --counts=\"\" to command line",
extra={'is_warning': True})
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Params>')
weights = read_binary_vector(pb)
find_next_tag(pb)
read_placeholder(pb, 1)
mapping_rule = {'layout': 'NCHW'}
embed_input(mapping_rule, 1, 'weights', weights)
ScaleShiftOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(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], 'layout': 'NCHW'}
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
InnerProduct.update_node_stat(node, mapping_rule)
return __class__.enabled
def extract(node):
pb = node.parameters
collect_until_token(pb, b'<Dim>')
dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<BlockDim>')
block_dim = read_binary_integer32_token(pb)
if block_dim != dim:
raise Error(
"Dim is not equal BlockDim for BatchNorm is not supported")
collect_until_token(pb, b'<Epsilon>')
eps = read_binary_float_token(pb)
collect_until_token(pb, b'<TargetRms>')
target_rms = read_binary_float_token(pb)
collect_until_token(pb, b'<TestMode>')
test_mode = read_binary_bool_token(pb)
if test_mode is not False:
raise Error("Test mode True for BatchNorm is not supported")
collect_until_token(pb, b'<StatsMean>')
mean = read_binary_vector(pb)
collect_until_token(pb, b'<StatsVar>')
var = read_binary_vector(pb)
scale = target_rms / np.sqrt(var + eps)
shift = -target_rms * mean / np.sqrt(var + eps)
attrs = {}
embed_input(attrs, 1, 'weights', scale)
embed_input(attrs, 2, 'biases', shift)
ScaleShiftOp.update_node_stat(node, attrs)
return __class__.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):
pb = node.parameters
collect_until_token(pb, b'<Bias>')
biases = read_binary_vector(pb)
find_next_tag(pb)
read_placeholder(pb, 1)
mapping_rule = {
'layout': 'NCHW',
'bias_term': True,
'out-size': biases.shape[0],
}
embed_input(mapping_rule, 2, 'biases', biases)
ScaleShiftOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(node):
pb = node.parameters
collect_until_token(pb, b'<LinearParams>')
weights, weights_shape = read_binary_matrix(pb)
tag = find_next_tag(pb)
read_placeholder(pb, 1)
if tag != '<BiasParams>':
raise Error('FixedAffineComponent must contain BiasParams')
biases = read_binary_vector(pb)
mapping_rule = {'out-size': weights_shape[0], 'layout': 'NCHW'}
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
InnerProduct.update_node_stat(node, mapping_rule)
return __class__.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<LinearParams>')
weights, weights_shape = read_binary_matrix(pb)
tag = find_next_tag(pb)
read_placeholder(pb, 1)
if tag != '<BiasParams>':
raise Error('FixedAffineComponent must contain BiasParams')
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