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
collect_until_token(pb, b'<PoolSize>')
kernel = read_binary_integer32_token(pb)
tag = find_next_tag(pb)
if tag == '<PoolStep>':
read_placeholder(pb, 1)
stride = read_binary_integer32_token(pb)
pool_step = stride
pool_stride = read_token_value(pb, b'<PoolStride>')
elif tag == '<PoolStride>':
stride = 1
pool_step = None
read_placeholder(pb, 1)
pool_stride = read_binary_integer32_token(pb)
else:
raise Error('Can not extract parameters for {}'.format(node))
mapping_rule = {
'window': np.array([1, 1, 1, kernel], dtype=np.int64),
'stride': np.array([1, 1, stride, stride], dtype=np.int64),
'pool_stride': pool_stride,
'pool_step': pool_step,
'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),
'pool_method': 'max',
}
mapping_rule.update(layout_attrs())
Pooling.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
pb = node.parameters
try:
collect_until_token(pb, b'<InputDim>')
except Error:
raise Error("<InputDim> was not found")
in_dim = read_binary_integer32_token(pb)
try:
collect_until_token(pb, b'<OutputDim>')
except Error:
raise Error("<OutputDim> was not found")
out_dim = read_binary_integer32_token(pb)
assert in_dim % out_dim == 0
group = in_dim / out_dim
try:
collect_until_token(pb, b'<P>')
except Error:
raise Error("<P> was not found")
p = read_binary_float_token(pb)
attrs = {
'group': group,
'p': p,
}
PNormOp.update_node_stat(node, attrs)
return cls.enabled
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 load_kalid_nnet1_model(graph, file_descr, name):
prev_layer_id = 'Parameter'
graph.add_node(prev_layer_id,
name=prev_layer_id,
kind='op',
op='Parameter',
parameters=None)
used_layers = set()
while True:
component_type = find_next_component(file_descr)
if component_type == end_of_nnet_tag.lower()[1:-1]:
break
layer_o = read_binary_integer32_token(file_descr)
layer_i = read_binary_integer32_token(file_descr)
if component_type == 'parallelcomponent':
prev_layer_id = load_parallel_component(file_descr, graph,
prev_layer_id)
find_end_of_component(file_descr, component_type)
continue
start_index = file_descr.tell()
end_tag, end_index = find_end_of_component(file_descr, component_type)
end_index -= len(end_tag)
layer_id = graph.unique_id(prefix=component_type)
graph.add_node(layer_id,
parameters=get_parameters(file_descr, start_index,
end_index),
op=component_type,
kind='op',
layer_i=layer_i,
layer_o=layer_o)
prev_node = Node(graph, prev_layer_id)
if prev_node.op == 'Parameter':
prev_node['shape'] = np.array([1, layer_i], dtype=np.int64)
prev_node.add_output_port(0)
Node(graph, layer_id).add_input_port(0)
graph.create_edge(
prev_node, Node(graph, layer_id), 0, 0,
create_edge_attrs(prev_layer_id, layer_id, prev_layer_id))
used_layers.add(prev_layer_id)
prev_layer_id = layer_id
log.debug('{} (type is {}) was loaded'.format(prev_layer_id,
component_type))
# Tensor names information corresponding to a node is stored on outgoing edges.
# As output nodes do not have outgoing edges, fake outputs are required. In the following code
# for each output Identity node is added, and tensor name for the output is kept
# on (output, fake output) edge. After Result nodes adding transformation fake outputs
# are deleted from graph.
output_layers = graph.nodes - used_layers
add_outputs_identity(
graph, output_layers, lambda g, output, fake_output: g.create_edge(
Node(g, output), Node(g, fake_output), 0, 0,
create_edge_attrs(output, fake_output, output)))
def read_binary_matrix(file_desc: io.BufferedReader, read_token: bool = True):
if read_token:
read_placeholder(file_desc)
rows_number = read_binary_integer32_token(file_desc)
cols_number = read_binary_integer32_token(file_desc)
# to compare: ((float *)a->buffer())[10]
return read_blob(file_desc,
rows_number * cols_number), (rows_number, cols_number)
def read_binary_vector(file_desc: io.BufferedReader,
read_token: bool = True,
dtype=np.float32):
if read_token:
read_placeholder(file_desc)
elements_number = read_binary_integer32_token(file_desc)
return read_blob(file_desc, elements_number, dtype)
def replace_op(self, graph: Graph, node: Node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
const_attrs = {
'name': 'indexes/{}'.format(node.id),
'value': np.array(weights),
'shape': [weights_size],
'data_type': np.int32
}
indexes_node = Const(graph).create_node(attrs=const_attrs)
perm_in_1 = Const(
graph, {
'value': np.array([1, 0], dtype=np.int64),
'shape': [2],
'data_type': np.int64
}).create_node()
axis_const = Const(graph, {'value': int64_array(0)}).create_node()
perm1_node = Transpose(graph, {
'name': 'input_permute'
}).create_node([node.in_node(0)])
perm1_node.in_port(0).connect(node.in_port(0).get_source())
perm1_node.in_port(1).connect(perm_in_1.out_port(0))
gather_node = Gather(graph, {}).create_node()
gather_node.in_port(0).connect(perm1_node.out_port(0))
gather_node.in_port(1).connect(indexes_node.out_port(0))
gather_node.in_port(2).connect(axis_const.out_port(0))
perm2_node = Transpose(graph, {'name': 'output_permute'}).create_node()
perm2_node.in_port(0).connect(gather_node.out_port(0))
perm2_node.in_port(1).connect(perm_in_1.out_port(0))
return [perm2_node.id]
def load_components(file_descr, graph, component_layer_map=None):
num_components = collect_until_token_and_read(file_descr, b'<NumComponents>')
log.debug('Network contains {} components'.format(num_components))
is_nnet3 = False if component_layer_map is None else True
if not is_nnet3:
collect_until_token(file_descr, b'<Components>')
all_components = list()
name = ""
for _ in range(num_components):
if is_nnet3:
name = collect_until_token_and_read(file_descr, b'<ComponentName>', np.string_)
component_type = find_next_component(file_descr)
if component_type == end_of_nnet_tag.lower()[1:-1]:
break
start_index = file_descr.tell()
end_tag, end_index = find_end_of_component(file_descr, component_type)
# read dim info where possible to simplify shape calculation for MemoryOffset
# shape calculation for MemoryOffset can't be done through shape of previous layer because
# it is separated in 2 parts to remove cycle from graph
file_descr.seek(start_index)
dim = 0
try:
collect_until_token(file_descr, b'<Dim>', size_search_zone=end_index - start_index)
cur_index = file_descr.tell()
if start_index < cur_index < end_index:
dim = read_binary_integer32_token(file_descr)
else:
file_descr.seek(start_index)
except Error:
file_descr.seek(start_index)
if is_nnet3:
if name in component_layer_map:
layer_id = component_layer_map[name][0]
for layer in component_layer_map[name]:
node = Node(graph, layer)
node['parameters'] = get_parameters(file_descr, start_index, end_index)
node['op'] = component_type
# Read dim info where possible to simplify shape calculation for MemoryOffset
for o_n_name, params in node.get_outputs():
o_n = Node(graph, o_n_name)
if o_n['op'] == 'MemoryOffset' and dim != 0:
o_n['parameters']['element_size'] = dim
else:
raise Error("Something wrong with layer {}".format(name))
else:
layer_id = graph.unique_id(prefix=component_type)
graph.add_node(layer_id,
parameters=get_parameters(file_descr, start_index, end_index),
op=component_type,
kind='op')
all_components.append(layer_id)
log.debug('{} (type is {}) was loaded'.format(layer_id, component_type))
return all_components
def replace_op(self, graph: Graph, node: Node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
node_name = node.soft_get('name', node.id)
const_attrs = {
'name': node_name + '/indexes',
'value': np.array(weights),
'shape': [weights_size],
'data_type': np.int32
}
indexes_node = Const(graph).create_node(attrs=const_attrs)
perm_in_1 = Const(graph, {'value': int64_array([1, 0]), 'name': node_name + '/order'}).create_node()
perm1_node = Transpose(graph, {'name': node_name + '/input_permute'}).create_node([node.in_node(0)])
perm1_node.in_port(0).connect(node.in_port(0).get_source())
perm1_node.in_port(1).connect(perm_in_1.out_port(0))
gather_node = create_op_with_const_inputs(graph, Gather, {2: int64_array(0)}, {'name': node_name + '/gather'})
gather_node.in_port(0).connect(perm1_node.out_port(0))
gather_node.in_port(1).connect(indexes_node.out_port(0))
perm2_node = Transpose(graph, {'name': node_name + '/output_permute'}).create_node()
perm2_node.in_port(0).connect(gather_node.out_port(0))
perm2_node.in_port(1).connect(perm_in_1.out_port(0))
return [perm2_node.id]
def load_kalid_nnet1_model(file_descr, name):
graph = Graph(name=name)
prev_layer_id = 'Input'
graph.add_node(prev_layer_id,
name=prev_layer_id,
kind='op',
op='Input',
parameters=None)
input_shape = []
while True:
component_type = find_next_component(file_descr)
if component_type == end_of_nnet_tag.lower()[1:-1]:
break
layer_o = read_binary_integer32_token(file_descr)
layer_i = read_binary_integer32_token(file_descr)
if component_type == 'parallelcomponent':
prev_layer_id = load_parallel_component(file_descr, graph,
prev_layer_id)
continue
start_index = file_descr.tell()
end_tag, end_index = find_end_of_component(file_descr, component_type)
end_index -= len(end_tag)
layer_id = graph.unique_id(prefix=component_type)
graph.add_node(layer_id,
parameters=get_parameters(file_descr, start_index,
end_index),
op=component_type,
kind='op',
layer_i=layer_i,
layer_o=layer_o)
prev_node = Node(graph, prev_layer_id)
if prev_node.op == 'Input':
prev_node['shape'] = np.array([1, layer_i], dtype=np.int64)
input_shape = np.array([1, layer_i], dtype=np.int64)
graph.add_edge(prev_layer_id, layer_id,
**create_edge_attrs(prev_layer_id, layer_id))
prev_layer_id = layer_id
log.debug('{} (type is {}) was loaded'.format(prev_layer_id,
component_type))
return graph, input_shape
def extract(node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
attrs = {'infer': copy_shape_infer}
embed_input(attrs, 1, 'indexes', weights)
Permute.update_node_stat(node, attrs)
return __class__.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):
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'<TimePeriod>')
time_period = read_binary_integer32_token(pb)
collect_until_token(pb, b'<DropoutProportion>')
dropout_proporion = read_binary_float_token(pb)
# collect_until_token(pb, b'<Continuous>')
Identity.update_node_stat(node, {})
return cls.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(node):
pb = node.parameters
num_slice_points = read_binary_integer32_token(pb)
mapping_rule = {
'axis': 1,
'slice_point': read_blob(pb, num_slice_points, np.int32),
'batch_dims': 0,
'spatial_dims': 1,
'infer': caffe_slice_infer
}
node.parameters.close()
Slice.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)
target_rms = 1
d_scaled = dim * target_rms**2
in_norm = np.zeros([dim], np.float64)
in_norm += 1.0 / d_scaled
in_norm = np.maximum(in_norm, 2.**(-66))
in_norm = np.power(in_norm, -0.5)
attrs = {}
embed_input(attrs, 1, 'weights', in_norm)
ScaleShiftOp.update_node_stat(node, attrs)
return __class__.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Dim>')
dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<Scale>')
scale = read_binary_float_token(pb)
# TODO add real batch here
attrs = {}
embed_input(attrs, 1, 'weights', np.full([dim], scale))
ScaleShiftOp.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pb = node.parameters
try:
collect_until_token(pb, b'<Dim>')
except Error:
try:
pb.seek(0)
collect_until_token(pb, b'<InputDim>')
except Error:
raise Error("Neither <Dim> nor <InputDim> were found")
in_dim = read_binary_integer32_token(pb)
try:
collect_until_token(pb, b'<TargetRms>')
target_rms = read_binary_float_token(pb)
except Error:
# model does not contain TargetRms
target_rms = 1.0
try:
collect_until_token(pb, b'<AddLogStddev>')
add_log = read_binary_bool_token(pb)
except Error:
# model does not contain AddLogStddev
add_log = False
if add_log is not False:
raise Error(
"AddLogStddev True in Normalize component is not supported")
scale = target_rms * np.sqrt(in_dim)
attrs = {
'eps': 0.00000001,
'across_spatial': 0,
'channel_shared': 1,
'in_dim': in_dim,
}
embed_input(attrs, 1, 'weights', [scale])
NormalizeOp.update_node_stat(node, attrs)
return cls.enabled
def test_read_binary_integer32_token(self):
stream = self.bytesio_from(
self.pack_value(4, 'B') + self.pack_value(32, self.uint32_fmt))
self.assertEqual(read_binary_integer32_token(stream), 32)
