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(cls, node):
pb = node.pb
model = node.model_pb
param = pb.scale_param
attrs = {
'axis': param.axis,
}
if model is None and len(pb.bottom) == 1:
# default weights and biases for scale layer if the caffemodel file doesn't contain them
model = NamedAttrsClass({
'blobs':
np.array([
NamedAttrsClass({'data': np.array([1])}),
NamedAttrsClass({'data': np.array([0])})
])
})
# scale with 1 input and 1 or 2 blobs
if model and len(model.blobs) != 0 and len(pb.bottom) == 1:
attrs.update(weights_biases(param.bias_term, model))
# 2 inputs + bias
elif len(pb.bottom) == 2 and param.bias_term:
if model is None or len(model.blobs) == 0:
# default bias for scale layer with 2 inputs if the caffemodel file doesn't contain them
model = NamedAttrsClass({
'blobs':
np.array([NamedAttrsClass({'data': np.array([0])})])
})
embed_input(attrs, 1, 'biases', model.blobs[0].data)
ScaleShiftOp.update_node_stat(node, attrs)
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
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):
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(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(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'<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'<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