def __init__(
self,
layer = [],
combine_fn = tf.minimum,
name ='elementwise_layer',
act = None,
):
Layer.__init__(self, name=name)
'''
if act:
#print(" [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
#self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
else:
#print(" [TL] ElementwiseLayer %s: size:%s fn:%s" % (
#self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
'''
self.outputs = layer[0].outputs
# #print(self.outputs._shape, type(self.outputs._shape))
for l in layer[1:]:
# assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" % (self.outputs.get_shape() , str(l.outputs.get_shape()))
self.outputs = combine_fn(self.outputs, l.outputs, name=name)
if act:
self.outputs = act(self.outputs)
self.all_layers = list(layer[0].all_layers)
self.all_params = list(layer[0].all_params)
self.all_drop = dict(layer[0].all_drop)
for i in range(1, len(layer)):
self.all_layers.extend(list(layer[i].all_layers))
self.all_params.extend(list(layer[i].all_params))
self.all_drop.update(dict(layer[i].all_drop))
self.all_layers = list_remove_repeat(self.all_layers)
self.all_params = list_remove_repeat(self.all_params)
def __init__(
self,
layer = [],
combine_fn = tf.minimum,
name ='elementwise_layer',
act = None,
):
Layer.__init__(self, name=name)
if act:
print(" [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" % (
self.name, layer[0].outputs.get_shape(), combine_fn.__name__, act.__name__))
else:
print(" [TL] ElementwiseLayer %s: size:%s fn:%s" % (
self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
self.outputs = layer[0].outputs
# print(self.outputs._shape, type(self.outputs._shape))
for l in layer[1:]:
# assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" % (self.outputs.get_shape() , str(l.outputs.get_shape()))
self.outputs = combine_fn(self.outputs, l.outputs, name=name)
if act:
self.outputs = act(self.outputs)
self.all_layers = list(layer[0].all_layers)
self.all_params = list(layer[0].all_params)
self.all_drop = dict(layer[0].all_drop)
for i in range(1, len(layer)):
self.all_layers.extend(list(layer[i].all_layers))
self.all_params.extend(list(layer[i].all_params))
self.all_drop.update(dict(layer[i].all_drop))
self.all_layers = list_remove_repeat(self.all_layers)
self.all_params = list_remove_repeat(self.all_params)
def __init__(
self,
layer=[],
combine_fn=tf.minimum, # 默认是取对应元素最小值
name='elementwise_layer',
act=None,
):
Layer.__init__(self, name=name) # [shortcut, residual]
if act:
print(" [TL] ElementwiseLayer %s: size:%s fn:%s, act:%s" %
(self.name, layer[0].outputs.get_shape(),
combine_fn.__name__, act.__name__))
else:
print(
" [TL] ElementwiseLayer %s: size:%s fn:%s" %
(self.name, layer[0].outputs.get_shape(), combine_fn.__name__)
) # ElementwiseLayer resnet_v1_50/block1/unit_1/bottleneck_v1/combine_layer: size:(?, 56, 56, 256) fn:add
self.outputs = layer[0].outputs # shortcut (?, 56, 56, 256)
# print(self.outputs._shape, type(self.outputs._shape))
for l in layer[1:]: # residual (?, 56, 56, 256)
# assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" % (self.outputs.get_shape() , str(l.outputs.get_shape()))
self.outputs = combine_fn(
self.outputs, l.outputs, name=name
) # shortcut (?, 56, 56, 256) + residual (?, 56, 56, 256)
if act:
self.outputs = act(self.outputs)
self.all_layers = list(layer[0].all_layers)
self.all_params = list(layer[0].all_params)
self.all_drop = dict(layer[0].all_drop)
for i in range(1, len(layer)):
self.all_layers.extend(list(layer[i].all_layers))
self.all_params.extend(list(layer[i].all_params))
self.all_drop.update(dict(layer[i].all_drop))
self.all_layers = list_remove_repeat(
self.all_layers
) # 剔除掉重复的layers，resnet_v1_50/conv1，resnet_v1_50/bn0，resnet_v1_50/prelu0
self.all_params = list_remove_repeat(self.all_params)
def __init__(
self,
layer=None,
shape=None,
name='resize_bilinear_layer',
):
Layer.__init__(self, name=name)
self.inputs = layer.outputs
if shape == None:
shape = tf.shape(self.inputs)[1:3, ]
self.outputs = tf.image.resize_bilinear(self.inputs, shape)
print(" [TL] ResizeBilinearLayer %s: %s" %
(self.name, self.outputs.get_shape()))
self.all_layers = list(layer.all_layers)
self.all_params = list(layer.all_params)
self.all_drop = dict(layer.all_drop)
self.all_layers = list_remove_repeat(self.all_layers)
self.all_params = list_remove_repeat(self.all_params)
