def __init__(self, n_filter=128, n_confmaps=19, n_pafmaps=38, data_format="channels_first"): super().__init__() self.data_format = data_format self.main_block = layers.LayerList([ Conv2d(n_filter=n_filter, in_channels=n_filter, act=tf.nn.relu, data_format=self.data_format), Conv2d(n_filter=n_filter, in_channels=n_filter, act=tf.nn.relu, data_format=self.data_format), Conv2d(n_filter=n_filter, in_channels=n_filter, act=tf.nn.relu, data_format=self.data_format) ]) self.conf_block=layers.LayerList([ Conv2d(n_filter=512,in_channels=n_filter,filter_size=(1,1),strides=(1,1),act=tf.nn.relu,W_init=initializer,\ b_init=initializer,data_format=self.data_format), Conv2d(n_filter=n_confmaps,in_channels=512,filter_size=(1,1),strides=(1,1),W_init=initializer,\ b_init=initializer,data_format=self.data_format) ]) self.paf_block=layers.LayerList([ Conv2d(n_filter=512,in_channels=n_filter,filter_size=(1,1),strides=(1,1),act=tf.nn.relu,W_init=initializer,\ b_init=initializer,data_format=self.data_format), Conv2d(n_filter=n_pafmaps,in_channels=512,filter_size=(1,1),strides=(1,1),W_init=initializer,\ b_init=initializer,data_format=self.data_format) ])
def __init__(self, n_filter=128, in_channels=185, n_confmaps=19, n_pafmaps=38, data_format="channels_first"): super().__init__() self.data_format = data_format self.block_1 = self.Refinement_block(n_filter=n_filter, in_channels=in_channels, data_format=self.data_format) self.block_2 = self.Refinement_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format) self.block_3 = self.Refinement_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format) self.block_4 = self.Refinement_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format) self.block_5 = self.Refinement_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format) self.conf_block=layers.LayerList([ Conv2d(n_filter=512,in_channels=n_filter,filter_size=(1,1),strides=(1,1),act=tf.nn.relu,W_init=initializer,b_init=initializer,\ data_format=self.data_format), Conv2d(n_filter=n_confmaps,in_channels=512,filter_size=(1,1),strides=(1,1),W_init=initializer,b_init=initializer,\ data_format=self.data_format) ]) self.paf_block=layers.LayerList([ Conv2d(n_filter=512,in_channels=n_filter,filter_size=(1,1),strides=(1,1),act=tf.nn.relu,W_init=initializer,b_init=initializer,\ data_format=self.data_format), Conv2d(n_filter=n_pafmaps,in_channels=512,filter_size=(1,1),strides=(1,1),W_init=initializer,b_init=initializer,\ data_format=self.data_format) ])
def nobn_dw_conv_block(n_filter, in_channels, filter_size=(3, 3), strides=(1, 1), W_init=initializer, b_init=initializer, data_format="channels_first"): layer_list = [] layer_list.append( DepthwiseConv2d(filter_size=filter_size, strides=strides, in_channels=in_channels, act=tf.nn.relu, W_init=initializer, b_init=None, data_format=data_format)) layer_list.append( Conv2d(n_filter=n_filter, filter_size=(1, 1), strides=(1, 1), in_channels=in_channels, act=tf.nn.relu, W_init=initializer, b_init=None, data_format=data_format)) return layers.LayerList(layer_list)
def __init__(self,in_channels=3,data_format="channels_first",pretrained=True): super().__init__() self.data_format=data_format self.pretrained=pretrained self.transpose=False self.out_channels=512 if(self.data_format=="channel_last"): self.main_block=tl.models.vgg19(pretrained=self.pretrained,end_with="conv4_2") else: if(self.pretrained): print("only channels_last pretrained vgg19 available, adding transpose") self.main_block=tl.models.vgg19(pretrained=self.pretrained,end_with="conv4_2") self.transpose=True else: self.main_block=layers.LayerList([ self.conv_block(n_filter=64,in_channels=3,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=64,in_channels=64,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), MaxPool2d(filter_size=(2,2),strides=(2,2),data_format=self.data_format), self.conv_block(n_filter=128,in_channels=64,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=128,in_channels=128,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), MaxPool2d(filter_size=(2,2),strides=(2,2),data_format=self.data_format), self.conv_block(n_filter=256,in_channels=128,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=256,in_channels=256,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=256,in_channels=256,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=256,in_channels=256,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), MaxPool2d(filter_size=(2,2),strides=(2,2),data_format=self.data_format), self.conv_block(n_filter=512,in_channels=256,filter_size=(3,3),strides=(1,1),act=tf.nn.relu), self.conv_block(n_filter=512,in_channels=512,filter_size=(3,3),strides=(1,1),act=tf.nn.relu) ])
def __init__(self, n_filter=128, in_channels=512, data_format="channels_first"): super().__init__() self.data_format = data_format self.init_layer = Conv2d(n_filter=n_filter, in_channels=in_channels, filter_size=(1, 1), act=tf.nn.relu, data_format=self.data_format) self.main_block = layers.LayerList([ conv_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format), conv_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format), conv_block(n_filter=n_filter, in_channels=n_filter, data_format=self.data_format), ]) self.end_layer = Conv2d(n_filter=n_filter, in_channels=n_filter, filter_size=(3, 3), act=tf.nn.relu, data_format=self.data_format)
def separable_block(n_filter=32,in_channels=3,filter_size=(3,3),strides=(1,1),dilation_rate=(1,1),act=tf.nn.relu,data_format="channels_first"): layer_list=[] layer_list.append(DepthwiseConv2d(filter_size=filter_size,strides=strides,in_channels=in_channels, dilation_rate=dilation_rate,W_init=initial_w,b_init=None,data_format=data_format)) layer_list.append(BatchNorm2d(decay=0.99,act=act,num_features=in_channels,data_format=data_format,is_train=True)) layer_list.append(Conv2d(n_filter=n_filter,filter_size=(1,1),strides=(1,1),in_channels=in_channels,W_init=initial_w,b_init=None,data_format=data_format)) layer_list.append(BatchNorm2d(decay=0.99,act=act,num_features=n_filter,data_format=data_format,is_train=True)) return layers.LayerList(layer_list)
def __init__(self,n_filter,in_channels,data_format="channels_first"): super().__init__() self.data_format=data_format self.init_layer=Conv2d(n_filter=n_filter,filter_size=(1,1),in_channels=in_channels,act=tf.nn.relu,data_format=self.data_format) self.main_block=layers.LayerList([ conv_block(n_filter=n_filter,in_channels=n_filter,data_format=self.data_format), conv_block(n_filter=n_filter,in_channels=n_filter,dilation_rate=(1,1),data_format=self.data_format) ])
def __init__(self,n_confmaps=19,n_pafmaps=38,in_channels=128,data_format="channels_first"): super().__init__() self.n_confmaps=n_confmaps self.n_pafmaps=n_pafmaps self.in_channels=in_channels self.data_format=data_format self.conf_block=layers.LayerList([ Conv2d(n_filter=128,in_channels=self.in_channels,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=128,in_channels=128,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=128,in_channels=128,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=512,in_channels=128,filter_size=(1,1),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=self.n_confmaps,in_channels=512,filter_size=(1,1),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format) ]) self.paf_block=layers.LayerList([ Conv2d(n_filter=128,in_channels=self.in_channels,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=128,in_channels=128,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=128,in_channels=128,filter_size=(3,3),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=512,in_channels=128,filter_size=(1,1),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format), Conv2d(n_filter=self.n_pafmaps,in_channels=512,filter_size=(1,1),strides=(1,1),padding="SAME",act=tf.nn.relu,W_init=initial_w,b_init=initial_b,data_format=self.data_format) ])
def __init__(self, data_format="channels_first"): super().__init__() self.data_format = data_format self.out_channels = 512 self.main_block = layers.LayerList([ conv_block(n_filter=32, in_channels=3, data_format=self.data_format, strides=(2, 2)), dw_conv_block(n_filter=64, in_channels=32, data_format=self.data_format), dw_conv_block(n_filter=128, in_channels=64, data_format=self.data_format, strides=(2, 2)), dw_conv_block(n_filter=128, in_channels=128, data_format=self.data_format), dw_conv_block(n_filter=256, in_channels=128, data_format=self.data_format, strides=(2, 2)), dw_conv_block(n_filter=256, in_channels=256, data_format=self.data_format), dw_conv_block(n_filter=512, in_channels=256, data_format=self.data_format), dw_conv_block(n_filter=512, in_channels=512, data_format=self.data_format, dilation_rate=(2, 2)), dw_conv_block(n_filter=512, in_channels=512, data_format=self.data_format), dw_conv_block(n_filter=512, in_channels=512, data_format=self.data_format), dw_conv_block(n_filter=512, in_channels=512, data_format=self.data_format), dw_conv_block(n_filter=512, in_channels=512, data_format=self.data_format) ])
def conv_block(n_filter, in_channels, filter_size=(3, 3), strides=(1, 1), dilation_rate=(1, 1), W_init=initializer, b_init=initializer, padding="SAME", data_format="channels_first"): layer_list = [] layer_list.append(Conv2d(n_filter=n_filter,filter_size=filter_size,strides=strides,in_channels=in_channels,\ dilation_rate=dilation_rate,padding=padding,W_init=initializer,b_init=initializer,data_format=data_format)) layer_list.append( BatchNorm2d(decay=0.99, act=tf.nn.relu, num_features=n_filter, data_format=data_format, is_train=True)) return layers.LayerList(layer_list)
def __init__(self, in_channels=3, scale_size=8, data_format="channels_first"): super().__init__() self.in_channels = in_channels self.data_format = data_format self.scale_size = scale_size if (self.scale_size == 8): strides = (1, 1) else: strides = (2, 2) self.out_channels = 384 self.main_block = layers.LayerList([ self.conv_block(n_filter=32, in_channels=3, filter_size=(3, 3), strides=(1, 1)), self.conv_block(n_filter=64, in_channels=32, filter_size=(3, 3), strides=(1, 1)), MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding="SAME", data_format=self.data_format), self.conv_block(n_filter=128, in_channels=64, filter_size=(3, 3), strides=(1, 1)), self.conv_block(n_filter=128, in_channels=128, filter_size=(3, 3), strides=(1, 1)), MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding="SAME", data_format=self.data_format), self.conv_block(n_filter=200, in_channels=128, filter_size=(3, 3), strides=(1, 1)), self.conv_block(n_filter=200, in_channels=200, filter_size=(3, 3), strides=strides), self.conv_block(n_filter=200, in_channels=200, filter_size=(3, 3), strides=(1, 1)), MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding="SAME", data_format=self.data_format), self.conv_block(n_filter=384, in_channels=200, filter_size=(3, 3), strides=(1, 1)), self.conv_block(n_filter=384, in_channels=384, filter_size=(3, 3), strides=strides) ])