minor changes in resnet (f1d8e45d) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/resnet50.py

+25 −27

Original line number	Diff line number	Diff line
		@@ -29,13 +29,12 @@ class ResNet50(TensorGraph):
		specifies number of classes
		"""

		def identity_block(input, kernel_size, filters):
		def identity_block(self, input, kernel_size, filters):
		filters1, filters2, filters3 = filters

		output = Conv2D(
		num_outputs=filters1,
		kernel_size=1,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[input])
		@@ -45,7 +44,6 @@ class ResNet50(TensorGraph):
		output = Conv2D(
		num_outputs=filters2,
		kernel_size=kernel_size,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[input])
		@@ -55,7 +53,6 @@ class ResNet50(TensorGraph):
		output = Conv2D(
		num_outputs=filters3,
		kernel_size=1,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[input])
		@@ -66,8 +63,9 @@ class ResNet50(TensorGraph):

		return output

		def conv_block(input, kernel_size, filters, strides=2):
		def conv_block(self, input, kernel_size, filters, strides=2):
		filters1, filters2, filters3 = filters

		output = Conv2D(
		num_outputs=filters1,
		kernel_size=1,
		@@ -81,7 +79,6 @@ class ResNet50(TensorGraph):
		output = Conv2D(
		num_outputs=filters2,
		kernel_size=kernel_size,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[output])
		@@ -99,12 +96,12 @@ class ResNet50(TensorGraph):
		shortcut = Conv2D(
		num_outputs=filters3,
		kernel_size=1,
		stride=strides,
		activation='linear',
		padding='same',
		in_layers=[input])
		shortcut = BatchNorm(in_layers=[shortcut])

		output = Add(in_layers[shortcut, output])
		output = Add(in_layers=[shortcut, output])
		output = ReLU(output)

		return output
		@@ -119,6 +116,7 @@ class ResNet50(TensorGraph):
		self.img_cols = img_cols
		self.img_rows = img_rows
		self.weights = weights
		self.classes = classes

		input = Feature(shape=(None, self.img_rows, self.img_cols, 3))
		labels = Label(shape=(None, self.classes))
		@@ -132,32 +130,32 @@ class ResNet50(TensorGraph):
		in_layers=[input])
		bn1 = BatchNorm(in_layers=[conv1])
		ac1 = ReLU(bn1)
		pool1 = MaxPool2D(ksize=[1, 2, 2, 1], in_layers=[bn1])
		pool1 = MaxPool2D(ksize=[1, 3, 3, 1], in_layers=[bn1])

		cb1 = conv_block(pool1, 3, [64, 64, 256], 1)
		id1 = identity_block(cb1, 3, [64, 64, 256])
		id1 = identity_block(id1, 3, [64, 64, 256])
		cb1 = self.conv_block(pool1, 3, [64, 64, 256], 1)
		id1 = self.identity_block(cb1, 3, [64, 64, 256])
		id1 = self.identity_block(id1, 3, [64, 64, 256])

		cb2 = conv_block(id1, 3, [128, 128, 512])
		id2 = identity_block(cb2, 3, [128, 128, 512])
		id2 = identity_block(id2, 3, [128, 128, 512])
		id2 = identity_block(id2, 3, [128, 128, 512])
		cb2 = self.conv_block(id1, 3, [128, 128, 512])
		id2 = self.identity_block(cb2, 3, [128, 128, 512])
		id2 = self.identity_block(id2, 3, [128, 128, 512])
		id2 = self.identity_block(id2, 3, [128, 128, 512])

		cb3 = conv_block(id2, 3, [256, 256, 1024])
		id3 = identity_block(cb3, 3, [256, 256, 1024])
		id3 = identity_block(id3, 3, [256, 256, 1024])
		id3 = identity_block(id3, 3, [256, 256, 1024])
		id3 = identity_block(cb3, 3, [256, 256, 1024])
		id3 = identity_block(id3, 3, [256, 256, 1024])
		cb3 = self.conv_block(id2, 3, [256, 256, 1024])
		id3 = self.identity_block(cb3, 3, [256, 256, 1024])
		id3 = self.identity_block(id3, 3, [256, 256, 1024])
		id3 = self.identity_block(id3, 3, [256, 256, 1024])
		id3 = self.identity_block(cb3, 3, [256, 256, 1024])
		id3 = self.identity_block(id3, 3, [256, 256, 1024])

		cb4 = conv_block(id3, 3, [512, 512, 2048])
		id4 = identity_block(cb4, 3, [512, 512, 2048])
		id4 = identity_block(id4, 3, [512, 512, 2048])
		cb4 = self.conv_block(id3, 3, [512, 512, 2048])
		id4 = self.identity_block(cb4, 3, [512, 512, 2048])
		id4 = self.identity_block(id4, 3, [512, 512, 2048])

		pool2 = MaxPool2D(ksize=[1, 7, 7, 1], in_layers=[id4])

		flatten = Flatten(in_layers=pool2)
		dense = Dense(classes, in_layers=flatten)
		flatten = Flatten(in_layers=[pool2])
		dense = Dense(classes, in_layers=[flatten])

		loss = SoftMaxCrossEntropy(in_layers=[labels, dense])
		loss = ReduceMean(in_layers=[loss])

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