Cleaned up code

layers_builder.py

@@ -0,0 +1,225 @@
+from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
+from keras.layers import BatchNormalization, Activation, Input, Dropout, ZeroPadding2D
+from keras.layers import merge, concatenate, Lambda, Reshape
+from keras.models import Model
+
+import tensorflow as tf
+
+
+
+def Interp(x, size=(60,60)):
+	print(x.shape)
+	new_height = size[0]
+	new_width = size[1]
+	resized = tf.image.resize_images(x, [new_height, new_width])
+	print(resized.shape)
+	return resized
+
+
+def Interp_zoom(x, zoom=8):
+	print(x.shape)
+	old_height = int(x.shape[1])
+	old_width = int(x.shape[2])
+	new_height = old_height + (old_height-1) * (zoom - 1)
+	new_width = old_width + (old_width-1) * (zoom - 1)
+	resized = tf.image.resize_images(x, [new_height, new_width])
+	return resized
+
+
+def residual_conv(prev, level,
+				pad=1, lvl=1, sub_lvl=1, modify_stride=False):
+	
+	lvl = str(lvl)
+	sub_lvl = str(sub_lvl)
+	names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_reduce" ,
+			"conv"+lvl+"_"+ sub_lvl +"_1x1_reduce_bn",
+			"conv"+lvl+"_"+ sub_lvl +"_3x3",
+			"conv"+lvl+"_"+ sub_lvl +"_3x3_bn",
+			"conv"+lvl+"_"+ sub_lvl +"_1x1_increase",
+			"conv"+lvl+"_"+ sub_lvl +"_1x1_increase_bn"]
+	if modify_stride == False:
+		prev = Conv2D(64 * level, (1,1), strides=(1,1), use_bias=False,
+					name=names[0])(prev)
+	elif modify_stride == True:
+		prev = Conv2D(64 * level, (1,1), strides=(2,2), use_bias=False,
+					name=names[0])(prev)
+
+	prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
+	prev = Activation('relu')(prev)
+
+	prev = ZeroPadding2D(padding=(pad,pad))(prev)
+	prev = Conv2D(64 * level, (3,3), 
+				strides=(1,1), dilation_rate=pad, use_bias=False,
+					name=names[2])(prev)
+	
+
+	prev = BatchNormalization(momentum=0.95, name=names[3])(prev)
+	prev = Activation('relu')(prev)
+	prev = Conv2D(256 * level, (1,1), strides=(1,1), use_bias=False,
+					name=names[4])(prev)
+	prev = BatchNormalization(momentum=0.95, name=names[5])(prev)
+	return prev
+
+
+def short_convolution_branch(prev, level,
+							lvl=1, sub_lvl=1, modify_stride=False):
+	lvl = str(lvl)
+	sub_lvl = str(sub_lvl)
+	names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_proj",
+			"conv"+lvl+"_"+ sub_lvl +"_1x1_proj_bn"
+			]
+
+	if modify_stride == False:		
+		prev = Conv2D(256 * level ,(1,1), strides=(1,1), use_bias=False,
+				name=names[0])(prev)
+	elif modify_stride == True:
+		prev = Conv2D(256 * level, (1,1), strides=(2,2), use_bias=False,
+				name=names[0])(prev)
+
+	prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
+	return prev
+
+
+def empty_branch(prev):
+	return prev
+
+
+def residual_short(prev_layer, level, pad=1, lvl=1, sub_lvl=1, modify_stride=False):
+
+	block_1 = residual_conv(prev_layer, level,
+						pad=pad, lvl=lvl, sub_lvl=sub_lvl,
+						modify_stride=modify_stride)
+
+	block_2 = short_convolution_branch(prev_layer, level,
+						lvl=lvl, sub_lvl=sub_lvl,
+						modify_stride=modify_stride)
+
+	return merge([block_1, block_2], mode='sum') 
+
+
+def residual_empty(prev_layer, level, pad=1, lvl=1, sub_lvl=1):
+	prev_layer = Activation('relu')(prev_layer)
+
+	block_1 = residual_conv(prev_layer, level, 
+						pad=pad, lvl=lvl, sub_lvl=sub_lvl)
+	block_2 = empty_branch(prev_layer)
+	return merge([block_1, block_2], mode='sum') 
+
+
+def interp_block(prev_layer, level, str_lvl=1):
+
+	str_lvl = str(str_lvl)
+
+	names = [
+		"conv5_3_pool"+str_lvl+"_conv",
+		"conv5_3_pool"+str_lvl+"_conv_bn"
+		]
+
+	kernel = (10*level, 10*level)
+	strides = (10*level, 10*level)
+	prev_layer = AveragePooling2D(kernel,strides=strides)(prev_layer)
+	prev_layer = Conv2D(512, (1,1), strides=(1,1), use_bias=False, name=names[0])(prev_layer)
+	prev_layer = BatchNormalization(momentum=0.95, name=names[1])(prev_layer)
+	prev_layer = Activation('relu')(prev_layer)
+	prev_layer = Lambda(Interp)(prev_layer)
+	return prev_layer
+
+
+def build_pspnet():
+	#Names for the first couple layers of model
+	names = ["conv1_1_3x3_s2",
+			"conv1_1_3x3_s2_bn",
+			"conv1_2_3x3",
+			"conv1_2_3x3_bn",
+			"conv1_3_3x3",
+			"conv1_3_3x3_bn"]
+
+	#---Short branch(only start of network)
+
+	inp = Input((473,473, 3))
+
+	cnv1 = ZeroPadding2D(padding=(1,1))(inp)
+	cnv1 = Conv2D(64, (3, 3), strides=(2, 2), use_bias=False, name=names[0])(cnv1) # "conv1_1_3x3_s2"
+	
+	bn1 = BatchNormalization(momentum=0.95, name=names[1])(cnv1)  # "conv1_1_3x3_s2/bn"
+	relu1 = Activation('relu')(bn1)				#"conv1_1_3x3_s2/relu"
+
+	cnv1 = ZeroPadding2D(padding=(1,1))(relu1)
+	cnv1 = Conv2D(64, (3, 3), strides=(1, 1), use_bias=False, name=names[2])(cnv1) #"conv1_2_3x3"
+	
+	bn1 = BatchNormalization(momentum=0.95, name=names[3])(cnv1)  #"conv1_2_3x3/bn"
+	relu1 = Activation('relu')(bn1) 				#"conv1_2_3x3/relu"
+
+	cnv1 = ZeroPadding2D(padding=(1,1))(relu1)
+	cnv1 = Conv2D(128, (3, 3), strides=(1, 1), use_bias=False, name=names[4])(cnv1) #"conv1_3_3x3"
+	
+	bn1 = BatchNormalization(momentum=0.95, name=names[5])(cnv1) 		#"conv1_3_3x3/bn"
+	relu1 = Activation('relu')(bn1)				#"conv1_3_3x3/relu"
+
+	res = ZeroPadding2D(padding=(1,1))(relu1)
+	res = MaxPooling2D(pool_size=(3,3), strides=(2,2))(res)  #"pool1_3x3_s2"
+	
+
+	#---Residual layers(body of network)
+
+	"""
+	Modify_stride --Used only once in first 3_1 convolutions block.
+	changes stride of first convolution from 1 -> 2
+	"""
+
+	#2_1- 2_3
+	res = residual_short(res, 1, pad=1, lvl=2, sub_lvl=1) 
+	for i in range(2):
+		res = residual_empty(res, 1, pad=1, lvl=2, sub_lvl=i+2) 
+
+	#3_1 - 3_3
+	res = residual_short(res, 2, pad=1, lvl=3, sub_lvl=1, modify_stride=True) 
+	for i in range(2): 
+		res = residual_empty(res, 2, pad=1, lvl=3, sub_lvl=i+2) 
+
+	#4_1 - 4_6
+	res = residual_short(res, 4, pad=2, lvl=4, sub_lvl=1) 
+	for i in range(5):
+		res = residual_empty(res, 4, pad=2, lvl=4, sub_lvl=i+2) 
+
+	#5_1 - 5_3
+	res = residual_short(res, 8, pad=4, lvl=5, sub_lvl=1) 
+	for i in range(2):
+		res = residual_empty(res, 8, pad=4, lvl=5, sub_lvl=i+2) 
+
+	#---Head of network
+	#---PSPNet concat layers with Interpolation
+
+	res = Activation('relu')(res)
+	interp_block1 = interp_block(res, 6, str_lvl=1)
+	interp_block2 = interp_block(res, 3, str_lvl=2)
+	interp_block3 = interp_block(res, 2, str_lvl=3)
+	interp_block4 = interp_block(res, 1, str_lvl=6)
+
+	#concat all these layers by 4th axis(3+1).  resulted shape=(1,60,60,4096)
+	res = concatenate([res,
+					interp_block1,
+					interp_block2,
+					interp_block3,
+					interp_block4], axis=3)
+
+	res = ZeroPadding2D(padding=(1,1))(res)
+	res = Conv2D(512, (3, 3), strides=(1, 1), use_bias=False, name="conv5_4")(res)
+	
+	res = BatchNormalization(momentum=0.95, name="conv5_4_bn")(res)
+	res = Activation('relu')(res)
+	#res = Dropout(0.1)(res) #used only in training
+	res = Conv2D(150, (1, 1), strides=(1, 1), name="conv6")(res)
+	res = Lambda(Interp_zoom)(res)
+
+
+	#Use softmax layer for pixelwise prediction
+	curr_width, curr_height, curr_channels = res._shape_as_list()[1:]
+
+	reshape = Reshape((curr_width*curr_height, curr_channels))(res)
+	activation = Activation('softmax')(reshape)
+	reshape = Reshape((curr_width, curr_height, curr_channels))(activation)
+
+	#End of model
+	model = Model(inputs=inp, outputs=reshape)
+	return model