mirror of
https://github.com/wassname/PSPNet-Keras-tensorflow.git
synced 2026-09-09 11:15:19 +08:00
Cleaned up code
This commit is contained in:
VladK
@@ -0,0 +1,225 @@
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from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
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from keras.layers import BatchNormalization, Activation, Input, Dropout, ZeroPadding2D
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from keras.layers import merge, concatenate, Lambda, Reshape
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from keras.models import Model
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import tensorflow as tf
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def Interp(x, size=(60,60)):
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print(x.shape)
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new_height = size[0]
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new_width = size[1]
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resized = tf.image.resize_images(x, [new_height, new_width])
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print(resized.shape)
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return resized
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def Interp_zoom(x, zoom=8):
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print(x.shape)
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old_height = int(x.shape[1])
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old_width = int(x.shape[2])
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new_height = old_height + (old_height-1) * (zoom - 1)
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new_width = old_width + (old_width-1) * (zoom - 1)
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resized = tf.image.resize_images(x, [new_height, new_width])
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return resized
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def residual_conv(prev, level,
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pad=1, lvl=1, sub_lvl=1, modify_stride=False):
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lvl = str(lvl)
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sub_lvl = str(sub_lvl)
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names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_reduce" ,
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"conv"+lvl+"_"+ sub_lvl +"_1x1_reduce_bn",
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"conv"+lvl+"_"+ sub_lvl +"_3x3",
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"conv"+lvl+"_"+ sub_lvl +"_3x3_bn",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_increase",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_increase_bn"]
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if modify_stride == False:
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prev = Conv2D(64 * level, (1,1), strides=(1,1), use_bias=False,
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name=names[0])(prev)
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elif modify_stride == True:
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prev = Conv2D(64 * level, (1,1), strides=(2,2), use_bias=False,
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name=names[0])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
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prev = Activation('relu')(prev)
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prev = ZeroPadding2D(padding=(pad,pad))(prev)
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prev = Conv2D(64 * level, (3,3),
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strides=(1,1), dilation_rate=pad, use_bias=False,
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name=names[2])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[3])(prev)
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prev = Activation('relu')(prev)
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prev = Conv2D(256 * level, (1,1), strides=(1,1), use_bias=False,
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name=names[4])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[5])(prev)
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return prev
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def short_convolution_branch(prev, level,
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lvl=1, sub_lvl=1, modify_stride=False):
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lvl = str(lvl)
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sub_lvl = str(sub_lvl)
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names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_proj",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_proj_bn"
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]
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if modify_stride == False:
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prev = Conv2D(256 * level ,(1,1), strides=(1,1), use_bias=False,
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name=names[0])(prev)
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elif modify_stride == True:
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prev = Conv2D(256 * level, (1,1), strides=(2,2), use_bias=False,
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name=names[0])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
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return prev
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def empty_branch(prev):
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return prev
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def residual_short(prev_layer, level, pad=1, lvl=1, sub_lvl=1, modify_stride=False):
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block_1 = residual_conv(prev_layer, level,
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pad=pad, lvl=lvl, sub_lvl=sub_lvl,
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modify_stride=modify_stride)
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block_2 = short_convolution_branch(prev_layer, level,
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lvl=lvl, sub_lvl=sub_lvl,
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modify_stride=modify_stride)
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return merge([block_1, block_2], mode='sum')
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def residual_empty(prev_layer, level, pad=1, lvl=1, sub_lvl=1):
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prev_layer = Activation('relu')(prev_layer)
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block_1 = residual_conv(prev_layer, level,
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pad=pad, lvl=lvl, sub_lvl=sub_lvl)
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block_2 = empty_branch(prev_layer)
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return merge([block_1, block_2], mode='sum')
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def interp_block(prev_layer, level, str_lvl=1):
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str_lvl = str(str_lvl)
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names = [
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"conv5_3_pool"+str_lvl+"_conv",
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"conv5_3_pool"+str_lvl+"_conv_bn"
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]
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kernel = (10*level, 10*level)
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strides = (10*level, 10*level)
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prev_layer = AveragePooling2D(kernel,strides=strides)(prev_layer)
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prev_layer = Conv2D(512, (1,1), strides=(1,1), use_bias=False, name=names[0])(prev_layer)
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prev_layer = BatchNormalization(momentum=0.95, name=names[1])(prev_layer)
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prev_layer = Activation('relu')(prev_layer)
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prev_layer = Lambda(Interp)(prev_layer)
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return prev_layer
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def build_pspnet():
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#Names for the first couple layers of model
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names = ["conv1_1_3x3_s2",
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"conv1_1_3x3_s2_bn",
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"conv1_2_3x3",
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"conv1_2_3x3_bn",
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"conv1_3_3x3",
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"conv1_3_3x3_bn"]
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#---Short branch(only start of network)
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inp = Input((473,473, 3))
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cnv1 = ZeroPadding2D(padding=(1,1))(inp)
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cnv1 = Conv2D(64, (3, 3), strides=(2, 2), use_bias=False, name=names[0])(cnv1) # "conv1_1_3x3_s2"
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bn1 = BatchNormalization(momentum=0.95, name=names[1])(cnv1) # "conv1_1_3x3_s2/bn"
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relu1 = Activation('relu')(bn1) #"conv1_1_3x3_s2/relu"
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cnv1 = ZeroPadding2D(padding=(1,1))(relu1)
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cnv1 = Conv2D(64, (3, 3), strides=(1, 1), use_bias=False, name=names[2])(cnv1) #"conv1_2_3x3"
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bn1 = BatchNormalization(momentum=0.95, name=names[3])(cnv1) #"conv1_2_3x3/bn"
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relu1 = Activation('relu')(bn1) #"conv1_2_3x3/relu"
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cnv1 = ZeroPadding2D(padding=(1,1))(relu1)
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cnv1 = Conv2D(128, (3, 3), strides=(1, 1), use_bias=False, name=names[4])(cnv1) #"conv1_3_3x3"
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bn1 = BatchNormalization(momentum=0.95, name=names[5])(cnv1) #"conv1_3_3x3/bn"
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relu1 = Activation('relu')(bn1) #"conv1_3_3x3/relu"
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res = ZeroPadding2D(padding=(1,1))(relu1)
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res = MaxPooling2D(pool_size=(3,3), strides=(2,2))(res) #"pool1_3x3_s2"
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#---Residual layers(body of network)
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"""
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Modify_stride --Used only once in first 3_1 convolutions block.
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changes stride of first convolution from 1 -> 2
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"""
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#2_1- 2_3
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res = residual_short(res, 1, pad=1, lvl=2, sub_lvl=1)
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for i in range(2):
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res = residual_empty(res, 1, pad=1, lvl=2, sub_lvl=i+2)
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#3_1 - 3_3
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res = residual_short(res, 2, pad=1, lvl=3, sub_lvl=1, modify_stride=True)
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for i in range(2):
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res = residual_empty(res, 2, pad=1, lvl=3, sub_lvl=i+2)
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#4_1 - 4_6
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res = residual_short(res, 4, pad=2, lvl=4, sub_lvl=1)
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for i in range(5):
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res = residual_empty(res, 4, pad=2, lvl=4, sub_lvl=i+2)
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#5_1 - 5_3
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res = residual_short(res, 8, pad=4, lvl=5, sub_lvl=1)
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for i in range(2):
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res = residual_empty(res, 8, pad=4, lvl=5, sub_lvl=i+2)
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#---Head of network
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#---PSPNet concat layers with Interpolation
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res = Activation('relu')(res)
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interp_block1 = interp_block(res, 6, str_lvl=1)
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interp_block2 = interp_block(res, 3, str_lvl=2)
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interp_block3 = interp_block(res, 2, str_lvl=3)
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interp_block4 = interp_block(res, 1, str_lvl=6)
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#concat all these layers by 4th axis(3+1). resulted shape=(1,60,60,4096)
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res = concatenate([res,
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interp_block1,
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interp_block2,
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interp_block3,
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interp_block4], axis=3)
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res = ZeroPadding2D(padding=(1,1))(res)
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res = Conv2D(512, (3, 3), strides=(1, 1), use_bias=False, name="conv5_4")(res)
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res = BatchNormalization(momentum=0.95, name="conv5_4_bn")(res)
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res = Activation('relu')(res)
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#res = Dropout(0.1)(res) #used only in training
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res = Conv2D(150, (1, 1), strides=(1, 1), name="conv6")(res)
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res = Lambda(Interp_zoom)(res)
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#Use softmax layer for pixelwise prediction
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curr_width, curr_height, curr_channels = res._shape_as_list()[1:]
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reshape = Reshape((curr_width*curr_height, curr_channels))(res)
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activation = Activation('softmax')(reshape)
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reshape = Reshape((curr_width, curr_height, curr_channels))(activation)
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#End of model
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model = Model(inputs=inp, outputs=reshape)
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return model
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@@ -1,19 +1,20 @@
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, UpSampling2D
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from keras.layers import BatchNormalization, Activation, Input, Dropout, ZeroPadding2D
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from keras.layers import Add, merge, concatenate, Lambda, Reshape
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from keras import backend as K
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import tensorflow as tf
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from keras.models import Model
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import numpy as np
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from PIL import Image
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import layers_builder as pspnet
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import tensorflow as tf
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import numpy as np
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import drawImage
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import argparse
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import time
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def load_weights():
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w = np.load('pspnet.npy').item()
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return w
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def set_weights(model, weights):
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print 'weights set start'
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for layer in model.layers:
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@@ -40,259 +41,24 @@ def set_weights(model, weights):
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print 'weights set finish'
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return model
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def Interp_(x, size=None, zoom=None):
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print(x.shape)
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old_height = int(x.shape[2])
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old_width = int(x.shape[3])
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if zoom is not None:
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zoom = int(zoom)
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new_height = old_height + (old_height-1) * (zoom - 1)
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new_width = old_width + (old_width-1) * (old_width - 1)
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elif size is not None:
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new_height = size[0]
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new_width = size[1]
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resized = tf.image.resize_images(x, [new_height, new_width])
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return resized
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def Interp(x, size=(60,60)):
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print(x.shape)
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new_height = size[0]
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new_width = size[1]
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resized = tf.image.resize_images(x, [new_height, new_width])
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print(resized.shape)
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return resized
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def Interp_zoom(x, zoom=8):
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print(x.shape)
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old_height = int(x.shape[1])
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old_width = int(x.shape[2])
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new_height = old_height + (old_height-1) * (zoom - 1)
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new_width = old_width + (old_width-1) * (zoom - 1)
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resized = tf.image.resize_images(x, [new_height, new_width])
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return resized
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#NOT USED---
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def add_common_layers(prev):
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prev = BatchNormalization(momentum=0.95)(prev)
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prev = Activation('relu')(prev)
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return prev
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def Conv(prev_layer, level, kernel=(1,1), strides=(1,1)):
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layer = Conv2D(64 * level, (1,1), strides=(1,1))(prev_layer)
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return layer
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#-----------
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def residual_conv(prev, level,
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pad=1, lvl=1, sub_lvl=1, modify_stride=False):
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lvl = str(lvl)
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sub_lvl = str(sub_lvl)
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names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_reduce" ,
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"conv"+lvl+"_"+ sub_lvl +"_1x1_reduce_bn",
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"conv"+lvl+"_"+ sub_lvl +"_3x3",
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"conv"+lvl+"_"+ sub_lvl +"_3x3_bn",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_increase",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_increase_bn"]
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if modify_stride == False:
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prev = Conv2D(64 * level, (1,1), strides=(1,1), use_bias=False,
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name=names[0])(prev)
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elif modify_stride == True:
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prev = Conv2D(64 * level, (1,1), strides=(2,2), use_bias=False,
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name=names[0])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
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prev = Activation('relu')(prev)
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prev = ZeroPadding2D(padding=(pad,pad))(prev)
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prev = Conv2D(64 * level, (3,3),
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strides=(1,1), dilation_rate=pad, use_bias=False,
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name=names[2])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[3])(prev)
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prev = Activation('relu')(prev)
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prev = Conv2D(256 * level, (1,1), strides=(1,1), use_bias=False,
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name=names[4])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[5])(prev)
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return prev
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def short_convolution_branch(prev, level,
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lvl=1, sub_lvl=1, modify_stride=False):
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lvl = str(lvl)
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sub_lvl = str(sub_lvl)
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names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_proj",
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"conv"+lvl+"_"+ sub_lvl +"_1x1_proj_bn"
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]
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if modify_stride == False:
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prev = Conv2D(256 * level ,(1,1), strides=(1,1), use_bias=False,
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name=names[0])(prev)
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elif modify_stride == True:
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prev = Conv2D(256 * level, (1,1), strides=(2,2), use_bias=False,
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name=names[0])(prev)
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prev = BatchNormalization(momentum=0.95, name=names[1])(prev)
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return prev
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def empty_branch(prev):
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return prev
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def residual_short(prev_layer, level, pad=1, lvl=1, sub_lvl=1, modify_stride=False):
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block_1 = residual_conv(prev_layer, level,
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pad=pad, lvl=lvl, sub_lvl=sub_lvl,
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modify_stride=modify_stride)
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block_2 = short_convolution_branch(prev_layer, level,
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lvl=lvl, sub_lvl=sub_lvl,
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modify_stride=modify_stride)
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return merge([block_1, block_2], mode='sum')
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def residual_empty(prev_layer, level, pad=1, lvl=1, sub_lvl=1):
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prev_layer = Activation('relu')(prev_layer)
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block_1 = residual_conv(prev_layer, level,
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pad=pad, lvl=lvl, sub_lvl=sub_lvl)
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block_2 = empty_branch(prev_layer)
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return merge([block_1, block_2], mode='sum')
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def interp_block(prev_layer, level, str_lvl=1):
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str_lvl = str(str_lvl)
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names = [
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"conv5_3_pool"+str_lvl+"_conv",
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"conv5_3_pool"+str_lvl+"_conv_bn"
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]
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kernel = (10*level, 10*level)
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strides = (10*level, 10*level)
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prev_layer = AveragePooling2D(kernel,strides=strides)(prev_layer)
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prev_layer = Conv2D(512, (1,1), strides=(1,1), use_bias=False, name=names[0])(prev_layer)
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prev_layer = BatchNormalization(momentum=0.95, name=names[1])(prev_layer)
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prev_layer = Activation('relu')(prev_layer)
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prev_layer = Lambda(Interp)(prev_layer)
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return prev_layer
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if __name__ == "__main__":
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#Names for the first layers of model
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names = ["conv1_1_3x3_s2",
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"conv1_1_3x3_s2_bn",
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"conv1_2_3x3",
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"conv1_2_3x3_bn",
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"conv1_3_3x3",
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"conv1_3_3x3_bn"]
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settings = None
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parser = argparse.ArgumentParser()
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parser.add_argument('--input-path', type=str, default='',
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required=True, help='Path the input image')
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parser.add_argument('--output-path', type=str, default='',
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required=True, help='Path to output')
|
||||
|
||||
#---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)
|
||||
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)
|
||||
|
||||
|
||||
settings, unparsed = parser.parse_known_args()
|
||||
|
||||
|
||||
model = pspnet.build_pspnet()
|
||||
|
||||
sess = tf.Session()
|
||||
K.set_session(sess)
|
||||
|
||||
|
||||
|
||||
with sess.as_default():
|
||||
#Load weights into variable
|
||||
npy_weights = load_weights()
|
||||
@@ -300,7 +66,7 @@ if __name__ == "__main__":
|
||||
model = set_weights(model, npy_weights)
|
||||
|
||||
#Load image, resize and paste into 4D tensor
|
||||
image = Image.open('test.jpg')
|
||||
image = Image.open(settings.input_path)
|
||||
data_im = np.asarray(image)
|
||||
data = np.zeros([1,473,473,3])
|
||||
data_im = np.resize(data_im, [473, 473, 3])
|
||||
@@ -329,6 +95,6 @@ if __name__ == "__main__":
|
||||
image, (im_Width, im_Height),
|
||||
predicted_classes)
|
||||
simpleSegmentImage = draw.drawSimpleSegment();
|
||||
simpleSegmentImage.save('out.jpg',"JPEG")
|
||||
simpleSegmentImage.save(settings.output_path,"JPEG")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user