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comment out unused functions
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deeppomf
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@ -6,7 +6,7 @@ from keras.models import load_model
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from keras.optimizers import Adam
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from keras.layers import Input, Conv2D, UpSampling2D, Dropout, LeakyReLU, BatchNormalization, Activation
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from keras.layers.merge import Concatenate
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from keras.applications import VGG16
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#from keras.applications import VGG16
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from keras import backend as K
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from libs.pconv_layer import PConv2D
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@ -26,36 +26,36 @@ class PConvUnet(object):
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# Set current epoch
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self.current_epoch = 0
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# VGG layers to extract features from (first maxpooling layers, see pp. 7 of paper)
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self.vgg_layers = [3, 6, 10]
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# # VGG layers to extract features from (first maxpooling layers, see pp. 7 of paper)
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# self.vgg_layers = [3, 6, 10]
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# Get the vgg16 model for perceptual loss
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self.vgg = self.build_vgg()
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# # Get the vgg16 model for perceptual loss
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# self.vgg = self.build_vgg()
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# Create UNet-like model
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self.model = self.build_pconv_unet()
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def build_vgg(self):
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"""
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Load pre-trained VGG16 from keras applications
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Extract features to be used in loss function from last conv layer, see architecture at:
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https://github.com/keras-team/keras/blob/master/keras/applications/vgg16.py
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"""
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# Input image to extract features from
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img = Input(shape=(self.img_rows, self.img_cols, 3))
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# def build_vgg(self):
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# """
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# Load pre-trained VGG16 from keras applications
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# Extract features to be used in loss function from last conv layer, see architecture at:
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# https://github.com/keras-team/keras/blob/master/keras/applications/vgg16.py
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# """
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# # Input image to extract features from
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# img = Input(shape=(self.img_rows, self.img_cols, 3))
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# Get the vgg network from Keras applications
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vgg = VGG16(weights="imagenet", include_top=False)
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# # Get the vgg network from Keras applications
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# vgg = VGG16(weights="imagenet", include_top=False)
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# Output the first three pooling layers
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vgg.outputs = [vgg.layers[i].output for i in self.vgg_layers]
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# # Output the first three pooling layers
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# vgg.outputs = [vgg.layers[i].output for i in self.vgg_layers]
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# Create model and compile
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model = Model(inputs=img, outputs=vgg(img))
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model.trainable = False
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model.compile(loss='mse', optimizer='adam')
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# # Create model and compile
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# model = Model(inputs=img, outputs=vgg(img))
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# model.trainable = False
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# model.compile(loss='mse', optimizer='adam')
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return model
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# return model
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def build_pconv_unet(self, train_bn=True, lr=0.0002):
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@ -111,76 +111,78 @@ class PConvUnet(object):
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# Compile the model
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model.compile(
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optimizer = Adam(lr=lr),
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loss=self.loss_total(loss_mask)
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loss='mse'
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#loss really isn't mse, but we don't need the vgg16 model for inference so we don't to have to download the vgg16 model
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#loss=self.loss_total(loss_mask)
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)
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return model
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def loss_total(self, mask):
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"""
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Creates a loss function which sums all the loss components
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and multiplies by their weights. See paper eq. 7.
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"""
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def loss(y_true, y_pred):
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# def loss_total(self, mask):
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# """
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# Creates a loss function which sums all the loss components
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# and multiplies by their weights. See paper eq. 7.
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# """
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# def loss(y_true, y_pred):
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# Compute predicted image with non-hole pixels set to ground truth
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y_comp = mask * y_true + (1-mask) * y_pred
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# # Compute predicted image with non-hole pixels set to ground truth
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# y_comp = mask * y_true + (1-mask) * y_pred
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# Compute the vgg features
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vgg_out = self.vgg(y_pred)
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vgg_gt = self.vgg(y_true)
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vgg_comp = self.vgg(y_comp)
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# # Compute the vgg features
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# vgg_out = self.vgg(y_pred)
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# vgg_gt = self.vgg(y_true)
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# vgg_comp = self.vgg(y_comp)
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# Compute loss components
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l1 = self.loss_valid(mask, y_true, y_pred)
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l2 = self.loss_hole(mask, y_true, y_pred)
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l3 = self.loss_perceptual(vgg_out, vgg_gt, vgg_comp)
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l4 = self.loss_style(vgg_out, vgg_gt)
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l5 = self.loss_style(vgg_comp, vgg_gt)
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l6 = self.loss_tv(mask, y_comp)
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# # Compute loss components
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# l1 = self.loss_valid(mask, y_true, y_pred)
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# l2 = self.loss_hole(mask, y_true, y_pred)
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# l3 = self.loss_perceptual(vgg_out, vgg_gt, vgg_comp)
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# l4 = self.loss_style(vgg_out, vgg_gt)
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# l5 = self.loss_style(vgg_comp, vgg_gt)
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# l6 = self.loss_tv(mask, y_comp)
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# Return loss function
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return l1 + 6*l2 + 0.05*l3 + 120*(l4+l5) + 0.1*l6
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# # Return loss function
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# return l1 + 6*l2 + 0.05*l3 + 120*(l4+l5) + 0.1*l6
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return loss
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# return loss
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def loss_hole(self, mask, y_true, y_pred):
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"""Pixel L1 loss within the hole / mask"""
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return self.l1((1-mask) * y_true, (1-mask) * y_pred)
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# def loss_hole(self, mask, y_true, y_pred):
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# """Pixel L1 loss within the hole / mask"""
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# return self.l1((1-mask) * y_true, (1-mask) * y_pred)
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def loss_valid(self, mask, y_true, y_pred):
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"""Pixel L1 loss outside the hole / mask"""
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return self.l1(mask * y_true, mask * y_pred)
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# def loss_valid(self, mask, y_true, y_pred):
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# """Pixel L1 loss outside the hole / mask"""
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# return self.l1(mask * y_true, mask * y_pred)
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def loss_perceptual(self, vgg_out, vgg_gt, vgg_comp):
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"""Perceptual loss based on VGG16, see. eq. 3 in paper"""
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loss = 0
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for o, c, g in zip(vgg_out, vgg_comp, vgg_gt):
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loss += self.l1(o, g) + self.l1(c, g)
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return loss
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# def loss_perceptual(self, vgg_out, vgg_gt, vgg_comp):
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# """Perceptual loss based on VGG16, see. eq. 3 in paper"""
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# loss = 0
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# for o, c, g in zip(vgg_out, vgg_comp, vgg_gt):
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# loss += self.l1(o, g) + self.l1(c, g)
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# return loss
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def loss_style(self, output, vgg_gt):
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"""Style loss based on output/computation, used for both eq. 4 & 5 in paper"""
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loss = 0
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for o, g in zip(output, vgg_gt):
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loss += self.l1(self.gram_matrix(o), self.gram_matrix(g))
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return loss
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# def loss_style(self, output, vgg_gt):
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# """Style loss based on output/computation, used for both eq. 4 & 5 in paper"""
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# loss = 0
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# for o, g in zip(output, vgg_gt):
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# loss += self.l1(self.gram_matrix(o), self.gram_matrix(g))
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# return loss
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def loss_tv(self, mask, y_comp):
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"""Total variation loss, used for smoothing the hole region, see. eq. 6"""
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# def loss_tv(self, mask, y_comp):
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# """Total variation loss, used for smoothing the hole region, see. eq. 6"""
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# Create dilated hole region using a 3x3 kernel of all 1s.
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kernel = K.ones(shape=(3, 3, mask.shape[3], mask.shape[3]))
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dilated_mask = K.conv2d(1-mask, kernel, data_format='channels_last', padding='same')
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# # Create dilated hole region using a 3x3 kernel of all 1s.
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# kernel = K.ones(shape=(3, 3, mask.shape[3], mask.shape[3]))
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# dilated_mask = K.conv2d(1-mask, kernel, data_format='channels_last', padding='same')
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# Cast values to be [0., 1.], and compute dilated hole region of y_comp
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dilated_mask = K.cast(K.greater(dilated_mask, 0), 'float32')
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P = dilated_mask * y_comp
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# # Cast values to be [0., 1.], and compute dilated hole region of y_comp
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# dilated_mask = K.cast(K.greater(dilated_mask, 0), 'float32')
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# P = dilated_mask * y_comp
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# Calculate total variation loss
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a = self.l1(P[:,1:,:,:], P[:,:-1,:,:])
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b = self.l1(P[:,:,1:,:], P[:,:,:-1,:])
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return a+b
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# # Calculate total variation loss
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# a = self.l1(P[:,1:,:,:], P[:,:-1,:,:])
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# b = self.l1(P[:,:,1:,:], P[:,:,:-1,:])
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# return a+b
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def fit(self, generator, epochs=10, plot_callback=None, *args, **kwargs):
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"""Fit the U-Net to a (images, targets) generator
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