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https://github.com/qurator-spk/eynollah.git
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training.models: re-use UNet decoder builder code
This commit is contained in:
Robert Sachunsky
parent
fcd10c3956
commit
daa084c367
1 changed files with 85 additions and 212 deletions
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@ -198,67 +198,82 @@ def resnet50(inputs, weight_decay=1e-6, pretraining=False):
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return f1, f2, f3, f4, f5
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def unet_decoder(img, f1, f2, f3, f4, f5, n_classes, light=False, task="segmentation", weight_decay=1e-6):
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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o = Conv2D(512 if light else 1024, (1, 1), padding='same',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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if light:
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f4 = Conv2D(512, (1, 1), padding='same',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
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f4 = BatchNormalization(axis=bn_axis)(f4)
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f4 = Activation('relu')(f4)
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o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
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o = concatenate([o, f4], axis=MERGE_AXIS)
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o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
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o = Conv2D(512, (3, 3), padding='valid',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
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o = concatenate([o, f3], axis=MERGE_AXIS)
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o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
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o = Conv2D(256, (3, 3), padding='valid',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
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o = concatenate([o, f2], axis=MERGE_AXIS)
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o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
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o = Conv2D(128, (3, 3), padding='valid',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
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o = concatenate([o, f1], axis=MERGE_AXIS)
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o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
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o = Conv2D(64, (3, 3), padding='valid',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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o = UpSampling2D((2, 2), data_format=IMAGE_ORDERING)(o)
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o = concatenate([o, img], axis=MERGE_AXIS)
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o = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(o)
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o = Conv2D(32, (3, 3), padding='valid',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('relu')(o)
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o = Conv2D(n_classes, (1, 1), padding='same',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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if task == "segmentation":
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o = BatchNormalization(axis=bn_axis)(o)
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o = Activation('softmax')(o)
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else:
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o = Activation('sigmoid')(o)
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return Model(img, o)
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def resnet50_unet_light(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
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assert input_height % 32 == 0
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assert input_width % 32 == 0
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img_input = Input(shape=(input_height, input_width, 3))
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f1, f2, f3, f4, f5 = resnet50(img_input, weight_decay, pretraining)
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v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
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v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
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v512_2048 = Activation('relu')(v512_2048)
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v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
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v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
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v512_1024 = Activation('relu')(v512_1024)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
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o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f3], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f2], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f1], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, img_input], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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if task == "segmentation":
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o = (BatchNormalization(axis=bn_axis))(o)
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o = (Activation('softmax'))(o)
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else:
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o = (Activation('sigmoid'))(o)
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model = Model(img_input, o)
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return model
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features = resnet50(img_input, weight_decay=weight_decay, pretraining=pretraining)
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return unet_decoder(img_input, *features, n_classes, light=True, task=task, weight_decay=weight_decay)
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def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
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assert input_height % 32 == 0
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@ -266,59 +281,9 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
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img_input = Input(shape=(input_height, input_width, 3))
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f1, f2, f3, f4, f5 = resnet50(img_input, weight_decay, pretraining)
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v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
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f5)
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v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
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v1024_2048 = Activation('relu')(v1024_2048)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
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o = (concatenate([o, f4], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f3], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f2], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f1], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, img_input], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
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if task == "segmentation":
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o = (BatchNormalization(axis=bn_axis))(o)
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o = (Activation('softmax'))(o)
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else:
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o = (Activation('sigmoid'))(o)
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model = Model(img_input, o)
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return model
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features = resnet50(img_input, weight_decay=weight_decay, pretraining=pretraining)
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return unet_decoder(img_input, *features, n_classes, light=False, task=task, weight_decay=weight_decay)
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def vit_resnet50_unet(num_patches,
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n_classes,
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@ -337,9 +302,9 @@ def vit_resnet50_unet(num_patches,
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transformer_mlp_head_units = [128, 64]
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inputs = Input(shape=(input_height, input_width, 3))
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f1, f2, f3, f4, f5 = resnet50(inputs, weight_decay, pretraining)
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features = resnet50(inputs, weight_decay=weight_decay, pretraining=pretraining)
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patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(x)
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patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(features[-1])
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# Encode patches.
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encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
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@ -360,59 +325,16 @@ def vit_resnet50_unet(num_patches,
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encoded_patches = Add()([x3, x2])
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encoded_patches = tf.reshape(encoded_patches,
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[-1, x.shape[1], x.shape[2],
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[-1,
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features[-1].shape[1],
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features[-1].shape[2],
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transformer_projection_dim // (transformer_patchsize_x *
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transformer_patchsize_y)])
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features[-1] = encoded_patches
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v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches)
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v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
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v1024_2048 = Activation('relu')(v1024_2048)
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o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
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o = (concatenate([o, f4],axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o ,f3], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f2], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, f1], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o, inputs],axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o)
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if task == "segmentation":
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o = (BatchNormalization(axis=bn_axis))(o)
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o = (Activation('softmax'))(o)
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else:
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o = (Activation('sigmoid'))(o)
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o = unet_decoder(inputs, *features, n_classes, task=task, weight_decay=weight_decay)
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model = Model(inputs=inputs, outputs=o)
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return model
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return Model(inputs, o)
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def vit_resnet50_unet_transformer_before_cnn(num_patches,
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n_classes,
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@ -431,11 +353,6 @@ def vit_resnet50_unet_transformer_before_cnn(num_patches,
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transformer_mlp_head_units = [128, 64]
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inputs = Input(shape=(input_height, input_width, 3))
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(inputs)
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# Encode patches.
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encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
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@ -463,59 +380,15 @@ def vit_resnet50_unet_transformer_before_cnn(num_patches,
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transformer_projection_dim // (transformer_patchsize_x *
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transformer_patchsize_y)])
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encoded_patches = Conv2D(3, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay), name='convinput')(encoded_patches)
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encoded_patches = Conv2D(3, (1, 1), padding='same',
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data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay),
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name='convinput')(encoded_patches)
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f1, f2, f3, f4, f5 = resnet50(encoded_patches, weight_decay, pretraining)
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features = resnet50(encoded_patches, weight_decay=weight_decay, pretraining=pretraining)
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v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(f5)
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v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
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v1024_2048 = Activation('relu')(v1024_2048)
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o = unet_decoder(inputs, *features, n_classes, task=task, weight_decay=weight_decay)
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o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
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o = (concatenate([o, f4],axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
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o = (concatenate([o ,f3], axis=MERGE_AXIS))
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o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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o = (BatchNormalization(axis=bn_axis))(o)
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o = Activation('relu')(o)
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|
||||
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
||||
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
||||
o = (BatchNormalization(axis=bn_axis))(o)
|
||||
o = Activation('relu')(o)
|
||||
|
||||
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
||||
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
||||
o = (BatchNormalization(axis=bn_axis))(o)
|
||||
o = Activation('relu')(o)
|
||||
|
||||
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||
o = (concatenate([o, inputs],axis=MERGE_AXIS))
|
||||
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
||||
o = (BatchNormalization(axis=bn_axis))(o)
|
||||
o = Activation('relu')(o)
|
||||
|
||||
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o)
|
||||
if task == "segmentation":
|
||||
o = (BatchNormalization(axis=bn_axis))(o)
|
||||
o = (Activation('softmax'))(o)
|
||||
else:
|
||||
o = (Activation('sigmoid'))(o)
|
||||
|
||||
model = Model(inputs=inputs, outputs=o)
|
||||
|
||||
return model
|
||||
return Model(inputs, o)
|
||||
|
||||
def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
|
||||
include_top=True
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue