Update app.py

app.py

@@ -6,6 +6,12 @@ from skimage.io import imshow, imsave
 import tensorflow
 import tensorflow as tf
 from tensorflow.keras import backend as K
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.metrics import MeanIoU
+from tensorflow.keras.utils import normalize, to_categorical
+from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
+from tensorflow.keras import layers
 
 size = 1024
 pach_size = 256
@@ -351,20 +357,7 @@ def unet_2( n_classes=2, height=pach_size, width=pach_size, channels=3, metrics
     
     encode = encoder_unet(inputs)
     decode = decoder(encode.output, inputs)
-    #print(type(decode.output))
-    #print(decode.output.shape)
-    
-    #encode_2 = encoder(decode.output, inputs)
-    #decode_2 = decoder(encode_2.output, inputs)
-    #########outputs = decode.output
-    #print(encode_2.output.shape)
-    #u7 = UpSampling2D((2, 2))(encode_2.output)
-    #u7 = Conv2D(32, (3, 3), activation='relu', padding='same')(u7)
-    #u7 = UpSampling2D((2, 2))(u7)
-    #u7 = Conv2D(64, (3, 3), activation='relu', padding='same')(u7)
-    #u7 = UpSampling2D((2, 2))(u7)
-    #u7 = Conv2D(128, (3, 3), activation='relu', padding='same')(u7)
-    #u7 = UpSampling2D((2, 2))(u7)  
+    
     outputs = decode.output 
     #outputs = Conv2D(n_classes, (1, 1), activation='softmax', padding='same', kernel_initializer='he_normal')(decode.output)
     #outputs = tf.reshape(encode_2.output[0], [None, 16, 16, 256])
@@ -385,183 +378,13 @@ def unet_2( n_classes=2, height=pach_size, width=pach_size, channels=3, metrics
 
 def unet_enssemble(n_classes=2, height=64, width=64, channels=3, metrics = ['accuracy']):
     x = Input((height, width, channels))
-    #x = inputs
-    
-    #augmented = data_augmentation(x)
-    #augmented_0 = data_augmentation_0(x)
-    #augmented_1 = data_augmentation_1(x)
-    #augmented_2 = data_augmentation_2(x)
-    #augmented_3 = data_augmentation_3(x)
-    #augmented_4 = data_augmentation_4(x)
-    #augmented_5 = data_augmentation_5(x)
-    #augmented = layers.GaussianNoise(0.1)(augmented)
-    
-    #out_x = concatenate([augmented, augmented_0], axis=0)
-    
-    #augmented = x
-    #BACKBONE = 'resnet152'
-    #BACKBONE = 'efficientnetb7'
-    #model5 = sm.Linknet(BACKBONE, encoder_weights='imagenet', classes=n_classes, activation='softmax')    
-    #model10 = sm.Unet(BACKBONE, 
-                    #pyramid_block_filters=32, 
-     #               encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #BACKBONE = 'vgg16'
-    #model7 = sm.FPN(BACKBONE, 
-                    #encoder_freeze = True,
-                    #pyramid_block_filters=16,
-     #               encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #BACKBONE = 'inceptionresnetv2'
-    #model8 = sm.FPN(BACKBONE, pyramid_block_filters=16, encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #BACKBONE = 'resnext50'
-    #BACKBONE = 'seresnet152'
-    #decode_filt=(256, 128, 64, 32, 16)
-    #BACKBONE = 'mobilenetv2'
-    #model10 = sm.FPN(BACKBONE, pyramid_block_filters=256, encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #model10_x1 = sm.Unet(BACKBONE, decoder_filters=decode_filt,
-     #                    decoder_block_type='upsampling',
-                         #decoder_block_type='transpose',
-      #                   encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #model10_x2 = sm.Linknet(BACKBONE, encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #BACKBONE = 'resnet18'
-    #BACKBONE = 'resnext50'
-    #BACKBONE = 'mobilenetv2'
-    #BACKBONE = 'efficientnetb7'
-    #model10 = sm.FPN(BACKBONE, 
-                     #encoder_freeze = True, 
-                     #pyramid_block_filters=16, 
-     #                encoder_weights='imagenet',
-      #               classes=n_classes, activation='softmax')
-    #BACKBONE = 'vgg16'
-    #model7 = sm.FPN(BACKBONE,
-     #               pyramid_block_filters=512,
-      #              encoder_weights='imagenet', classes=n_classes, activation='softmax')
-    #model9 = create_cct_model(n_classes=n_classes, height = height, width = width, channels = n_channels)
-    #reshaped = tf.reshape(encoded_patches , [-1,256,256,64])
-    
-    #model7 = unet_2( n_classes=n_classes, height = height, width = width, channels = 3)
-    model10 = unet_2( n_classes=n_classes, height = height, width = width, channels = 3)
-    #model10_xx = unet_2( n_classes=n_classes, height = height, width = width, channels = 3)
-    #model8 = unet_2( n_classes=n_classes,
-     #               height = height, width = width, channels = n_channels)(augmented)
-    ###model8_x = unet_2( n_classes=n_classes,
-          ###          height = height, width = width, channels = n_channels)(x)
-    
-    
-
-    #model1 = get_model(inputs=x, n_classes=n_classes, height = height, width = width, channels = n_channels)
-    #model2 = DeeplabV3Plus(model_input=x, image_size=256, num_classes=n_classes)
-    #model4 = unet_2(inputs=x, n_classes=n_classes, height = height, width = width, channels = n_channels)
-    #model3 = swin_unet_2d_base(x, filter_num_begin, depth, stack_num_down, stack_num_up, 
-     #                 patch_size, num_heads, window_size, num_mlp, 
-      #                shift_window=shift_window, name='swin_unet')
-    #print(model1.output.shape, model2.output.shape)
-    #model5.trainable = False
-    #model6.trainable = False
-    
-    #out = model11(augmented)
-    #out = Conv2D(3, (3, 3), activation=activation_funtion, padding='same')(out)
-    #out = K.flatten(out)
-    #out = K.reshape(out,(-1,256,256,1))
-    #out = model11(x)
-    #out = unet_2(inputs=augmented, n_classes=n_classes, height = height, width = width, channels = n_channels)
-    
-    #quantize_model_7 = tfmot.quantization.keras.quantize_model
-    # q_aware stands for for quantization aware.
-    #q_aware_model_7 = quantize_model(model7)
-    #quantize_model_11 = tfmot.quantization.keras.quantize_model
-    # q_aware stands for for quantization aware.
-    #q_aware_model_11 = quantize_model(model11)
     
+    model10 = unet_2( n_classes=n_classes, height = height, width = width, channels = 3)
     
-    out = model10(x)
-    #out = layers.GaussianNoise(0.1+np.random.random()*0.4)(out)
-    #out = layers.GaussianNoise(0.1)(out)
-    #out = concatenate([q_aware_model_7(augmented), q_aware_model_11(augmented)], axis=3)
-    #out = concatenate([model6(augmented), model8(augmented), model6(x), model8(x)], axis=3)
-    #out = concatenate([model10_x1(augmented), model10_x1(x), model10_x1(augmented_0)], axis=3)
-    #out_7 = concatenate([model11(augmented), model7(augmented)], axis=3)
-    
-    #out = concatenate([x, out], axis=3)
-    #out = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(out)
-    #out = Conv2D(3, (3, 3), activation=activation_funtion, padding='same')(out)    
-    #out = model7(out)
     
     
+    out = model10(x)
     
-        
-    #out = model10_x(attention_weights)
-    #model11 = Conv2D(32, (3, 3), activation=activation_funtion, padding='same')(model11)
-    #model7 = Conv2D(32, (3, 3), activation=activation_funtion, padding='same')(model7)
-    #out = concatenate([model10_x(x), model10_x(augmented), model10_x(augmented_0)], axis=3)
-    #out = concatenate([ model7(x), model11(x),
-     #                 model7(augmented_0), model11(augmented_0),
-      #                model7(augmented_1), model11(augmented_1),
-       #               model7(augmented_2), model11(augmented_2),
-        #              model7(augmented_3), model11(augmented_3),
-         #             model7(augmented_4), model11(augmented_4),
-          #            model7(augmented_5), model11(augmented_5)],axis=3)
-    
-    #out = tf.keras.layers.PReLU()(out)
-    #out = Conv2D(64, (3, 3), activation=activation_funtion, padding='same')(out)
-    #out = BatchNormalization()(out)
-    #out = Dropout(0.2)(out)
-    
-    #####out = hybrid_pool_layer((2,2))(out)
-    
-    #a = tf.keras.layers.AveragePooling2D(padding='same')(out)
-    #a = Lambda(lambda xx : xx*alpha)(a)
-    #m = tf.keras.layers.MaxPooling2D(padding='same')(out)
-    #m = Lambda(lambda xx : xx*(1-alpha))(m)
-    #out = tf.keras.layers.Add()([a,m])
-    #out = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(out)
-    
-    #out = layers.add([model1.output, model2.output])
-    #out = layers.multiply([model1.output, model2.output])
-    ##out = layers.add([model, encode.output])
-    ##out = layers.multiply([model, encode.output])
-    
-    #out = Conv2D(128, (3, 3), activation=activation_funtion, padding='same')(out)    
-    #out = BatchNormalization()(out)
-    #out = Conv2D(64, (3, 3), activation=activation_funtion, padding='same')(out)
-    #out = SpikingActivation("relu")(out)
-    #out = BatchNormalization()(out)
-    #out = Dropout(0.2)(out)
-    #out = Conv2D(32, (3, 3), activation=activation_funtion, padding='same')(out)    
-    #out = BatchNormalization()(out)    
-    #out = Conv2D(64, (3, 3), activation=activation_funtion, padding='same')(out)    
-    #out = BatchNormalization()(out)
-    #out = Dropout(0.2)(out)
-    #out = tf.keras.layers.PReLU()(out)
-    #out = Conv2D(64, (3, 3), activation=activation_funtion, padding='same')(out)    
-    #out = BatchNormalization()(out)
-    #out = Dropout(0.2)(out)
-    #out = tf.keras.layers.PReLU()(out)
-    
-    #out = concatenate([conv_out_jump, out], axis=3)
-    #out = Conv2D(256, (3, 3), activation=activation_funtion, padding='same')(out)
-    #out = BatchNormalization()(out)
-    #out = Dropout(0.2)(out)
-    
-    #out = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(out)
-    #out = UpSampling2D((2, 2))(out)
-    
-    #out_list = []
-    #for i in range(1,23):
-     #   outputs1 = Conv2D(n_classes-i, (1, 1), activation='softmax')(out)
-      #  out_list.append(outputs1)
-    #outputs2 = Conv2D(n_classes-1, (1, 1), activation='softmax')(out)
-    #outputs3 = Conv2D(n_classes-2, (1, 1), activation='softmax')(out)
-    #outputs4 = Conv2D(n_classes-3, (1, 1), activation='softmax')(out)
-    #outputs5 = Conv2D(n_classes-4, (1, 1), activation='softmax')(out)
-    #outputs6 = Conv2D(n_classes-5, (1, 1), activation='softmax')(out)
-    #outputs7 = Conv2D(n_classes-6, (1, 1), activation='softmax')(out)
-    #outputs8 = Conv2D(n_classes-7, (1, 1), activation='softmax')(out)
-    #outputs9 = Conv2D(n_classes-8, (1, 1), activation='softmax')(out)
-    
-    #out_list = [outputs1, outputs2, outputs3, outputs4, outputs5, outputs6, outputs7, outputs8, outputs9]
-    #outputs = Conv2D(n_classes, (1, 1), activation='softmax')(encode.output)
-    #outputs = concatenate(out_list, axis=3)
-    #outputs = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(outputs)
     outputs = Conv2D(n_classes, (1, 1), activation='softmax',  padding='same')(out)