Transformer+CNN structure is added to vision transformer type

config_params.json

@@ -2,9 +2,9 @@
     "backbone_type" : "transformer",
     "task": "binarization",
     "n_classes" : 2,
-    "n_epochs" : 1,
+    "n_epochs" : 2,
     "input_height" : 224,
-    "input_width" : 672,
+    "input_width" : 224,
     "weight_decay" : 1e-6,
     "n_batch" : 1,
     "learning_rate": 1e-4,
@@ -22,10 +22,14 @@
     "scaling_flip" : false,
     "rotation": false,
     "rotation_not_90": false,
-    "transformer_num_patches_xy": [7, 7],
-    "transformer_patchsize_x": 3,
-    "transformer_patchsize_y": 1,
-    "transformer_projection_dim": 192,
+    "transformer_num_patches_xy": [56, 56],
+    "transformer_patchsize_x": 4,
+    "transformer_patchsize_y": 4,
+    "transformer_projection_dim": 64,
+    "transformer_mlp_head_units": [128, 64],
+    "transformer_layers": 1,
+    "transformer_num_heads": 1,
+    "transformer_cnn_first": false,
     "blur_k" : ["blur","guass","median"],
     "scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4],
     "brightness" : [1.3, 1.5, 1.7, 2],

models.py

@@ -5,10 +5,10 @@ from tensorflow.keras.layers import *
 from tensorflow.keras import layers
 from tensorflow.keras.regularizers import l2
 
-mlp_head_units = [2048, 1024]
-#projection_dim = 64
-transformer_layers = 8
-num_heads = 4
+##mlp_head_units = [512, 256]#[2048, 1024]
+###projection_dim = 64
+##transformer_layers = 2#8
+##num_heads = 1#4
 resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
 IMAGE_ORDERING = 'channels_last'
 MERGE_AXIS = -1
@@ -36,7 +36,8 @@ class Patches(layers.Layer):
             rates=[1, 1, 1, 1],
             padding="VALID",
         )
-        patch_dims = patches.shape[-1]
+        #patch_dims = patches.shape[-1]
+        patch_dims = tf.shape(patches)[-1]
         patches = tf.reshape(patches, [batch_size, -1, patch_dims])
         return patches
     def get_config(self):
@@ -393,13 +394,13 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
     return model
 
 
-def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
+def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=[128, 64], transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
     inputs = layers.Input(shape=(input_height, input_width, 3))
     
-    transformer_units = [
-        projection_dim * 2,
-        projection_dim,
-    ]  # Size of the transformer layers
+    #transformer_units = [
+        #projection_dim * 2,
+        #projection_dim,
+    #]  # Size of the transformer layers
     IMAGE_ORDERING = 'channels_last'
     bn_axis=3
 
@@ -459,7 +460,7 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, projec
         # Layer normalization 2.
         x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
         # MLP.
-        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
+        x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1)
         # Skip connection 2.
         encoded_patches = layers.Add()([x3, x2])
     
@@ -515,6 +516,125 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, projec
     
     return model
 
+def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=[128, 64], transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
+    inputs = layers.Input(shape=(input_height, input_width, 3))
+    
+    ##transformer_units = [
+        ##projection_dim * 2,
+        ##projection_dim,
+    ##]  # Size of the transformer layers
+    IMAGE_ORDERING = 'channels_last'
+    bn_axis=3
+    
+    patches = Patches(patch_size_x, patch_size_y)(inputs)
+    # Encode patches.
+    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)
+    
+    for _ in range(transformer_layers):
+        # Layer normalization 1.
+        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
+        # Create a multi-head attention layer.
+        attention_output = layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=projection_dim, dropout=0.1
+        )(x1, x1)
+        # Skip connection 1.
+        x2 = layers.Add()([attention_output, encoded_patches])
+        # Layer normalization 2.
+        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
+        # MLP.
+        x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1)
+        # Skip connection 2.
+        encoded_patches = layers.Add()([x3, x2])
+    
+    encoded_patches = tf.reshape(encoded_patches, [-1, input_height, input_width , int( projection_dim / (patch_size_x * patch_size_y) )])
+    
+    encoded_patches = Conv2D(3, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay), name='convinput')(encoded_patches)
+    
+    x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(encoded_patches)
+    x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
+    f1 = x
+
+    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
+    x = Activation('relu')(x)
+    x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
+    
+    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
+    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
+    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
+    f2 = one_side_pad(x)
+
+    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
+    f3 = x 
+
+    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
+    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
+    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
+    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
+    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
+    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
+    f4 = x 
+
+    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
+    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
+    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
+    f5 = x 
+    
+    if pretraining:
+        model = Model(encoded_patches, x).load_weights(resnet50_Weights_path)
+
+    v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(x)
+    v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
+    v1024_2048 = Activation('relu')(v1024_2048)
+    
+    o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
+    o = (concatenate([o, f4],axis=MERGE_AXIS))
+    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
+    o = (Conv2D(512, (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 ,f3], axis=MERGE_AXIS))
+    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
+    o = (Conv2D(256, (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, 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
+
 def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
     include_top=True
     assert input_height%32 == 0

train.py

@@ -70,10 +70,14 @@ def config_params():
     brightness = None #  Brighten image for augmentation.
     flip_index = None  #  Flip image for augmentation.
     continue_training = False  # Set to true if you would like to continue training an already trained a model.
-    transformer_patchsize_x = None  # Patch size of vision transformer patches.
-    transformer_patchsize_y = None
-    transformer_num_patches_xy = None  # Number of patches for vision transformer.
-    transformer_projection_dim = 64 # Transformer projection dimension
+    transformer_patchsize_x = None  # Patch size of vision transformer patches in x direction.
+    transformer_patchsize_y = None # Patch size of vision transformer patches in y direction.
+    transformer_num_patches_xy = None  # Number of patches for vision transformer in x and y direction respectively.
+    transformer_projection_dim = 64 # Transformer projection dimension. Default value is 64.
+    transformer_mlp_head_units = [128, 64] # Transformer Multilayer Perceptron (MLP) head units. Default value is [128, 64]
+    transformer_layers = 8 # transformer layers. Default value is 8.
+    transformer_num_heads = 4 # Transformer number of heads. Default value is 4.
+    transformer_cnn_first = True # We have two types of vision transformers. In one type, a CNN is applied first, followed by a transformer. In the other type, this order is reversed. If transformer_cnn_first is true, it means the CNN will be applied before the transformer. Default value is true.
     index_start = 0  #  Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
     dir_of_start_model = ''  # Directory containing pretrained encoder to continue training the model.
     is_loss_soft_dice = False  # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
@@ -94,7 +98,9 @@ def run(_config, n_classes, n_epochs, input_height,
         brightening, binarization, blur_k, scales, degrade_scales,
         brightness, dir_train, data_is_provided, scaling_bluring,
         scaling_brightness, scaling_binarization, rotation, rotation_not_90,
-        thetha, scaling_flip, continue_training, transformer_projection_dim, transformer_patchsize_x, transformer_patchsize_y,
+        thetha, scaling_flip, continue_training, transformer_projection_dim,
+        transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
+        transformer_patchsize_x, transformer_patchsize_y,
         transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
         pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
     
@@ -218,12 +224,7 @@ def run(_config, n_classes, n_epochs, input_height,
                 num_patches_y = transformer_num_patches_xy[1]
                 num_patches = num_patches_x * num_patches_y
                 
-                ##if not (num_patches == (input_width / 32) * (input_height / 32)):
-                    ##print("Error: transformer num patches error. Parameter transformer_num_patches_xy should be set to (input_width/32) = {} and (input_height/32) =  {}".format(int(input_width / 32), int(input_height / 32)) )
-                    ##sys.exit(1)
-                #if not (transformer_patchsize == 1):
-                    #print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
-                    #sys.exit(1)
+                if transformer_cnn_first:
                     if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
                         print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
                         sys.exit(1)
@@ -234,10 +235,22 @@ def run(_config, n_classes, n_epochs, input_height,
                         print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
                         sys.exit(1)
                         
-                model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
+                    
+                    model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
+                else:
+                    if (input_height != (num_patches_y * transformer_patchsize_y) ):
+                        print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y)")
+                        sys.exit(1)
+                    if (input_width != (num_patches_x * transformer_patchsize_x) ):
+                        print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x)")
+                        sys.exit(1)
+                    if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
+                        print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
+                        sys.exit(1)
+                    model = vit_resnet50_unet_transformer_before_cnn(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
         
         #if you want to see the model structure just uncomment model summary.
-        #model.summary()
+        model.summary()
 
         
         if (task == "segmentation" or task == "binarization"):