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machine based reading order training is integrated
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3 changed files with 109 additions and 0 deletions
55
models.py
55
models.py
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@ -544,4 +544,59 @@ def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=
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return model
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def machine_based_reading_order_model(n_classes,input_height=224,input_width=224,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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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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x1 = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
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x1 = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x1)
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x1 = BatchNormalization(axis=bn_axis, name='bn_conv1')(x1)
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x1 = Activation('relu')(x1)
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x1 = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x1)
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x1 = conv_block(x1, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
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x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='b')
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x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='c')
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x1 = conv_block(x1, 3, [128, 128, 512], stage=3, block='a')
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x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='b')
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x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='c')
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x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='d')
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x1 = conv_block(x1, 3, [256, 256, 1024], stage=4, block='a')
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x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='b')
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x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='c')
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x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='d')
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x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='e')
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x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='f')
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x1 = conv_block(x1, 3, [512, 512, 2048], stage=5, block='a')
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x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='b')
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x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='c')
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if pretraining:
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Model(img_input , x1).load_weights(resnet50_Weights_path)
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x1 = AveragePooling2D((7, 7), name='avg_pool1')(x1)
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flattened = Flatten()(x1)
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o = Dense(256, activation='relu', name='fc512')(flattened)
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o=Dropout(0.2)(o)
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o = Dense(256, activation='relu', name='fc512a')(o)
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o=Dropout(0.2)(o)
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o = Dense(n_classes, activation='sigmoid', name='fc1000')(o)
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model = Model(img_input , o)
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return model
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31
train.py
31
train.py
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@ -313,4 +313,35 @@ def run(_config, n_classes, n_epochs, input_height,
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with open(os.path.join( os.path.join(dir_output,'model_best'), "config.json"), "w") as fp:
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json.dump(_config, fp) # encode dict into JSON
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elif task=='reading_order':
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configuration()
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model = machine_based_reading_order_model(n_classes,input_height,input_width,weight_decay,pretraining)
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dir_flow_train_imgs = os.path.join(dir_train, 'images')
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dir_flow_train_labels = os.path.join(dir_train, 'labels')
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classes = os.listdir(dir_flow_train_labels)
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num_rows =len(classes)
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#ls_test = os.listdir(dir_flow_train_labels)
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#f1score_tot = [0]
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indexer_start = 0
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opt = SGD(lr=0.01, momentum=0.9)
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opt_adam = tf.keras.optimizers.Adam(learning_rate=0.0001)
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model.compile(loss="binary_crossentropy",
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optimizer = opt_adam,metrics=['accuracy'])
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for i in range(n_epochs):
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history = model.fit(generate_arrays_from_folder_reading_order(dir_flow_train_labels, dir_flow_train_imgs, n_batch, input_height, input_width, n_classes), steps_per_epoch=num_rows / n_batch, verbose=1)
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model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
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with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
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json.dump(_config, fp) # encode dict into JSON
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'''
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if f1score>f1score_tot[0]:
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f1score_tot[0] = f1score
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model_dir = os.path.join(dir_out,'model_best')
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model.save(model_dir)
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'''
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23
utils.py
23
utils.py
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@ -268,6 +268,29 @@ def IoU(Yi, y_predi):
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#print("Mean IoU: {:4.3f}".format(mIoU))
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return mIoU
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def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batchsize, height, width, n_classes):
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all_labels_files = os.listdir(classes_file_dir)
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ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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while True:
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for i in all_labels_files:
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file_name = i.split('.')[0]
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img = cv2.imread(os.path.join(modal_dir,file_name+'.png'))
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label_class = int( np.load(os.path.join(classes_file_dir,i)) )
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ret_x[batchcount, :,:,0] = img[:,:,0]/3.0
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ret_x[batchcount, :,:,2] = img[:,:,2]/3.0
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ret_x[batchcount, :,:,1] = img[:,:,1]/5.0
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ret_y[batchcount, :] = label_class
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batchcount+=1
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if batchcount>=batchsize:
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yield (ret_x, ret_y)
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ret_x= np.zeros((batchsize, height, width, 3))#.astype(np.int16)
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ret_y= np.zeros((batchsize, n_classes)).astype(np.int16)
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batchcount = 0
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def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
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c = 0
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