add xception

add xception model file

model/xception/xception_keras_imagegen.py

@@ -0,0 +1,176 @@
+#coding=GB18030
+
+from math import ceil
+import numpy as np
+import time
+from keras.applications import Xception
+from keras.applications.inception_v3 import InceptionV3
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
+from keras.layers import Dense, Dropout
+from keras.models import Model
+from keras.optimizers import *
+from utils_dist import SGDRScheduler, CustomModelCheckpoint, SequenceData
+from keras.callbacks import TensorBoard
+from keras.utils import training_utils
+from keras.utils import multi_gpu_model
+from keras.preprocessing.image import ImageDataGenerator
+import tensorflow as tf
+
+import os
+#os.environ["CUDA_VISIBLE_DEVICES"] = '0, 1'
+
+np.random.seed(1024)
+
+NBR_MODELS = 500
+
+FINE_TUNE = False
+warmup_epochs = 1
+base_lr = 0.0125
+momentum = 0.9
+LEARNING_RATE = 0.0005
+NBR_EPOCHS = 1500
+BATCH_SIZE = 32
+IMG_WIDTH = 299
+IMG_HEIGHT = 299
+monitor_index = 'val_acc'
+USE_CLASS_WEIGHTS = False
+RANDOM_SCALE = True
+encoding = "gbk"
+resume_from_epoch = 0
+
+train_path = '/data/work_image/train_file/'
+val_path = '/data/work_image/val_file/'
+train_file_list = './data/500/train_file_796.txt'
+val_file_list = './data/500/val_file_796.txt'
+new_classes = ''
+best_model_path = "./data/500/"
+best_model_file = ""
+last_mt_time = 0
+##get the last modify h5 file
+for maindir, subdirs, file_name_list in os.walk(best_model_path):
+    for file in file_name_list:
+        if file.find('.h5') == -1:
+            continue
+
+        mt_time = os.path.getmtime(os.path.join(maindir, file))
+        if last_mt_time < mt_time:
+            last_mt_time = mt_time
+            best_model_file = os.path.join(maindir, file)
+            FINE_TUNE = True
+
+
+
+if __name__ == "__main__":
+    # Pin a server GPU to be used by this process
+
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.gpu_options.per_process_gpu_memory_fraction = 0.9
+    K.set_session(tf.Session(config=config))
+
+    # ['/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1']
+    nbr_gpus = len(training_utils._get_available_devices()) - 1
+
+    verbose = 1 
+    resume_from_epoch = 0 
+
+    train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        width_shift_range=0.4,
+        height_shift_range=0.4,
+        rotation_range=90,
+        zoom_range=0.7,
+        horizontal_flip=True,
+        vertical_flip=True)
+    test_datagen = ImageDataGenerator(rescale=1./255)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_path,
+        target_size=(299,299),
+        batch_size=BATCH_SIZE,
+        class_mode='categorical')
+
+    val_generator = test_datagen.flow_from_directory(
+        val_path,
+        target_size=(299,299),
+        batch_size=BATCH_SIZE,
+        class_mode='categorical')        
+
+
+    print('Loading Xception Weights ...')
+    with tf.device('/cpu:0'):
+        xception_path = 'xception_model/xception_weights_tf_dim_ordering_tf_kernels_notop.h5'
+        inception = Xception(include_top=False, weights=xception_path,
+                                input_tensor=None, input_shape=(IMG_WIDTH, IMG_HEIGHT, 3), pooling='avg')
+        output = inception.get_layer(index=-1).output
+        output = Dropout(0.5)(output)
+        output = Dense(NBR_MODELS, activation='softmax', name='predictions')(output)
+        for layer in inception.layers:
+           layer.trainable = False
+        model = Model(outputs=output, inputs=inception.input)
+
+    if FINE_TUNE:
+        print('Loading Xception Weights in file %s' % best_model_file)
+        #model = multi_gpu_model(model, gpus=2)
+        model.load_weights(best_model_file)
+
+        if new_classes:
+            with open(new_classes) as f:
+                NBR_MODELS = len(f.readlines())
+
+            f.close()
+            print('use fine tune.....')
+            output = model.get_layer(index=-2).output
+            output = Dense(NBR_MODELS, activation='softmax', name='predictions')(output)
+            model = Model(outputs=output, inputs=inception.input)
+
+    print('Training model begins...')
+
+    optimizer = SGD(lr=LEARNING_RATE , momentum=0.9, decay=0.0, nesterov=True)
+    # optimizer = Adam(lr=LEARNING_RATE, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
+    # optimizer = Nadam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004)
+
+
+    #model = multi_gpu_model(model, gpus=2) #若使用多GPU训练则去掉注释
+    model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
+
+    out_model_name = best_model_path + 'Xception_bestmodel_' + time.strftime('%y%m%d', time.localtime()) + "_500.h5"
+    best_model = CustomModelCheckpoint(model, out_model_name, monitor_index=monitor_index)
+    reduce_lr = ReduceLROnPlateau(monitor=monitor_index, factor=0.5, patience=10, verbose=1, min_lr=0)
+    early_stop = EarlyStopping(monitor=monitor_index, patience=5, verbose=1, min_delta=0.001)
+
+    try:
+        train_data_lines = open(train_file_list, 'r', encoding=encoding).readlines()
+    except UnicodeDecodeError:
+        train_data_lines = open(train_file_list, 'r', encoding='UTF-8').readlines()
+    node_nbr_train = len(train_data_lines)
+    print('# Train Images: {}.'.format(node_nbr_train))
+    steps_per_epoch = int(ceil(node_nbr_train / BATCH_SIZE))
+
+    try:
+        val_data_lines = open(val_file_list, 'r', encoding=encoding).readlines()
+    except UnicodeDecodeError:
+        val_data_lines = open(val_file_list, 'r', encoding='UTF-8').readlines()
+    node_nbr_val = len(val_data_lines)
+    print('# Val Images: {}.'.format(node_nbr_val))
+    validation_steps = int(ceil(node_nbr_val / BATCH_SIZE))
+
+    gpu_device_name = tf.test.gpu_device_name()
+    print('gpu_device_name:',gpu_device_name)
+
+    callbacks = list()
+    callbacks.append(reduce_lr)
+    callbacks.append(best_model)
+    callbacks.append(early_stop)
+    callbacks.append(TensorBoard(log_dir='./tb_log'))
+
+    model.fit_generator(train_generator,
+                        steps_per_epoch=steps_per_epoch, 
+                        epochs=NBR_EPOCHS, verbose=verbose,
+                        validation_data=val_generator,
+                        validation_steps=validation_steps,
+                        callbacks=callbacks,
+                        shuffle=True,
+                        max_queue_size=128, workers=4, use_multiprocessing=True)
+