From 662eb2fe94892b8a8eabc7158cb39a5fc095284a Mon Sep 17 00:00:00 2001 From: xiaochus Date: Fri, 2 Feb 2018 21:45:22 +0800 Subject: [PATCH] add train method. --- README.md | 58 +++- data/convert.py | 35 ++ images/MobileNetv2.png | Bin 1126429 -> 1124602 bytes mobilenet_v2.py | 15 +- python | 765 +++++++++++++++++++++++++++++++++++++++++ train.py | 158 +++++++++ 6 files changed, 1016 insertions(+), 15 deletions(-) create mode 100644 data/convert.py create mode 100644 python create mode 100644 train.py diff --git a/README.md b/README.md index 2789ec3..18481f7 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ # MobileNet v2 -A Keras 2 implementation of MobileNet V2. +A Python 3 and Keras 2 implementation of MobileNet V2 and provide train method. -According to the paper:[Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381) +According to the paper: [Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381). -Currently only the network structure is defined, and the training function will be updated later. ## Requirement +- OpenCV 3.4 - Python 3.5 - Tensorflow-gpu 1.2.0 - Keras 2.1.3 @@ -22,14 +22,56 @@ Each line describes a sequence of 1 or more identical (modulo stride) layers, re ![residual block architectures](/images/stru.jpg) -**Architectures of this implementation with (224, 224, 3) inputs and 1000 output:** -![architectures](/images/MobileNetv2.png) +## Train the model -##Reference -- [Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381) +The recommended size of the image in the paper is 224 * 224. The ```data\convert.py``` file provide a demo of resize cifar-100 dataset to this size. -##Copyright +**The dataset folder structure is as follows:** + + data + --train + --class 0 + --class 1 + .... + --class n + --validation + --class 0 + --class 1 + .... + --class n + +**Run command below to train the model:** + +``` +python train.py --classes num_classes --batch batch_size --epochs epochs --size image_size +``` + +The ```.h5``` weight file was saved at model folder. If you want to do fine tune the trained model, you can run the following command. However, it should be noted that the size of the input image should be consistent with the original model. + +``` +python train.py --classes num_classes --batch batch_size --epochs epochs --size image_size --weights weights_path +``` + +**Parameter explanation** + +- --classes, The number of classes of dataset. +- --size, The image size of train sample. +- --batch, The number of train samples per batch. +- --epochs, The number of train iterations. +- --weights, Fine tune with other weights. + +## Reference + + @article{MobileNetv2, + title={Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentatio}, + author={Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen}, + journal={arXiv preprint arXiv:1801.04381}, + year={2018} + } + + +## Copyright See [LICENSE](LICENSE) for details. diff --git a/data/convert.py b/data/convert.py new file mode 100644 index 0000000..8e976cb --- /dev/null +++ b/data/convert.py @@ -0,0 +1,35 @@ +""" +A sample of convert the cifar100 dataset to 224 * 224 size train\val data. +""" +import cv2 +import os +from keras.datasets import cifar100 + + +def convert(): + train = 'train//' + val = 'validation//' + + (X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode='fine') + + for i in range(len(X_train)): + x = X_train[i] + y = y_train[i] + path = train + str(y[0]) + x = cv2.resize(x, (224, 224), interpolation=cv2.INTER_CUBIC) + if not os.path.exists(path): + os.makedirs(path) + cv2.imwrite(path + '//' + str(i) + '.jpg', x) + + for i in range(len(X_test)): + x = X_train[i] + y = y_train[i] + path = val + str(y[0]) + x = cv2.resize(x, (224, 224), interpolation=cv2.INTER_CUBIC) + if not os.path.exists(path): + os.makedirs(path) + cv2.imwrite(path + '//' + str(i) + '.jpg', x) + + +if __name__ == '__main__': + convert() diff --git a/images/MobileNetv2.png b/images/MobileNetv2.png index 8f678375b959ef1edc71c3bf1d9608bc4313270c..5e55c13103c3f3cddd54d5f2d7cf79b317c413b5 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[>.............................] - ETA: 17:20 - loss: 4.7237 - acc: 0.0239 + 56/1562 [>.............................] - ETA: 17:18 - loss: 4.7241 - acc: 0.0240 + 57/1562 [>.............................] - ETA: 17:17 - loss: 4.7238 - acc: 0.0236 + 58/1562 [>.............................] - ETA: 17:14 - loss: 4.7208 - acc: 0.0237 + 59/1562 [>.............................] - ETA: 17:12 - loss: 4.7197 - acc: 0.0233 + 60/1562 [>.............................] - ETA: 17:08 - loss: 4.7174 - acc: 0.0240 + 61/1562 [>.............................] - ETA: 17:04 - loss: 4.7148 - acc: 0.0236 + 62/1562 [>.............................] - ETA: 17:06 - loss: 4.7188 - acc: 0.0237 + 63/1562 [>.............................] - ETA: 17:06 - loss: 4.7194 - acc: 0.0238 + 64/1562 [>.............................] - ETA: 17:04 - loss: 4.7198 - acc: 0.0244 + 65/1562 [>.............................] - ETA: 17:07 - loss: 4.7157 - acc: 0.0240 + 66/1562 [>.............................] - ETA: 17:05 - loss: 4.7145 - acc: 0.0237 + 67/1562 [>.............................] - ETA: 17:11 - loss: 4.7131 - acc: 0.0233 + 68/1562 [>.............................] - ETA: 17:13 - loss: 4.7145 - acc: 0.0234 + 69/1562 [>.............................] - ETA: 17:08 - loss: 4.7095 - acc: 0.0231 + 70/1562 [>.............................] - ETA: 17:08 - loss: 4.7074 - acc: 0.0232 + 71/1562 [>.............................] - ETA: 17:04 - loss: 4.7038 - acc: 0.0233 + 72/1562 [>.............................] - ETA: 17:02 - loss: 4.7025 - acc: 0.0234 + 73/1562 [>.............................] - ETA: 16:59 - loss: 4.6981 - acc: 0.0235 + 74/1562 [>.............................] - ETA: 16:54 - loss: 4.6958 - acc: 0.0236 + 75/1562 [>.............................] - ETA: 16:54 - loss: 4.6986 - acc: 0.0233 + 76/1562 [>.............................] - ETA: 16:50 - loss: 4.6995 - acc: 0.0230 + 77/1562 [>.............................] - ETA: 16:47 - loss: 4.7037 - acc: 0.0227 + 78/1562 [>.............................] - ETA: 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[>.............................] - ETA: 16:13 - loss: 4.6929 - acc: 0.0240 + 91/1562 [>.............................] - ETA: 16:10 - loss: 4.6939 - acc: 0.0240 + 92/1562 [>.............................] - ETA: 16:08 - loss: 4.6954 - acc: 0.0238 + 93/1562 [>.............................] - ETA: 16:06 - loss: 4.6934 - acc: 0.0239 + 94/1562 [>.............................] - ETA: 16:04 - loss: 4.6960 - acc: 0.0236 + 95/1562 [>.............................] - ETA: 16:02 - loss: 4.6939 - acc: 0.0234 + 96/1562 [>.............................] - ETA: 16:00 - loss: 4.6940 - acc: 0.0231 + 97/1562 [>.............................] - ETA: 16:04 - loss: 4.6902 - acc: 0.0232 + 98/1562 [>.............................] - ETA: 16:05 - loss: 4.6881 - acc: 0.0230 + 99/1562 [>.............................] - ETA: 16:02 - loss: 4.6853 - acc: 0.0230 + 100/1562 [>.............................] - ETA: 15:59 - loss: 4.6863 - acc: 0.0234 + 101/1562 [>.............................] - ETA: 15:58 - loss: 4.6860 - 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4.6560 - acc: 0.0232 + 125/1562 [=>............................] - ETA: 15:23 - loss: 4.6548 - acc: 0.0232 + 126/1562 [=>............................] - ETA: 15:22 - loss: 4.6566 - acc: 0.0231 + 127/1562 [=>............................] - ETA: 15:20 - loss: 4.6559 - acc: 0.0231 + 128/1562 [=>............................] - ETA: 15:18 - loss: 4.6546 - acc: 0.0232 + 129/1562 [=>............................] - ETA: 15:16 - loss: 4.6524 - acc: 0.0235 + 130/1562 [=>............................] - ETA: 15:14 - loss: 4.6517 - acc: 0.0238 + 131/1562 [=>............................] - ETA: 15:12 - loss: 4.6539 - acc: 0.0236 + 132/1562 [=>............................] - ETA: 15:10 - loss: 4.6517 - acc: 0.0237 + 133/1562 [=>............................] - ETA: 15:11 - loss: 4.6495 - acc: 0.0240 + 134/1562 [=>............................] - ETA: 15:09 - loss: 4.6467 - acc: 0.0240 + 135/1562 [=>............................] - ETA: 15:09 - loss: 4.6475 - acc: 0.0238 + 136/1562 [=>............................] - ETA: 15:07 - loss: 4.6484 - acc: 0.0239 + 137/1562 [=>............................] - ETA: 15:06 - loss: 4.6472 - acc: 0.0240 + 138/1562 [=>............................] - ETA: 15:09 - loss: 4.6466 - acc: 0.0242 + 139/1562 [=>............................] - ETA: 15:08 - loss: 4.6468 - acc: 0.0243 + 140/1562 [=>............................] - ETA: 15:07 - loss: 4.6473 - acc: 0.0241 + 141/1562 [=>............................] - ETA: 15:06 - loss: 4.6460 - acc: 0.0239 + 142/1562 [=>............................] - ETA: 15:04 - loss: 4.6456 - acc: 0.0238 + 143/1562 [=>............................] - ETA: 15:03 - loss: 4.6444 - acc: 0.0236 + 144/1562 [=>............................] - ETA: 15:01 - loss: 4.6453 - acc: 0.0237 + 145/1562 [=>............................] - ETA: 14:59 - loss: 4.6438 - acc: 0.0237 + 146/1562 [=>............................] - ETA: 14:58 - loss: 4.6422 - acc: 0.0238 + 147/1562 [=>............................] - ETA: 14:57 - loss: 4.6427 - acc: 0.0242 + 148/1562 [=>............................] - ETA: 14:55 - loss: 4.6438 - acc: 0.0245 + 149/1562 [=>............................] - ETA: 14:54 - loss: 4.6446 - acc: 0.0243 + 150/1562 [=>............................] - ETA: 14:54 - loss: 4.6432 - acc: 0.0244 + 151/1562 [=>............................] - ETA: 14:52 - loss: 4.6434 - acc: 0.0244 + 152/1562 [=>............................] - ETA: 14:51 - loss: 4.6416 - acc: 0.0243 + 153/1562 [=>............................] - ETA: 14:49 - loss: 4.6420 - acc: 0.0243 + 154/1562 [=>............................] - ETA: 14:50 - loss: 4.6412 - acc: 0.0244 + 155/1562 [=>............................] - ETA: 14:48 - loss: 4.6401 - acc: 0.0246 + 156/1562 [=>............................] - ETA: 14:47 - loss: 4.6387 - acc: 0.0244 + 157/1562 [==>...........................] - ETA: 14:46 - loss: 4.6372 - acc: 0.0243 + 158/1562 [==>...........................] - ETA: 14:45 - loss: 4.6361 - acc: 0.0241 + 159/1562 [==>...........................] - ETA: 14:43 - loss: 4.6374 - acc: 0.0240 + 160/1562 [==>...........................] - ETA: 14:42 - loss: 4.6362 - acc: 0.0238 + 161/1562 [==>...........................] - ETA: 14:41 - loss: 4.6331 - acc: 0.0243 + 162/1562 [==>...........................] - ETA: 14:39 - loss: 4.6319 - acc: 0.0241 + 163/1562 [==>...........................] - ETA: 14:38 - loss: 4.6334 - acc: 0.0242 + 164/1562 [==>...........................] - ETA: 14:38 - loss: 4.6329 - acc: 0.0242 + 165/1562 [==>...........................] - ETA: 14:36 - loss: 4.6310 - acc: 0.0242 + 166/1562 [==>...........................] - ETA: 14:35 - loss: 4.6300 - acc: 0.0241 + 167/1562 [==>...........................] - ETA: 14:33 - loss: 4.6310 - acc: 0.0240 + 168/1562 [==>...........................] - ETA: 14:32 - loss: 4.6312 - acc: 0.0240 + 169/1562 [==>...........................] - ETA: 14:31 - loss: 4.6326 - acc: 0.0239 + 170/1562 [==>...........................] - ETA: 14:30 - loss: 4.6334 - acc: 0.0237 + 171/1562 [==>...........................] - ETA: 14:29 - loss: 4.6342 - acc: 0.0238 + 172/1562 [==>...........................] - ETA: 14:28 - loss: 4.6320 - acc: 0.0240 + 173/1562 [==>...........................] - ETA: 14:27 - loss: 4.6319 - acc: 0.0238 + 174/1562 [==>...........................] - ETA: 14:25 - loss: 4.6307 - acc: 0.0237 + 175/1562 [==>...........................] - ETA: 14:24 - loss: 4.6309 - acc: 0.0239 + 176/1562 [==>...........................] - ETA: 14:23 - loss: 4.6287 - acc: 0.0240 + 177/1562 [==>...........................] - ETA: 14:22 - loss: 4.6276 - acc: 0.0240 + 178/1562 [==>...........................] - ETA: 14:20 - loss: 4.6281 - acc: 0.0239 + 179/1562 [==>...........................] - ETA: 14:19 - loss: 4.6288 - acc: 0.0237 + 180/1562 [==>...........................] - ETA: 14:17 - loss: 4.6300 - acc: 0.0238 + 181/1562 [==>...........................] - ETA: 14:16 - loss: 4.6287 - acc: 0.0238 + 182/1562 [==>...........................] - ETA: 14:15 - loss: 4.6262 - acc: 0.0237 + 183/1562 [==>...........................] - ETA: 14:13 - loss: 4.6270 - acc: 0.0237 + 184/1562 [==>...........................] - ETA: 14:11 - loss: 4.6271 - acc: 0.0238 + 185/1562 [==>...........................] - ETA: 14:10 - loss: 4.6269 - acc: 0.0242 + 186/1562 [==>...........................] - ETA: 14:09 - loss: 4.6248 - acc: 0.0242 + 187/1562 [==>...........................] - ETA: 14:08 - loss: 4.6251 - acc: 0.0241 + 188/1562 [==>...........................] - ETA: 14:07 - loss: 4.6270 - acc: 0.0239 + 189/1562 [==>...........................] - ETA: 14:05 - loss: 4.6295 - acc: 0.0238 + 190/1562 [==>...........................] - ETA: 14:04 - loss: 4.6305 - acc: 0.0238 + 191/1562 [==>...........................] - ETA: 14:03 - loss: 4.6274 - acc: 0.0242 + 192/1562 [==>...........................] - ETA: 14:02 - loss: 4.6271 - acc: 0.0243 + 193/1562 [==>...........................] - ETA: 14:00 - loss: 4.6269 - acc: 0.0243 + 194/1562 [==>...........................] - ETA: 13:59 - loss: 4.6286 - acc: 0.0242 + 195/1562 [==>...........................] - ETA: 13:58 - loss: 4.6297 - acc: 0.0240 + 196/1562 [==>...........................] - ETA: 13:57 - loss: 4.6302 - acc: 0.0239 + 197/1562 [==>...........................] - ETA: 13:57 - loss: 4.6307 - acc: 0.0240 + 198/1562 [==>...........................] - ETA: 13:56 - loss: 4.6302 - acc: 0.0240 + 199/1562 [==>...........................] - ETA: 13:55 - loss: 4.6297 - acc: 0.0239 + 200/1562 [==>...........................] - ETA: 13:53 - loss: 4.6284 - acc: 0.0241 + 201/1562 [==>...........................] - ETA: 13:52 - loss: 4.6279 - acc: 0.0241 + 202/1562 [==>...........................] - ETA: 13:51 - loss: 4.6287 - acc: 0.0243 + 203/1562 [==>...........................] - ETA: 13:49 - loss: 4.6292 - acc: 0.0242 + 204/1562 [==>...........................] - ETA: 13:48 - loss: 4.6301 - acc: 0.0241 + 205/1562 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[=============>................] - ETA: 7:43 - loss: 4.5930 - acc: 0.0338 + 761/1562 [=============>................] - ETA: 7:43 - loss: 4.5931 - acc: 0.0338 \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000..5a173aa --- /dev/null +++ b/train.py @@ -0,0 +1,158 @@ +""" +Train the MobileNet V2 model +""" +import os +import sys +import argparse +import pandas as pd + +from mobilenet_v2 import MobileNetv2 + +from keras.optimizers import RMSprop +from keras.preprocessing.image import ImageDataGenerator +from keras.callbacks import EarlyStopping +from keras.layers import Conv2D, Reshape, Activation +from keras.models import Model + + +def main(argv): + parser = argparse.ArgumentParser() + # Required arguments. + parser.add_argument( + "--classes", + help="The number of classes of dataset.") + # Optional arguments. + parser.add_argument( + "--size", + default=224, + help="The image size of train sample.") + parser.add_argument( + "--batch", + default=32, + help="The number of train samples per batch.") + parser.add_argument( + "--epochs", + default=100, + help="The number of train iterations.", + ) + parser.add_argument( + "--weights", + default=False, + help="Fine tune with other weights.", + ) + + args = parser.parse_args() + + train(int(args.batch), int(args.epochs), int(args.classes), int(args.size), args.weights) + + +def generate(batch, size): + """Data generation and augmentation + + # Arguments + batch: Integer, batch size. + size: Integer, image size. + + # Returns + train_generator: train set generator + validation_generator: validation set generator + count1: Integer, number of train set. + count2: Integer, number of test set. + """ + + # Using the data Augmentation in traning data + ptrain = 'data/train' + pval = 'data/validation' + + datagen1 = ImageDataGenerator( + rescale=1. / 255, + shear_range=0.2, + zoom_range=0.2, + rotation_range=90, + width_shift_range=0.2, + height_shift_range=0.2, + horizontal_flip=True) + + datagen2 = ImageDataGenerator(rescale=1. / 255) + + train_generator = datagen1.flow_from_directory( + ptrain, + target_size=(size, size), + batch_size=batch, + class_mode='categorical') + + validation_generator = datagen2.flow_from_directory( + pval, + target_size=(size, size), + batch_size=batch, + class_mode='categorical') + + count1 = 0 + for root, dirs, files in os.walk(ptrain): + for each in files: + count1 += 1 + + count2 = 0 + for root, dirs, files in os.walk(pval): + for each in files: + count2 += 1 + + return train_generator, validation_generator, count1, count2 + + +def fine_tune(num_classes, weights, model): + """Re-build model with current num_classes. + + # Arguments + num_classes, Integer, The number of classes of dataset. + tune, String, The pre_trained model weights. + model, Model, The model structure. + """ + model.load_weights(weights) + + x = model.get_layer('Dropout').output + x = Conv2D(num_classes, (1, 1), padding='same')(x) + x = Activation('softmax', name='softmax')(x) + output = Reshape((num_classes,))(x) + + model = Model(inputs=model.input, outputs=output) + + return model + + +def train(batch, epochs, num_classes, size, weights): + """Train the model. + + # Arguments + batch: Integer, The number of train samples per batch. + epochs: Integer, The number of train iterations. + num_classes, Integer, The number of classes of dataset. + size: Integer, image size. + weights, String, The pre_trained model weights. + """ + + train_generator, validation_generator, count1, count2 = generate(batch, size) + + model = MobileNetv2((size, size, 3), num_classes) + if weights: + model = fine_tune(num_classes, weights, model) + + rmsprop = RMSprop() + earlystop = EarlyStopping(monitor='val_acc', patience=10, verbose=0, mode='auto') + model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['accuracy']) + + hist = model.fit_generator( + train_generator, + validation_data=validation_generator, + steps_per_epoch=count1 // batch, + validation_steps=count2 // batch, + epochs=epochs, + callbacks=[earlystop]) + + df = pd.DataFrame.from_dict(hist.history) + df.to_csv('model/hist.csv', encoding='utf-8', index=False) + model.save_weights('model/weights.h5') + + +if __name__ == '__main__': + main(sys.argv)