train_T3D_keras.py

# Code to train T3D model
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard
from keras.optimizers import Adam, SGD
import keras.backend as K
import traceback

from T3D_keras import densenet161_3D_DropOut, densenet121_3D_DropOut, densenet201_3D_Dropout
from get_video import video_gen
import time, datetime

# there is a minimum number of frames that the network must have, values below 10 gives -- ValueError: Negative dimension size caused by subtracting 3 from 2 for 'conv3d_7/convolution'
# paper uses 224x224, but in that case also the above error occurs
FRAMES_PER_VIDEO = 32  # old value:20
FRAME_HEIGHT = 256
FRAME_WIDTH = 256
FRAME_CHANNEL = 3
NUM_CLASSES = 2
BATCH_SIZE = 2
EPOCHS = 5
MODEL_FILE_NAME = 'T3D_saved_model.h5'


def train():
    sample_input = np.empty(
        [FRAMES_PER_VIDEO, FRAME_HEIGHT, FRAME_WIDTH, FRAME_CHANNEL], dtype=np.uint8)

    # Read Dataset
    d_train = pd.read_csv(os.path.join('train.csv'))
    d_valid = pd.read_csv(os.path.join('test.csv'))
    # Split data into random training and validation sets
    nb_classes = len(set(d_train['class']))

    video_train_generator = video_gen(
        d_train, FRAMES_PER_VIDEO, FRAME_HEIGHT, FRAME_WIDTH, FRAME_CHANNEL, nb_classes, batch_size=BATCH_SIZE)
    video_val_generator = video_gen(
        d_valid, FRAMES_PER_VIDEO, FRAME_HEIGHT, FRAME_WIDTH, FRAME_CHANNEL, nb_classes, batch_size=BATCH_SIZE)

    # Get Model
    # model = densenet121_3D_DropOut(sample_input.shape, nb_classes)
    model = densenet161_3D_DropOut(sample_input.shape, nb_classes)
    # model = densenet201_3D_Dropout(sample_input.shape, nb_classes)

    checkpoint = ModelCheckpoint('T3D_saved_model_weights.hdf5', monitor='val_loss',
                                 verbose=1, save_best_only=True, mode='min', save_weights_only=True)
    earlyStop = EarlyStopping(monitor='val_loss', mode='min', patience=100)
    reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5,
                                       patience=20,
                                       verbose=1, mode='min', min_delta=0.0001, cooldown=2, min_lr=1e-6)

    tensorboard = TensorBoard(log_dir="./logs/{}/{}epochs_{}fpv_{}".format(model.name, EPOCHS, FRAMES_PER_VIDEO, datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
    callbacks_list = [checkpoint, reduceLROnPlat, earlyStop, tensorboard]

    # compile model
    optim = Adam(lr=1e-4, decay=1e-6)
    #optim = SGD(lr = 0.1, momentum=0.9, decay=1e-4, nesterov=True)
    model.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy'])

    if os.path.exists('./T3D_saved_model_weights.hdf5'):
        print('Pre-existing model weights found, loading weights.......')
        model.load_weights('./T3D_saved_model_weights.hdf5')
        print('Weights loaded')

    # train model
    print('Training started....')

    train_steps = len(d_train)//BATCH_SIZE
    val_steps = len(d_valid)//BATCH_SIZE

    history = model.fit_generator(
        video_train_generator,
        steps_per_epoch=train_steps,
        epochs=EPOCHS,
        validation_data=video_val_generator,
        validation_steps=val_steps,
        verbose=1,
        callbacks=callbacks_list,
        use_multiprocessing=True,
        workers=2
    )
    model.save(MODEL_FILE_NAME)


if __name__ == '__main__':
    try:
        train()
    except Exception as err:
        print('Error:', err)
        traceback.print_tb(err.__traceback__)
    finally:
        # Destroying the current TF graph to avoid clutter from old models / layers
        K.clear_session()