def fit_model(model,
train_data,
test_data,
weights_dir,
input_shape,
optical_flow=False,
project_dir='./'):
try:
# using sequence or image_from_sequnece generator
if optical_flow:
train_generator = sequence_generator(train_data, BatchSize,
input_shape, N_CLASSES)
test_generator = sequence_generator(test_data, BatchSize,
input_shape, N_CLASSES)
else:
train_generator = image_from_sequence_generator(
train_data, BatchSize, input_shape, N_CLASSES)
test_generator = image_from_sequence_generator(
test_data, BatchSize, input_shape, N_CLASSES)
# frames_dir = '/home/changan/ActionRecognition/data/flow_images'
# train_generator = image_generator(train_data, frames_dir, BatchSize, input_shape, N_CLASSES, mean_sub=False,
# normalization=True, random_crop=True, horizontal_flip=True)
# test_generator = image_generator(test_data, frames_dir, BatchSize, input_shape, N_CLASSES, mean_sub=False,
# normalization=True, random_crop=True, horizontal_flip=True)
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
print('Start fitting model')
while True:
checkpointer = keras.callbacks.ModelCheckpoint(
weights_dir, save_best_only=True, save_weights_only=True)
earlystopping = keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=20,
verbose=2,
mode='auto')
tensorboard = keras.callbacks.TensorBoard(log_dir='./logs/try',
histogram_freq=0,
write_graph=True,
write_images=True)
model.fit_generator(
train_generator,
steps_per_epoch=200,
epochs=2000,
validation_data=test_generator,
validation_steps=100,
verbose=2,
callbacks=[checkpointer, tensorboard, earlystopping])
data_dir = os.path.join(project_dir, 'data')
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
UCF_dir = os.path.join(data_dir, 'UCF-101')
regenerate_data(data_dir, list_dir, UCF_dir)
except KeyboardInterrupt:
print('Training is interrupted')
from utils.UCF_preprocessing import regenerate_data
import os
import sys
dataset = 'ucf'
if len(sys.argv) > 1:
dataset = sys.argv[1]
cwd = os.getcwd()
data_dir = os.path.join(cwd, 'data')
if 'hmdb' in dataset.lower():
list_dir = os.path.join(data_dir, 'hmdb51_test_train_splits')
UCF_dir = os.path.join(data_dir, 'hmdb51_org')
else:
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
UCF_dir = os.path.join(data_dir, 'UCF-101')
regenerate_data(data_dir, list_dir, UCF_dir)
from utils.UCF_preprocessing import regenerate_data
import os
import sys
dataset = 'ucf'
if len(sys.argv) > 1:
dataset = sys.argv[1]
cwd = os.getcwd()
data_dir = os.path.join(cwd, 'data')
if 'hmdb' in dataset.lower():
list_dir = os.path.join(data_dir, 'hmdb51_test_train_splits')
UCF_dir = os.path.join(data_dir, 'hmdb51_org')
else:
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
UCF_dir = os.path.join(data_dir, 'UCF-101')
regenerate_data(data_dir, list_dir, UCF_dir, temporal='DMD')