check_randomness("Before fitting")
logging.info("Fitting the network...")
history = model.fit(train_x, train_y,
validation_data=(val_x,val_y),
epochs=EPOCHS,
batch_size=BATCH_SIZE,
sample_weight=train_y_weights,
callbacks=[metrics_callback],
shuffle=False)
if SHOW_PLOTS :
plots.plot_accuracy(history)
plots.plot_loss(history)
plots.plot_prf(metrics_callback)
if SAVE_MODEL :
model.save(MODEL_PATH)
logging.info("Model saved in %s", MODEL_PATH)
else :
logging.info("Loading existing model from %s...",MODEL_PATH)
model = load_model(MODEL_PATH)
logging.info("Completed loading model from file")
logging.info("Predicting on test set...")
output = model.predict(x=test_x, verbose=1)
logging.debug("Shape of output array: %s",np.shape(output))
def main(
df_path:
str = '/project/cq-training-1/project1/data/catalog.helios.public.20100101-20160101.pkl',
image_size: int = 32,
model: str = 'dummy',
epochs: int = 20,
optimizer: str = 'adam',
lr: float = 1e-4,
batch_size: int = 100,
subset_perc: float = 1,
subset_dates: bool = False,
saved_model_dir: str = None,
seq_len: int = 6,
seed: bool = True,
scale_label: bool = True,
use_csky: bool = False,
cache: bool = True,
timesteps_minutes: int = 15):
# Warning if no GPU detected
if len(tf.config.list_physical_devices('GPU')) == 0:
logger.warning('No GPU detected, training will run on CPU.')
elif len(tf.config.list_physical_devices('GPU')) > 1:
logger.warning(
'Multiple GPUs detected, training will run on only one GPU.')
if subset_dates and subset_perc != 1:
raise Exception(
f'Invalid configuration. Argument --subset_dates=True and --subset_perc={subset_perc}.'
)
# Set random seed
if seed:
tf.random.set_seed(SEED)
np.random.seed(SEED)
# Load dataframe
logger.info('Loading and preprocessing dataframe...')
df = pd.read_pickle(df_path)
df = preprocessing.preprocess(df, shuffle=False, scale_label=scale_label)
metadata = data.Metadata(df, scale_label)
# Pre-crop data
logger.info('Getting crops...')
images = data.Images(metadata, image_size)
# images.crop(dest=SLURM_TMPDIR)
images.crop(dest=images.shared_storage)
# Split into train and valid
if subset_dates:
metadata_train, metadata_valid = metadata.split_with_dates()
else:
metadata, _ = metadata.split(1 - subset_perc)
metadata_train, metadata_valid = metadata.split(VALID_PERC)
nb_train_examples = metadata_train.get_number_of_examples()
nb_valid_examples = metadata_valid.get_number_of_examples()
logger.info(
f'Number of training examples : {nb_train_examples}, number of validation examples : \
{nb_valid_examples}')
# Create model
if model == 'dummy':
model = baselines.DummyModel()
elif model == 'sunset':
model = baselines.SunsetModel()
elif model == 'cnndem':
model = baselines.ConvDemModel(image_size)
elif model == 'sunset3d':
model = baselines.Sunset3DModel()
elif model == 'convlstm':
model = baselines.ConvLSTM()
elif model == 'cnngru':
model = CnnGru(seq_len)
elif model == 'cnngruatt':
model = CnnGruAtt(seq_len)
elif model == 'cnnlstm':
model = LSTM_Resnet(seq_len)
elif model == 'resnet':
model = baselines.ResNetModel()
else:
raise Exception(f'Model "{model}" not recognized.')
# Load model weights
if saved_model_dir is not None:
model.load_weights(os.path.join(saved_model_dir, "model"))
# Loss and optimizer
mse = tf.keras.losses.MeanSquaredError()
if optimizer == 'adam':
optimizer = tf.keras.optimizers.Adam(lr)
elif optimizer == 'sgd':
optimizer = tf.keras.optimizers.SGD(lr)
else:
raise Exception(f'Optimizer "{optimizer}" not recognized.')
# Create data loader
dataloader_train = SequenceDataset(
metadata_train,
images,
seq_len,
batch_size,
timesteps=datetime.timedelta(minutes=timesteps_minutes),
cache=cache)
dataloader_valid = SequenceDataset(
metadata_valid,
images,
seq_len,
batch_size,
timesteps=datetime.timedelta(minutes=timesteps_minutes),
cache=cache)
# Training loop
logger.info('Training...')
losses = {'train': [], 'valid': []}
best_valid_loss = float('inf')
for epoch in range(epochs):
train_epoch(model, dataloader_train, batch_size, mse, optimizer,
nb_train_examples, scale_label, use_csky)
test_epoch(model, dataloader_valid, batch_size, mse, nb_valid_examples,
scale_label, use_csky)
train_loss = np.sqrt(train_mse_metric.result().numpy())
valid_loss = np.sqrt(valid_mse_metric.result().numpy())
csky_valid_loss = np.sqrt(valid_csky_mse_metric.result().numpy())
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
utils.save_model(model)
# Logs
logger.info(
f'Epoch {epoch} - Train Loss : {train_loss:.4f}, Valid Loss : {valid_loss:.4f}, Csky Valid Loss : \
{csky_valid_loss:.4f}')
losses['train'].append(train_loss)
losses['valid'].append(valid_loss)
with train_summary_writer.as_default():
tf.summary.scalar('loss', train_loss, step=epoch)
with test_summary_writer.as_default():
tf.summary.scalar('loss', valid_loss, step=epoch)
# Plot losses
plots.plot_loss(losses['train'], losses['valid'], csky_valid_loss)
if value < 60000:
correct_test_losses.append(value)
else:
correct_test_losses.append(correct_test_losses[-1])
test_losses = np.array(correct_test_losses)
correct_test_bpd = []
for value in test_bpd:
if value < 60000/(3072*np.log(2)):
correct_test_bpd.append(value)
else:
correct_test_bpd.append(correct_test_bpd[-1])
test_bpd = np.array(correct_test_bpd)
print(test_bpd)
# Plot
plot_loss(train_losses, 'Train Loss', 'output/cifar/train_loss.png')
plot_loss(test_losses, 'Test Loss', 'output/cifar/test_loss.png')
plot_bpd(train_bpd, 'Train bits/dim', 'output/cifar/train_bpd.png')
plot_bpd(test_bpd, 'Test bits/dim', 'output/cifar/test_bpd.png')
# 2) Dog CIFAR-10
model = Trainer(lr=1e-5, epochs=50, device='cpu', subset=True, label='dog')
print('[==> Visualize training images ...')
model.visualize(fname='output/dogs_cifar_trainset.png')
model.build()
# Load pre-trained model
print('[==> Loading pre-trained model')
model.load_model('input/pre_trained/dogs_cifar/net_final.model')
print('> Sampling')
model.save_samples('output/dogs_cifar/pre_trained_dogs_cifar.png')
print('> Plotting loss and bits/dim')
