ts.MEAN,
ts.STD,
ts.IMAGE_HEIGHT,
ts.IMAGE_WIDTH,
random_hor_flipping=ts.HORIZONTAL_FLIPPING,
random_ver_flipping=ts.VERTICAL_FLIPPING,
random_90x_rotation=ts.ROTATION_90X,
random_rotation=ts.ROTATION,
random_color_jittering=ts.COLOR_JITTERING,
use_coordinates=ts.USE_COORDINATES)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=opt.batchsize,
shuffle=False,
num_workers=opt.nworkers,
pin_memory=pin_memory,
collate_fn=test_align_collate)
# Define Model
model = Model(opt.dataset, ts.N_CLASSES, ts.MAX_N_OBJECTS,
use_instance_segmentation=ts.USE_INSTANCE_SEGMENTATION,
use_coords=ts.USE_COORDINATES, load_model_path=opt.model,
usegpu=opt.usegpu)
# Train Model
model.fit(ts.CRITERION, ts.DELTA_VAR, ts.DELTA_DIST, ts.NORM, ts.LEARNING_RATE,
ts.WEIGHT_DECAY, ts.CLIP_GRAD_NORM, ts.LR_DROP_FACTOR,
ts.LR_DROP_PATIENCE, ts.OPTIMIZE_BG, ts.OPTIMIZER, ts.TRAIN_CNN,
opt.nepochs, ts.CLASS_WEIGHTS, train_loader, test_loader,
model_save_path, opt.debug)
random_hor_flipping=ts.HORIZONTAL_FLIPPING,
random_ver_flipping=ts.VERTICAL_FLIPPING,
random_transposing=ts.TRANSPOSING,
random_90x_rotation=ts.ROTATION_90X,
random_rotation=ts.ROTATION,
random_color_jittering=ts.COLOR_JITTERING,
random_grayscaling=ts.GRAYSCALING,
random_channel_swapping=ts.CHANNEL_SWAPPING,
random_gamma=ts.GAMMA_ADJUSTMENT,
random_resolution=ts.RESOLUTION_DEGRADING)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=opt.batchsize,
shuffle=False,
num_workers=opt.nworkers,
pin_memory=pin_memory,
collate_fn=test_align_collate)
# Define Model
model = Model(opt.dataset, ts.MODEL_NAME, ts.N_CLASSES, ts.MAX_N_OBJECTS,
use_instance_segmentation=ts.USE_INSTANCE_SEGMENTATION,
use_coords=ts.USE_COORDINATES, load_model_path=opt.model,
usegpu=opt.usegpu)
# Train Model
model.fit(ts.CRITERION, ts.DELTA_VAR, ts.DELTA_DIST, ts.NORM, ts.LEARNING_RATE,
ts.WEIGHT_DECAY, ts.CLIP_GRAD_NORM, ts.LR_DROP_FACTOR,
ts.LR_DROP_PATIENCE, ts.OPTIMIZE_BG, ts.OPTIMIZER, ts.TRAIN_CNN,
opt.nepochs, ts.CLASS_WEIGHTS, train_loader, test_loader,
model_save_path, opt.debug)
model = Model(args.n_stations, s.MOVING_HORIZON, s.ACTIVATION, s.CRITERION, usegpu=args.usegpu)
# Train First RNN
[X_train, y_train], [X_val, y_val], [X_test, y_test] = data.load_data_lstm_1()
rnn_model_num = 1
print '#' * 10 + ' RNN 1 ' + '#' * 10
train_loader = torch.utils.data.DataLoader(Loader((X_train, y_train)), batch_size=args.batch_size, shuffle=True,
num_workers=args.n_workers, pin_memory=pin_memory)
val_loader = torch.utils.data.DataLoader(Loader((X_val, y_val)), batch_size=args.batch_size, shuffle=False,
num_workers=args.n_workers, pin_memory=pin_memory)
model.fit(rnn_model_num, s.LEARNING_RATE, s.WEIGHT_DECAY, s.CLIP_GRAD_NORM, s.LR_DROP_FACTOR, s.LR_DROP_PATIENCE, s.PATIENCE,
s.OPTIMIZER, s.N_EPOCHS[rnn_model_num - 1],
train_loader, val_loader, model_save_path.format(rnn_model_num))
# Train Other RNNs
for rnn_model_num in range(2, s.MOVING_HORIZON + 1):
X_train, y_train = data.load_data(X_train, y_train, model, rnn_model_num - 1)
X_val, y_val = data.load_data(X_val, y_val, model, rnn_model_num - 1)
print '#' * 10 + ' RNN {} '.format(rnn_model_num) + '#' * 10
train_loader = torch.utils.data.DataLoader(Loader((X_train, y_train)), batch_size=args.batch_size, shuffle=True,
num_workers=args.n_workers, pin_memory=pin_memory)
val_loader = torch.utils.data.DataLoader(Loader((X_val, y_val)), batch_size=args.batch_size, shuffle=False,
num_workers=args.n_workers, pin_memory=pin_memory)
model.fit(rnn_model_num, s.LEARNING_RATE, s.WEIGHT_DECAY, s.CLIP_GRAD_NORM, s.LR_DROP_FACTOR, s.LR_DROP_PATIENCE, s.PATIENCE,
s.OPTIMIZER, s.N_EPOCHS[rnn_model_num - 1],
