learning_rate_scheduler=scheduler)
else:
print("Making directories")
os.makedirs(args.folder, exist_ok=True)
start_epoch, val_metric_per_epoch = 0, []
shutil.copy2(args.params, args.folder)
param_shapes = print_para(model)
num_batches = 0
for epoch_num in range(start_epoch,
params['trainer']['num_epochs'] + start_epoch):
train_results = []
norms = []
model.train()
for b, (time_per_batch, batch) in enumerate(
time_batch(train_loader if args.no_tqdm else tqdm(train_loader),
reset_every=ARGS_RESET_EVERY)):
batch = _to_gpu(batch)
optimizer.zero_grad()
output_dict = model(**batch)
loss = output_dict['loss'].mean(
) + output_dict['cnn_regularization_loss'].mean()
loss.backward()
num_batches += 1
if scheduler:
scheduler.step_batch(num_batches)
norms.append(
clip_grad_norm(model.named_parameters(),
max_norm=params['trainer']['grad_norm'],
clip=True,
params = Params.from_file(args.params)
print("Loading {} for {}".format(params['model'].get('type', 'WTF?'), mode), flush=True)
model = Model.from_params(vocab=vcr_dataset.vocab, params=params['model'])
for submodule in model.detector.backbone.modules():
if isinstance(submodule, BatchNorm2d):
submodule.track_running_stats = False
model_state = torch.load(getattr(args, f'{mode}_ckpt'), map_location=device_mapping(-1))
model.load_state_dict(model_state)
model = DataParallel(model).cuda() if NUM_GPUS > 1 else model.cuda()
model.eval()
test_probs = []
test_ids = []
for b, (time_per_batch, batch) in enumerate(time_batch(test_loader)):
with torch.no_grad():
batch = _to_gpu(batch)
output_dict = model(**batch)
test_probs.append(output_dict['label_probs'].detach().cpu().numpy())
test_ids += [m['annot_id'] for m in batch['metadata']]
if (b > 0) and (b % 10 == 0):
print("Completed {}/{} batches in {:.3f}s".format(b, len(test_loader), time_per_batch * 10), flush=True)
probs_grp.append(np.concatenate(test_probs, 0))
ids_grp.append(test_ids)
################################################################################
# This is the part you'll care about if you want to submit to the leaderboard!
################################################################################