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jblitzar
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github-python

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@torch.no_grad()
def validate_mrc(model, val_loader, task):
LOGGER.info("start running MRC validation...")
val_loss = 0
n_feat = 0
st = time()
tot_score = 0
for i, batch in enumerate(val_loader):
prediction_soft_label = model(
batch, task=task, compute_loss=False)
if "kl" in task:
prediction_soft_label = F.log_softmax(
prediction_soft_label, dim=-1)
label_targets = batch['label_targets']
loss = F.kl_div(
prediction_soft_label, label_targets, reduction='sum')
tot_score += compute_accuracy_for_soft_targets(
prediction_soft_label, label_targets)
else:
# background class should not be the target
cls_label_targets = label_targets[:, 1:].max(dim=-1)[1] + 1
loss = F.cross_entropy(
prediction_soft_label, cls_label_targets,
ignore_index=0, reduction='sum')
tot_score += compute_accuracy_for_soft_targets(
prediction_soft_label[:, 1:], label_targets[:, 1:])
val_loss += loss.item()
n_feat += batch['img_mask_tgt'].sum().item()
val_loss = sum(all_gather_list(val_loss))
tot_score = sum(all_gather_list(tot_score))
n_feat = sum(all_gather_list(n_feat))
tot_time = time()-st
val_loss /= n_feat
val_acc = tot_score / n_feat
val_log = {'loss': val_loss,
'acc': val_acc,
'feat_per_s': n_feat/tot_time}
LOGGER.info(f"validation finished in {int(tot_time)} seconds, "
f"score: {val_acc*100:.2f}")
return val_log
def compute_accuracy_for_soft_targets(out, labels):
outputs = out.max(dim=-1)[1]
labels = labels.max(dim=-1)[1] # argmax
n_correct = (outputs == labels).sum().item()
return n_correct
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
# NOTE: train tasks and val tasks cannot take command line arguments
parser.add_argument('--compressed_db', action='store_true',
help='use compressed LMDB')
parser.add_argument("--model_config", type=str,
help="path to model structure config json")
parser.add_argument("--checkpoint", default=None, type=str,
help="path to model checkpoint (*.pt)")
parser.add_argument(
"--output_dir", default=None, type=str,
help="The output directory where the model checkpoints will be "
"written.")
parser.add_argument('--mrm_prob', default=0.15, type=float,
help='probability to mask in MRM training')
# Prepro parameters
parser.add_argument('--max_txt_len', type=int, default=60,
help='max number of tokens in text (BERT BPE)')
parser.add_argument('--conf_th', type=float, default=0.2,
help='threshold for dynamic bounding boxes '
'(-1 for fixed)')
parser.add_argument('--max_bb', type=int, default=100,
help='max number of bounding boxes')
parser.add_argument('--min_bb', type=int, default=10,
help='min number of bounding boxes')
parser.add_argument('--num_bb', type=int, default=36,
help='static number of bounding boxes')
# training parameters
parser.add_argument("--train_batch_size", default=4096, type=int,
help="Total batch size for training. "
"(batch by tokens)")
parser.add_argument("--val_batch_size", default=4096, type=int,
help="Total batch size for validation. "
"(batch by tokens)")
parser.add_argument('--gradient_accumulation_steps', type=int, default=16,
help="Number of updates steps to accumualte before "
"performing a backward/update pass.")
parser.add_argument("--learning_rate", default=3e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--valid_steps", default=1000, type=int,
help="Run validation every X steps")
parser.add_argument("--num_train_steps", default=100000, type=int,