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- # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-
- """Pretrain VIT"""
-
- import torch
- import torch.nn.functional as F
- from functools import partial
- from megatron import get_args, get_timers, print_rank_0
- from megatron.core.enums import ModelType
- from megatron.data.vit_dataset import build_train_valid_datasets
- from megatron.model.vision.classification import VitClassificationModel
- from megatron.model.vision.classification import MitClassificationModel
- from megatron.training import pretrain
- from megatron.utils import average_losses_across_data_parallel_group
-
-
- def model_provider(pre_process=True, post_process=True):
- """Build the model."""
-
- args = get_args()
-
- if args.vision_backbone_type == 'vit':
- print_rank_0("building VIT model ...")
- model = VitClassificationModel(num_classes=args.num_classes,
- pre_process=pre_process,
- post_process=post_process)
- elif args.vision_backbone_type == 'mit':
- print_rank_0("building MIT model ...")
- model = MitClassificationModel(num_classes=args.num_classes,
- pre_process=pre_process,
- post_process=post_process)
- else:
- raise Exception('{} vision backbone is not supported.'.format(
- args.vision_backbone_type))
- return model
-
-
- def get_batch(data_iterator):
- """Build the batch."""
- data = next(data_iterator)
-
- # only data parallelism; no need for broadcast
- images = data[0].cuda()
- labels = data[1].cuda()
-
- return images, labels
-
-
- def loss_func(labels, output_tensor):
- logits = output_tensor.contiguous().float()
- loss = F.cross_entropy(logits, labels)
-
- outputs = torch.argmax(logits, -1)
- correct = (outputs == labels).float()
- accuracy = torch.mean(correct)
-
- averaged_loss = average_losses_across_data_parallel_group([loss, accuracy])
-
- return loss, {"loss": averaged_loss[0], "accuracy": averaged_loss[1]}
-
-
- def forward_step(data_iterator, model):
- """Forward step."""
- timers = get_timers()
-
- # Get the batch.
- timers("batch-generator", log_level=2).start()
- (
- images,
- labels,
- ) = get_batch(data_iterator)
- timers("batch-generator").stop()
-
- # Forward model. lm_labels
- output_tensor = model(images)
-
- return output_tensor, partial(loss_func, labels)
-
- def train_valid_test_datasets_provider(train_val_test_num_samples):
- """Build train, valid, and test datasets."""
- args = get_args()
-
- print_rank_0(
- "> building train, validation, and test datasets " "for VIT ..."
- )
- train_ds, valid_ds = build_train_valid_datasets(
- data_path=args.data_path,
- image_size=(args.img_h, args.img_w)
- )
- print_rank_0("> finished creating VIT datasets ...")
-
- return train_ds, valid_ds, None
-
-
- if __name__ == "__main__":
-
- pretrain(
- train_valid_test_datasets_provider,
- model_provider,
- ModelType.encoder_or_decoder,
- forward_step,
- args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True}
- )
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