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collate_fns=[vqa_collate_fn,None])
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if not args.evaluate:
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print("Creating model for searching")
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search_model = blip_vqa(client=client, pretrained=config['pretrained'], image_size=config['image_size'],
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vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],
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search=True)
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search_model = search_model.to(device)
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print_params_and_flops('vqa', search_model, device)
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search_model_without_ddp = search_model
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if args.distributed:
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search_model = torch.nn.parallel.DistributedDataParallel(search_model, device_ids=[args.gpu])
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search_model_without_ddp = search_model.module
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if not args.amp:
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optimizer = torch.optim.AdamW(
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params=[{'params':[param for name, param in list(search_model.named_parameters()) if not ('alpha' in name)]}],
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lr=config['init_lr'],
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weight_decay=config['weight_decay']
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)
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else:
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optimizer = torch.optim.AdamW(
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[{'params':[param for name, param in list(search_model.named_parameters()) if not ('alpha' in name)],
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'lr': config['init_lr'], 'weight_decay': config['weight_decay']},
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{'params':[param for name, param in list(search_model.named_parameters()) if ('alpha' in name)],
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'lr': 0, 'weight_decay': 0}]
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)
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print("Start searching")
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scaler = torch.cuda.amp.GradScaler() if args.amp else None
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for epoch in range(0, config['max_epoch']):
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if args.evaluate:
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break
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if args.distributed:
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train_loader.sampler.set_epoch(epoch)
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train(search_model, train_loader, optimizer, epoch, device, config, search=True, scaler=scaler)
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dist.barrier()
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search_model.module.print_compression_statistics()
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#### Model ####
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print("Creating model for training")
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model = blip_vqa(client=client, pretrained=config['pretrained'], image_size=config['image_size'],
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vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])
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msg = model.load_state_dict(search_model_without_ddp.state_dict(), strict=False)
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model.compress(search_model_without_ddp)
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else:
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print("Creating model for evaluation")
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model = blip_vqa(client=client, pretrained='', image_size=config['image_size'],
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vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],
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evaluate=True)
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model.prune_if_compressed(client, config['pretrained'])
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model = model.to(device)
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print_params_and_flops('vqa', model, device)
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model_without_ddp = model
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if args.distributed:
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
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model_without_ddp = model.module
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optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
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best = 0
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best_epoch = 0
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print("Start training")
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scaler = torch.cuda.amp.GradScaler() if (not args.evaluate and args.amp) else None
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for epoch in range(0, config['max_epoch']):
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if not args.evaluate:
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if args.distributed:
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train_loader.sampler.set_epoch(epoch)
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cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
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train_stats = train(model, train_loader, optimizer, epoch, device, config, scaler=scaler)
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else:
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break
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if utils.is_main_process():
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log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
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'epoch': epoch,
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}
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# with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
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# f.write(json.dumps(log_stats) + "\n")
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print("LOG: ", log_stats)
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save_obj = {
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'model': model_without_ddp.state_dict(),
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# 'optimizer': optimizer.state_dict(),
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# 'config': config,
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# 'epoch': epoch,
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}
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if client is not None:
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with io.BytesIO() as f:
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torch.save(save_obj, f)
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client.put(os.path.join('s3://BucketName/ProjectName', args.output_dir, 'checkpoint_%02d.pth'%epoch), f.getvalue())
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else:
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torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))
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dist.barrier()
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