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|
| import argparse |
| import time |
|
|
| import torch |
| from mmcv.cnn import get_model_complexity_info |
| from mmcv.cnn.utils.flops_counter import flops_to_string, params_to_string |
| from models.intern_vit_6b import InternViT6B |
| from tqdm import tqdm |
|
|
| parser = argparse.ArgumentParser(description='Hyperparams') |
| parser.add_argument('config', nargs='?', type=str, default=None) |
| args = parser.parse_args() |
|
|
| configs = { |
| 'a': { |
| 'embed_dim': 3968, |
| 'num_heads': 62, |
| 'mlp_ratio': 4, |
| 'depth': 32 |
| }, |
| 'e': { |
| 'embed_dim': 3200, |
| 'num_heads': 50, |
| 'mlp_ratio': 4, |
| 'depth': 48 |
| }, |
| 'f': { |
| 'embed_dim': 3200, |
| 'num_heads': 25, |
| 'mlp_ratio': 4, |
| 'depth': 48 |
| }, |
| 'g': { |
| 'embed_dim': 2496, |
| 'num_heads': 39, |
| 'mlp_ratio': 8, |
| 'depth': 48 |
| }, |
| 'i': { |
| 'embed_dim': 2816, |
| 'num_heads': 44, |
| 'mlp_ratio': 4, |
| 'depth': 64 |
| }, |
| 'm': { |
| 'embed_dim': 2496, |
| 'num_heads': 39, |
| 'mlp_ratio': 4, |
| 'depth': 80 |
| }, |
| } |
|
|
|
|
| def sa_flops(h, w, dim): |
| return 2 * h * w * h * w * dim |
|
|
|
|
| def get_flops(model, input_shape): |
| flops, params = get_model_complexity_info(model, |
| input_shape, |
| as_strings=False) |
| _, H, W = input_shape |
| print(flops, params) |
| for i in range(model.depth): |
| flops += sa_flops(H // model.patch_size, W // model.patch_size, |
| model.embed_dim) |
| return flops_to_string(flops), params_to_string(params) |
|
|
|
|
| if __name__ == '__main__': |
|
|
| input_shape = (3, 224, 224) |
|
|
| config = configs[args.config] |
| print(config) |
| model = InternViT6B(in_chans=3, |
| patch_size=14, |
| img_size=224, |
| pretrain_size=224, |
| qkv_bias=False, |
| drop_path_rate=0.0, |
| embed_dim=config['embed_dim'], |
| num_heads=config['num_heads'], |
| mlp_ratio=config['mlp_ratio'], |
| init_values=0.1, |
| qk_normalization=True, |
| depth=config['depth'], |
| use_flash_attn=True, |
| with_cp=True, |
| freeze_vit=True, |
| cls_target='cls_patch_concat', |
| num_classes=0, |
| attn_pool_num_heads=16, |
| clip_embed_dim=768, |
| norm_type='rms').to(torch.bfloat16) |
|
|
| for k, v in model.named_parameters(): |
| v.requires_grad = True |
|
|
| if torch.cuda.is_available(): |
| model.cuda() |
| model.eval() |
|
|
| flops, params = get_flops(model, input_shape) |
| split_line = '=' * 30 |
| print(f'{split_line}\nInput shape: {input_shape}\n' |
| f'Flops: {flops}\nParams: {params}\n{split_line}') |
| print('!!!Please be cautious if you use the results in papers. ' |
| 'You may need to check if all ops are supported and verify that the ' |
| 'flops computation is correct.') |
|
|
| image = torch.rand(128, 3, 224, 224).to(torch.bfloat16).cuda() |
| torch.cuda.synchronize() |
| start_time = time.time() |
| with torch.no_grad(): |
| for i in tqdm(range(10)): |
| out = model(image) |
| torch.cuda.synchronize() |
| end_time = time.time() |
|
|
| print('warmup time: ', end_time - start_time) |
|
|
| torch.cuda.synchronize() |
| start_time = time.time() |
| with torch.no_grad(): |
| for i in tqdm(range(50)): |
| out = model(image) |
| torch.cuda.synchronize() |
| end_time = time.time() |
| print('using time: ', (end_time - start_time)) |
| print('FPS: ', 50 * 128 / (end_time - start_time)) |
| print(config) |
|
|
