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run_videomae_vis.py
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run_videomae_vis.py
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# -*- coding: utf-8 -*-
import argparse
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from PIL import Image
from pathlib import Path
from timm.models import create_model
import utils
import modeling_pretrain
from datasets import DataAugmentationForVideoMAE
from torchvision.transforms import ToPILImage
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from decord import VideoReader, cpu
from torchvision import transforms
from transforms import *
from masking_generator import TubeMaskingGenerator
class DataAugmentationForVideoMAE(object):
def __init__(self, args):
self.input_mean = [0.485, 0.456, 0.406] # IMAGENET_DEFAULT_MEAN
self.input_std = [0.229, 0.224, 0.225] # IMAGENET_DEFAULT_STD
normalize = GroupNormalize(self.input_mean, self.input_std)
self.train_augmentation = GroupCenterCrop(args.input_size)
self.transform = transforms.Compose([
self.train_augmentation,
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
if args.mask_type == 'tube':
self.masked_position_generator = TubeMaskingGenerator(
args.window_size, args.mask_ratio
)
def __call__(self, images):
process_data , _ = self.transform(images)
return process_data, self.masked_position_generator()
def __repr__(self):
repr = "(DataAugmentationForVideoMAE,\n"
repr += " transform = %s,\n" % str(self.transform)
repr += " Masked position generator = %s,\n" % str(self.masked_position_generator)
repr += ")"
return repr
def get_args():
parser = argparse.ArgumentParser('VideoMAE visualization reconstruction script', add_help=False)
parser.add_argument('img_path', type=str, help='input video path')
parser.add_argument('save_path', type=str, help='save video path')
parser.add_argument('model_path', type=str, help='checkpoint path of model')
parser.add_argument('--mask_type', default='random', choices=['random', 'tube'],
type=str, help='masked strategy of video tokens/patches')
parser.add_argument('--num_frames', type=int, default= 16)
parser.add_argument('--sampling_rate', type=int, default= 4)
parser.add_argument('--decoder_depth', default=4, type=int,
help='depth of decoder')
parser.add_argument('--input_size', default=224, type=int,
help='videos input size for backbone')
parser.add_argument('--device', default='cuda:0',
help='device to use for training / testing')
parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true')
parser.add_argument('--mask_ratio', default=0.75, type=float,
help='ratio of the visual tokens/patches need be masked')
# Model parameters
parser.add_argument('--model', default='pretrain_videomae_base_patch16_224', type=str, metavar='MODEL',
help='Name of model to vis')
parser.add_argument('--drop_path', type=float, default=0.0, metavar='PCT',
help='Drop path rate (default: 0.1)')
return parser.parse_args()
def get_model(args):
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
drop_path_rate=args.drop_path,
drop_block_rate=None,
decoder_depth=args.decoder_depth
)
return model
def main(args):
print(args)
device = torch.device(args.device)
cudnn.benchmark = True
model = get_model(args)
patch_size = model.encoder.patch_embed.patch_size
print("Patch size = %s" % str(patch_size))
args.window_size = (args.num_frames // 2, args.input_size // patch_size[0], args.input_size // patch_size[1])
args.patch_size = patch_size
model.to(device)
checkpoint = torch.load(args.model_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
model.eval()
if args.save_path:
Path(args.save_path).mkdir(parents=True, exist_ok=True)
with open(args.img_path, 'rb') as f:
vr = VideoReader(f, ctx=cpu(0))
duration = len(vr)
new_length = 1
new_step = 1
skip_length = new_length * new_step
# frame_id_list = [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49, 53, 57, 61]
tmp = np.arange(0,32, 2) + 60
frame_id_list = tmp.tolist()
# average_duration = (duration - skip_length + 1) // args.num_frames
# if average_duration > 0:
# frame_id_list = np.multiply(list(range(args.num_frames)),
# average_duration)
# frame_id_list = frame_id_list + np.random.randint(average_duration,
# size=args.num_frames)
video_data = vr.get_batch(frame_id_list).asnumpy()
print(video_data.shape)
img = [Image.fromarray(video_data[vid, :, :, :]).convert('RGB') for vid, _ in enumerate(frame_id_list)]
transforms = DataAugmentationForVideoMAE(args)
img, bool_masked_pos = transforms((img, None)) # T*C,H,W
# print(img.shape)
img = img.view((args.num_frames , 3) + img.size()[-2:]).transpose(0,1) # T*C,H,W -> T,C,H,W -> C,T,H,W
# img = img.view(( -1 , args.num_frames) + img.size()[-2:])
bool_masked_pos = torch.from_numpy(bool_masked_pos)
with torch.no_grad():
# img = img[None, :]
# bool_masked_pos = bool_masked_pos[None, :]
img = img.unsqueeze(0)
print(img.shape)
bool_masked_pos = bool_masked_pos.unsqueeze(0)
img = img.to(device, non_blocking=True)
bool_masked_pos = bool_masked_pos.to(device, non_blocking=True).flatten(1).to(torch.bool)
outputs = model(img, bool_masked_pos)
#save original video
mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, :, None, None, None]
std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, :, None, None, None]
ori_img = img * std + mean # in [0, 1]
imgs = [ToPILImage()(ori_img[0,:,vid,:,:].cpu()) for vid, _ in enumerate(frame_id_list) ]
for id, im in enumerate(imgs):
im.save(f"{args.save_path}/ori_img{id}.jpg")
img_squeeze = rearrange(ori_img, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c', p0=2, p1=patch_size[0], p2=patch_size[0])
img_norm = (img_squeeze - img_squeeze.mean(dim=-2, keepdim=True)) / (img_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)
img_patch = rearrange(img_norm, 'b n p c -> b n (p c)')
img_patch[bool_masked_pos] = outputs
#make mask
mask = torch.ones_like(img_patch)
mask[bool_masked_pos] = 0
mask = rearrange(mask, 'b n (p c) -> b n p c', c=3)
mask = rearrange(mask, 'b (t h w) (p0 p1 p2) c -> b c (t p0) (h p1) (w p2) ', p0=2, p1=patch_size[0], p2=patch_size[1], h=14, w=14)
#save reconstruction video
rec_img = rearrange(img_patch, 'b n (p c) -> b n p c', c=3)
# Notice: To visualize the reconstruction video, we add the predict and the original mean and var of each patch.
rec_img = rec_img * (img_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6) + img_squeeze.mean(dim=-2, keepdim=True)
rec_img = rearrange(rec_img, 'b (t h w) (p0 p1 p2) c -> b c (t p0) (h p1) (w p2)', p0=2, p1=patch_size[0], p2=patch_size[1], h=14, w=14)
imgs = [ ToPILImage()(rec_img[0, :, vid, :, :].cpu().clamp(0,0.996)) for vid, _ in enumerate(frame_id_list) ]
for id, im in enumerate(imgs):
im.save(f"{args.save_path}/rec_img{id}.jpg")
#save masked video
img_mask = rec_img * mask
imgs = [ToPILImage()(img_mask[0, :, vid, :, :].cpu()) for vid, _ in enumerate(frame_id_list)]
for id, im in enumerate(imgs):
im.save(f"{args.save_path}/mask_img{id}.jpg")
if __name__ == '__main__':
opts = get_args()
main(opts)