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4940c8b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # -*- 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 datasets import DataAugmentationForVideoMAE
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='tube', choices=['random', 'tube', 'tubelet'],
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_small_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)')
# Tubelet params
parser.add_argument('--add_tubelets', action='store_true')
parser.set_defaults(add_tubelets=True)
parser.add_argument('--use_objects', action='store_true')
parser.set_defaults(use_objects=True)
parser.add_argument('--motion_type', type=str, default='gaussian')
parser.add_argument('--scales', type=str, default='[32, 48, 56, 64, 96, 128]')
parser.add_argument('--loc_velocity', type=int, default=12)
parser.add_argument('--mixed_tubelet', action='store_true')
parser.set_defaults(mixed_tubelet=False)
parser.add_argument('--visible_frames', type=str, default=None)
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)
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