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sample_video-diffusion.py
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sample_video-diffusion.py
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import torch
from video_diffusion_pytorch.wip_video_diffusion_pytorch import Unet3D, GaussianDiffusion, Trainer, num_to_groups, video_tensor_to_gif
import os
import numpy as np
import argparse
from einops import rearrange
import wandb
def parse_arguments():
parser = argparse.ArgumentParser(description='Video Diffusion Sampling')
parser.add_argument('--image_size', type=int, default=64, help='Image size')
parser.add_argument('--channels', type=int, default=3, help='Image Channel size')
parser.add_argument('--num_frames', type=int, default=16, help='Number of video frames')
parser.add_argument('--base_channel_size', type=int, default=256, help='Base channel size of 3D U-Net')
parser.add_argument('--timeemb_linears', type=int, default=2, help='Number of temporal layers')
parser.add_argument('--attn_heads', type=int, default=8, help='Number of attention heads')
parser.add_argument('--attn_dim_head', type=int, default=64, help='Dimension of attention head')
parser.add_argument('--ssm_hidden_dim', type=int, default=16, help='Hidden dimension of SSM')
parser.add_argument('--ssm_linear_dim', type=int, default=None, help='Linear dimension of MLPs in SSM Layers')
parser.add_argument('--ssm_version', type=int, default=None, help='Version of SSM Layers')
parser.add_argument('--temporal_layer', type=str, default='attn', help='Architecture of temporal layers')
parser.add_argument('--timesteps', type=int, default=1000, help='Number of steps')
parser.add_argument('--loss_type', type=str, default='l2', help='Loss type')
parser.add_argument('--train_batch_size', type=int, default=16, help='Training batch size')
parser.add_argument('--train_lr', type=float, default=1e-4, help='Training learning rate')
parser.add_argument('--beta1', type=float, default=0.9, help='Beta1 for Adam')
parser.add_argument('--beta2', type=float, default=0.999, help='Beta2 for Adam')
parser.add_argument('--save_and_sample_every', type=int, default=1000, help='Save and sample frequency')
parser.add_argument('--train_num_steps', type=int, default=700000, help='Total training steps')
parser.add_argument('--gradient_accumulate_every', type=int, default=4, help='Gradient accumulation steps')
parser.add_argument('--ema_decay', type=float, default=0.995, help='Exponential moving average decay')
parser.add_argument('--amp', type=bool, default=False, help='Use mixed precision')
parser.add_argument('--dataset', type=str, default='movingmnist', help='Dataset name')
parser.add_argument('--folder', type=str, default='/groups/gcb50389/yuta.oshima/video_datasets/mnist_test_seq.npy', help='Data folder')
parser.add_argument('--results_folder', type=str, default='/groups/gcb50389/yuta.oshima/s5_diffusion_results/results', help='Results folder')
parser.add_argument('--num_samples', type=int, default=100, help='Number of samples')
parser.add_argument('--sample_batch_size', type=int, default=16, help='Sampling batch size')
parser.add_argument('--sample_save_every', type=int, default=100, help='Sample save frequency')
parser.add_argument('--milestone', type=int, help='Milestone for loading model')
parser.add_argument('--device_ids', nargs='+', type=int, default=[0, 1, 2], help='List of GPU IDs')
parser.add_argument('--seed', type=int, default=0, help='Random seed')
# parser.add_argument('--csv_path', type=str, default="/home/acd13972py/Video-LLaMA/video_captions.csv", help='CSV path')
# parser.add_argument('--use_cond', type=bool, default=True, help='use text condition or not')
args = parser.parse_args()
return args
def main(args):
torch.backends.cudnn.benchmark = True
torch.manual_seed(args.seed)
wandb_run = wandb.init(
project='iclr_ssm_diffusion_sampling',
entity="shim0114",
config=vars(args))
model = Unet3D(
dim = args.base_channel_size,
dim_mults = (1, 2, 4, 8),
channels = args.channels,
timeemb_linears = args.timeemb_linears,
attn_heads = args.attn_heads,
attn_dim_head = args.attn_dim_head,
ssm_hidden_dim = args.ssm_hidden_dim,
ssm_linear_dim = args.ssm_linear_dim,
ssm_version = args.ssm_version,
temporal_arch = args.temporal_layer,
)
from thop import profile
img = torch.randn(1, args.channels, args.num_frames, args.image_size, args.image_size).to('cuda:0')
t = torch.tensor([1]).to('cuda:0')
flops, params = profile(model.to('cuda:0'), inputs=(img, t))
print(params)
print(flops)
model = torch.nn.DataParallel(model, device_ids=args.device_ids) # DP (old ver)
diffusion = GaussianDiffusion(
model,
image_size = args.image_size,
channels = args.channels,
num_frames = args.num_frames,
timesteps = args.timesteps, # number of steps
loss_type = args.loss_type # L1 or L2
)
diffusion = diffusion.cuda()
# diffusion = torch.nn.DataParallel(diffusion, device_ids=args.device_ids) # DP (new ver)
trainer = Trainer(
diffusion,
dataset=args.dataset, # dataset name
folder=args.folder, # this folder path needs to contain all your training data, as .gif files, of correct image size and number of frames
results_folder = args.results_folder,
train_batch_size = args.train_batch_size,
train_lr = args.train_lr,
beta1 = args.beta1,
beta2 = args.beta2,
save_and_sample_every = args.save_and_sample_every,
train_num_steps = args.train_num_steps, # total training steps
gradient_accumulate_every = args.gradient_accumulate_every, # gradient accumulation steps
ema_decay = args.ema_decay, # exponential moving average decay
amp = args.amp, # turn on mixed precision
num_gpus = len(args.device_ids), # number of GPUs
)
trainer.load(args.milestone)
### Sampling ###
if args.num_samples % args.sample_batch_size * args.sample_save_every != 0:
raise ValueError(f'num_samples must be divisible by sample_batch_size * sample_save_every')
if args.sample_batch_size * args.sample_save_every < 4:
raise ValueError(f'sample_batch_size * sample_save_every must be greater than 4')
total_samples = 0
idx = 0
num_samples = args.sample_batch_size * args.sample_save_every
batches = num_to_groups(num_samples, args.sample_batch_size)
while not total_samples > args.num_samples: # crashを想定し，1回分余裕を見る
videos_list = list(map(lambda n: trainer.ema_model.sample(batch_size=n), batches))
videos_list = torch.cat(videos_list, dim = 0)
print(videos_list.shape)
total_samples += args.sample_batch_size * args.sample_save_every
idx += 1
# Log samples as a grid
log_videos_list = torch.nn.functional.pad(videos_list[:4], (2, 2, 2, 2))
one_gif = rearrange(log_videos_list, '(i j) c f h w -> c f (i h) (j w)', i = 2) # 2x2 grid
video_path = os.path.join(args.results_folder, str(f'fakes_gif/{args.milestone}_{args.seed}_{idx}.gif'))
video_tensor_to_gif(one_gif, video_path)
wandb_run.log({args.dataset + '/fakes': wandb.Image(video_path)}) ### Changed ###
# Save samples as .npy
videos_list = videos_list.cpu().numpy()
np.save(os.path.join(args.results_folder, f'fakes/{args.milestone}_{args.seed}_{total_samples}.npy'), videos_list)
wandb_run.log({args.dataset + '/total_fakes': total_samples})
if __name__ == "__main__":
args = parse_arguments()
if not os.path.exists(os.path.join(args.results_folder, 'fakes')):
os.makedirs(os.path.join(args.results_folder, 'fakes'), exist_ok=True)
os.makedirs(os.path.join(args.results_folder, 'fakes_gif'), exist_ok=True)
main(args)