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import importlib
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import os
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import os.path as osp
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import shutil
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import sys
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from pathlib import Path
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import av
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import numpy as np
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import torch
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import torchvision
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from einops import rearrange
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from PIL import Image
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def seed_everything(seed):
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import random
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import numpy as np
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed % (2**32))
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random.seed(seed)
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def import_filename(filename):
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spec = importlib.util.spec_from_file_location("mymodule", filename)
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module = importlib.util.module_from_spec(spec)
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sys.modules[spec.name] = module
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spec.loader.exec_module(module)
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return module
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def delete_additional_ckpt(base_path, num_keep):
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dirs = []
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for d in os.listdir(base_path):
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if d.startswith("checkpoint-"):
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dirs.append(d)
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num_tot = len(dirs)
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if num_tot <= num_keep:
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return
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del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep]
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for d in del_dirs:
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path_to_dir = osp.join(base_path, d)
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if osp.exists(path_to_dir):
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shutil.rmtree(path_to_dir)
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def save_videos_from_pil(pil_images, path, fps):
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save_fmt = Path(path).suffix
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os.makedirs(os.path.dirname(path), exist_ok=True)
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width, height = pil_images[0].size
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if save_fmt == ".mp4":
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codec = "libx264"
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container = av.open(path, "w")
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stream = container.add_stream(codec, rate=fps)
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stream.width = width
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stream.height = height
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stream.pix_fmt = 'yuv420p'
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stream.bit_rate = 10000000
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stream.options["crf"] = "18"
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for pil_image in pil_images:
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av_frame = av.VideoFrame.from_image(pil_image)
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container.mux(stream.encode(av_frame))
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container.mux(stream.encode())
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container.close()
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elif save_fmt == ".gif":
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pil_images[0].save(
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fp=path,
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format="GIF",
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append_images=pil_images[1:],
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save_all=True,
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duration=(1 / fps * 1000),
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loop=0,
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)
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else:
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raise ValueError("Unsupported file type. Use .mp4 or .gif.")
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def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
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videos = rearrange(videos, "b c t h w -> t b c h w")
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height, width = videos.shape[-2:]
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outputs = []
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for x in videos:
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x = torchvision.utils.make_grid(x, nrow=n_rows)
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x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
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if rescale:
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x = (x + 1.0) / 2.0
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x = (x * 255).numpy().astype(np.uint8)
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x = Image.fromarray(x)
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outputs.append(x)
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os.makedirs(os.path.dirname(path), exist_ok=True)
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save_videos_from_pil(outputs, path, fps)
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def read_frames(video_path):
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container = av.open(video_path)
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video_stream = next(s for s in container.streams if s.type == "video")
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frames = []
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for packet in container.demux(video_stream):
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for frame in packet.decode():
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image = Image.frombytes(
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"RGB",
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(frame.width, frame.height),
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frame.to_rgb().to_ndarray(),
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)
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frames.append(image)
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return frames
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def get_fps(video_path):
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container = av.open(video_path)
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video_stream = next(s for s in container.streams if s.type == "video")
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fps = video_stream.average_rate
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container.close()
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return fps
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