# coding=utf-8 # Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np import PIL.Image import torch from parameterized import parameterized from diffusers.video_processor import VideoProcessor np.random.seed(0) torch.manual_seed(0) class VideoProcessorTest(unittest.TestCase): def get_dummy_sample(self, input_type): batch_size = 1 num_frames = 5 num_channels = 3 height = 8 width = 8 def generate_image(): return PIL.Image.fromarray(np.random.randint(0, 256, size=(height, width, num_channels)).astype("uint8")) def generate_4d_array(): return np.random.rand(num_frames, height, width, num_channels) def generate_5d_array(): return np.random.rand(batch_size, num_frames, height, width, num_channels) def generate_4d_tensor(): return torch.rand(num_frames, num_channels, height, width) def generate_5d_tensor(): return torch.rand(batch_size, num_frames, num_channels, height, width) if input_type == "list_images": sample = [generate_image() for _ in range(num_frames)] elif input_type == "list_list_images": sample = [[generate_image() for _ in range(num_frames)] for _ in range(num_frames)] elif input_type == "list_4d_np": sample = [generate_4d_array() for _ in range(num_frames)] elif input_type == "list_list_4d_np": sample = [[generate_4d_array() for _ in range(num_frames)] for _ in range(num_frames)] elif input_type == "list_5d_np": sample = [generate_5d_array() for _ in range(num_frames)] elif input_type == "5d_np": sample = generate_5d_array() elif input_type == "list_4d_pt": sample = [generate_4d_tensor() for _ in range(num_frames)] elif input_type == "list_list_4d_pt": sample = [[generate_4d_tensor() for _ in range(num_frames)] for _ in range(num_frames)] elif input_type == "list_5d_pt": sample = [generate_5d_tensor() for _ in range(num_frames)] elif input_type == "5d_pt": sample = generate_5d_tensor() return sample def to_np(self, video): # List of images. if isinstance(video[0], PIL.Image.Image): video = np.stack([np.array(i) for i in video], axis=0) # List of list of images. elif isinstance(video, list) and isinstance(video[0][0], PIL.Image.Image): frames = [] for vid in video: all_current_frames = np.stack([np.array(i) for i in vid], axis=0) frames.append(all_current_frames) video = np.stack([np.array(frame) for frame in frames], axis=0) # List of 4d/5d {ndarrays, torch tensors}. elif isinstance(video, list) and isinstance(video[0], (torch.Tensor, np.ndarray)): if isinstance(video[0], np.ndarray): video = np.stack(video, axis=0) if video[0].ndim == 4 else np.concatenate(video, axis=0) else: if video[0].ndim == 4: video = np.stack([i.cpu().numpy().transpose(0, 2, 3, 1) for i in video], axis=0) elif video[0].ndim == 5: video = np.concatenate([i.cpu().numpy().transpose(0, 1, 3, 4, 2) for i in video], axis=0) # List of list of 4d/5d {ndarrays, torch tensors}. elif ( isinstance(video, list) and isinstance(video[0], list) and isinstance(video[0][0], (torch.Tensor, np.ndarray)) ): all_frames = [] for list_of_videos in video: temp_frames = [] for vid in list_of_videos: if vid.ndim == 4: current_vid_frames = np.stack( [i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(1, 2, 0) for i in vid], axis=0, ) elif vid.ndim == 5: current_vid_frames = np.concatenate( [i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(0, 2, 3, 1) for i in vid], axis=0, ) temp_frames.append(current_vid_frames) temp_frames = np.stack(temp_frames, axis=0) all_frames.append(temp_frames) video = np.concatenate(all_frames, axis=0) # Just 5d {ndarrays, torch tensors}. elif isinstance(video, (torch.Tensor, np.ndarray)) and video.ndim == 5: video = video if isinstance(video, np.ndarray) else video.cpu().numpy().transpose(0, 1, 3, 4, 2) return video @parameterized.expand(["list_images", "list_list_images"]) def test_video_processor_pil(self, input_type): video_processor = VideoProcessor(do_resize=False, do_normalize=True) input = self.get_dummy_sample(input_type=input_type) for output_type in ["pt", "np", "pil"]: out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type) out_np = self.to_np(out) input_np = self.to_np(input).astype("float32") / 255.0 if output_type != "pil" else self.to_np(input) assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}" @parameterized.expand(["list_4d_np", "list_5d_np", "5d_np"]) def test_video_processor_np(self, input_type): video_processor = VideoProcessor(do_resize=False, do_normalize=True) input = self.get_dummy_sample(input_type=input_type) for output_type in ["pt", "np", "pil"]: out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type) out_np = self.to_np(out) input_np = ( (self.to_np(input) * 255.0).round().astype("uint8") if output_type == "pil" else self.to_np(input) ) assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}" @parameterized.expand(["list_4d_pt", "list_5d_pt", "5d_pt"]) def test_video_processor_pt(self, input_type): video_processor = VideoProcessor(do_resize=False, do_normalize=True) input = self.get_dummy_sample(input_type=input_type) for output_type in ["pt", "np", "pil"]: out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type) out_np = self.to_np(out) input_np = ( (self.to_np(input) * 255.0).round().astype("uint8") if output_type == "pil" else self.to_np(input) ) assert np.abs(input_np - out_np).max() < 1e-6, f"Decoded output does not match input for {output_type=}"