import sys from PIL import Image from torchvision import transforms # from transformers import OFATokenizer, OFAModel # from transformers.models.ofa.generate import sequence_generator # from generate import sequence_generator from transformers import DPTImageProcessor, DPTForDepthEstimation import os.path from argparse import ArgumentParser from torch.utils import data import json import torch import torch.distributed as dist import os import os.path as osp from os.path import join as opj import pandas as pd from random import randint import cv2 import torch from torch.utils.data import Dataset, DataLoader import decord import glob import subprocess import time import numpy as np import os os.environ["HF_DATASETS_OFFLINE"] = "1" import decord from decord import cpu #glpn from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation import torch import numpy as np from PIL import Image import requests import io import cv2 import numpy as np from decord import VideoReader, cpu try: from petrel_client.client import Client petrel_backend_imported = True except (ImportError, ModuleNotFoundError): petrel_backend_imported = False def get_video_loader(use_petrel_backend: bool = True, enable_mc: bool = True, conf_path: str = None): if petrel_backend_imported and use_petrel_backend: _client = Client(conf_path=conf_path, enable_mc=enable_mc) else: _client = None def _loader(video_path): if _client is not None and 's3:' in video_path: video_path = io.BytesIO(_client.get(video_path)) vr = VideoReader(video_path, num_threads=1, ctx=cpu(0)) return vr return _loader class my_dataset(Dataset): def __init__(self, args): super().__init__() self.args = args self.shuffle = True self.resolution = args.resolution # 对于动态大小视频无用 self.loader = get_video_loader() if args.train_file.endswith('.csv'): self.train_file = pd.read_csv(args.train_file) elif args.train_file.endswith('.json'): # coco_vat_vat0_11_all_id_rootfolder_clsidx_spacy.json # 格式： id : { 'idx_list' : [0], 'root_folder' : 'coco_vat_9' } if hasattr(args, 'part_nums') and args.part_nums >1: self.part_nums = args.part_nums else: self.part_nums = 100000 self.part_index = args.part_index t1 = time.time() with open(args.train_file, 'r', encoding='utf-8') as f: self.train_file = json.load(f) if type(self.train_file) is str: self.train_file = json.loads(self.train_file) self.id_list = list(self.train_file.keys()) #============================= # obtain subset of self.id_list self.id_list = self.id_list[self.part_nums*(self.part_index-1):self.part_nums*self.part_index] print(f'Nums of train_file is {len(self.id_list)},part_index:{self.part_index}, first:{self.id_list[0]}') self.no_caption_id_list = [] for idx,id in enumerate(self.id_list): caption_json = osp.join('/apdcephfs_cq3/share_1311970/A_Youtube',self.train_file[id]['root_folder'],f'{id}_depth_f8glpn_folder') mp4_path = osp.join('/apdcephfs_cq3/share_1311970/A_Youtube',self.train_file[id]['root_folder'],f'{id}.mp4') if not os.path.exists(caption_json) and os.path.exists(mp4_path): self.no_caption_id_list.append(mp4_path) # else: # print(f'{caption_json} is exist!') if idx%10000==0: print(f'Time_cost:{time.time()-t1}s, idx:{idx}, caption_json:{caption_json}') try: print(f'Nums of no_depth_folder_id_list is {len(self.no_caption_id_list)}, first:{self.no_caption_id_list[0]}') except: print(f'Nums of no_depth_folder_id_list is {len(self.no_caption_id_list)}') t2 = time.time() print(f'Time cost:{t2-t1}s') # DPT # self.patch_resize_transform = DPTImageProcessor.from_pretrained("Intel/dpt-large", cache_dir= args.weights_folder) # glpn self.patch_resize_transform = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu", cache_dir= args.weights_folder) print('Dataset nums is {}'.format(self.__len__())) time.sleep(10) def __len__(self): return len(self.no_caption_id_list) def random_sample(self): return self.__getitem__(randint(0, self.__len__() - 1)) def sequential_sample(self, idx): if idx >= self.__len__() - 1: return self.__getitem__(0) return self.__getitem__(idx + 1) def skip_sample(self, idx): if self.shuffle: return self.random_sample() return self.sequential_sample(idx=idx) def resize_frame(self, frame): height, width = frame.shape[:2] if height < width: new_height = 256 new_width = int(width * (new_height / height)) else: new_width = 256 new_height = int(height * (new_width / width)) # resized_frame = cv2.resize(frame, (new_width, new_height)) resized_frame = cv2.resize(frame, (448, 796)) # 576*448 796,448 return resized_frame, new_width, new_height # frame的形状是和new_w, new_w不一样的！！ def get_frames_from_video_decord(self, batchsize=1, video_path=None, caption_nums_per_video=8): # 加载视频 video_path = video_path vr = self.loader(video_path) frame_width, frame_height = vr[0].shape[1], vr[0].shape[0] # 确定要提取的帧数 num_frames = caption_nums_per_video # 计算每隔多少帧提取一次 total_frames = len(vr) step = total_frames // num_frames # 用于存储提取的图像的tensor frames = [] height = width = 0 # 直接读取指定帧 for i in range(num_frames): # 计算要提取的帧的索引 idx = i * step # 读取该帧 frame = vr[idx].asnumpy() frame, new_width, new_height = self.resize_frame(frame) if i == 0: height, width = new_height, new_width # 转换为PIL Image并进行缩放 # Image_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) Image_frame = Image.fromarray(frame) frame = self.patch_resize_transform(images=Image_frame, return_tensors="pt").pixel_values.unsqueeze(0) # 将numpy数组转换为tensor并存储在frames中 frames.append(frame) # vr.close() frames = torch.cat(frames, 1).squeeze(0) return frames, height, width def get_frames_from_video_opencv(self, batchsize=1, video_path=None, caption_nums_per_video=8): # 加载视频 video_path = video_path cap = cv2.VideoCapture(video_path) frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 确定要提取的帧数 num_frames = caption_nums_per_video # 计算每隔多少帧提取一次 total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) step = total_frames // num_frames # 用于存储提取的图像的tensor # frames = torch.empty(num_frames, 3, frame_height, frame_width) frames = [] height = width = 0 # frame_idx_list=[] 把视频的帧序号存储下来 # 直接读取指定帧 for i in range(num_frames): # 计算要提取的帧的索引 idx = i * step # frame_idx_list.append(idx) # 设置当前帧为所需的帧 cap.set(cv2.CAP_PROP_POS_FRAMES, idx) # 读取该帧 ret, frame = cap.read() frame, new_width, new_height = self.resize_frame(frame) # frame size是按照(w=448, h=576)resize的，但是new_h, new_w是按照原视频宽高比例缩放到短边为256，这样可以保持视频物体比例并且减少内存占用 # print(f'{frame_width},{frame_height }===== {frame.shape} ') if i==0: # height, width, _ = frame.shape height, width = new_height, new_width # print(f'{frame.shape}, {new_width}, {new_height}!!!') #(576, 448, 3), 256, 455!!! if not ret: break # ret, frame = cap.read() # frame.shape (h,w,3) # frame = self.resize_frame(frame) # # print(f'{frame_width},{frame_height }===== {frame.shape} ') # if i==0: # height, width, _ = frame.shape # if not ret: # break # 转换为PIL Image并进行缩放 Image_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) # frame = self.patch_resize_transform(Image_frame) frame = self.patch_resize_transform(images=Image_frame, return_tensors="pt").pixel_values.unsqueeze(0) # print(frame.shape) # 将numpy数组转换为tensor并存储在frames中 # frames[i] = frame frames.append(frame) # 打印输出frames的形状 # print(frames.shape) cap.release() frames = torch.cat(frames, 1).squeeze(0) return frames, height, width def __getitem__(self, idx): try: # video_id = self.filter_train_file[idx] # video_path = opj(self.vat_root, video_id) video_path = self.no_caption_id_list[idx] video_id = video_path.split('/')[-1].split('.')[0] # 假如多个程序一起跑，其他已经生成了，就跳过 caption_video_json = video_path.replace('.mp4', '_depth_f8glpn_folder') if os.path.exists(caption_video_json): print('parallel task has process it :{}'.format(caption_video_json)) # return '===========', None, torch.random(8,3,self.resolution, self.resolution) return self.skip_sample(idx) if not osp.exists(video_path): print('video {} is not exists and skip this idx! '.format(video_path)) return self.skip_sample(idx) video_frames, height, width = self.get_frames_from_video_opencv( video_path = video_path, caption_nums_per_video = args.caption_nums_per_video) # video_frames, height, width = self.get_frames_from_video_decord( video_path = video_path, caption_nums_per_video = args.caption_nums_per_video) return video_id, video_path, video_frames, height, width except Exception as e: print('Read video error in {},{} and we have skip this !, this will not cause error!'.format(idx,e)) return self.skip_sample(idx) def synchronize(): if not dist.is_available(): return if not dist.is_initialized(): return world_size = dist.get_world_size() if world_size == 1: return dist.barrier() def depth_estimation(args): ######################################## model start ############################# """https://huggingface.co/docs/transformers/main/en/model_doc/dpt""" weights_folder = args.weights_folder print(f'args.weights_folder is {args.weights_folder}') model_name = 'glpn' if model_name == 'glpn': # glpn feature_extractor = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu", cache_dir= weights_folder) model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu", cache_dir= weights_folder).cuda(args.local_rank) else: # DPT processor = DPTImageProcessor.from_pretrained("Intel/dpt-large", cache_dir= weights_folder) model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", cache_dir= weights_folder).cuda(args.local_rank) if args.rank==0: print('模型初始化完成') model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank) model.eval() if args.rank==0: print('DDP model') ######################################## model over ############################# ######################################## dataset start ############################# if args.rank == 0: print('dataset 初始化') train_dataset = my_dataset(args) if args.rank == 0: print('dataset_len: ',train_dataset.__len__()) print('loading dataset is complete!') train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset, num_replicas=args.world_size, rank=args.rank ) if args.rank == 0: print('正在同步') synchronize() if args.rank == 0: print('dataloader 初始化') dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True, sampler=train_sampler, drop_last=True ) ######################################## dataset over ############################# ######################################## depth_estimation start ############################# for index, (video_ids, video_paths, videos_frames_, h_list, w_list) in enumerate(dataloader): bs, cap_nums, c, h, w = videos_frames_.shape videos_frames = videos_frames_.view(-1, c, h, w ).cuda(args.local_rank) torch.cuda.empty_cache() try: with torch.no_grad(): outputs = model(videos_frames) predicted_depth = outputs.predicted_depth # (bs*cap_nums, h, w) predicted_depth = predicted_depth.view(bs, cap_nums, h, w) # print(f'predicted_depth.shape:{predicted_depth.shape}') # interpolate to original size for bs_idx, sample in enumerate(predicted_depth): # import ipdb # ipdb.set_trace() pic_folder = video_paths[bs_idx].replace('.mp4','_depth_f8glpn_folder') os.makedirs(pic_folder, exist_ok=True) for frame_idx, frame in enumerate(predicted_depth[bs_idx]): prediction = torch.nn.functional.interpolate( frame.unsqueeze(0).unsqueeze(0), # torch.Size([1, 1, 384, 384]) size=(h_list[bs_idx],w_list[bs_idx]), mode="bicubic", align_corners=False, ) # torch.Size([1, 1, h=480, w=640]) # print('prediction.shape:{prediction.shape}') # visualize the prediction output = prediction.squeeze().cpu().numpy() # formatted = (output * 255 / np.max(output)).astype("uint8") # depth = Image.fromarray(formatted) # size (576, 1024) # depth.save(f"{pic_folder}/{frame_idx}.png") max_depth = 10 if np.any(output>10): print(f"{pic_folder} > 10") output_1k = np.clip(output, 0, max_depth)*1000 cv2.imwrite(f"{pic_folder}/{frame_idx}.png", output_1k.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) print(f'{pic_folder} is succeed!') # sys.exit(0) del videos_frames, outputs, predicted_depth except Exception as e: print(f'Error:{e}!') del videos_frames ######################################## depth_estimation over ############################# def init_distributed_mode(args): args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ['WORLD_SIZE']) args.local_rank = int(os.environ['LOCAL_RANK']) torch.cuda.set_device(args.local_rank) args.dist_backend = 'nccl' args.dist_url = 'env://' torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank, timeout=datetime.timedelta(seconds=5400)) torch.distributed.barrier() import utils.misc as misc def main(args): misc.init_distributed_mode(args) if args.rank == 0: print('进程组初始化完成') print("started") print("started caption_json count!") # glob1(json_path=args.exist_caption_id_list_json) # 'coco_vat_exist_caption_id_list_03141026.json' ###########################################################3 import time t1=time.time() depth_estimation(args) t2 = time.time() if args.rank == 0: print('Time : ',t2-t1,' s') dist.destroy_process_group() # 销毁进程组 def test_dataset(args): train_dataset = my_dataset(args) loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers) from time import time for i, sample in enumerate(loader): video_ids, video_paths, videos_frames, h, w = sample print(i, video_ids, video_paths, videos_frames.shape, h, w) def glpn(): from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation import torch import numpy as np from PIL import Image import requests max_depth = 10 # url = "http://images.cocodataset.org/val2017/000000039769.jpg" # image = Image.open(requests.get(url, stream=True).raw) image = Image.open('hallo.png') weights_folder = './glpn' feature_extractor = GLPNFeatureExtractor.from_pretrained("vinvino02/glpn-nyu", cache_dir= weights_folder) model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu", cache_dir= weights_folder) # prepare image for the model inputs = feature_extractor(images=image, return_tensors="pt") # import ipdb # ipdb.set_trace() # print(inputs.shape) with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) # visualize the prediction output = prediction.squeeze().cpu().numpy() # formatted = (output * 255 / np.max(output)).astype("uint8") # depth = Image.fromarray(formatted) # print(f'min_output:{torch.min(output)}, max_output:{torch.max(output)}!!') output_1k = np.clip(output, 0, max_depth)*1000 import ipdb ipdb.set_trace() cv2.imwrite('./saved_10000jpg.jpg', output_1k.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) # cv2.imwrite('./image1.jpg', output_1k.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) cv2.imwrite('./saved_10000png.png', output_1k.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) # cv2.imwrite('./image2.png', output_1k.astype("uint16"), [cv2.IMWRITE_PNG_COMPRESSION, 0]) # glpn() if __name__ == "__main__": import time # time.sleep(10000) import datetime # 获取当前时间 now = datetime.datetime.now() # 获取当前月份 month = now.month # 获取当前日期 day = now.day # 获取当前小时 hour = now.hour parser = ArgumentParser() parser.add_argument('--caption_nums_per_video', type=int, default=8, help='process rank') parser.add_argument('--batch_size', type=int, default=2) parser.add_argument('--train_file', type=str, required=True) parser.add_argument('--vat_root', type=str,default=None) parser.add_argument('--num_workers', type=int, default=1) parser.add_argument('--resolution', type=int, default=480) # parser.add_argument('--exist_caption_id_list_json', type=str, default=f'coco_vat_exist_caption_id_list_{month}{day}{hour}.json',help='') parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local_rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') # --dist_on_itp ddp parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') parser.add_argument('--distributed', default=True, help='url used to set up distributed training') parser.add_argument('--gpus', default=[0, 1, 2, 3], help='DP CUDA devices') parser.add_argument('--part_index', default=1, type=int, help='used to split train_file_id into different parts, and generate caption from part_index 1 to ....') parser.add_argument('--part_nums', default=1000, type=int, help='used to split train_file_id into different parts, and generate caption from part_index 1 to ....') parser.add_argument('--weights_folder', type=str,default='/apdcephfs_cq3/share_1311970/A_ofa/glpn') args = parser.parse_args() # test_dataset(args) # import ipdb # ipdb.set_trace() main(args) synchronize() # success_file=f"part_{args.part_index}_success" success_file=f"/apdcephfs_cq3/share_1311970/A_depth_glpn/part_{args.part_index}_success" os.system(f"touch {success_file}") """ HF_DATASETS_OFFLINE=1 python3 -m torch.distributed.launch --nproc_per_node 1 --master_port 29504 depth_ddp_glpn.py \ --train_file /apdcephfs_cq3/share_1311970/A_Youtube/coco_vat_vat0_11_all_id_rootfolder_clsidx_spacy.json \ --num_workers 1 --batch_size 2 \ --part_index 92 \ --part_nums 10000 \ --weights_folder /apdcephfs_cq3/share_1311970/A_ofa/glpn \ --resolution 0 \ --caption_nums_per_video 8 """