from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from __future__ import print_function import os import torch from torch.utils.data import (SequentialSampler) import numpy as np import random from thop import profile from metrics import logging_rank import time import argparse from sklearn import preprocessing from transformers import BertTokenizer, AutoTokenizer, AutoModel from tensorboardX import SummaryWriter from modules.file_utils import PYTORCH_PRETRAINED_BERT_CACHE from modules.tokenization_clip import SimpleTokenizer as ClipTokenizer from modules.modeling import BirdModel_VT, BirdPreTrainedModel, BirdModel from modules.optimization import BertAdam from dataloaders.dataloader import DATALOADER_DICT from modules.until_module import get_dual_matrix from util import parallel_apply, get_logger from torch.cuda.amp import autocast, GradScaler torch.distributed.init_process_group(backend="nccl") global logger def get_args(description='CLIP4Clip on Retrieval Task'): parser = argparse.ArgumentParser(description=description) parser.add_argument("--do_pretrain", action='store_true', help="Whether to run training.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_params", action='store_true', help="text the params of the model.") parser.add_argument("--use_frame_fea", action='store_true', help="whether use frame feature matching text") parser.add_argument('--task', type=str, default="retrieval", choices=["retrieval_VT", "retrieval"], help="choose downstream task.") parser.add_argument('--dataset', type=str, default="bird", choices=["bird", "msrvtt", "vatex", "msvd"], help="choose dataset.") parser.add_argument('--num_thread_reader', type=int, default=1, help='') parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate') parser.add_argument('--text_lr', type=float, default=0.00001, help='text encoder learning rate') parser.add_argument('--epochs', type=int, default=20, help='upper epoch limit') parser.add_argument('--batch_size', type=int, default=256, help='batch size') parser.add_argument('--batch_size_val', type=int, default=3500, help='batch size eval') parser.add_argument('--lr_decay', type=float, default=0.9, help='Learning rate exp epoch decay') parser.add_argument('--weight_decay', type=float, default=0.2, help='Learning rate exp epoch decay') parser.add_argument('--n_display', type=int, default=100, help='Information display frequence') parser.add_argument('--seed', type=int, default=42, help='random seed') parser.add_argument('--max_words', type=int, default=32, help='') parser.add_argument('--max_frames', type=int, default=12, help='') parser.add_argument('--top_frames', type=int, default=3, help='') parser.add_argument('--frame_sample', type=str, default="uniform", choices=["uniform", "random", "uniform_random"], help='frame sample strategy') parser.add_argument('--frame_sample_len', type=str, default="fix", choices=["dynamic", "fix"], help='use dynamic frame length of fix frame length') parser.add_argument('--language', type=str, default="chinese", choices=["chinese", "english"], help='language for text encoder') parser.add_argument('--use_temp', action='store_true', help='whether to use temporal transformer') parser.add_argument("--logdir", default=None, type=str, required=False, help="log dir for tensorboardX writer") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") parser.add_argument("--cross_model", default="cross-base", type=str, required=False, help="Cross module") parser.add_argument("--init_model", default=None, type=str, required=False, help="Initial model.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% of training.") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument('--n_gpu', type=int, default=1, help="Changed in the execute process.") parser.add_argument("--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3") parser.add_argument('--enable_amp', action='store_true', help="whether to use pytorch amp") parser.add_argument("--world_size", default=0, type=int, help="distribted training") parser.add_argument("--local_rank", default=0, type=int, help="distribted training") parser.add_argument("--rank", default=0, type=int, help="distribted training") parser.add_argument('--coef_lr', type=float, default=1., help='coefficient for bert branch.') args = parser.parse_args() # Check paramenters if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( args.gradient_accumulation_steps)) if not args.do_train and not args.do_eval and not args.do_params: raise ValueError("At least one of `do_train` or `do_eval` or 'do_params' must be True.") args.batch_size = int(args.batch_size / args.gradient_accumulation_steps) return args def set_seed_logger(args): global logger # predefining random initial seeds random.seed(args.seed) os.environ['PYTHONHASHSEED'] = str(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) # if you are using multi-GPU. torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True world_size = torch.distributed.get_world_size() torch.cuda.set_device(args.local_rank) args.world_size = world_size rank = torch.distributed.get_rank() args.rank = rank if not os.path.exists(args.output_dir): os.makedirs(args.output_dir, exist_ok=True) logger = get_logger(os.path.join(args.output_dir, "log.txt")) if args.local_rank == 0: if args.logdir: args.writer = SummaryWriter(args.logdir) logger.info("Effective parameters:") for key in sorted(args.__dict__): logger.info(" <<< {}: {}".format(key, args.__dict__[key])) return args def init_device(args, local_rank): global logger device = torch.device("cuda" if torch.cuda.is_available() else "cpu", local_rank) n_gpu = torch.cuda.device_count() logger.info("device: {} n_gpu: {}".format(device, n_gpu)) args.n_gpu = n_gpu if args.batch_size % args.n_gpu != 0 or args.batch_size_val % args.n_gpu != 0: raise ValueError( "Invalid batch_size/batch_size_val and n_gpu parameter: {}%{} and {}%{}, should be == 0".format( args.batch_size, args.n_gpu, args.batch_size_val, args.n_gpu)) return device, n_gpu def init_model(args, device, n_gpu, local_rank): if args.init_model: model_state_dict = torch.load(args.init_model, map_location='cpu') else: model_state_dict = None # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed') if args.task == "retrieval_VT": model = BirdModel_VT.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args) elif args.task == "retrieval": model = BirdModel.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args) else: raise Exception('wrong task! task should in [retrieve_VT, retrieve]') # args.writer.add_graph(model) model.to(device) return model def prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, local_rank, coef_lr=1.): if hasattr(model, 'module'): model = model.module param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] decay_param_tp = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_decay)] no_decay_param_tp = [(n, p) for n, p in param_optimizer if any(nd in n for nd in no_decay)] decay_clip_param_tp = [(n, p) for n, p in decay_param_tp if "visual_encoder.visual." in n] decay_chinesebert_param_tp = [(n, p) for n, p in decay_param_tp if "text_encoder." in n] decay_noclip_param_tp = [(n, p) for n, p in decay_param_tp if ("visual_encoder.visual." not in n) and ("text_encoder." not in n)] no_decay_clip_param_tp = [(n, p) for n, p in no_decay_param_tp if "visual_encoder.visual." in n] no_decay_text_param_tp = [(n, p) for n, p in no_decay_param_tp if "text_encoder." in n] no_decay_noclip_param_tp = [(n, p) for n, p in no_decay_param_tp if ("visual_encoder.visual." not in n) and ("text_encoder." not in n)] weight_decay = args.weight_decay optimizer_grouped_parameters = [ {'params': [p for n, p in decay_clip_param_tp], 'weight_decay': weight_decay, 'lr': args.lr * coef_lr}, {'params': [p for n, p in decay_chinesebert_param_tp], 'weight_decay': weight_decay, 'lr': args.text_lr}, {'params': [p for n, p in decay_noclip_param_tp], 'weight_decay': weight_decay}, {'params': [p for n, p in no_decay_clip_param_tp], 'weight_decay': 0.0, 'lr': args.lr * coef_lr}, {'params': [p for n, p in no_decay_text_param_tp], 'weight_decay': 0.0, 'lr': args.text_lr}, {'params': [p for n, p in no_decay_noclip_param_tp], 'weight_decay': 0.0} ] scheduler = None optimizer = BertAdam(optimizer_grouped_parameters, lr=args.lr, warmup=args.warmup_proportion, schedule='warmup_cosine', b1=0.9, b2=0.98, e=1e-6, t_total=num_train_optimization_steps, weight_decay=weight_decay, max_grad_norm=1.0) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) # if args.local_rank == 0: # for name, parameters in model.named_parameters(): # logger.info("name:{} requires_grad:{} size:{}".format(name, parameters.requires_grad, parameters.size())) return optimizer, scheduler, model def save_model(epoch, args, model, type_name=""): # Only save the model it-self model_to_save = model.module if hasattr(model, 'module') else model output_model_file = os.path.join( args.output_dir, "pytorch_model.bin.{}{}".format("" if type_name == "" else type_name + ".", epoch)) torch.save(model_to_save.state_dict(), output_model_file) logger.info("Model saved to %s", output_model_file) return output_model_file def load_model(epoch, args, n_gpu, device, model_file=None): if model_file is None or len(model_file) == 0: model_file = os.path.join(args.output_dir, "pytorch_model.bin.{}".format(epoch)) if os.path.exists(model_file): model_state_dict = torch.load(model_file, map_location='cpu') if args.local_rank == 0: logger.info("Model loaded from %s", model_file) # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed') if args.task == "retrieval": model = BirdModel.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args) elif args.task == "retrieval_VT": model = BirdModel_VT.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args) else: model = None model.to(device) else: model = None return model def train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer, scheduler, scaler, global_step, local_rank=0): global logger torch.cuda.empty_cache() model.train() log_step = args.n_display start_time = time.time() total_loss = 0 load_start_time = time.time() for step, batch in enumerate(train_dataloader): load_finish_time = time.time() if global_step % log_step == 0 and local_rank == 0: logger.info("data loader time:{}".format(load_finish_time - load_start_time)) global_step += 1 if n_gpu == 1: # multi-gpu does scattering it-self batch = tuple(t.to(device=device, non_blocking=True) for t in batch) with autocast(enabled=args.enable_amp): if args.task == "retrieval_VT": query_ids, query_mask, video_data, video_frame, title_ids, title_mask, idx = batch loss = model(query_ids, query_mask, video_data, video_frame, title_ids, title_mask, idx, global_step) elif args.task == "retrieval": query_ids, query_mask, video_data, video_frame, idx = batch loss = model(query_ids, query_mask, video_data, video_frame, idx, global_step) else: raise ValueError("wrong task type:{}".format(args.task)) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps forward_time = time.time() if args.enable_amp: scaler.scale(loss).backward() else: loss.backward() total_loss += float(loss) backward_time = time.time() if global_step % log_step == 0 and local_rank == 0: logger.info("forward_time:{},backward_time:{}".format(forward_time - load_finish_time, backward_time - forward_time)) if (step + 1) % args.gradient_accumulation_steps == 0: torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) if scheduler is not None: scheduler.step() # Update learning rate schedule if args.enable_amp: scaler.step(optimizer) scaler.update() else: optimizer.step() optimizer.zero_grad() if global_step % log_step == 0 and local_rank == 0: logger.info("Epoch: %d/%s, Step: %d/%d, Lr: %s, Loss: %f, Time/step: %f", epoch + 1, args.epochs, step + 1, len(train_dataloader), "-".join([str('%.9f' % itm) for itm in sorted(list(set(optimizer.get_lr())))]), float(loss), (time.time() - start_time) / (log_step * args.gradient_accumulation_steps)) if args.logdir: # args.writer.add_scalar('loss', loss.item(), global_step=global_step) args.writer.add_scalars('lr', {"lr%d" % i: itm for i, itm in enumerate(sorted(list(set(optimizer.get_lr()))))}, global_step=global_step) start_time = time.time() load_start_time = time.time() total_loss = total_loss / len(train_dataloader) return total_loss, global_step def _run_on_single_gpu(model, batch_query_output_list, batch_visual_output_list, batch_title_output_list, batch_frame_output_list): sim_matrix = [] sim_matrix_title = [] sim_matrix_frame = [] for idx1, query_output in enumerate(batch_query_output_list): each_row = [] title_each_row = [] frame_each_row = [] for idx2, (visual_output, title_output, frame_output) in enumerate(zip(batch_visual_output_list, batch_title_output_list, batch_frame_output_list)): b1b2_logits = model.loose_similarity(query_output, visual_output) title_logits = model.loose_similarity(query_output, title_output) frame_logits = model.loose_similarity(query_output, frame_output) frame_logits = torch.topk(frame_logits, k=model.top_frames, dim=2)[0] frame_logits = torch.mean(frame_logits, dim=2) b1b2_logits = b1b2_logits.cpu().detach().numpy() title_logits = title_logits.cpu().detach().numpy() frame_logits = frame_logits.cpu().detach().numpy() each_row.append(b1b2_logits) title_each_row.append(title_logits) frame_each_row.append(frame_logits) # logger.info("b1b2_logits:{}".format(b1b2_logits.shape)) # logger.info("frame_logits:{}".format(frame_logits.shape)) each_row = np.concatenate(tuple(each_row), axis=-1) # logger.info("each_row:{}".format(each_row.shape)) title_each_row = np.concatenate(tuple(title_each_row), axis=-1) # frame_each_row = np.concatenate(tuple(frame_each_row), axis=-1) frame_each_row = np.concatenate(tuple(frame_each_row), axis=1) # logger.info("frame_each_row:{}".format(frame_each_row.shape)) # sim_matrix.append(preprocessing.scale(each_row, axis=1)) sim_matrix.append(each_row) sim_matrix_title.append(title_each_row) sim_matrix_frame.append(frame_each_row) # logger.info("sim_matrix:{}".format(sim_matrix)) return sim_matrix, sim_matrix_title, sim_matrix_frame def eval_epoch(args, model, test_dataloader, device, n_gpu): torch.cuda.empty_cache() if hasattr(model, 'module'): model = model.module.to(device) else: model = model.to(device) model.eval() logger.info("args.task:{}".format(args.task)) # if multi_sentence_ == True: compute the similarity with multi-sentences retrieval multi_sentence_ = False cut_off_points_, sentence_num_, video_num_ = [], -1, -1 if hasattr(test_dataloader.dataset, 'multi_sentence_per_video') \ and test_dataloader.dataset.multi_sentence_per_video: multi_sentence_ = True cut_off_points_ = test_dataloader.dataset.cut_off_points # used to tag the label when calculate the metric sentence_num_ = test_dataloader.dataset.sentence_num # used to cut the sentence representation video_num_ = test_dataloader.dataset.video_num # used to cut the video representation cut_off_points_ = [itm - 1 for itm in cut_off_points_] logger.info("multi_sentence_:{}".format(multi_sentence_)) with torch.no_grad(): batch_query_output_list, batch_visual_output_list = [], [] batch_title_output_list = [] batch_frame_output_list = [] total_video_num = 0 # ---------------------------- # 1. cache the features # ---------------------------- for bid, batch in enumerate(test_dataloader): batch = tuple(t.to(device) for t in batch) if args.task == "retrieval_VT": query_ids, query_mask, video, video_frame, title_ids, title_mask = batch elif args.task == "retrieval": query_ids, query_mask, video, video_frame = batch else: raise ValueError("wrong task type:{}".format(args.task)) print("bid:{}/{}".format(bid, len(test_dataloader)), end="\r") if multi_sentence_: # multi-sentences retrieval means: one frame clip has two or more descriptions. b, *_t = video.shape # logger.info("query_ids.shape:{}".format(query_ids.shape)) # logger.info("video.shape:{}".format(video.shape)) query_output = model.text_encoder(query_ids, query_mask) batch_query_output_list.append(query_output) title_output = torch.zeros_like(query_output) batch_title_output_list.append(title_output) s_, e_ = total_video_num, total_video_num + b filter_inds = [itm - s_ for itm in cut_off_points_ if s_ <= itm < e_] if len(filter_inds) > 0: video = video[filter_inds, ...] visual_output, frame_output = model.visual_encoder(video, video_frame) # frame_output = torch.mean(frame_output, dim=1) batch_visual_output_list.append(visual_output) batch_frame_output_list.append(frame_output) total_video_num += b else: query_output = model.text_encoder(query_ids, query_mask) visual_output, frame_output = model.visual_encoder(video, video_frame) # frame_output = torch.mean(frame_output, dim=1) if args.task == "retrieval_VT": title_output = model.text_encoder(title_ids, title_mask) logger.info("title_output.shape:{}".format(title_output.shape)) elif args.task == "retrieval": title_output = torch.zeros_like(query_output) else: raise ValueError("wrong task type:{}".format(args.task)) # logger.info("query_output.shape:{}".format(query_output.shape)) # logger.info("weight_VTM:{},weight_FTM:{},exp:{}".format(model.weight_VTM, model.weight_FTM, # model.text_encoder.logit_scale.exp())) logger.info("visual_output.shape:{}".format(visual_output.shape)) logger.info("frame_output.shape:{}".format(frame_output.shape)) batch_query_output_list.append(query_output) batch_visual_output_list.append(visual_output) batch_title_output_list.append(title_output) batch_frame_output_list.append(frame_output) # ---------------------------------- # 2. calculate the similarity # ---------------------------------- logger.info("n_gpu:{}".format(n_gpu)) # logger.info("model.weight_sum:{}".format(model.weight_sum)) if n_gpu > 1: device_ids = list(range(n_gpu)) batch_t_output_splits = [] batch_v_output_splits = [] batch_title_output_splits = [] batch_frame_output_splits = [] bacth_len = len(batch_query_output_list) split_len = (bacth_len + n_gpu - 1) // n_gpu for dev_id in device_ids: s_, e_ = dev_id * split_len, (dev_id + 1) * split_len if dev_id == 0: batch_t_output_splits.append(batch_query_output_list[s_:e_]) batch_v_output_splits.append(batch_visual_output_list) batch_title_output_splits.append(batch_title_output_list) batch_frame_output_splits.append(batch_frame_output_list) else: devc = torch.device('cuda:{}'.format(str(dev_id))) devc_batch_list = [b.to(devc) for b in batch_query_output_list[s_:e_]] batch_t_output_splits.append(devc_batch_list) devc_batch_list = [b.to(devc) for b in batch_visual_output_list] batch_v_output_splits.append(devc_batch_list) devc_batch_list = [b.to(devc) for b in batch_title_output_list] batch_title_output_splits.append(devc_batch_list) devc_batch_list = [b.to(devc) for b in batch_frame_output_list] batch_frame_output_splits.append(devc_batch_list) parameters_tuple_list = [(batch_t_output_splits[dev_id], batch_v_output_splits[dev_id], batch_title_output_splits[dev_id], batch_frame_output_splits[dev_id]) for dev_id in device_ids] parallel_outputs_tuple = parallel_apply(_run_on_single_gpu, model, parameters_tuple_list, device_ids) sim_matrix = [] sim_matrix_title = [] sim_matrix_frame = [] for idx in range(len(parallel_outputs_tuple)): parallel_outputs, parallel_outputs_title, parallel_outputs_frame = parallel_outputs_tuple[idx] sim_matrix += parallel_outputs sim_matrix_title += parallel_outputs_title sim_matrix_frame += parallel_outputs_frame sim_matrix = np.concatenate(tuple(sim_matrix), axis=0) sim_matrix_title = np.concatenate(tuple(sim_matrix_title), axis=0) sim_matrix_frame = np.concatenate(tuple(sim_matrix_frame), axis=0) else: sim_matrix_tuple = _run_on_single_gpu(model, batch_query_output_list, batch_visual_output_list, batch_title_output_list, batch_frame_output_list) sim_matrix, sim_matrix_title, sim_matrix_frame = sim_matrix_tuple sim_matrix = np.concatenate(tuple(sim_matrix), axis=0) sim_matrix_title = np.concatenate(tuple(sim_matrix_title), axis=0) sim_matrix_frame = np.concatenate(tuple(sim_matrix_frame), axis=0) batch_visual_output_list = torch.cat(batch_visual_output_list, dim=0) batch_frame_output_list = torch.cat(batch_frame_output_list, dim=0) batch_visual_output_list = batch_visual_output_list.cpu().detach().numpy() batch_frame_output_list = batch_frame_output_list.cpu().detach().numpy() # np.save("/ai/swxdisk/data/vatex/features/Chinese_batch_visual_output_list", batch_visual_output_list) # np.save("/ai/swxdisk/data/vatex/features/Chinese_batch_frame_output_list", batch_frame_output_list) np.save("/ai/swxdisk/data/vatex/features/English_batch_visual_output_list", batch_visual_output_list) np.save("/ai/swxdisk/data/vatex/features/English_batch_frame_output_list", batch_frame_output_list) # logger.info("sim_matrix:{}".format(sim_matrix.shape)) # logger.info("sim_matrix_frame:{}".format(sim_matrix_frame.shape)) # np.save("/ai/swxdisk/data/msrvtt/visualize/sim_matrix", sim_matrix) # np.save("/ai/swxdisk/data/msrvtt/visualize/sim_matrix_frame_top2", sim_matrix_frame) # sim_matrix_frame = np.topk(sim_matrix_frame, k=model.top_frames, dim=2)[0] # sim_matrix_frame = np.mean(sim_matrix_frame, dim=2) if args.use_frame_fea: sim_matrix += sim_matrix_frame if args.task == "retrieval_VT": # logger.info("sim_matrix_title:{}".format(sim_matrix_title)) weight_title = model.weight_title sim_matrix += weight_title * sim_matrix_title # sim_matrix = weight_title * sim_matrix_title logger.info("sim matrix size: {}".format(np.array(sim_matrix).shape)) # sim_matrix = get_dual_matrix(sim_matrix) tv_metrics = logging_rank(sim_matrix, multi_sentence_, cut_off_points_, logger) return tv_metrics def main(): global logger args = get_args() args = set_seed_logger(args) device, n_gpu = init_device(args, args.local_rank) # get text pretrained path pretrained_text = "hfl/chinese-roberta-wwm-ext" args.pretrained_text = pretrained_text if args.language == "chinese": tokenizer = BertTokenizer.from_pretrained(pretrained_text) else: tokenizer = ClipTokenizer() model = init_model(args, device, n_gpu, args.local_rank) ## #################################### # freeze testing ## #################################### ''' assert args.freeze_layer_num <= 12 and args.freeze_layer_num >= -1 if hasattr(model, "visual_encoder") and args.freeze_layer_num > -1: for name, param in model.visual_encoder.named_parameters(): # top layers always need to train if name.find("ln_final.") == 0 or name.find("text_projection") == 0 or name.find("logit_scale") == 0 \ or name.find("visual.ln_post.") == 0 or name.find("visual.proj") == 0: continue # need to train elif name.find("visual.transformer.resblocks.") == 0 or name.find("transformer.resblocks.") == 0: layer_num = int(name.split(".resblocks.")[1].split(".")[0]) if layer_num >= args.freeze_layer_num: continue # need to train if args.linear_patch == "3d" and name.find("conv2."): continue else: # paramenters which < freeze_layer_num will be freezed param.requires_grad = False ''' assert args.dataset in DATALOADER_DICT test_dataloader, test_length = DATALOADER_DICT[args.dataset]["test"](args, tokenizer) if args.local_rank == 0: logger.info("***** Running test *****") logger.info(" Num examples = %d", test_length) logger.info(" Batch size = %d", args.batch_size_val) logger.info(" Num steps = %d", len(test_dataloader)) if args.do_train: train_dataloader, train_length, train_sampler = DATALOADER_DICT[args.dataset]["train"](args, tokenizer) num_train_optimization_steps = (int(len(train_dataloader) + args.gradient_accumulation_steps - 1) / args.gradient_accumulation_steps) * args.epochs # logger.info("train_dataloader len = {}".format(len(train_dataloader))) # logger.info("gradient_accumulation_steps = {}".format(args.gradient_accumulation_steps)) coef_lr = args.coef_lr optimizer, scheduler, model = prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, args.local_rank, coef_lr=coef_lr) if args.local_rank == 0: logger.info("***** Running training *****") logger.info(" Num examples = %d", train_length) logger.info(" Batch size = %d", args.batch_size) logger.info(" Num steps = %d", num_train_optimization_steps * args.gradient_accumulation_steps) best_score = 0.00001 best_output_model_file = "None" global_step = 0 if args.enable_amp: scaler = GradScaler() else: scaler = None for epoch in range(args.epochs): train_sampler.set_epoch(epoch) tr_loss, global_step = train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer, scheduler, scaler, global_step, local_rank=args.local_rank) if args.local_rank == 0: logger.info("Epoch %d/%s Finished, Train Loss: %f", epoch + 1, args.epochs, tr_loss) # for name, param in model.named_parameters(): # args.writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch) # writer.add_histogram(name + '/grad', param.requires_grad_().clone().cpu().data.numpy(), epoch) if epoch % 1 == 0: ## Uncomment if want to save checkpoint output_model_file = save_model(epoch, args, model, type_name="") # if epoch == 100: metrics = eval_epoch(args, model, test_dataloader, device, n_gpu) if args.logdir: args.writer.add_scalars('metrics', {'R1': metrics["R1"], 'R5': metrics["R5"], 'R10': metrics["R10"]}, global_step=epoch) if best_score < metrics["R1"]: best_score = metrics["R1"] best_output_model_file = output_model_file logger.info("The best model is: {}, the R1 is: {:.4f}".format(best_output_model_file, best_score)) elif args.do_eval: if args.local_rank == 0: eval_epoch(args, model, test_dataloader, device, n_gpu) elif args.do_params: logger.info("do_params begin!") # total = sum([param.nelement() for param in model.parameters()]) total = sum(p.numel() for p in model.parameters()) logger.info("Number of parameter: %.2fM" % (total / 1e6)) for bid, batch in enumerate(test_dataloader): batch = tuple(t.to(device) for t in batch) query_ids, query_mask, pos_video_data, pos_title_ids, pos_title_mask, = batch flops, params = profile(model, (query_ids, query_mask, pos_video_data, pos_title_ids, pos_title_mask,)) print('flops: %.2f G, params: %.2f M' % (flops / 1e9, params / 1e6)) break if args.local_rank == 0 and args.logdir: args.writer.close() if __name__ == "__main__": main()