From 29366eff5396812f5deaa52f0d1811f181ac0462 Mon Sep 17 00:00:00 2001 From: "haoning.wu" Date: Tue, 21 Nov 2023 21:44:54 +0800 Subject: [PATCH] data release note --- README.md | 5 +- divide.yml | 91 + dover/models/conv_backbone.py | 2 - dover/version.py | 2 +- .../DIVIDE_MaxWell/train_labels.txt | 3634 +++++++++++++++++ .../DIVIDE_MaxWell/val_labels.txt | 909 +++++ get_divide_dataset/README.md | 59 + training_with_divide.py | 601 +++ 8 files changed, 5298 insertions(+), 5 deletions(-) create mode 100644 divide.yml create mode 100644 examplar_data_labels/DIVIDE_MaxWell/train_labels.txt create mode 100644 examplar_data_labels/DIVIDE_MaxWell/val_labels.txt create mode 100644 get_divide_dataset/README.md create mode 100644 training_with_divide.py diff --git a/README.md b/README.md index 0994c25..0f68a56 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ # DOVER Official Code for [ICCV2023] Paper *"Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives"*. -Official Code, Demo, Weights for the [Disentangled Objective Video Quality Evaluator (DOVER)](arxiv.org/abs/2211.04894v3). +Official Code, Demo, Weights for the [Disentangled Objective Video Quality Evaluator (DOVER)](arxiv.org/abs/2211.04894). +- 21 Nov, 2023: The release note of [DIVIDE database](get_divide_dataset/) is updated. - 1 Aug, 2023: ONNX conversion script for DOVER has been released. Short tip: after installation, run [this](https://github.com/VQAssessment/DOVER/blob/master/convert_to_onnx.py) and then [this](https://github.com/VQAssessment/DOVER/blob/master/onnx_inference.py). -- 17 Jul, 2023: DOVER has been accepted by ICCV2023. We will release the DIVIDE-3k dataset to train DOVER++ via fully-supervised LVBS soon. +- 17 Jul, 2023: DOVER has been accepted by ICCV2023. - 9 Feb, 2023: **DOVER-Mobile** is available! Evaluate on CPU with Very High Speed! - 16 Jan, 2023: Full Training Code Available (include LVBS). See below. - 10 Dec, 2022: Now the evaluation tool can directly predict a fused score for any video. See [here](https://github.com/QualityAssessment/DOVER#new-get-the-fused-quality-score-for-use). diff --git a/divide.yml b/divide.yml new file mode 100644 index 0000000..21fccbf --- /dev/null +++ b/divide.yml @@ -0,0 +1,91 @@ +name: DOVER +num_epochs: 20 +l_num_epochs: 10 +warmup_epochs: 2.5 +ema: true +save_model: true +batch_size: 16 +num_workers: 8 +split_seed: 42 + +wandb: + project_name: DOVER + +data: + val-dividemaxwell: + type: ViewDecompositionDataset + args: + weight: 0.598 + phase: test + anno_file: ./examplar_data_labels/DIVIDE_MaxWell/val_labels.txt + data_prefix: ../datasets/DIVIDE_MaxWell/videos/ + sample_types: + technical: + fragments_h: 7 + fragments_w: 7 + fsize_h: 32 + fsize_w: 32 + aligned: 32 + clip_len: 32 + frame_interval: 2 + num_clips: 3 + aesthetic: + size_h: 224 + size_w: 224 + clip_len: 32 + frame_interval: 2 + t_frag: 32 + num_clips: 1 + train-dividemaxwell: + type: ViewDecompositionDataset + args: + weight: 0.598 + phase: train + fully_supervised: True + + anno_file: ./examplar_data_labels/DIVIDE_MaxWell/train_labels.txt + data_prefix: ../datasets/DIVIDE_MaxWell/videos/ + sample_types: + technical: + fragments_h: 7 + fragments_w: 7 + fsize_h: 32 + fsize_w: 32 + aligned: 32 + clip_len: 32 + frame_interval: 2 + num_clips: 1 + aesthetic: + size_h: 224 + size_w: 224 + clip_len: 32 + frame_interval: 2 + t_frag: 32 + num_clips: 1 + + +model: + type: DOVER + args: + backbone: + technical: + type: swin_tiny_grpb + checkpoint: true + pretrained: + aesthetic: + type: conv_tiny + backbone_preserve_keys: technical,aesthetic + divide_head: true + vqa_head: + in_channels: 768 + hidden_channels: 64 + +optimizer: + lr: !!float 1e-3 + backbone_lr_mult: !!float 1e-1 + wd: 0.05 + +test_load_path: ./pretrained_weights/DOVER.pth + + + diff --git a/dover/models/conv_backbone.py b/dover/models/conv_backbone.py index 2c358fb..3aab784 100644 --- a/dover/models/conv_backbone.py +++ b/dover/models/conv_backbone.py @@ -4,8 +4,6 @@ from timm.models.layers import trunc_normal_, DropPath from timm.models.registry import register_model -from open_clip import CLIP3D -import open_clip class GRN(nn.Module): """ GRN (Global Response Normalization) layer diff --git a/dover/version.py b/dover/version.py index ce20dbd..02fe57b 100644 --- a/dover/version.py +++ b/dover/version.py @@ -1,4 +1,4 @@ -__version__ = "0.5.0" +__version__ = "1.0.0" def parse_version_info(version_str): diff --git a/examplar_data_labels/DIVIDE_MaxWell/train_labels.txt b/examplar_data_labels/DIVIDE_MaxWell/train_labels.txt new file mode 100644 index 0000000..f26dd16 --- /dev/null +++ b/examplar_data_labels/DIVIDE_MaxWell/train_labels.txt @@ -0,0 +1,3634 @@ +0001.mp4, 1.057142857, 1.142857143, 1.057142857 +0002.mp4, 1.228571429, 1.228571429, 1.142857143 +0003.mp4, 1.25, 1.321428571, 1.142857143 +0004.mp4, 1.171428571, 1.4, 1.142857143 +0005.mp4, 1.171428571, 1.542857143, 1.171428571 +0006.mp4, 1.230769231, 1.461538462, 1.192307692 +0007.mp4, 1.321428571, 1.392857143, 1.285714286 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file mode 100644 index 0000000..78bb89a --- /dev/null +++ b/get_divide_dataset/README.md @@ -0,0 +1,59 @@ +# Data Release Note + +_v1.0.0: merge_divide_and_maxwell_splits_ + +The DIVIDE-3k dataset was originally proposed in DOVER, as the first dataset with aesthetic and technical labels in addition to overall labels. In this dataset, we invite trained subjects to conduct a subjective study in-lab. + +In the project [ExplainableVQA](https://github.com/VQAssessment/ExplainableVQA), we further invite the same group of trained subjects to provide 13 dimensions of **explanation-level** labels for the DIVIDE-3k (*i.e.* the MaxWell database). These subjects also further labeled around 1K new videos to expand the database. + +As the videos in MaxWell is a superset of the videos in the DIVIDE-3k, after the internal discussion of our team, we decide to unify the `train` and `val` splits in the two datasets, and re-run DOVER and DOVER++ on the merged `DIVIDE-MaxWell` database (*a superset of DIVIDE-3k, each one labeled with aesthetic and technical, and overall perspective scores*) on the same official split as provided in MaxWell, *i.e.* 3634 training videos (80%), and 909 validation videos. The results of both variants are shown as follows, while the model checkpoint of DOVER++ (on the official train / test sets) will be uploaded soon to faciliate further research. + + +## Download Videos + +Download the videos in [Hugging Face Datasets](https://huggingface.co/datasets/teowu/DIVIDE-MaxWell/resolve/main/videos.zip). + +## Labels + +The labels are provided here for the official [training set](../examplar_data_labels/DIVIDE_MaxWell/train_labels.txt) and official [validation set](../examplar_data_labels/DIVIDE_MaxWell/val_labels.txt) + +## Training for DOVER++ + +To run DOVER++ (enhance end-to-end training with *Aesthetic+Technical+overall scores*), the scripts are as follows: + +```shell +python training_with_divide.py --train train-dividemaxwell --val val-dividemaxwell +``` + +## Results on the Updated Train-Test Splits + +As we have changed the train-test split, the results for FAST-VQA (technical branch of DOVER), DOVER and DOVER++ are also changed. See the following table for their results. + +### Zero-Shot + +- DOVER (pre-trained on LSVQ) +SROCC: 0.7477 | PLCC: 0.7546 | KROCC: 0.5510 + +- FAST-VQA (==technical branch in DOVER, pre-trained on LSVQ) +SROCC: 0.7204 | PLCC: 0.7282 | KROCC: 0.5286 + +- Aesthetic Branch in DOVER (pre-trained on LSVQ) +SROCC: 0.7184 | PLCC: 0.7293 | KROCC: 0.5249 + +- SAQI (based on CLIP) +*To be updated soon.* + +- NIQE +*To be updated soon.* + +### Fine-tuned + +- VSFA +SROCC: 0.6671 | PLCC: 0.6784 | KROCC: 0.4875 + +- BVQA (Zheng *et al*, 2022) +SROCC: 0.7418 | PLCC: 0.7394 | KROCC: 0.5341 + +- DOVER++ (trained on the training set of DIVIDE-MaxWell) +*To be updated soon.* + diff --git a/training_with_divide.py b/training_with_divide.py new file mode 100644 index 0000000..d8c11e6 --- /dev/null +++ b/training_with_divide.py @@ -0,0 +1,601 @@ +import torch + +import cv2 +import random +import os.path as osp +import argparse +from scipy.stats import spearmanr, pearsonr +from scipy.stats.stats import kendalltau as kendallr +import numpy as np +from time import time +from tqdm import tqdm +import pickle +import math +import wandb +import yaml +from collections import OrderedDict + +from functools import reduce +from thop import profile +import copy + + +import dover.models as models +import dover.datasets as datasets + + + +def rank_loss(y_pred, y): + ranking_loss = torch.nn.functional.relu( + (y_pred - y_pred.t()) * torch.sign((y.t() - y)) + ) + scale = 1 + torch.max(ranking_loss) + return ( + torch.sum(ranking_loss) / y_pred.shape[0] / (y_pred.shape[0] - 1) / scale + ).float() + + +def gaussian(y, eps=1e-8): + return (y - y.mean()) / (y.std() + 1e-8) + + +def plcc_loss(y_pred, y): + sigma_hat, m_hat = torch.std_mean(y_pred, unbiased=False) + y_pred = (y_pred - m_hat) / (sigma_hat + 1e-8) + sigma, m = torch.std_mean(y, unbiased=False) + y = (y - m) / (sigma + 1e-8) + loss0 = torch.nn.functional.mse_loss(y_pred, y) / 4 + rho = torch.mean(y_pred * y) + loss1 = torch.nn.functional.mse_loss(rho * y_pred, y) / 4 + return ((loss0 + loss1) / 2).float() + + +def rescaled_l2_loss(y_pred, y): + y_pred_rs = (y_pred - y_pred.mean()) / y_pred.std() + y_rs = (y - y.mean()) / (y.std() + eps) + return torch.nn.functional.mse_loss(y_pred_rs, y_rs) + + +def rplcc_loss(y_pred, y, eps=1e-8): + ## Literally (1 - PLCC) / 2 + y_pred, y = gaussian(y_pred), gaussian(y) + cov = torch.sum(y_pred * y) / y_pred.shape[0] + # std = (torch.std(y_pred) + eps) * (torch.std(y) + eps) + return (1 - cov) / 2 + + +def self_similarity_loss(f, f_hat, f_hat_detach=False): + if f_hat_detach: + f_hat = f_hat.detach() + return 1 - torch.nn.functional.cosine_similarity(f, f_hat, dim=1).mean() + + +def contrastive_similarity_loss(f, f_hat, f_hat_detach=False, eps=1e-8): + if f_hat_detach: + f_hat = f_hat.detach() + intra_similarity = torch.nn.functional.cosine_similarity(f, f_hat, dim=1).mean() + cross_similarity = torch.nn.functional.cosine_similarity(f, f_hat, dim=0).mean() + return (1 - intra_similarity) / (1 - cross_similarity + eps) + + +def rescale(pr, gt=None): + if gt is None: + pr = (pr - np.mean(pr)) / np.std(pr) + else: + pr = ((pr - np.mean(pr)) / np.std(pr)) * np.std(gt) + np.mean(gt) + return pr + + +sample_types = ["aesthetic", "technical"] + + +def finetune_epoch( + ft_loader, + model, + model_ema, + optimizer, + scheduler, + device, + epoch=-1, + need_upsampled=False, + need_feat=False, + need_fused=False, + need_separate_sup=True, + plus_plus=True, +): + model.train() + for i, data in enumerate(tqdm(ft_loader, desc=f"Training in epoch {epoch}")): + optimizer.zero_grad() + video = {} + for key in sample_types: + if key in data: + video[key] = data[key].to(device) + + y = data["gt_label"].float().detach().to(device).unsqueeze(-1) + + scores = model(video, inference=False, reduce_scores=False) + if len(scores) > 1: + y_pred = reduce(lambda x, y: x + y, scores) + else: + y_pred = scores[0] + y_pred = y_pred.mean((-3, -2, -1)) + + frame_inds = data["frame_inds"] + + + loss = 0 # p_loss + 0.3 * r_loss + + if need_separate_sup: + p_loss_a = plcc_loss(scores[0].mean((-3, -2, -1)), y) + p_loss_b = plcc_loss(scores[1].mean((-3, -2, -1)), y) + r_loss_a = rank_loss(scores[0].mean((-3, -2, -1)), y) + r_loss_b = rank_loss(scores[1].mean((-3, -2, -1)), y) + loss += ( + p_loss_a + p_loss_b + 0.3 * r_loss_a + 0.3 * r_loss_b + ) # + 0.2 * o_loss + wandb.log( + { + "train/plcc_loss_a": p_loss_a.item(), + "train/plcc_loss_b": p_loss_b.item(), + } + ) + + if plus_plus: + if i == 0: + print("In DOVER++ training") + y_a = data["gt_label_a"].float().detach().to(device).unsqueeze(-1) + y_t = data["gt_label_t"].float().detach().to(device).unsqueeze(-1) + p_loss_a = plcc_loss(scores[0].mean((-3, -2, -1)), y_a) + p_loss_b = plcc_loss(scores[1].mean((-3, -2, -1)), y_t) + r_loss_a = rank_loss(scores[0].mean((-3, -2, -1)), y_a) + r_loss_b = rank_loss(scores[1].mean((-3, -2, -1)), y_t) + loss += 0.5 * ( + p_loss_a + p_loss_b + 0.3 * r_loss_a + 0.3 * r_loss_b + ) # + 0.2 * o_loss + wandb.log( + { + "train/plcc_loss_a": p_loss_a.item(), + "train/plcc_loss_b": p_loss_b.item(), + } + ) + + wandb.log( + {"train/total_loss": loss.item(),} + ) + + loss.backward() + optimizer.step() + scheduler.step() + + # ft_loader.dataset.refresh_hypers() + + if model_ema is not None: + model_params = dict(model.named_parameters()) + model_ema_params = dict(model_ema.named_parameters()) + for k in model_params.keys(): + model_ema_params[k].data.mul_(0.999).add_( + model_params[k].data, alpha=1 - 0.999 + ) + model.eval() + + +def profile_inference(inf_set, model, device): + video = {} + data = inf_set[0] + for key in sample_types: + if key in data: + video[key] = data[key].to(device).unsqueeze(0) + with torch.no_grad(): + + flops, params = profile(model, (video,)) + print( + f"The FLOps of the Variant is {flops/1e9:.1f}G, with Params {params/1e6:.2f}M." + ) + + +def inference_set( + inf_loader, + model, + device, + best_, + save_model=False, + suffix="s", + save_name="divide", + save_type="head", +): + + results = [] + + best_s, best_p, best_k, best_r = best_ + + for i, data in enumerate(tqdm(inf_loader, desc="Validating")): + result = dict() + video, video_up = {}, {} + for key in sample_types: + if key in data: + video[key] = data[key].to(device) + ## Reshape into clips + b, c, t, h, w = video[key].shape + video[key] = ( + video[key] + .reshape( + b, c, data["num_clips"][key], t // data["num_clips"][key], h, w + ) + .permute(0, 2, 1, 3, 4, 5) + .reshape( + b * data["num_clips"][key], c, t // data["num_clips"][key], h, w + ) + ) + if key + "_up" in data: + video_up[key] = data[key + "_up"].to(device) + ## Reshape into clips + b, c, t, h, w = video_up[key].shape + video_up[key] = ( + video_up[key] + .reshape(b, c, data["num_clips"], t // data["num_clips"], h, w) + .permute(0, 2, 1, 3, 4, 5) + .reshape(b * data["num_clips"], c, t // data["num_clips"], h, w) + ) + # .unsqueeze(0) + with torch.no_grad(): + result["pr_labels"] = model(video, reduce_scores=True).cpu().numpy() + if len(list(video_up.keys())) > 0: + result["pr_labels_up"] = model(video_up).cpu().numpy() + + result["gt_label"] = data["gt_label"].item() + del video, video_up + results.append(result) + + ## generate the demo video for video quality localization + gt_labels = [r["gt_label"] for r in results] + pr_labels = [np.mean(r["pr_labels"][:]) for r in results] + pr_labels = rescale(pr_labels, gt_labels) + + s = spearmanr(gt_labels, pr_labels)[0] + p = pearsonr(gt_labels, pr_labels)[0] + k = kendallr(gt_labels, pr_labels)[0] + r = np.sqrt(((gt_labels - pr_labels) ** 2).mean()) + + wandb.log( + { + f"val_{suffix}/SRCC-{suffix}": s, + f"val_{suffix}/PLCC-{suffix}": p, + f"val_{suffix}/KRCC-{suffix}": k, + f"val_{suffix}/RMSE-{suffix}": r, + } + ) + + del results, result # , video, video_up + torch.cuda.empty_cache() + + if s + p > best_s + best_p and save_model: + state_dict = model.state_dict() + + if save_type == "head": + head_state_dict = OrderedDict() + for key, v in state_dict.items(): + if "head" in key: + head_state_dict[key] = v + print("Following keys are saved (for head-only):", head_state_dict.keys()) + torch.save( + {"state_dict": head_state_dict, "validation_results": best_,}, + f"pretrained_weights/{save_name}_{suffix}_finetuned.pth", + ) + else: + torch.save( + {"state_dict": state_dict, "validation_results": best_,}, + f"pretrained_weights/{save_name}_{suffix}_finetuned.pth", + ) + + best_s, best_p, best_k, best_r = ( + max(best_s, s), + max(best_p, p), + max(best_k, k), + min(best_r, r), + ) + + wandb.log( + { + f"val_{suffix}/best_SRCC-{suffix}": best_s, + f"val_{suffix}/best_PLCC-{suffix}": best_p, + f"val_{suffix}/best_KRCC-{suffix}": best_k, + f"val_{suffix}/best_RMSE-{suffix}": best_r, + } + ) + + print( + f"For {len(inf_loader)} videos, \nthe accuracy of the model: [{suffix}] is as follows:\n SROCC: {s:.4f} best: {best_s:.4f} \n PLCC: {p:.4f} best: {best_p:.4f} \n KROCC: {k:.4f} best: {best_k:.4f} \n RMSE: {r:.4f} best: {best_r:.4f}." + ) + + return best_s, best_p, best_k, best_r + + # torch.save(results, f'{args.save_dir}/results_{dataset.lower()}_s{32}*{32}_ens{args.famount}.pkl') + + +def main(): + + parser = argparse.ArgumentParser() + parser.add_argument( + "-o", "--opt", type=str, default="divide.yml", help="the option file" + ) + + parser.add_argument( + "-t", "--train", type=str, default="train-dividemaxwell", help="training set" + ) + + parser.add_argument( + "-v", "--val", type=str, default="val-dividemaxwell", help="training set" + ) + + args = parser.parse_args() + with open(args.opt, "r") as f: + opt = yaml.safe_load(f) + print(opt) + + ## adaptively choose the device + + device = "cuda" if torch.cuda.is_available() else "cpu" + + ## defining model and loading checkpoint + + bests_ = [] + + if opt.get("split_seed", -1) > 0: + num_splits = 10 + else: + print("No random splits, follow default") + num_splits = 1 + + + for split in range(num_splits): + model = getattr(models, opt["model"]["type"])(**opt["model"]["args"]).to(device) + + train_datasets = {} + for key in opt["data"]: + if key.startswith("train"): + train_dataset = getattr(datasets, opt["data"][key]["type"])( + opt["data"][key]["args"] + ) + train_datasets[key] = train_dataset + print(len(train_dataset.video_infos)) + + train_loaders = {} + for key, train_dataset in train_datasets.items(): + train_loaders[key] = torch.utils.data.DataLoader( + train_dataset, + batch_size=opt["batch_size"], + num_workers=opt["num_workers"], + shuffle=True, + ) + + val_datasets = {} + for key in opt["data"]: + if key.startswith("val"): + val_dataset = getattr(datasets, opt["data"][key]["type"])( + opt["data"][key]["args"] + ) + print(len(val_dataset.video_infos)) + val_datasets[key] = val_dataset + + val_loaders = {} + for key, val_dataset in val_datasets.items(): + val_loaders[key] = torch.utils.data.DataLoader( + val_dataset, + batch_size=1, + num_workers=opt["num_workers"], + pin_memory=True, + ) + + run = wandb.init( + project=opt["wandb"]["project_name"], + name=opt["name"] + f"_divide_maxwell_train_split" + if num_splits > 1 + else opt["name"], + reinit=True, + settings=wandb.Settings(start_method="thread"), + ) + + state_dict = torch.load(opt["test_load_path"], map_location=device) + + head_removed_state_dict = OrderedDict() + for key, v in state_dict.items(): + if "head" not in key: + head_removed_state_dict[key] = v + + # Allowing empty head weight + model.load_state_dict(state_dict, strict=False) + + if opt["ema"]: + from copy import deepcopy + + model_ema = deepcopy(model) + else: + model_ema = None + + # profile_inference(val_dataset, model, device) + + # finetune the model + + param_groups = [] + + for key, value in dict(model.named_children()).items(): + if "backbone" in key: + param_groups += [ + { + "params": value.parameters(), + "lr": opt["optimizer"]["lr"] + * opt["optimizer"]["backbone_lr_mult"], + } + ] + else: + param_groups += [ + {"params": value.parameters(), "lr": opt["optimizer"]["lr"]} + ] + + optimizer = torch.optim.AdamW( + lr=opt["optimizer"]["lr"], + params=param_groups, + weight_decay=opt["optimizer"]["wd"], + ) + warmup_iter = 0 + for train_loader in train_loaders.values(): + warmup_iter += int(opt["warmup_epochs"] * len(train_loader)) + max_iter = int((opt["num_epochs"] + opt["l_num_epochs"]) * len(train_loader)) + lr_lambda = ( + lambda cur_iter: cur_iter / warmup_iter + if cur_iter <= warmup_iter + else 0.5 * (1 + math.cos(math.pi * (cur_iter - warmup_iter) / max_iter)) + ) + + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda,) + + bests = {} + bests_n = {} + for key in val_loaders: + bests[key] = -1, -1, -1, 1000 + bests_n[key] = -1, -1, -1, 1000 + + for key, value in dict(model.named_children()).items(): + if "backbone" in key: + for param in value.parameters(): + param.requires_grad = False + + for epoch in range(opt["l_num_epochs"]): + print(f"Linear Epoch {epoch}:") + for key, train_loader in train_loaders.items(): + finetune_epoch( + train_loader, + model, + model_ema, + optimizer, + scheduler, + device, + epoch, + opt.get("need_upsampled", False), + opt.get("need_feat", False), + opt.get("need_fused", False), + ) + for key in val_loaders: + bests[key] = inference_set( + val_loaders[key], + model_ema if model_ema is not None else model, + device, + bests[key], + save_model=opt["save_model"], + save_name=opt["name"] + "_head_" + args.train + f"_{split}", + suffix=key + "_s", + ) + if model_ema is not None: + bests_n[key] = inference_set( + val_loaders[key], + model, + device, + bests_n[key], + save_model=opt["save_model"], + save_name=opt["name"] + + "_head_" + + args.train + + f"_{split}", + suffix=key + "_n", + ) + else: + bests_n[key] = bests[key] + + if opt["l_num_epochs"] >= 0: + for key in val_loaders: + print( + f"""For the linear transfer process on {key} with {len(val_loaders[key])} videos, + the best validation accuracy of the model-s is as follows: + SROCC: {bests[key][0]:.4f} + PLCC: {bests[key][1]:.4f} + KROCC: {bests[key][2]:.4f} + RMSE: {bests[key][3]:.4f}.""" + ) + + print( + f"""For the linear transfer process on {key} with {len(val_loaders[key])} videos, + the best validation accuracy of the model-n is as follows: + SROCC: {bests_n[key][0]:.4f} + PLCC: {bests_n[key][1]:.4f} + KROCC: {bests_n[key][2]:.4f} + RMSE: {bests_n[key][3]:.4f}.""" + ) + + for key, value in dict(model.named_children()).items(): + if "backbone" in key: + for param in value.parameters(): + param.requires_grad = True + + for epoch in range(opt["num_epochs"]): + print(f"End-to-end Epoch {epoch}:") + for key, train_loader in train_loaders.items(): + finetune_epoch( + train_loader, + model, + model_ema, + optimizer, + scheduler, + device, + epoch, + opt.get("need_upsampled", False), + opt.get("need_feat", False), + opt.get("need_fused", False), + ) + for key in val_loaders: + bests[key] = inference_set( + val_loaders[key], + model_ema if model_ema is not None else model, + device, + bests[key], + save_model=opt["save_model"], + save_name=opt["name"] + "_head_" + args.train + f"_{split}", + suffix=key + "_s", + save_type="full", + ) + if model_ema is not None: + bests_n[key] = inference_set( + val_loaders[key], + model, + device, + bests_n[key], + save_model=opt["save_model"], + save_name=opt["name"] + + "_head_" + + args.train + + f"_{split}", + suffix=key + "_n", + save_type="full", + ) + else: + bests_n[key] = bests[key] + + if opt["num_epochs"] >= 0: + for key in val_loaders: + print( + f"""For the end-to-end transfer process on {key} with {len(val_loaders[key])} videos, + the best validation accuracy of the model-s is as follows: + SROCC: {bests[key][0]:.4f} + PLCC: {bests[key][1]:.4f} + KROCC: {bests[key][2]:.4f} + RMSE: {bests[key][3]:.4f}.""" + ) + + print( + f"""For the end-to-end transfer process on {key} with {len(val_loaders[key])} videos, + the best validation accuracy of the model-n is as follows: + SROCC: {bests_n[key][0]:.4f} + PLCC: {bests_n[key][1]:.4f} + KROCC: {bests_n[key][2]:.4f} + RMSE: {bests_n[key][3]:.4f}.""" + ) + + for key, value in dict(model.named_children()).items(): + if "backbone" in key: + for param in value.parameters(): + param.requires_grad = True + + run.finish() + + +if __name__ == "__main__": + main()