import os import numpy as np import math import sys from typing import Iterable, Optional import torch from mixup import Mixup from timm.utils import accuracy, ModelEma import utils_mae as utils from scipy.special import softmax import gc import pickle def train_class_batch(model, samples, target, criterion): outputs = model(samples) loss = criterion(outputs, target) return loss, outputs def get_loss_scale_for_deepspeed(model): optimizer = model.optimizer return optimizer.loss_scale if hasattr(optimizer, "loss_scale") else optimizer.cur_scale def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer, device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None, log_writer=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None, num_training_steps_per_epoch=None, update_freq=None): model.train(True) metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 10 if loss_scaler is None: model.zero_grad() model.micro_steps = 0 else: optimizer.zero_grad() for data_iter_step, (samples, targets, _, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): step = data_iter_step // update_freq if step >= num_training_steps_per_epoch: continue it = start_steps + step # global training iteration # Update LR & WD for the first acc if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0: for i, param_group in enumerate(optimizer.param_groups): if lr_schedule_values is not None: param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"] if wd_schedule_values is not None and param_group["weight_decay"] > 0: param_group["weight_decay"] = wd_schedule_values[it] samples = samples.to(device, non_blocking=True) targets = targets.to(device, non_blocking=True) if mixup_fn is not None: samples, targets = mixup_fn(samples, targets) if loss_scaler is None: samples = samples.half() loss, output = train_class_batch( model, samples, targets, criterion) else: with torch.cuda.amp.autocast(): loss, output = train_class_batch( model, samples, targets, criterion) loss_value = loss.item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) sys.exit(1) if loss_scaler is None: loss /= update_freq model.backward(loss) model.step() if (data_iter_step + 1) % update_freq == 0: # model.zero_grad() # Deepspeed will call step() & model.zero_grad() automatic if model_ema is not None: model_ema.update(model) grad_norm = None loss_scale_value = get_loss_scale_for_deepspeed(model) else: # this attribute is added by timm on one optimizer (adahessian) is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order loss /= update_freq grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm, parameters=model.parameters(), create_graph=is_second_order, update_grad=(data_iter_step + 1) % update_freq == 0) if (data_iter_step + 1) % update_freq == 0: optimizer.zero_grad() if model_ema is not None: model_ema.update(model) loss_scale_value = loss_scaler.state_dict()["scale"] torch.cuda.synchronize() if mixup_fn is None: class_acc = (output.max(-1)[-1] == targets).float().mean() else: class_acc = None metric_logger.update(loss=loss_value) metric_logger.update(class_acc=class_acc) metric_logger.update(loss_scale=loss_scale_value) min_lr = 10. max_lr = 0. for group in optimizer.param_groups: min_lr = min(min_lr, group["lr"]) max_lr = max(max_lr, group["lr"]) metric_logger.update(lr=max_lr) metric_logger.update(min_lr=min_lr) weight_decay_value = None for group in optimizer.param_groups: if group["weight_decay"] > 0: weight_decay_value = group["weight_decay"] metric_logger.update(weight_decay=weight_decay_value) metric_logger.update(grad_norm=grad_norm) if log_writer is not None: log_writer.update(loss=loss_value, head="loss") log_writer.update(class_acc=class_acc, head="loss") log_writer.update(loss_scale=loss_scale_value, head="opt") log_writer.update(lr=max_lr, head="opt") log_writer.update(min_lr=min_lr, head="opt") log_writer.update(weight_decay=weight_decay_value, head="opt") log_writer.update(grad_norm=grad_norm, head="opt") log_writer.set_step() # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def validation_one_epoch(data_loader, model, device): criterion = torch.nn.CrossEntropyLoss() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Val:' # switch to evaluation mode model.eval() for batch in metric_logger.log_every(data_loader, 10, header): videos = batch[0] target = batch[1] videos = videos.to(device, non_blocking=True) target = target.to(device, non_blocking=True) # compute output with torch.cuda.amp.autocast(): output = model(videos) loss = criterion(output, target) acc1, acc5 = accuracy(output, target, topk=(1, 5)) batch_size = videos.shape[0] metric_logger.update(loss=loss.item()) metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) # gather the stats from all processes metric_logger.synchronize_between_processes() print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def final_test(data_loader, model, device, file): criterion = torch.nn.CrossEntropyLoss() metric_logger = utils.MetricLogger(delimiter=" ") header = 'Test:' # switch to evaluation mode model.eval() final_result = [] for batch in metric_logger.log_every(data_loader, 10, header): videos = batch[0] target = batch[1] ids = batch[2] chunk_nb = batch[3] split_nb = batch[4] videos = videos.to(device, non_blocking=True) target = target.to(device, non_blocking=True) # compute output with torch.cuda.amp.autocast(): output = model(videos) loss = criterion(output, target) for i in range(output.size(0)): string = "{} {} {} {} {}\n".format(ids[i], \ str(output.data[i].cpu().numpy().tolist()), \ str(int(target[i].cpu().numpy())), \ str(int(chunk_nb[i].cpu().numpy())), \ str(int(split_nb[i].cpu().numpy()))) final_result.append(string) acc1, acc5 = accuracy(output, target, topk=(1, 5)) batch_size = videos.shape[0] metric_logger.update(loss=loss.item()) metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) if not os.path.exists(file): os.mknod(file) with open(file, 'w') as f: f.write("{}, {}\n".format(acc1, acc5)) for line in final_result: f.write(line) # gather the stats from all processes metric_logger.synchronize_between_processes() print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def merge(eval_path, num_tasks): dict_feats = {} dict_label = {} dict_pos = {} print("Reading individual output files") for x in range(num_tasks): file = os.path.join(eval_path, str(x) + '.txt') lines = open(file, 'r').readlines()[1:] for line in lines: line = line.strip() name = line.split('[')[0] label = line.split(']')[1].split(' ')[1] chunk_nb = line.split(']')[1].split(' ')[2] split_nb = line.split(']')[1].split(' ')[3] data = np.fromstring(line.split('[')[1].split(']')[0], dtype=float, sep=',') data = softmax(data) if not name in dict_feats: dict_feats[name] = [] dict_label[name] = 0 dict_pos[name] = [] if chunk_nb + split_nb in dict_pos[name]: continue dict_feats[name].append(data) dict_pos[name].append(chunk_nb + split_nb) dict_label[name] = label print("Computing final results") input_lst = [] print(len(dict_feats)) for i, item in enumerate(dict_feats): input_lst.append([i, item, dict_feats[item], dict_label[item]]) from multiprocessing import Pool p = Pool(64) ans = p.map(compute_video, input_lst) top1 = [x[1] for x in ans] top5 = [x[2] for x in ans] pred = [x[0] for x in ans] label = [x[3] for x in ans] final_top1 ,final_top5 = np.mean(top1), np.mean(top5) return final_top1*100 ,final_top5*100 def compute_video(lst): i, video_id, data, label = lst feat = [x for x in data] feat = np.mean(feat, axis=0) pred = np.argmax(feat) top1 = (int(pred) == int(label)) * 1.0 top5 = (int(label) in np.argsort(-feat)[:5]) * 1.0 return [pred, top1, top5, int(label)] def merge_mean_per_class(eval_path, num_tasks,nb_classes): dict_feats = {} dict_label = {} dict_pos = {} #print("Reading individual output files") for x in range(num_tasks): file = os.path.join(eval_path, str(x) + '.txt') lines = open(file, 'r').readlines()[1:] for line in lines: line = line.strip() name = line.split('[')[0] label = line.split(']')[1].split(' ')[1] chunk_nb = line.split(']')[1].split(' ')[2] split_nb = line.split(']')[1].split(' ')[3] data = np.fromstring(line.split('[')[1].split(']')[0], dtype=float, sep=',') data = softmax(data) if not name in dict_feats: dict_feats[name] = [] dict_label[name] = 0 dict_pos[name] = [] if chunk_nb + split_nb in dict_pos[name]: continue dict_feats[name].append(data) dict_pos[name].append(chunk_nb + split_nb) dict_label[name] = label print("Computing mean per class results") input_lst = [] all_pred = [] all_label = [] classes = torch.arange(nb_classes) classwise_top1 = [0 for c in classes] classwise_top5 = [0 for c in classes] actual_nb_classes = nb_classes cnt = 0 for c in classes: input_lst = [] for i, item in enumerate(dict_feats): if int(dict_label[item]) == c: input_lst.append([i, item, dict_feats[item], dict_label[item]]) cnt += len(input_lst) # p = Pool(4) # ans = p.map(compute_video, input_lst) if len(input_lst) == 0: actual_nb_classes -= 1 print(f"Class {c} is not present in test set, skip") continue ans = [] for i in input_lst: ans.append(compute_video(i)) top1 = [x[1] for x in ans] top5 = [x[2] for x in ans] pred = [x[0] for x in ans] label = [x[3] for x in ans] # for i in pred: # all_pred.append(i) # for j in label: # all_label.append(j) final_top1 ,final_top5 = np.mean(top1), np.mean(top5) classwise_top1[c] = final_top1*100 classwise_top5[c] = final_top5*100 del input_lst del ans del top1 del top5 del pred del label gc.collect() assert cnt == len(dict_feats) # pred_cnt = 0 # for idx, p in enumerate(all_pred): # if int(p) == int(all_label[idx]): # pred_cnt += 1 # print(pred_cnt/len(all_pred)) classwise_top1_path = os.path.join(eval_path, "classwise_top1.pkl") with open(classwise_top1_path, 'wb') as file: pickle.dump(classwise_top1, file) classwise_top1 = np.sum(classwise_top1) / actual_nb_classes classwise_top5 = np.sum(classwise_top5) / actual_nb_classes return classwise_top1,classwise_top5