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engine.py
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engine.py
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# ------------------------------------------
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
# ------------------------------------------
# Modification:
# Added code for Simple Continual Learning datasets
# -- Jaeho Lee, dlwogh9344@khu.ac.kr
# ------------------------------------------
"""
Train and eval functions used in main.py
"""
import math
import sys
import os
import datetime
import json
from typing import Iterable
from pathlib import Path
import torch
import numpy as np
from timm.utils import accuracy
import utils
def train_one_epoch(model: torch.nn.Module, criterion, data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, max_norm: float = 0, set_training_mode=True,
task_id=-1, class_mask=None, args = None,):
model.train(set_training_mode)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('Lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('Loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
header = f'Train: Epoch[{epoch+1:{int(math.log10(args.epochs))+1}}/{args.epochs}]'
for input, target in metric_logger.log_every(data_loader, args.print_freq, header):
input = input.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
output = model(input)
# here is the trick to mask out classes of non-current tasks
if args.train_mask and class_mask is not None:
mask = class_mask[task_id]
not_mask = np.setdiff1d(np.arange(args.nb_classes), mask)
not_mask = torch.tensor(not_mask, dtype=torch.int64).to(device)
output = output.index_fill(dim=1, index=not_mask, value=float('-inf'))
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
if not math.isfinite(loss.item()):
print("Loss is {}, stopping training".format(loss.item()))
sys.exit(1)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
metric_logger.update(Loss=loss.item())
metric_logger.update(Lr=optimizer.param_groups[0]["lr"])
metric_logger.meters['Acc@1'].update(acc1.item(), n=input.shape[0])
metric_logger.meters['Acc@5'].update(acc5.item(), n=input.shape[0])
# 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 evaluate(model: torch.nn.Module, data_loader, device, task_id=-1, class_mask=None, args=None,):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test: [Task {}]'.format(task_id + 1)
# switch to evaluation mode
model.eval()
with torch.no_grad():
for input, target in metric_logger.log_every(data_loader, args.print_freq, header):
input = input.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
output = model(input)
if args.task_inc and class_mask is not None:
#adding mask to output logits
mask = class_mask[task_id]
mask = torch.tensor(mask, dtype=torch.int64).to(device)
logits_mask = torch.ones_like(output, device=device) * float('-inf')
logits_mask = logits_mask.index_fill(1, mask, 0.0)
output = output + logits_mask
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
metric_logger.meters['Loss'].update(loss.item())
metric_logger.meters['Acc@1'].update(acc1.item(), n=input.shape[0])
metric_logger.meters['Acc@5'].update(acc5.item(), n=input.shape[0])
# 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.meters['Acc@1'], top5=metric_logger.meters['Acc@5'], losses=metric_logger.meters['Loss']))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate_till_now(model: torch.nn.Module, data_loader, device, task_id=-1, class_mask=None, acc_matrix=None, args=None,):
stat_matrix = np.zeros((3, args.num_tasks)) # 3 for Acc@1, Acc@5, Loss
for i in range(task_id+1):
test_stats = evaluate(model=model,data_loader=data_loader[i]['val'], device=device, task_id=i, class_mask=class_mask, args=args)
stat_matrix[0, i] = test_stats['Acc@1']
stat_matrix[1, i] = test_stats['Acc@5']
stat_matrix[2, i] = test_stats['Loss']
acc_matrix[i, task_id] = test_stats['Acc@1']
avg_stat = np.divide(np.sum(stat_matrix, axis=1), task_id+1)
diagonal = np.diag(acc_matrix)
result_str = "[Average accuracy till task{}]\tAcc@1: {:.4f}\tAcc@5: {:.4f}\tLoss: {:.4f}".format(task_id+1, avg_stat[0], avg_stat[1], avg_stat[2])
if task_id > 0:
forgetting = np.mean((np.max(acc_matrix, axis=1) -
acc_matrix[:, task_id])[:task_id])
backward = np.mean((acc_matrix[:, task_id] - diagonal)[:task_id])
result_str += "\tForgetting: {:.4f}\tBackward: {:.4f}".format(forgetting, backward)
print(result_str)
return test_stats
def train_and_evaluate(model: torch.nn.Module, criterion, data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, class_mask=None, args = None,):
# create matrix to save end-of-task accuracies
acc_matrix = np.zeros((args.num_tasks, args.num_tasks))
for task_id in range(args.num_tasks):
for epoch in range(args.epochs):
train_stats = train_one_epoch(model=model, criterion=criterion, data_loader=data_loader[task_id]['train'],
optimizer=optimizer, device=device, epoch=epoch, max_norm=args.clip_grad,
set_training_mode=True, task_id=task_id, class_mask=class_mask, args=args,)
test_stats = evaluate_till_now(model=model, data_loader=data_loader, device=device,
task_id=task_id, class_mask=class_mask, acc_matrix=acc_matrix, args=args)
if args.output_dir:
Path(os.path.join(args.output_dir, 'checkpoint')).mkdir(parents=True, exist_ok=True)
checkpoint_path = os.path.join(args.output_dir, 'checkpoint/task{}_checkpoint.pth'.format(task_id+1))
state_dict = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
'args': args,
}
utils.save_on_master(state_dict, checkpoint_path)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,}
if args.output_dir:
with open(os.path.join(args.output_dir, '{}_stats.txt'.format(datetime.datetime.now().strftime('log_%Y_%m_%d_%H_%M'))), 'a') as f:
f.write(json.dumps(log_stats) + '\n')