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engine.py
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# ------------------------------------------
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
# ------------------------------------------
# Modification:
# Added code for dualprompt implementation
# -- Jaeho Lee, [email protected]
# ------------------------------------------
"""
Train and eval functions used in main.py
"""
import math
import sys
import os
import datetime
import json
import time
from typing import Iterable
from pathlib import Path
import torch
import torch.nn.functional as F
from torch import optim
import numpy as np
from torch.nn import MSELoss
from timm.utils import accuracy
from timm.optim import create_optimizer
import copy
import utils
from torch.distributions.multivariate_normal import MultivariateNormal
import logging
# for attribute matching of tasks
from attribute_matching import num_new_prompts
from timm.scheduler import create_scheduler
def train_one_epoch(model: torch.nn.Module,
criterion, data_loader: Iterable,
device: torch.device, epoch: int, max_norm: float = 0,
optimizer=None,
old_prompt_matcher = None,
old_prompt = None,
set_training_mode=True, task_id=-1, class_mask=None, args = None,
old_num_k=5,):
model.train(set_training_mode)
s = old_num_k
# Freezing previous tasks' filters
for name, param in model.named_parameters():
if name.find('e_prompt.v_conv_vals') >=0 or name.find('e_prompt.k_conv_vals') >=0:
for i in range(s):
if name.find('.{}.weight'.format(i)) >=0 or name.find('.{}.bias'.format(i)) >=0:
param.requires_grad = False
if args.distributed and utils.get_world_size() > 1:
data_loader.sampler.set_epoch(epoch)
metric_logger = utils.MetricLogger(delimiter=" ")
# metric_logger.add_meter('Lr_head', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
if args.SLCA:
metric_logger.add_meter('Lr_cls', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('Lr_rps', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
else:
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, task_id=task_id, train=set_training_mode)
logits = output['logits']
# Masking and computing loss
known_classes = task_id*len(class_mask[0])
cur_targets = torch.where(target-known_classes>=0,target-known_classes,-100)
loss = criterion(logits[:, known_classes:], cur_targets) # base criterion (CrossEntropyLoss)
if args.use_e_prompt or args.use_g_prompt:
if task_id > 0:
l1_loss = 0.0
for old_wt, new_wt in zip(old_prompt_matcher.parameters(), model.e_prompt.prompt_embed_matcher.parameters()):
l1_loss += torch.norm(old_wt.detach() - new_wt, p=1)
loss = loss + 0.01 * l1_loss
prompt_loss = torch.norm(old_prompt.detach() - model.e_prompt.prompt, p=1)
loss = loss + 0.01 * prompt_loss
acc1, acc5 = accuracy(logits, 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()
if args.use_clip_grad:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
torch.cuda.synchronize()
metric_logger.update(Loss=loss.item())
if args.SLCA:
metric_logger.update(Lr_cls=optimizer.param_groups[0]["lr"])
metric_logger.update(Lr_rps=optimizer.param_groups[1]["lr"])
else:
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)
logging.info("Averaged stats: {}".format(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()
correct = 0
total = 0
# Batchwise Eval time
start_eval_time = time.time()
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)
# compute output
output = model(input, task_id=task_id)
logits = output['logits']
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(logits, device=device) * float('-inf')
logits_mask = logits_mask.index_fill(1, mask, 0.0)
logits = logits + logits_mask
loss = criterion(logits, target)
# print('Loss')
predicts = torch.max(logits, dim=1)[1]
correct += (predicts == target).sum()
total += len(target)
acc1, acc5 = accuracy(logits, 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])
end_eval_time = time.time()
# 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']))
logging.info('* 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']))
print(f"Batchwise eval time for task {task_id+1} = {(end_eval_time - start_eval_time)/len(data_loader)}")
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}, total, correct
@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
total, correct = 0, 0
for i in range(task_id+1):
logging.info('Evaluating task {}...'.format(i+1))
test_stats, temp_total, temp_correct = evaluate(model=model, data_loader=data_loader[i]['val'],
device=device, task_id=i, class_mask=class_mask, args=args,)
total += temp_total
correct += temp_correct
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)
final_acc = np.divide(correct.cpu(), total)*100.0
result_str = "[Average accuracy till task{}]\tAcc@1: {:.4f}\tAcc@5: {:.4f}\tLoss: {:.4f}".format(task_id+1, final_acc, 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)
logging.info(result_str)
return test_stats
def train_and_evaluate(model: torch.nn.Module,
criterion, data_loader: Iterable, lr_scheduler, 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))
old_num_k = 0
for task_id in range(args.num_tasks):
if task_id>0:
model.head.update(len(class_mask[task_id]))
# Create new optimizer for each task to clear optimizer status
not_n_params = []
n_params = []
if args.SLCA:
milestones = [18] if "CIFAR" in args.dataset else [40]
lrate_decay = 0.1
param_list = list(model.parameters())
if task_id:
for n, p in model.named_parameters():
if n.find('norm1')>=0 or n.find('norm2') >= 0 or n.startswith('norm') or n.find('fc_norm') >= 0:
# print(f'Param: {n} Param.requires_grad: {p.requires_grad}')
n_params.append(p)
else:
not_n_params.append(p)
network_params = [{'params': not_n_params, 'lr': args.lr, 'weight_decay': args.weight_decay},
{'params': n_params, 'lr': 0.005*args.lr, 'weight_decay': args.weight_decay}]
else:
network_params = [{'params': param_list, 'lr': args.lr, 'weight_decay': args.weight_decay}]
if not args.SLCA:
print("Using adam optimizer")
print("Reinitialising optimizer")
optimizer = optim.Adam(network_params, weight_decay=args.weight_decay)
if args.sched != 'constant':
# lr_scheduler, _ = create_scheduler(args, optimizer)
# Create cosine lr scheduler
lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=0)
elif args.sched == 'constant':
lr_scheduler = None
else:
optimizer = optim.SGD(network_params, lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer, milestones=milestones, gamma=lrate_decay)
if args.use_e_prompt or args.use_g_prompt:
old_prompt = copy.deepcopy(model.e_prompt.prompt.clone().detach())
old_prompt_matcher = copy.deepcopy(model.e_prompt.prompt_embed_matcher)
curr_num_k = num_new_prompts(class_mask, task_id, args) # Returns number of prompts to be added for this task
model.e_prompt.process_new_task(old_num_k, old_num_k + curr_num_k)
else:
curr_num_k = 0
old_prompt_matcher = None
old_prompt = None
print("Task number: ", task_id)
for epoch in range(args.epochs):
logging.info('Training for task {} epoch {}/{}'.format(task_id, epoch, 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,
old_prompt_matcher=old_prompt_matcher,
old_prompt=old_prompt,
set_training_mode=True, task_id=task_id, class_mask=class_mask, args=args,
old_num_k=old_num_k,)
if lr_scheduler:
lr_scheduler.step()
if args.use_e_prompt or args.use_g_prompt:
old_num_k += curr_num_k
eval_start_time = time.time()
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)
eval_end_time = time.time()
print(f"Eval time for task {task_id+1} = {eval_end_time - eval_start_time}")
if args.output_dir and utils.is_main_process():
path = args.output_dir+'_'+args.dataset
Path(os.path.join(path, 'checkpoint')).mkdir(parents=True, exist_ok=True)
checkpoint_path = os.path.join(path, 'checkpoint/task{}_checkpoint.pth'.format(task_id+1))
state_dict = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
'args': args,
}
if args.sched is not None and args.sched != 'constant':
state_dict['lr_scheduler'] = lr_scheduler.state_dict()
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 and utils.is_main_process():
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')