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train_fun.py
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import math
from tqdm import tqdm
import os
import pandas as pd
import torch
from torch.utils.tensorboard import SummaryWriter
import logging
import os
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.nn.functional as F
import yaml
class TrainUtils:
def __init__(self, model, train_loader, optimizer, args, args_dict, memory_loader, test_loader):
self.model = model
self.train_loader = train_loader
self.optimizer = optimizer
self.args = args
self.memory_loader = memory_loader
self.test_loader = test_loader
self.writer = SummaryWriter() #auto creates runs/Apr07_18-01-17_d1007 DIR
self.path = os.path.join(self.args.results_dir,self.writer.log_dir)
if not os.path.exists(self.path):
os.makedirs(self.path)
with open(os.path.join(self.path,'config.yml'), 'w') as file:
documents = yaml.dump(args_dict, file)
logging.basicConfig(filename=os.path.join(self.path, 'training.log'), level=logging.DEBUG)
print("\n\n directory: {}".format(self.path))
def train_one_epoch(self, net , data_loader, train_optimizer, epoch, args):
# train for one epoch
net.train()
#self.model.train()
self.adjust_learning_rate(train_optimizer, epoch, args)
total_loss, total_num, train_bar = 0.0, 0, tqdm(data_loader)
for images, _ in train_bar:
im_1 = images[0]
im_2 = images[1]
im_1, im_2 = im_1.cuda(non_blocking=True), im_2.cuda(non_blocking=True)
loss, logits, labels = net(im_1, im_2)
if(self.n_iter % 50 == 0):
self.top1, self.top5 = self.accuracy(logits, labels, topk=(1, 5))
self.n_iter +=1
# self.writer.add_scalar('loss', loss, global_step=n_iter)
# self.writer.add_scalar('acc/top1', top1[0], global_step=epoch)
# self.writer.add_scalar('acc/top5', top5[0], global_step=epoch)
# self.writer.add_scalar('learning_rate', train_optimizer.param_groups[0]['lr'], global_step=epoch)
train_optimizer.zero_grad()
loss.backward()
train_optimizer.step()
total_num += data_loader.batch_size
total_loss += loss.item() * data_loader.batch_size
train_bar.set_description(
'Train Epoch: [{}/{}], lr: {:.6f}, Loss: {:.4f}'.format(epoch, args.epochs, train_optimizer.param_groups[0]['lr'],
total_loss / total_num))
return total_loss / total_num
# lr scheduler for training
def adjust_learning_rate(self, optimizer, epoch, args):
"""Decay the learning rate based on schedule"""
lr = args.lr
if args.cos: # cosine lr schedule
lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
else: # stepwise lr schedule
for milestone in args.schedule:
lr *= 0.1 if epoch >= milestone else 1.
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(self, output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
# test using a knn monitor
def test(self,net, memory_data_loader, test_data_loader, epoch, args):
net.eval()
classes = len(memory_data_loader.dataset.classes)
total_top1, total_top5, total_num, feature_bank = 0.0, 0.0, 0, []
with torch.no_grad():
# generate feature bank
for data, target in tqdm(memory_data_loader, desc='Feature extracting'):
feature = net(data.cuda(non_blocking=True))
feature = F.normalize(feature, dim=1)
feature_bank.append(feature)
# [D, N]
feature_bank = torch.cat(feature_bank, dim=0).t().contiguous()
# [N]
if(args.dataset == 'stl10'):
feature_labels = torch.tensor(memory_data_loader.dataset.labels, device=feature_bank.device)
else:
feature_labels = torch.tensor(memory_data_loader.dataset.targets, device=feature_bank.device)
# loop test data to predict the label by weighted knn search
test_bar = tqdm(test_data_loader)
for data, target in test_bar:
data, target = data.cuda(non_blocking=True), target.cuda(non_blocking=True)
feature = net(data)
feature = F.normalize(feature, dim=1)
pred_labels = self.knn_predict(feature, feature_bank, feature_labels, classes, args.knn_k, args.knn_t)
total_num += data.size(0)
total_top1 += (pred_labels[:, 0] == target).float().sum().item()
test_bar.set_description('Test Epoch: [{}/{}] Acc@1:{:.2f}%'.format(epoch, args.epochs, total_top1 / total_num * 100))
return total_top1 / total_num * 100
# knn monitor as in InstDisc https://arxiv.org/abs/1805.01978
# implementation follows http://github.com/zhirongw/lemniscate.pytorch and https://github.com/leftthomas/SimCLR
def knn_predict(self, feature, feature_bank, feature_labels, classes, knn_k, knn_t):
# compute cos similarity between each feature vector and feature bank ---> [B, N]
sim_matrix = torch.mm(feature, feature_bank)
# [B, K]
sim_weight, sim_indices = sim_matrix.topk(k=knn_k, dim=-1)
# [B, K]
sim_labels = torch.gather(feature_labels.expand(feature.size(0), -1), dim=-1, index=sim_indices)
sim_weight = (sim_weight / knn_t).exp()
# counts for each class
one_hot_label = torch.zeros(feature.size(0) * knn_k, classes, device=sim_labels.device)
# [B*K, C]
print(sim_labels.view(-1,1))
one_hot_label = one_hot_label.scatter(dim=-1, index=sim_labels.view(-1, 1), value=1.0)
# weighted score ---> [B, C]
pred_scores = torch.sum(one_hot_label.view(feature.size(0), -1, classes) * sim_weight.unsqueeze(dim=-1), dim=1)
pred_labels = pred_scores.argsort(dim=-1, descending=True)
return pred_labels
def train(self, epoch_start=1):
# training loop
epoch_start = 1
self.n_iter= 0
if(self.args.resume is ''):
logging.info(f"Start MoCo training for {self.args.epochs} epochs.")
for epoch in range(epoch_start, self.args.epochs + 1):
train_loss = self.train_one_epoch(self.model, self.train_loader, self.optimizer, epoch, self.args)
logging.info("Epoch: {}\ttrain_loss: {:.3f}\tAcc@1: {:.3f}\tAcc@5: {:.3f}".format(epoch,train_loss,self.top1[0], self.top5[0]))
torch.save({'epoch': epoch, 'state_dict': self.model.state_dict(), 'optimizer' : self.optimizer.state_dict(),}, os.path.join(self.args.results_dir, self.writer.log_dir,'model.pth'))
logging.info(f"Model, metadata and training log has been saved at {self.path}.")
def knn_train(self, epoch_start=1):
# training loop
self.n_iter= 0
if(self.args.resume is ''):
logging.info(f"Start MoCo training for {self.args.epochs} epochs. knn testing")
for epoch in range(epoch_start, self.args.epochs + 1):
train_loss = self.train_one_epoch(self.model, self.train_loader, self.optimizer, epoch, self.args)
test_acc_1 = self.test(self.model.encoder_q, self.memory_loader, self.test_loader, epoch, self.args)
logging.info("Epoch: {}\ttrain_loss: {:.3f}\ttest_Acc@1: {:.3f}".format(epoch,train_loss,test_acc_1))
torch.save({'epoch': epoch, 'state_dict': self.model.state_dict(), 'optimizer' : self.optimizer.state_dict(),}, os.path.join(self.args.results_dir, self.writer.log_dir,'model.pth'))
logging.info(f"Model, metadata and training log has been saved at {self.path}.")