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Contrastive_trainer.py
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Contrastive_trainer.py
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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File ：L5 -> Contrastive_train
@IDE ：PyCharm
@Author ：DIPTE
@Date ：2020/8/3 0:25
@Desc ：
=================================================='''
import time
import datetime
import torch
import torch.nn as nn
import numpy as np
from loss_function import *
from average_meter import AverageMeter
class Trainer(object):
def __init__(self, epochs, dataloaders, model, optimizer, scheduler, device,
margin, ckpt_tag,criterion):
self.epochs = epochs
self.dataloaders = dataloaders
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.device = device
self.margin = margin
self.ckpt_tag = ckpt_tag
# save best model
self.best_val_acc = -100
self.criterion = criterion#ContrastiveLoss(margin=0.8)
def train(self):
for epoch in range(self.epochs):
self.train_epoch(epoch, 'train')
self.eval_epoch(epoch, 'LFW')
print("Best acc on LFW: {}, best threshold: {}".format(self.best_val_acc,
self.best_threshold))
def train_epoch(self, epoch, phase):
loss_ = AverageMeter()
accuracy_ = AverageMeter()
self.model.train()
self.margin.train()
for batch_idx, sample in enumerate(self.dataloaders[phase]):
imageL, imageR, label = sample[0].to(self.device), \
sample[1].to(self.device), sample[2].to(self.device)
self.optimizer.zero_grad()
with torch.set_grad_enabled(True):
outputL, outputR = self.model(imageL), self.model(imageR)
acc = 0
loss = self.criterion([outputL, outputR], label)
loss.backward()
self.optimizer.step()
loss_.update(loss, label.size(0))
accuracy_.update(acc, label.size(0))
if batch_idx % 40 == 0:
print('Train Epoch: {} [{:08d}/{:08d} ({:02.0f}%)]\tLoss:{:.6f}\tAcc:{:.6f} LR:{:.7f}'.format(
epoch, batch_idx * len(label), len(self.dataloaders[phase].dataset),
100. * batch_idx / len(self.dataloaders[phase]), loss.item(), 0,
self.optimizer.param_groups[0]['lr']))
self.scheduler.step()
print("Train Epoch Loss: {:.6f} Accuracy: {:.6f}".format(loss_.avg,
accuracy_.avg))
torch.save(self.model.state_dict(),
'./checkpoints/{}_{}_Contrastive_{:04d}.pth'.format(self.ckpt_tag,
str(self.margin), epoch))
torch.save(self.margin.state_dict(),
'./checkpoints/{}_512_{}_Contrastive_{:04d}.pth'.format(self.ckpt_tag,
str(self.margin), epoch))
def eval_epoch(self, epoch, phase):
feature_ls = feature_rs = flags = folds = None
# sample = {'pair':[img_l, img_r], 'label': 1/-1}
for batch_idx, sample in enumerate(self.dataloaders[phase]):
img_l = sample['pair'][0].to(self.device)
img_r = sample['pair'][1].to(self.device)
flag = sample['label'].numpy()
fold = sample['fold'].numpy()
feature_l, feature_r = self.getDeepFeature(img_l, img_r)
feature_l, feature_r = feature_l.cpu().numpy(), feature_r.cpu().numpy()
if (feature_ls is None) and (feature_rs is None):
feature_ls = feature_l
feature_rs = feature_r
flags = flag
folds = fold
else:
feature_ls = np.concatenate((feature_ls, feature_l), 0)
feature_rs = np.concatenate((feature_rs, feature_r), 0)
flags = np.concatenate((flags, flag), 0)
folds = np.concatenate((folds, fold), 0)
accs, thresholds = self.evaluation_10_fold(feature_ls, feature_rs, flags, folds,
method='cos_distance')
print("Eval Epoch Average Acc: {:.4f}, Average Threshold: {:.4f}".format(
np.mean(accs), np.mean(thresholds)))
if np.mean(accs) > self.best_val_acc:
self.best_val_acc = np.mean(accs)
torch.save(self.model.state_dict(),
'./checkpoints/{}_{}_Contrastive_best.pth'.format(self.ckpt_tag,
str(self.margin)))
torch.save(self.margin.state_dict(),
'./checkpoints/{}_512_{}_Contrastive_best.pth'.format(self.ckpt_tag, str(self.margin)))
self.best_threshold = np.mean(thresholds)
def getDeepFeature(self, img_l, img_r):
self.model.eval()
with torch.no_grad():
feature_l = self.model(img_l)
feature_r = self.model(img_r)
return feature_l, feature_r
def evaluation_10_fold(self, feature_ls, feature_rs, flags, folds,
method='l2_distance'):
accs = np.zeros(10)
thresholds = np.zeros(10)
for i in range(10):
val_fold = (folds != i)
test_fold = (folds == i)
# minus by mean
mu = np.mean(np.concatenate((feature_ls[val_fold, :],
feature_rs[val_fold, :]),
0), 0)
feature_ls = feature_ls - mu
feature_rs = feature_rs - mu
# normalization
feature_ls = feature_ls / np.expand_dims(np.sqrt(np.sum(np.power(feature_ls, 2), 1)), 1)
feature_rs = feature_rs / np.expand_dims(np.sqrt(np.sum(np.power(feature_rs, 2), 1)), 1)
if method == 'l2_distance':
scores = np.sum(np.power((feature_ls - feature_rs), 2), 1)
elif method == 'cos_distance':
scores = np.sum(np.multiply(feature_ls, feature_rs), 1)
else:
raise NameError("Distance Method not supported")
thresholds[i] = self.getThreshold(scores[val_fold], flags[val_fold], 10000, method)
accs[i] = self.getAccuracy(scores[test_fold], flags[test_fold], thresholds[i], method)
return accs, thresholds
def getThreshold(self, scores, flags, thrNum, method='l2_distance'):
accs = np.zeros((2 * thrNum + 1, 1))
thresholds = np.arange(-thrNum, thrNum + 1) * 3 / thrNum
# print(thresholds)
# print(np.min(scores))
# print(np.max(scores))
for i in range(2 * thrNum + 1):
accs[i] = self.getAccuracy(scores, flags, thresholds[i], method)
max_index = np.squeeze(accs == np.max(accs))
best_threshold = np.mean(thresholds[max_index]) # multi best threshold
return best_threshold
def getAccuracy(self, scores, flags, threshold, method='l2_distance'):
if method == 'l2_distance':
pred_flags = np.where(scores < threshold, 1, -1)
elif method == 'cos_distance':
pred_flags = np.where(scores > threshold, 1, -1)
acc = np.sum(pred_flags == flags) / pred_flags.shape[0]
return acc