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reverse_grad.py
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reverse_grad.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torch.autograd import Function
from model import encoder, domain_classifier, feature_extractor_1
from LoadData import DATASET
from LoadData_1 import DATASET_1
import sys
import grad_rever_function as my_function
import argparse
import os
import random
import shutil
import time
import warnings
class ToRGB(object):
def __init__(self):
pass
def __call__(self, sample):
sample = sample.convert('RGB')
return sample
### basic setting ###
cuda = torch.cuda.is_available()
device = torch.device('cuda' if cuda else 'cpu')
download = True
BATCH_SIZE = 30
EP = 30
### ------------- ###
mean, std = np.array([0.5, 0.5, 0.5]), np.array([0.5, 0.5, 0.5])
rgb_transform = transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
gray2rgb_transform = transforms.Compose([
ToRGB(),
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
def train(cls_model, domain_clf, optimizer, ep, train_loader, test_loader, src_name, tar_name):
loss_fn_cls = nn.CrossEntropyLoss()
loss_fn_domain = nn.MSELoss()
ac_list, loss_list = [], []
max_= 0
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(len(train_loader), batch_time, data_time, losses, top1,
top5, prefix="Epoch: [{}]".format(ep))
end = time.time()
for i in range(ep):
cls_model.train()
domain_clf.train()
print(i)
for index, (src_batch, tar_batch) in enumerate(zip(train_loader, test_loader)):
if index % 10 == 0:
print('start')
data_time.update(time.time() - end)
p = float(index + i * min([len(train_loader), len(test_loader)])) / ep / min([len(train_loader), len(test_loader)])
alpha = 2. / (1. + np.exp(-10 * p)) - 1
alpha = 1
x, y = src_batch
x = x.to(device)
y = y.to(device)
y = y.view(-1)
tar_x, _ = tar_batch
tar_x = tar_x.to(device)
src_pred, src_feature = cls_model(x)
_, tar_feature = cls_model(tar_x)
label_loss = loss_fn_cls(src_pred, y)
losses.update(label_loss.item(), x.size(0))
src_domain = domain_clf(src_feature, alpha)
tar_domain = domain_clf(tar_feature, alpha)
src_domain_label = torch.ones(x.size(0)).to(device)
tar_domain_label = torch.zeros(tar_x.size(0)).to(device)
domain_loss = loss_fn_domain(src_domain, src_domain_label) + loss_fn_domain(tar_domain, tar_domain_label)
cls_loss = loss_fn_cls(src_pred, y)
loss = cls_loss + domain_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
if index % 10 == 0:
print('[%d]/[%d]' % (index, min([len(train_loader), len(test_loader)])))
batch_time.update(time.time() - end)
end = time.time()
if index % 10 == 0:
progress.print(i)
cls_model.eval()
domain_clf.eval()
ac = 0
total_loss=0
with torch.no_grad():
for batch in test_loader:
x, y = batch
x = x.to(device)
y = y.to(device)
y = y.view(-1)
pred, _ = cls_model(x)
loss = loss_fn_cls(pred, y)
ac += np.sum(np.argmax(pred.cpu().detach().numpy(), axis=1) == y.cpu().detach().numpy())
total_loss += loss.item()
print('Accuracy : [%.3f], Avg Loss : [%.4f]' % ((ac / len(test_loader) / BATCH_SIZE), (total_loss / len(test_loader))) )
ac_list.append(ac/len(test_loader)/BATCH_SIZE)
loss_list.append(total_loss / len(test_loader) / BATCH_SIZE)
if (ac / len(test_loader) / BATCH_SIZE) > max_:
max_ = (ac / len(test_loader) / BATCH_SIZE)
torch.save(cls_model.state_dict(), './model/reverse_grad_'+src_name+'2'+tar_name+'.pth')
return ac_list, loss_list
def main(src, tar):
clf = encoder().to(device)
domain_clf = domain_classifier().to(device)
optimizer = optim.Adam(list(clf.parameters()) + list(domain_clf.parameters()) , lr=1e-4)
### dataloader ###
if src == 'mnist':
src_train_set = dset.MNIST('./dataset/mnist', train=True, download=True, transform=gray2rgb_transform)
elif src == 'mnistm':
src_train_set = DATASET('./dataset/mnistm/train', './dataset/mnistm/train.csv', transforms=rgb_transform)
elif src == 'svhn':
src_train_set = dset.SVHN(root='./dataset/svhn/', download=download, transform=rgb_transform)
elif src == 'usps':
src_train_set = DATASET('./dataset/usps/train', './dataset/usps/train.csv', transforms=gray2rgb_transform)
if tar == 'svhn':
tar_train_set = dset.SVHN(root='./dataset/svhn/', download=download, transform = rgb_transform)
elif tar == 'mnist':
tar_train_set = dset.MNIST('./dataset/mnist', train=True, download=True, transform=gray2rgb_transform)
elif tar == 'mnistm':
tar_train_set = DATASET('./dataset/mnistm/train', './dataset/mnistm/train.csv', transform=rgb_transform)
elif tar == 'usps':
tar_train_set = DATASET('./dataset/usps/train', './dataset/usps/train.csv', transform=rgb_transform)
src_train_loader = torch.utils.data.DataLoader(
dataset = src_train_set,
batch_size = BATCH_SIZE,
shuffle = True,
pin_memory=True
)
tar_train_loader = torch.utils.data.DataLoader(
dataset = tar_train_set,
batch_size = BATCH_SIZE,
shuffle = True,
pin_memory=True
)
# train
ac_list, loss_list = train(clf, domain_clf, optimizer, EP, src_train_loader, tar_train_loader, src, tar)
ac_list = np.array(ac_list).flatten()
# plot tsne
loss_list = np.array(loss_list).flatten()
epoch = [i for i in range(EP)]
my_function.tsne_plot(clf, src_train_loader, tar_train_loader, src, tar, BATCH_SIZE, 'reverse_grad')
### plot learning curve ###
plt.figure()
plt.plot(epoch, ac_list)
plt.xlabel('EPOCH')
plt.ylabel('Accuracy')
plt.title('domian_adapt : ' + src + ' to ' + tar)
plt.savefig('./learning_curve/domian_adapt_' + src + '_to_' + tar + '_accuracy.jpg')
plt.figure()
plt.plot(epoch, loss_list)
plt.xlabel('EPOCH')
plt.ylabel('Loss')
plt.title('domian_adapt : ' + src + ' to ' + tar)
plt.savefig('./learning_curve/domian_adapt_' + src + '_to_' + tar + '_loss.jpg')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, *meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def print(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
if __name__ == '__main__':
source, target = sys.argv[1:]
main(source, target)