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solver.py
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solver.py
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import torch
import torch.nn as nn
import os
from torch import optim
from model import encoder, classifier, discriminator
from sklearn.metrics import confusion_matrix, accuracy_score
from utils import adjust_alpha
from data_loader import get_loader
import copy
import torch.nn.functional as F
from torchsummary import summary
class Solver(object):
def __init__(self, args):
self.args = args
self.s_train_loader, self.s_test_loader, self.t_train_loader, self.t_test_loader = get_loader(args)
self.ce = nn.CrossEntropyLoss()
self.bce = nn.BCELoss()
self.best_acc = 0
self.time_taken = None
self.enc = encoder(self.args).cuda()
self.clf = classifier(self.args).cuda()
self.fd = discriminator(self.args).cuda()
print('--------Network--------')
print(self.enc)
print(self.clf)
print('--------Feature Disc--------')
print(self.fd)
self.fake_label = torch.FloatTensor(self.args.batch_size, 1).fill_(0).cuda()
self.real_label = torch.FloatTensor(self.args.batch_size, 1).fill_(1).cuda()
if not args.method == 'src':
if os.path.exists(os.path.join(self.args.model_path, 'src_enc.pt')):
print("Loading Source model...")
self.enc.load_state_dict(torch.load(os.path.join(self.args.model_path, 'src_enc.pt')))
self.clf.load_state_dict(torch.load(os.path.join(self.args.model_path, 'src_clf.pt')))
else:
print("Training Source model...")
self.src()
self.test()
def test_dataset(self, db='t_test'):
self.enc.eval()
self.clf.eval()
actual = []
pred = []
if db.lower() == 's_train':
loader = self.s_train_loader
elif db.lower() == 's_test':
loader = self.s_test_loader
elif db.lower() == 't_train':
loader = self.t_train_loader
else:
loader = self.t_test_loader
for data in loader:
img, label = data
img = img.cuda()
with torch.no_grad():
class_out = self.clf(self.enc(img))
_, predicted = torch.max(class_out.data, 1)
actual += label.tolist()
pred += predicted.tolist()
acc = accuracy_score(y_true=actual, y_pred=pred) * 100
cm = confusion_matrix(y_true=actual, y_pred=pred, labels=range(self.args.num_classes))
return acc, cm
def test(self):
s_train_acc, cm = self.test_dataset('s_train')
print("Source Tr Acc: %.2f" % (s_train_acc))
if self.args.cm:
print(cm)
s_test_acc, cm = self.test_dataset('s_test')
print("Source Te Acc: %.2f" % (s_test_acc))
if self.args.cm:
print(cm)
t_train_acc, cm = self.test_dataset('t_train')
print("Target Tr Acc: %.2f" % (t_train_acc))
if self.args.cm:
print(cm)
t_test_acc, cm = self.test_dataset('t_test')
print("Target Te Acc: %.2f" % (t_test_acc))
if self.args.cm:
print(cm)
return s_train_acc, s_test_acc, t_train_acc, t_test_acc
def src(self):
total_iters = 0
self.best_acc = 0
s_iter_per_epoch = len(iter(self.s_train_loader))
self.args.src_test_epoch = max(self.args.src_epochs // 10, 1)
self.optimizer = optim.Adam(list(self.enc.parameters()) + list(self.clf.parameters()), self.args.lr,
betas=[0.5, 0.999], weight_decay=self.args.weight_decay)
for epoch in range(self.args.src_epochs):
self.clf.train()
self.enc.train()
for i, (source, s_labels) in enumerate(self.s_train_loader):
total_iters += 1
source, s_labels = source.cuda(), s_labels.cuda()
s_logits = self.clf(self.enc(source))
s_clf_loss = self.ce(s_logits, s_labels)
loss = s_clf_loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if i % 50 == 0 or i == (s_iter_per_epoch - 1):
print('Ep: %d/%d, iter: %d/%d, total_iters: %d, s_err: %.4f'
% (epoch + 1, self.args.src_epochs, i + 1, s_iter_per_epoch, total_iters, s_clf_loss))
if (epoch + 1) % self.args.src_test_epoch == 0:
s_test_acc, cm = self.test_dataset('s_test')
print("Source test acc: %0.2f" % (s_test_acc))
if self.args.cm:
print(cm)
if s_test_acc > self.best_acc:
self.best_acc = s_test_acc
best_enc = copy.deepcopy(self.enc.state_dict())
best_clf = copy.deepcopy(self.clf.state_dict())
torch.save(best_enc, os.path.join(self.args.model_path, 'src_enc.pt'))
torch.save(best_clf, os.path.join(self.args.model_path, 'src_clf.pt'))
self.enc.load_state_dict(best_enc)
self.clf.load_state_dict(best_clf)
def dann(self):
s_iter_per_epoch = len(self.s_train_loader)
t_iter_per_epoch = len(self.t_train_loader)
min_len = min(s_iter_per_epoch, t_iter_per_epoch)
total_iters = 0
print("Source iters per epoch: %d" % (s_iter_per_epoch))
print("Target iters per epoch: %d" % (t_iter_per_epoch))
print("iters per epoch: %d" % (min(s_iter_per_epoch, t_iter_per_epoch)))
self.optimizer = optim.Adam(list(self.enc.parameters()) + list(self.clf.parameters()) + list(self.fd.parameters()), self.args.lr,
betas=[0.5, 0.999], weight_decay=self.args.weight_decay)
for epoch in range(self.args.adapt_epochs):
self.clf.train()
self.enc.train()
self.fd.train()
for i, (source_data, target_data) in enumerate(zip(self.s_train_loader, self.t_train_loader)):
total_iters += 1
alpha = adjust_alpha(i, epoch, min_len, self.args.adapt_epochs)
source, s_labels = source_data
source, s_labels = source.cuda(), s_labels.cuda()
target, t_labels = target_data
target, t_labels = target.cuda(), t_labels.cuda()
s_deep = self.enc(source)
s_out = self.clf(s_deep)
t_deep = self.enc(target)
t_out = self.clf(t_deep)
s_fd_out = self.fd(s_deep, alpha=alpha)
t_fd_out = self.fd(t_deep, alpha=alpha)
s_domain_err = self.bce(s_fd_out, self.real_label)
t_domain_err = self.bce(t_fd_out, self.fake_label)
disc_loss = s_domain_err + t_domain_err
s_clf_loss = self.ce(s_out, s_labels)
loss = s_clf_loss + disc_loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if i % 50 == 0 or i == (min_len - 1):
print('Ep: %d/%d, iter: %d/%d, total_iters: %d, s_err: %.4f, d_err: %.4f, alpha: %.4f'
% (epoch + 1, self.args.adapt_epochs, i + 1, min_len, total_iters, s_clf_loss, disc_loss, alpha))
if (epoch + 1) % self.args.adapt_test_epoch == 0:
t_test_acc, cm = self.test_dataset('t_test')
print("Target test acc: %0.2f" % (t_test_acc))
if self.args.cm:
print(cm)
torch.save(self.enc.state_dict(), os.path.join(self.args.model_path, 'dann_enc.pt'))
torch.save(self.clf.state_dict(), os.path.join(self.args.model_path, 'dann_clf.pt'))
torch.save(self.fd.state_dict(), os.path.join(self.args.model_path, 'dann_disc.pt'))