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resnet50_dwt_mec_officehome.py
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resnet50_dwt_mec_officehome.py
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"""
File modified from:
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
from __future__ import print_function
import sys
sys.path.append('utils')
import argparse
import os
import numpy as np
from PIL import Image
import scipy.io as sio
import cv2
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
import torch.nn.functional as F
from torchvision import datasets, models, transforms
import batch_norm
import folder
import consensus_loss
import whitening
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class whitening_scale_shift(nn.Module):
def __init__(self, planes, group_size, running_mean, running_variance, track_running_stats=True, affine=True):
super(whitening_scale_shift, self).__init__()
self.planes = planes
self.group_size = group_size
self.track_running_stats = track_running_stats
self.affine = affine
self.running_mean = running_mean
self.running_variance = running_variance
self.wh = whitening.WTransform2d(self.planes,
self.group_size,
running_m=self.running_mean,
running_var=self.running_variance,
track_running_stats=self.track_running_stats)
if self.affine:
self.gamma = nn.Parameter(torch.ones(self.planes, 1, 1))
self.beta = nn.Parameter(torch.zeros(self.planes, 1, 1))
def forward(self, x):
out = self.wh(x)
if self.affine:
out = out * self.gamma + self.beta
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, layer, sub_layer, bn_dict, group_size=4, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.expansion = 4
self.conv1 = conv1x1(inplanes, planes)
if layer == 1:
self.bns1 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.bnt1 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.bnt1_aug = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.wh.running_variance'],
affine=False)
self.gamma1 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.gamma'])
self.beta1 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.beta'])
else:
self.bns1 = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_var'],
affine=False)
self.bnt1 = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_var'],
affine=False)
self.bnt1_aug = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.running_var'],
affine=False)
self.gamma1 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.weight'].view(-1, 1, 1))
self.beta1 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn1.bias'].view(-1, 1, 1))
self.conv2 = conv3x3(planes, planes, stride)
if layer == 1:
self.bns2 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.bnt2 = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.bnt2_aug = whitening_scale_shift(planes=planes,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.wh.running_variance'],
affine=False)
self.gamma2 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.gamma'])
self.beta2 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.beta'])
else:
self.bns2 = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_var'],
affine=False)
self.bnt2 = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_var'],
affine=False)
self.bnt2_aug = batch_norm.BatchNorm2d(num_features=planes,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.running_var'],
affine=False)
self.gamma2 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.weight'].view(-1, 1, 1))
self.beta2 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn2.bias'].view(-1, 1, 1))
self.conv3 = conv1x1(planes, planes * self.expansion)
if layer == 1:
self.bns3 = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.bnt3 = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.bnt3_aug = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.wh.running_variance'],
affine=False)
self.gamma3 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.gamma'])
self.beta3 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.beta'])
else:
self.bns3 = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_var'],
affine=False)
self.bnt3 = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_var'],
affine=False)
self.bnt3_aug = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.running_var'],
affine=False)
self.gamma3 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.weight'].view(-1, 1, 1))
self.beta3 = nn.Parameter(bn_dict['layer' + str(layer) + '.' + str(sub_layer) + '.bn3.bias'].view(-1, 1, 1))
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
if self.downsample is not None:
if layer == 1:
self.downsample_bns = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_bnt = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_bnt_aug = whitening_scale_shift(planes=planes * self.expansion,
group_size=group_size,
running_mean=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_mean'],
running_variance=bn_dict['layer' + str(layer) + '.0.downsample_bn.wh.running_variance'],
affine=False)
self.downsample_gamma = nn.Parameter(bn_dict['layer' + str(layer) + '.0.downsample_bn.gamma'])
self.downsample_beta = nn.Parameter(bn_dict['layer' + str(layer) + '.0.downsample_bn.beta'])
else:
self.downsample_bns = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_bnt = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_bnt_aug = batch_norm.BatchNorm2d(num_features=planes * self.expansion,
running_m=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_mean'],
running_v=bn_dict['layer' + str(layer) + '.0.downsample_bn.running_var'],
affine=False)
self.downsample_gamma = nn.Parameter(bn_dict['layer' + str(layer) + '.0.downsample_bn.weight'].view(-1, 1, 1))
self.downsample_beta = nn.Parameter(bn_dict['layer' + str(layer) + '.0.downsample_bn.bias'].view(-1, 1, 1))
def forward(self, x):
if self.training:
# to do
identity = x
out = self.conv1(x)
out_s, out_t, out_t_dup = torch.split(out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns1(out_s), torch.cat((self.bnt1(out_t), self.bnt1_aug(out_t_dup)), dim=0) ), dim=0) * self.gamma1 + self.beta1
out = self.relu(out)
out = self.conv2(out)
out_s, out_t, out_t_dup = torch.split(out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns2(out_s), torch.cat((self.bnt2(out_t), self.bnt2_aug(out_t_dup)), dim=0) ), dim=0) * self.gamma2 + self.beta2
out = self.relu(out)
out = self.conv3(out)
out_s, out_t, out_t_dup = torch.split(out, split_size_or_sections=out.shape[0] // 3, dim=0)
out = torch.cat((self.bns3(out_s), torch.cat((self.bnt3(out_t), self.bnt3_aug(out_t_dup)), dim=0) ), dim=0) * self.gamma3 + self.beta3
if self.downsample is not None:
identity = self.downsample(x)
identity_s, identity_t, identity_t_dup = torch.split(identity, split_size_or_sections=identity.shape[0] // 3, dim=0)
identity = torch.cat((self.downsample_bns(identity_s),
torch.cat((self.downsample_bnt(identity_t), self.downsample_bnt_aug(identity_t_dup)), dim=0) ), dim=0) * self.downsample_gamma + self.downsample_beta
out = out.clone() + identity
out = self.relu(out)
else:
identity = x
out = self.conv1(x)
out = self.bnt1(out) * self.gamma1 + self.beta1
out = self.relu(out)
out = self.conv2(out)
out = self.bnt2(out) * self.gamma2 + self.beta2
out = self.relu(out)
out = self.conv3(out)
out = self.bnt3(out) * self.gamma3 + self.beta3
if self.downsample is not None:
identity = self.downsample(x)
identity = self.downsample_bnt(identity) * self.downsample_gamma + self.downsample_beta
out = out.clone() + identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, state_dict, num_classes=65, zero_init_residual=False, group_size=4):
super(ResNet, self).__init__()
self.inplanes = 64
self.bn_dict = compute_bn_stats(state_dict)
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bns1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.bnt1 = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.bnt1_aug = whitening_scale_shift(planes=64,
group_size=group_size,
running_mean=self.bn_dict['bn1.wh.running_mean'],
running_variance=self.bn_dict['bn1.wh.running_variance'],
affine=False)
self.gamma1 = nn.Parameter(self.bn_dict['bn1.gamma'])
self.beta1 = nn.Parameter(self.bn_dict['bn1.beta'])
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], self.bn_dict, layer=1)
self.layer2 = self._make_layer(block, 128, layers[1], self.bn_dict, stride=2, layer=2)
self.layer3 = self._make_layer(block, 256, layers[2], self.bn_dict, stride=2, layer=3)
self.layer4 = self._make_layer(block, 512, layers[3], self.bn_dict, stride=2, layer=4)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc_out = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, bn_dict, layer=1, group_size=4, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
#nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, layer, 0, bn_dict, group_size, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, layer, i, bn_dict, group_size))
return nn.Sequential(*layers)
def forward(self, x):
if self.training:
x = self.conv1(x)
x_s, x_t, x_t_dup = torch.split(x, split_size_or_sections=x.shape[0] // 3, dim=0)
x = torch.cat((self.bns1(x_s), torch.cat((self.bnt1(x_t), self.bnt1_aug(x_t_dup)), dim=0) ), dim=0) * self.gamma1 + self.beta1
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_out(x)
else:
x = self.conv1(x)
x = self.bnt1(x) * self.gamma1 + self.beta1
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_out(x)
return x
def resnet50(weights_path, device):
state_dict_ = torch.load(weights_path, map_location=device)
state_dict_model = state_dict_['state_dict']
modified_state_dict = {}
for key in state_dict_model.keys():
mod_key = key[7:]
modified_state_dict.update({mod_key: state_dict_model[key]})
model = ResNet(Bottleneck, [3, 4, 6, 3], modified_state_dict)
model.load_state_dict(modified_state_dict, strict=False)
return model
def eval_pass_collect_stats(args, model, device, target_test_loader):
# Run a bunch of forward passes to collect the target statistics before evaluating on the test set
model.train(mode=True)
with torch.no_grad():
for i in range(10):
print("Pass {} ...".format(i))
for data, _ in target_test_loader:
data = torch.cat((data, data, data), dim=0) # dont care about source statistics after its trained.
data = data.to(device)
output = model(data)
def train_infinite_collect_stats(args, model, device, source_train_loader,
target_train_loader, optimizer, lambda_mec_loss,
target_test_loader):
source_iter = iter(source_train_loader)
target_iter = iter(target_train_loader)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[6000], gamma=0.1)
for i in range(args.num_iters):
model.train()
exp_lr_scheduler.step()
try:
source_data, source_y = next(source_iter)
except:
source_iter = iter(source_train_loader)
source_data, source_y = next(source_iter)
try:
target_data, target_data_dup, _ = next(target_iter)
except:
target_iter = iter(target_train_loader)
target_data, target_data_dup, _ = next(target_iter)
data = torch.cat((source_data, target_data, target_data_dup), dim=0) # concat the source and target mini-batches
data, source_y = data.to(device), source_y.to(device)
optimizer.zero_grad()
output = model(data)
source_output, target_output, target_output_dup = torch.split(output, split_size_or_sections=output.shape[0] // 3, dim=0)
mec_criterion = consensus_loss.MinEntropyConsensusLoss(num_classes=args.num_classes, device=device)
cls_loss = F.nll_loss(F.log_softmax(source_output), source_y)
mec_loss = lambda_mec_loss * mec_criterion(target_output, target_output_dup)
loss = cls_loss + mec_loss
loss.backward()
optimizer.step()
if i % args.log_interval == 0:
print('Train Iter: [{}/{}]\tClassification Loss: {:.6f} \t MEC Loss: {:.6f}'.format(
i, args.num_iters, cls_loss.item(), mec_loss.item()
))
if (i + 1) % args.check_acc_step == 0:
test(args, model, device, target_test_loader)
print("Training is complete...")
print("Running a bunch of forward passes to estimate the population statistics of target...")
eval_pass_collect_stats(args, model, device, target_test_loader)
print("Finally computing the precision on the test set...")
test(args, model, device, target_test_loader)
def test(args, model, device, target_test_loader):
model.eval()
test_loss = 0.
correct = 0
with torch.no_grad():
for data, target in target_test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(F.log_softmax(output, dim=1), target, size_average=False).item()
pred = F.softmax(output, dim=1).max(1, keepdim=True)[1] # get the index of max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(target_test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
test_loss, correct, len(target_test_loader.dataset),
100. * correct / len(target_test_loader.dataset)))
return 100. * correct / len(target_test_loader.dataset)
def compute_bn_stats(state_dict):
#state_dict = state_dict = torch.load(path) #'/home/sroy/.torch/models/resnet50-19c8e357.pth'
bn_key_names = []
for name, param in state_dict.items():
if name.find('bn') != -1:
bn_key_names.append(name)
elif name.find('downsample') != -1:
bn_key_names.append(name)
# keeping only the batch norm specific elements in the dictionary
bn_dict = {k: v for k, v in state_dict.items() if k in bn_key_names}
return bn_dict
def _random_affine_augmentation(x):
M = np.float32([[1 + np.random.normal(0.0, 0.1), np.random.normal(0.0, 0.1), 0],
[np.random.normal(0.0, 0.1), 1 + np.random.normal(0.0, 0.1), 0]])
rows, cols = x.shape[1:3]
dst = cv2.warpAffine(np.transpose(x.numpy(), [1, 2, 0]), M, (cols,rows))
dst = np.transpose(dst, [2, 0, 1])
return torch.from_numpy(dst)
def _gaussian_blur(x, sigma=0.1):
ksize = int(sigma + 0.5) * 8 + 1
dst = cv2.GaussianBlur(x.numpy(), (ksize, ksize), sigma)
return torch.from_numpy(dst)
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch DWT-MEC OfficeHome')
parser.add_argument('--num_workers', default=2, type=int)
parser.add_argument('--source_batch_size', type=int, default=18, help='input source batch size for training (default: 20)')
parser.add_argument('--target_batch_size', type=int, default=18, help='input target batch size for training (default: 20)')
parser.add_argument('--test_batch_size', type=int, default=10, help='input batch size for testing (default: 10)')
parser.add_argument('--s_dset_path', type=str, default='../data/OfficeHomeDataset_10072016/Art', help="The source dataset path")
parser.add_argument('--t_dset_path', type=str, default='../data/OfficeHomeDataset_10072016/Clipart', help="The target dataset path")
parser.add_argument('--resnet_path', type=str, default='../data/models/model_best_gr_4.pth.tar', help="The pre-trained model path")
parser.add_argument('--img_resize', type=int, default=256, help='size of the input image')
parser.add_argument('--img_crop_size', type=int, default=224, help='size of the cropped image')
parser.add_argument('--num_iters', type=int, default=10000, help='number of iterations to train (default: 10000)')
parser.add_argument('--check_acc_step', type=int, default=100, help='number of iterations steps to check validation accuracy (default: 10)')
parser.add_argument('--lr_change_step', type=int, default=1000)
parser.add_argument('--lr', type=float, default=1e-2, help='learning rate (default: 0.01)')
parser.add_argument('--num_classes', type=int, default=65, help='number of classes in the dataset')
parser.add_argument('--sgd_momentum', type=float, default=0.5, help='SGD momentum (default: 0.5)')
parser.add_argument('--running_momentum', type=float, default=0.1, help='Running momentum for domain statistics(default: 0.1)')
parser.add_argument('--lambda_mec_loss', type=float, default=0.1, help='Value of lambda for the entropy loss (default: 0.1)')
parser.add_argument('--log_interval', type=int, default=10, help='how many batches to wait before logging training status')
parser.add_argument('--seed', type=int, default=1, help='random seed (default: 1)')
args = parser.parse_args()
# set the seed
torch.manual_seed(args.seed)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# transformation on the source data during training and test data during test
data_transform = transforms.Compose([
transforms.Resize((args.img_resize, args.img_resize)), # spatial size of vgg-f input
transforms.RandomCrop((args.img_crop_size, args.img_crop_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# transformation on the target data
data_transform_dup = transforms.Compose([
transforms.Resize((args.img_resize, args.img_resize)),
transforms.RandomCrop((args.img_crop_size, args.img_crop_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Lambda(lambda x: _random_affine_augmentation(x)),
transforms.Lambda(lambda x: _gaussian_blur(x)),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# train data sets
source_dataset = folder.ImageFolder(root=args.s_dset_path,
transform=data_transform)
target_dataset = folder.ImageFolder(root=args.t_dset_path,
transform=data_transform,
transform_aug=data_transform_dup)
# test data sets
target_dataset_test = folder.ImageFolder(root=args.t_dset_path,
transform=data_transform)
# '''''''''''' Train loaders ''''''''''''''' #
source_trainloader = torch.utils.data.DataLoader(source_dataset,
batch_size=args.source_batch_size,
shuffle=True,
num_workers=args.num_workers,
drop_last=True)
target_trainloader = torch.utils.data.DataLoader(target_dataset,
batch_size=args.source_batch_size,
shuffle=True,
num_workers=args.num_workers,
drop_last=True)
# '''''''''''' Test loader ''''''''''''''' #
target_testloader = torch.utils.data.DataLoader(target_dataset_test,
batch_size=args.test_batch_size,
shuffle=True,
num_workers=args.num_workers)
model = resnet50(args.resnet_path, device).to(device)
final_layer_params = []
rest_of_the_net_params = []
for name, param in model.named_parameters():
if name.startswith('fc_out'):
final_layer_params.append(param)
else:
rest_of_the_net_params.append(param)
optimizer = optim.SGD([
{'params': rest_of_the_net_params},
{'params': final_layer_params, 'lr': args.lr}
], lr=args.lr * 0.1, momentum=0.9, weight_decay=5e-4)
train_infinite_collect_stats(args=args,
model=model,
device=device,
source_train_loader=source_trainloader,
target_train_loader=target_trainloader,
optimizer=optimizer,
lambda_mec_loss=args.lambda_mec_loss,
target_test_loader=target_testloader)
if __name__ == '__main__':
main()