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import os
import json
import time
import argparse
import pathlib
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torch.optim.lr_scheduler import _LRScheduler
import traceback
CIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)
MILESTONES = [60, 120, 160]
class WideBasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropout_rate, stride=1):
super(WideBasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.dropout = nn.Dropout(p=dropout_rate)
self.bn2 = nn.BatchNorm2d(out_planes)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
if in_planes != out_planes:
self.shortcut = nn.Conv2d(
in_planes,
out_planes,
kernel_size=1,
stride=stride,
padding=0,
bias=False,
)
else:
self.shortcut = nn.Identity()
def forward(self, x):
out = self.relu(self.bn1(x))
skip_x = x if isinstance(self.shortcut, nn.Identity) else out
out = self.conv1(out)
out = self.relu(self.bn2(out))
out = self.dropout(out)
out = self.conv2(out)
out += self.shortcut(skip_x)
return out
class WideResNet(nn.Module):
def __init__(self, depth, widen_factor, num_classes, dropout_rate):
super(WideResNet, self).__init__()
assert (depth - 4) % 6 == 0, "Wide-resnet depth should be 6n+4"
n = (depth - 4) / 6
n_stages = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
self.conv1 = nn.Conv2d(3, n_stages[0], kernel_size=3, stride=1, padding=1, bias=False)
self.stage1 = self._make_wide_stage(WideBasicBlock, n_stages[0], n_stages[1], n, dropout_rate, stride=1)
self.stage2 = self._make_wide_stage(WideBasicBlock, n_stages[1], n_stages[2], n, dropout_rate, stride=2)
self.stage3 = self._make_wide_stage(WideBasicBlock, n_stages[2], n_stages[3], n, dropout_rate, stride=2)
self.bn1 = nn.BatchNorm2d(n_stages[3])
self.relu = nn.ReLU(inplace=True)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.linear = nn.Linear(n_stages[3], num_classes)
self._init_params()
@staticmethod
def _make_wide_stage(block, in_planes, out_planes, num_blocks, dropout_rate, stride):
stride_list = [stride] + [1] * (int(num_blocks) - 1)
in_planes_list = [in_planes] + [out_planes] * (int(num_blocks) - 1)
blocks = []
for _in_planes, _stride in zip(in_planes_list, stride_list):
blocks.append(block(_in_planes, out_planes, dropout_rate, _stride))
return nn.Sequential(*blocks)
def _init_params(self):
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):
if m.affine:
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
out = self.conv1(x)
out = self.stage1(out)
out = self.stage2(out)
out = self.stage3(out)
out = self.relu(self.bn1(out))
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def wide_resnet_28_10_old():
return WideResNet(
depth=28,
widen_factor=10,
num_classes=100,
dropout_rate=0.0,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument("--out_dir", type=str, default="run_1")
parser.add_argument("--in_channels", type=int, default=3)
parser.add_argument("--data_root", type=str, default='./datasets/cifar100/')
parser.add_argument("--learning_rate", type=float, default=0.1)
parser.add_argument("", type=int, default=200)
parser.add_argument("--val_per_epoch", type=int, default=5)
config = parser.parse_args()
try:
final_infos = {}
all_results = {}
pathlib.Path(config.out_dir).mkdir(parents=True, exist_ok=True)
model = wide_resnet_28_10_old().cuda()
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: F.pad(x.unsqueeze(0),
(4, 4, 4, 4), mode='reflect').squeeze()),
transforms.ToPILImage(),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(CIFAR100_TRAIN_MEAN, CIFAR100_TRAIN_STD),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(CIFAR100_TRAIN_MEAN, CIFAR100_TRAIN_STD)
])
train_dataset = datasets.CIFAR100(root=config.data_root, train=True,
download=True, transform=transform_train)
test_dataset = datasets.CIFAR100(root=config.data_root, train=False,
download=True, transform=transform_test)
train_loader = DataLoader(train_dataset, shuffle=True, num_workers=config.num_workers, batch_size=config.batch_size)
test_loader = DataLoader(test_dataset, shuffle=True, num_workers=config.num_workers, batch_size=config.batch_size)
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate, momentum=0.9, weight_decay=5e-4,
nesterov=True)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, len(train_loader) * config.max_epoch)
best_acc = 0.0
start_time = time.time()
for cur_epoch in tqdm(range(1, config.max_epoch + 1)):
model.train()
for batch_idx, (images, labels) in enumerate(tqdm(train_loader)):
images, labels = images.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
scheduler.step()
print(f'Finished epoch {cur_epoch} training.')
if (cur_epoch % config.val_per_epoch == 0 and cur_epoch != 0) or cur_epoch == (config.max_epoch - 1):
model.eval()
correct = 0.0
for images, labels in tqdm(test_loader):
images, labels = images.cuda(), labels.cuda()
with torch.no_grad():
outputs = model(images)
_, preds = outputs.max(1)
correct += preds.eq(labels).sum()
cur_acc = correct.float() / len(test_loader.dataset)
print(f"Epoch: {cur_epoch}, Accuracy: {correct.float() / len(test_loader.dataset)}")
if cur_acc > best_acc:
best_acc = cur_acc
best_epoch = cur_epoch
torch.save(model.state_dict(), os.path.join(config.out_dir, 'best.pth'))
final_infos = {
"cifar100": {
"means": {
"best_acc": best_acc.item(),
"epoch": best_epoch
}
}
}
with open(os.path.join(config.out_dir, "final_info.json"), "w") as f:
json.dump(final_infos, f)
except Exception as e:
print("Original error in subprocess:", flush=True)
traceback.print_exc(file=open(os.path.join(config.out_dir, "traceback.log"), "w"))
raise |