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"""
ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
"""
import torch.nn as nn
import torch.nn.functional as F
# imports
import os
import torch
from pytorch_lightning import LightningModule, Trainer
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision import transforms
from torchvision.datasets import CIFAR10
# from pytorch_lightning.callbacks import ModelSummary
# from lightning.pytorch.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks import ModelCheckpoint, ModelSummary
import torchvision.transforms as transforms
PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
AVAIL_GPUS = min(1, torch.cuda.device_count())
BATCH_SIZE = 256 if AVAIL_GPUS else 64
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class CIFAR10Model(LightningModule):
def __init__(self, block, num_blocks, num_classes=10, data_dir=PATH_DATASETS, learning_rate=0.01):
super(CIFAR10Model, self).__init__()
self.in_planes = 64
# Define transformations using Albumentations
normalize = transforms.Normalize(mean=(0.4914, 0.4822, 0.4465), std=(0.2470, 0.2434, 0.2615))
random_crop = transforms.RandomCrop((32, 32))
horizontal_flip = transforms.RandomHorizontalFlip()
to_tensor = transforms.ToTensor()
self.transform = transforms.Compose([
random_crop,
horizontal_flip,
to_tensor,
normalize
])
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
self.accuracy = Accuracy(task="MULTICLASS", num_classes=10)
self.data_dir = data_dir
self.learning_rate = learning_rate
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.cross_entropy(logits, y)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
self.accuracy(preds, y)
self.log("val_loss", loss, prog_bar=True)
self.log("val_acc", self.accuracy, prog_bar=True)
return loss
def test_step(self, batch, batch_idx):
return self.validation_step(batch, batch_idx)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)
return optimizer
def prepare_data(self):
CIFAR10(self.data_dir, train=True, download=True)
CIFAR10(self.data_dir, train=False, download=True)
def setup(self, stage=None):
if stage == "fit" or stage is None:
cifar10_full = CIFAR10(self.data_dir, train=True, transform=self.transform)
train_size = int(len(cifar10_full) * 0.9)
val_size = len(cifar10_full) - train_size
self.cifar10_train, self.cifar10_val = random_split(cifar10_full, [train_size, val_size])
if stage == "test" or stage is None:
self.cifar10_test = CIFAR10(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self):
return DataLoader(self.cifar10_train, batch_size=BATCH_SIZE, num_workers=os.cpu_count())
def val_dataloader(self):
return DataLoader(self.cifar10_val, batch_size=BATCH_SIZE, num_workers=os.cpu_count(), persistent_workers=True)
def test_dataloader(self):
return DataLoader(self.cifar10_test, batch_size=BATCH_SIZE, num_workers=os.cpu_count())
def ResNet18():
return CIFAR10Model(BasicBlock, [2, 2, 2, 2])
def ResNet34():
return ResNet(BasicBlock, [3, 4, 6, 3])
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