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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
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
from torchmetrics.functional import accuracy
from torchvision import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
import albumentations as A
from albumentations.pytorch import ToTensorV2
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 LitResNet(pl.LightningModule):
def __init__(self, block, num_blocks, num_classes=10,batch_size=128):
super(LitResNet, self).__init__()
self.batch_size = batch_size
self.in_planes = 64
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)
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
y_hat = self(x)
# Calculate loss
loss = F.cross_entropy(y_hat, y)
#Calculate accuracy
acc = accuracy(y_hat, y)
self.log_dict(
{"train_loss": loss, "train_acc": acc},
on_step=True,
on_epoch=True,
prog_bar=True,
logger=True,
)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
acc = accuracy(y_hat, y)
self.log_dict(
{"val_loss": loss, "val_acc": acc},
on_step=True,
on_epoch=True,
prog_bar=True,
logger=True,
)
return loss
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
argmax_pred = y_hat.argmax(dim=1).cpu()
loss = F.cross_entropy(y_hat, y)
acc = accuracy(y_hat, y)
self.log_dict(
{"test_loss": loss, "test_acc": acc},
on_step=True,
on_epoch=True,
prog_bar=True,
logger=True,
)
# Update the confusion matrix
self.confusion_matrix.update(y_hat, y)
# Store the predictions, labels and incorrect predictions
x, y, y_hat, argmax_pred = (
x.cpu(),
y.cpu(),
y_hat.cpu(),
argmax_pred.cpu(),
)
self.pred_store["test_preds"] = torch.cat(
(self.pred_store["test_preds"], argmax_pred), dim=0
)
self.pred_store["test_labels"] = torch.cat(
(self.pred_store["test_labels"], y), dim=0
)
for d, t, p, o in zip(x, y, argmax_pred, y_hat):
if p.eq(t.view_as(p)).item() == False:
self.pred_store["test_incorrect"].append(
(d.cpu(), t, p, o[p.item()].cpu())
)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
def LitResNet18():
return LitResNet(BasicBlock, [2, 2, 2, 2])
def LitResNet34():
return LitResNet(BasicBlock, [3, 4, 6, 3])
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