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import torch
import torch.nn.functional as F
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch import nn, optim
from torch.utils.data import DataLoader
from tqdm import tqdm
import pytorch_lightning as pl
import torchmetrics
from torchmetrics import Metric
import torchvision
class BasicBlock(nn.Module):
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()
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 CustomBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(CustomBlock, self).__init__()
self.inner_layer = nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
)
self.res_block = BasicBlock(out_channels, out_channels)
def forward(self, x):
x = self.inner_layer(x)
r = self.res_block(x)
out = x + r
return out
class CustomResNet(pl.LightningModule):
def __init__(self, input_size, learning_rate, num_classes=10):
super().__init__()
self.lr = learning_rate
self.loss_fn = nn.CrossEntropyLoss()
self.accuracy = torchmetrics.Accuracy(
task="multiclass", num_classes=num_classes
)
self.accuracy1 = torchmetrics.Accuracy(
task="multiclass", num_classes=num_classes
)
self.f1_score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)
self.prep_layer = nn.Sequential(
nn.Conv2d(
in_channels=3,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer_1 = CustomBlock(in_channels=64, out_channels=128)
self.layer_2 = nn.Sequential(
nn.Conv2d(
in_channels=128,
out_channels=256,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer_3 = CustomBlock(in_channels=256, out_channels=512)
self.max_pool = nn.Sequential(nn.MaxPool2d(kernel_size=4))
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.prep_layer(x)
x = self.layer_1(x)
x = self.layer_2(x)
x = self.layer_3(x)
x = self.max_pool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def training_step(self, batch, batch_idx):
x, y = batch
loss, scores, y = self._common_step(batch, batch_idx)
accuracy = self.accuracy(scores, y)
f1_score = self.f1_score(scores, y)
self.log_dict(
{
"train_loss": loss,
"train_accuracy": accuracy,
"train_f1_score": f1_score,
},
on_step=False,
on_epoch=True,
prog_bar=True,
)
return {"loss": loss, "scores": scores, "y": y}
def validation_step(self, batch, batch_idx):
loss, scores, y = self._common_step(batch, batch_idx)
accuracy = self.accuracy1(scores, y)
#self.log("val_loss", loss)
self.log_dict(
{
"val_loss": loss,
"val_accuracy": accuracy,
#"train_f1_score": f1_score,
},
on_step=False,
on_epoch=True,
prog_bar=True,
)
return {"loss": loss, "scores": scores, "y": y}
def test_step(self, batch, batch_idx):
loss, scores, y = self._common_step(batch, batch_idx)
self.log("test_loss", loss)
return loss
def _common_step(self, batch, batch_idx):
x, y = batch
#x = x.reshape(x.size(0), -1)
scores = self.forward(x)
loss = self.loss_fn(scores, y)
return loss, scores, y
def predict_step(self, batch, batch_idx):
x, y = batch
#x = x.reshape(x.size(0), -1)
scores = self.forward(x)
preds = torch.argmax(scores, dim=1)
return preds
def configure_optimizers(self):
return optim.Adam(self.parameters(), lr=self.lr) |