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# imports
import albumentations as A
import lightning as L
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim as optim
from albumentations.pytorch import ToTensorV2
from model import MyResNet
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import preprocess_image, show_cam_on_image
from torch import nn
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader
from torchmetrics.functional import accuracy
from torchvision import datasets, transforms
means = [0.4914, 0.4822, 0.4465]
stds = [0.2470, 0.2435, 0.2616]
class CustomResnetTransforms:
def train_transforms(means, stds):
return A.Compose(
[
A.Normalize(mean=means, std=stds, always_apply=True),
A.PadIfNeeded(min_height=36, min_width=36, always_apply=True),
A.RandomCrop(height=32, width=32, always_apply=True),
A.HorizontalFlip(),
A.Cutout(num_holes=1, max_h_size=8, max_w_size=8, fill_value=0, p=1.0),
ToTensorV2(),
]
)
def test_transforms(means, stds):
return A.Compose(
[
A.Normalize(mean=means, std=stds, always_apply=True),
ToTensorV2(),
]
)
class Cifar10SearchDataset(datasets.CIFAR10):
def __init__(self, root="~/data", train=True, download=True, transform=None):
super().__init__(root=root, train=train, download=download, transform=transform)
def __getitem__(self, index):
image, label = self.data[index], self.targets[index]
if self.transform is not None:
transformed = self.transform(image=image)
image = transformed["image"]
return image, label
class LitCIFAR10(L.LightningModule):
def __init__(self, data_dir='./data', learning_rate=0.01, batch_size = 512):
super().__init__()
# Set our init args as class attributes
self.data_dir = data_dir
self.lr = learning_rate
self.batch_size = batch_size
# Hardcode some dataset specific attributes
self.num_classes = 10
self.train_transforms = CustomResnetTransforms.train_transforms(means, stds)
self.test_transforms = CustomResnetTransforms.test_transforms(means, stds)
# Define PyTorch model
self.model = MyResNet()
self.criterion = nn.CrossEntropyLoss()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = self.criterion(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y, task='multiclass',
num_classes=10)
# Calling self.log will surface up scalars for you in TensorBoard
self.log("train_loss", loss, prog_bar=True, enable_graph = True, on_step=False, on_epoch=True)
self.log("train_acc", acc, prog_bar=True, enable_graph = True, on_step=False, on_epoch=True)
# print("train_loss", loss)
# print("train_acc", acc)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = self.criterion(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y, task='multiclass',
num_classes=10)
# Calling self.log will surface up scalars for you in TensorBoard
self.log("val_loss", loss, prog_bar=True, enable_graph = True, on_step=False, on_epoch=True)
self.log("val_acc", acc, prog_bar=True, enable_graph = True, on_step=False, on_epoch=True)
return loss
def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(), lr=self.lr, weight_decay=1e-4)
steps_per_epoch = (len(self.trainset) // self.batch_size)+1
scheduler_dict = {
"scheduler": OneCycleLR(
optimizer,
max_lr = self.lr,
steps_per_epoch=steps_per_epoch,
epochs=self.trainer.max_epochs,
pct_start=5/self.trainer.max_epochs,
div_factor=100,
three_phase=False,
final_div_factor=100,
anneal_strategy='linear'
),
"interval": "step",
}
return {"optimizer": optimizer, "lr_scheduler": scheduler_dict}
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
self.trainset = Cifar10SearchDataset(root=self.data_dir, train=True,
download=True, transform=self.train_transforms)
self.valset = Cifar10SearchDataset(root=self.data_dir, train=False,
download=True, transform=self.test_transforms)
def train_dataloader(self):
return DataLoader(self.trainset, batch_size=self.batch_size, num_workers=0, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.valset, batch_size=self.batch_size, num_workers=0, pin_memory=True)
def get_misclassified_images(model, testset, mu, sigma, device):
model.eval()
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mu, sigma)
])
misclassified_images, misclassified_predictions, true_targets = [], [], []
with torch.no_grad():
for data_, target in testset:
data = transform(data_).to(device)
data = data.unsqueeze(0)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
if pred.item()!=target:
misclassified_images.append(data_)
misclassified_predictions.append(pred.item())
true_targets.append(target)
return misclassified_images, misclassified_predictions, true_targets
def plot_misclassified(image, pred, target, classes):
nrows = 4
ncols = 5
_, ax = plt.subplots(nrows, ncols, figsize=(20, 15))
for i in range(nrows):
for j in range(ncols):
index = i * ncols + j
ax[i, j].axis("off")
ax[i, j].set_title(f"Prediction: {classes[pred[index]]}\nTarget: {classes[target[index]]}")
ax[i, j].imshow(image[index])
plt.show()
class ClassifierOutputTarget:
def __init__(self, category):
self.category = category
def __call__(self, model_output):
if len(model_output.shape) == 1:
return model_output[self.category]
return model_output[:, self.category]
def plot_grad_cam_images(images, pred, target, classes, model):
nrows = 4
ncols = 5
fig, ax = plt.subplots(nrows, ncols, figsize=(20,15))
device = 'cuda' if torch.cuda.is_available() else 'cpu'
for i in range(nrows):
for j in range(ncols):
index = i * ncols + j
img = images[index]
input_tensor = preprocess_image(img,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
target_layers = [model.model.layer3[-1]]
targets = [ClassifierOutputTarget(target[index])]
cam = GradCAM(model=model, target_layers=target_layers, use_cuda = device)
grayscale_cam = cam(input_tensor=input_tensor, targets = targets)
#grayscale_cam = cam(input_tensor=input_tensor)
grayscale_cam = grayscale_cam[0, :]
rgb_img = np.float32(img) / 255
visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight = 0.6)
index = i * ncols + j
ax[i, j].axis("off")
ax[i, j].set_title(f"Prediction: {classes[pred[index]]}\nTarget: {classes[target[index]]}")
ax[i, j].imshow(visualization)
plt.show() |