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import numpy as np
import random
import matplotlib.pyplot as plt
import torch
import torchvision
from torchinfo import summary
from torch_lr_finder import LRFinder
def find_lr(model, optimizer, criterion, device, trainloader, numiter, startlr, endlr):
lr_finder = LRFinder(
model=model, optimizer=optimizer, criterion=criterion, device=device
)
lr_finder.range_test(
train_loader=trainloader,
start_lr=startlr,
end_lr=endlr,
num_iter=numiter,
step_mode="exp",
)
lr_finder.plot()
lr_finder.reset()
def one_cycle_lr(optimizer, maxlr, steps, epochs):
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer=optimizer,
max_lr=maxlr,
steps_per_epoch=steps,
epochs=epochs,
pct_start=5 / epochs,
div_factor=100,
three_phase=False,
final_div_factor=100,
anneal_strategy="linear",
)
return scheduler
def show_random_images_for_each_class(train_data, num_images_per_class=16):
for c, cls in enumerate(train_data.classes):
rand_targets = random.sample(
[n for n, x in enumerate(train_data.targets) if x == c],
k=num_images_per_class,
)
show_img_grid(np.transpose(train_data.data[rand_targets], axes=(0, 3, 1, 2)))
plt.title(cls)
def show_img_grid(data):
try:
grid_img = torchvision.utils.make_grid(data.cpu().detach())
except:
data = torch.from_numpy(data)
grid_img = torchvision.utils.make_grid(data)
plt.figure(figsize=(10, 10))
plt.imshow(grid_img.permute(1, 2, 0))
def show_random_images(data_loader):
data, target = next(iter(data_loader))
show_img_grid(data)
def show_model_summary(model, batch_size):
summary(
model=model,
input_size=(batch_size, 3, 32, 32),
col_names=["input_size", "output_size", "num_params", "kernel_size"],
verbose=1,
)
def lossacc_plots(results):
plt.plot(results["epoch"], results["trainloss"])
plt.plot(results["epoch"], results["testloss"])
plt.legend(["Train Loss", "Validation Loss"])
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.title("Loss vs Epochs")
plt.show()
plt.plot(results["epoch"], results["trainacc"])
plt.plot(results["epoch"], results["testacc"])
plt.legend(["Train Acc", "Validation Acc"])
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.title("Accuracy vs Epochs")
plt.show()
def lr_plots(results, length):
plt.plot(range(length), results["lr"])
plt.xlabel("Epochs")
plt.ylabel("Learning Rate")
plt.title("Learning Rate vs Epochs")
plt.show()
def get_misclassified(model, testloader, device, mis_count=10):
misimgs, mistgts, mispreds = [], [], []
with torch.no_grad():
for data, target in testloader:
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
misclassified = torch.argwhere(pred.squeeze() != target).squeeze()
for idx in misclassified:
if len(misimgs) >= mis_count:
break
misimgs.append(data[idx])
mistgts.append(target[idx])
mispreds.append(pred[idx].squeeze())
return misimgs, mistgts, mispreds
# def plot_misclassified(misimgs, mistgts, mispreds, classes):
# fig, axes = plt.subplots(len(misimgs) // 2, 2)
# fig.tight_layout()
# for ax, img, tgt, pred in zip(axes.ravel(), misimgs, mistgts, mispreds):
# ax.imshow((img / img.max()).permute(1, 2, 0).cpu())
# ax.set_title(f"{classes[tgt]} | {classes[pred]}")
# ax.grid(False)
# ax.set_axis_off()
# plt.show()
def get_misclassified_data(model, device, test_loader, count):
"""
Function to run the model on test set and return misclassified images
:param model: Network Architecture
:param device: CPU/GPU
:param test_loader: DataLoader for test set
"""
# Prepare the model for evaluation i.e. drop the dropout layer
model.eval()
# List to store misclassified Images
misclassified_data = []
# Reset the gradients
with torch.no_grad():
# Extract images, labels in a batch
for data, target in test_loader:
# Migrate the data to the device
data, target = data.to(device), target.to(device)
# Extract single image, label from the batch
for image, label in zip(data, target):
# Add batch dimension to the image
image = image.unsqueeze(0)
# Get the model prediction on the image
output = model(image)
# Convert the output from one-hot encoding to a value
pred = output.argmax(dim=1, keepdim=True)
# If prediction is incorrect, append the data
if pred != label:
misclassified_data.append((image, label, pred))
if len(misclassified_data) >= count:
break
return misclassified_data[:count]
def plot_misclassified(data, classes, size=(10, 10), rows=2, cols=5, inv_normalize=None):
fig = plt.figure(figsize=size)
number_of_samples = len(data)
for i in range(number_of_samples):
plt.subplot(rows, cols, i + 1)
img = data[i][0].squeeze().to('cpu')
if inv_normalize is not None:
img = inv_normalize(img)
plt.imshow(np.transpose(img, (1, 2, 0)))
plt.title(f"Label: {classes[data[i][1].item()]} \n Prediction: {classes[data[i][2].item()]}")
plt.xticks([])
plt.yticks([])
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