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helper_functions.py
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helper_functions.py
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"""
A series of helper functions used throughout the course.
If a function gets defined once and could be used over and over, it'll go in here.
"""
import torch
import matplotlib.pyplot as plt
import numpy as np
from torch import nn
import os
import zipfile
from pathlib import Path
import requests
# Walk through an image classification directory and find out how many files (images)
# are in each subdirectory.
import os
def walk_through_dir(dir_path):
"""
Walks through dir_path returning its contents.
Args:
dir_path (str): target directory
Returns:
A print out of:
number of subdiretories in dir_path
number of images (files) in each subdirectory
name of each subdirectory
"""
for dirpath, dirnames, filenames in os.walk(dir_path):
print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
def plot_decision_boundary(model: torch.nn.Module, X: torch.Tensor, y: torch.Tensor):
"""Plots decision boundaries of model predicting on X in comparison to y.
Source - https://madewithml.com/courses/foundations/neural-networks/ (with modifications)
"""
# Put everything to CPU (works better with NumPy + Matplotlib)
model.to("cpu")
X, y = X.to("cpu"), y.to("cpu")
# Setup prediction boundaries and grid
x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1
y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))
# Make features
X_to_pred_on = torch.from_numpy(np.column_stack((xx.ravel(), yy.ravel()))).float()
# Make predictions
model.eval()
with torch.inference_mode():
y_logits = model(X_to_pred_on)
# Test for multi-class or binary and adjust logits to prediction labels
if len(torch.unique(y)) > 2:
y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # mutli-class
else:
y_pred = torch.round(torch.sigmoid(y_logits)) # binary
# Reshape preds and plot
y_pred = y_pred.reshape(xx.shape).detach().numpy()
plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
# Plot linear data or training and test and predictions (optional)
def plot_predictions(
train_data, train_labels, test_data, test_labels, predictions=None
):
"""
Plots linear training data and test data and compares predictions.
"""
plt.figure(figsize=(10, 7))
# Plot training data in blue
plt.scatter(train_data, train_labels, c="b", s=4, label="Training data")
# Plot test data in green
plt.scatter(test_data, test_labels, c="g", s=4, label="Testing data")
if predictions is not None:
# Plot the predictions in red (predictions were made on the test data)
plt.scatter(test_data, predictions, c="r", s=4, label="Predictions")
# Show the legend
plt.legend(prop={"size": 14})
# Calculate accuracy (a classification metric)
def accuracy_fn(y_true, y_pred):
"""Calculates accuracy between truth labels and predictions.
Args:
y_true (torch.Tensor): Truth labels for predictions.
y_pred (torch.Tensor): Predictions to be compared to predictions.
Returns:
[torch.float]: Accuracy value between y_true and y_pred, e.g. 78.45
"""
correct = torch.eq(y_true, y_pred).sum().item()
acc = (correct / len(y_pred)) * 100
return acc
def print_train_time(start, end, device=None):
"""Prints difference between start and end time.
Args:
start (float): Start time of computation (preferred in timeit format).
end (float): End time of computation.
device ([type], optional): Device that compute is running on. Defaults to None.
Returns:
float: time between start and end in seconds (higher is longer).
"""
total_time = end - start
print(f"\nTrain time on {device}: {total_time:.3f} seconds")
return total_time
# Plot loss curves of a model
def plot_loss_curves(results):
"""Plots training curves of a results dictionary.
Args:
results (dict): dictionary containing list of values, e.g.
{"train_loss": [...],
"train_acc": [...],
"test_loss": [...],
"test_acc": [...]}
"""
loss = results["train_loss"]
test_loss = results["test_loss"]
accuracy = results["train_acc"]
test_accuracy = results["test_acc"]
epochs = range(len(results["train_loss"]))
plt.figure(figsize=(15, 7))
# Plot loss
plt.subplot(1, 2, 1)
plt.plot(epochs, loss, label="train_loss")
plt.plot(epochs, test_loss, label="test_loss")
plt.title("Loss")
plt.xlabel("Epochs")
plt.legend()
# Plot accuracy
plt.subplot(1, 2, 2)
plt.plot(epochs, accuracy, label="train_accuracy")
plt.plot(epochs, test_accuracy, label="test_accuracy")
plt.title("Accuracy")
plt.xlabel("Epochs")
plt.legend()
# Pred and plot image function from notebook 04
# See creation: https://www.learnpytorch.io/04_pytorch_custom_datasets/#113-putting-custom-image-prediction-together-building-a-function
from typing import List
import torchvision
def pred_and_plot_image(
model: torch.nn.Module,
image_path: str,
class_names: List[str] = None,
transform=None,
device: torch.device = "cuda" if torch.cuda.is_available() else "cpu",
):
"""Makes a prediction on a target image with a trained model and plots the image.
Args:
model (torch.nn.Module): trained PyTorch image classification model.
image_path (str): filepath to target image.
class_names (List[str], optional): different class names for target image. Defaults to None.
transform (_type_, optional): transform of target image. Defaults to None.
device (torch.device, optional): target device to compute on. Defaults to "cuda" if torch.cuda.is_available() else "cpu".
Returns:
Matplotlib plot of target image and model prediction as title.
Example usage:
pred_and_plot_image(model=model,
image="some_image.jpeg",
class_names=["class_1", "class_2", "class_3"],
transform=torchvision.transforms.ToTensor(),
device=device)
"""
# 1. Load in image and convert the tensor values to float32
target_image = torchvision.io.read_image(str(image_path)).type(torch.float32)
# 2. Divide the image pixel values by 255 to get them between [0, 1]
target_image = target_image / 255.0
# 3. Transform if necessary
if transform:
target_image = transform(target_image)
# 4. Make sure the model is on the target device
model.to(device)
# 5. Turn on model evaluation mode and inference mode
model.eval()
with torch.inference_mode():
# Add an extra dimension to the image
target_image = target_image.unsqueeze(dim=0)
# Make a prediction on image with an extra dimension and send it to the target device
target_image_pred = model(target_image.to(device))
# 6. Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)
target_image_pred_probs = torch.softmax(target_image_pred, dim=1)
# 7. Convert prediction probabilities -> prediction labels
target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)
# 8. Plot the image alongside the prediction and prediction probability
plt.imshow(
target_image.squeeze().permute(1, 2, 0)
) # make sure it's the right size for matplotlib
if class_names:
title = f"Pred: {class_names[target_image_pred_label.cpu()]} | Prob: {target_image_pred_probs.max().cpu():.3f}"
else:
title = f"Pred: {target_image_pred_label} | Prob: {target_image_pred_probs.max().cpu():.3f}"
plt.title(title)
plt.axis(False)
def set_seeds(seed: int=42):
"""Sets random sets for torch operations.
Args:
seed (int, optional): Random seed to set. Defaults to 42.
"""
# Set the seed for general torch operations
torch.manual_seed(seed)
# Set the seed for CUDA torch operations (ones that happen on the GPU)
torch.cuda.manual_seed(seed)
def download_data(source: str,
destination: str,
remove_source: bool = True) -> Path:
"""Downloads a zipped dataset from source and unzips to destination.
Args:
source (str): A link to a zipped file containing data.
destination (str): A target directory to unzip data to.
remove_source (bool): Whether to remove the source after downloading and extracting.
Returns:
pathlib.Path to downloaded data.
Example usage:
download_data(source="https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip",
destination="pizza_steak_sushi")
"""
# Setup path to data folder
data_path = Path("data/")
image_path = data_path / destination
# If the image folder doesn't exist, download it and prepare it...
if image_path.is_dir():
print(f"[INFO] {image_path} directory exists, skipping download.")
else:
print(f"[INFO] Did not find {image_path} directory, creating one...")
image_path.mkdir(parents=True, exist_ok=True)
# Download pizza, steak, sushi data
target_file = Path(source).name
with open(data_path / target_file, "wb") as f:
request = requests.get(source)
print(f"[INFO] Downloading {target_file} from {source}...")
f.write(request.content)
# Unzip pizza, steak, sushi data
with zipfile.ZipFile(data_path / target_file, "r") as zip_ref:
print(f"[INFO] Unzipping {target_file} data...")
zip_ref.extractall(image_path)
# Remove .zip file
if remove_source:
os.remove(data_path / target_file)
return image_path