PyTNet/evaluate.py at master · Sushmitha-Katti/PyTNet · GitHub

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# -*- coding: utf-8 -*-
"""Evaluate.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1e1OPp4X5jViLwOsEtLJpxnE8R9iySR7x
"""

import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision


def show_misclassified_images(model, device, dataset, classes,number = 25):
  misclassified_images = []

  for images, labels in dataset:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            for i in range(len(labels)):
              if(len(misclassified_images)<number and predicted[i]!=labels[i]):
                misclassified_images.append([images[i],predicted[i],labels[i]])
            if(len(misclassified_images)>number):
              break
  return misclassified_images

# --------------------------------------Plot Misclassified Images-------------------------------------------------

def plot_misclassified_images(misclassified_images, classes, Figsize = (20,20),number = 25) :

  fig = plt.figure(figsize = Figsize)
  for i in range(25):
        sub = fig.add_subplot(5, 5, i+1)
        img = misclassified_images[i][0].cpu()
        img = img *0.29  + 0.6
        npimg = img.numpy()
        plt.imshow(np.transpose(npimg,(1, 2, 0)),interpolation='none')

        sub.set_title("P={}\nA={}".format(str(classes[misclassified_images[i][1].data.cpu().numpy()]),str(classes[misclassified_images[i][2].data.cpu().numpy()])), fontsize = 10)
        sub.axis('off')
  plt.tight_layout()


# ----------------------------Evaluate Accuracy---------------------------------------------------------------------

def evaluate_accuracy(model, device, test_loader):
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %.2f %%' % (
        100 * correct / total))


# --------------------------------Evaluate classWise Accuracy------------------------------------------------------

def evaluate_classwise_accuracy(model, device, classes, test_loader):
    class_correct = list(0. for i in range(len(classes)))
    class_total = list(0. for i in range(len(classes)))

    with torch.no_grad():
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            c = (predicted == labels).squeeze().to('cpu').flatten()
            for i in range(len(c)):
            	label = labels[i]
            	class_correct[label] += c[i].item()
            	class_total[label] += 1

    for i in range(len(classes)):
        print('Accuracy of %5s : %2d%%' % (
            classes[i], 100 * class_correct[i] / class_total[i]))


# ---------------------------------Plot Accuracy/Loss Curves----------------------------------------------------

import matplotlib.pyplot as plt
"""
args:
points should be of type list of tuples or lists
Ex : [{[x: xpoints, y: ypoints, label: title, xlabel: "" , ylabel: " "}),([....points],label)]
"""
def plot_curve(curves,title,Figsize = (7,7)):
    fig = plt.figure(figsize=Figsize)
    ax = plt.subplot()
    for curve in curves:
        if("x" not in curve):
            ax.plot(curve["y"], label=curve.get("label", "label"))
        else:
            ax.plot(curve["x"],curve["y"], label=curve.get("label","label"))
        plt.xlabel(curve.get("xlabel","x-axis"))
        plt.ylabel(curve.get("ylabel","y-axis"))
        plt.title(title)
    ax.legend()
    plt.show()