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Following the Pytorch Transfer learning tutorial, I am interested in reporting only train and test accuracy as well as confusion matrix (say using sklearn confusionmatrix). How can I do that? The current tutorial only reports train/val accuracy and I am having hard time figuring how to incorporate the sklearn confusionmatrix code there. Link to original tutorial here: https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

%matplotlib inline
from graphviz import Digraph
import torch
from torch.autograd import Variable
# Author: Sasank Chilamkurthy

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

plt.ion()
data_transforms = 
 'train': transforms.Compose([
 transforms.RandomResizedCrop(224),
 transforms.RandomHorizontalFlip(),
 transforms.ToTensor(),
 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ]),
 'val': transforms.Compose([
 transforms.Resize(256),
 transforms.CenterCrop(224),
 transforms.ToTensor(),
 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ]),



data_dir = "images"
image_datasets = x: datasets.ImageFolder(os.path.join(data_dir, x),
 data_transforms[x])
 for x in ['train', 'val']
dataloaders = x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
 shuffle=True, num_workers=4)
 for x in ['train', 'val']
dataset_sizes = x: len(image_datasets[x]) for x in ['train', 'val']
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

def imshow(inp, title=None):
 """Imshow for Tensor."""
 inp = inp.numpy().transpose((1, 2, 0))
 mean = np.array([0.485, 0.456, 0.406])
 std = np.array([0.229, 0.224, 0.225])
 inp = std * inp + mean
 inp = np.clip(inp, 0, 1)
 plt.imshow(inp)
 if title is not None:
 plt.title(title)
 plt.pause(0.001) # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
 since = time.time()

 best_model_wts = copy.deepcopy(model.state_dict())
 best_acc = 0.0

 for epoch in range(num_epochs):
 print('Epoch /'.format(epoch, num_epochs - 1))
 print('-' * 10)

 # Each epoch has a training and validation phase
 for phase in ['train', 'val']:
 if phase == 'train':
 scheduler.step()
 model.train() # Set model to training mode
 else:
 model.eval() # Set model to evaluate mode

 running_loss = 0.0
 running_corrects = 0

 # Iterate over data.
 for inputs, labels in dataloaders[phase]:
 inputs = inputs.to(device)
 labels = labels.to(device)

 # zero the parameter gradients
 optimizer.zero_grad()

 # forward
 # track history if only in train
 with torch.set_grad_enabled(phase == 'train'):
 outputs = model(inputs)
 _, preds = torch.max(outputs, 1)
 loss = criterion(outputs, labels)

 # backward + optimize only if in training phase
 if phase == 'train':
 loss.backward()
 optimizer.step()

 # statistics
 running_loss += loss.item() * inputs.size(0)
 running_corrects += torch.sum(preds == labels.data)

 epoch_loss = running_loss / dataset_sizes[phase]
 epoch_acc = running_corrects.double() / dataset_sizes[phase]

 print(' Loss: :.4f Acc: :.4f'.format(
 phase, epoch_loss, epoch_acc))

 # deep copy the model
 if phase == 'val' and epoch_acc > best_acc:
 best_acc = epoch_acc
 best_model_wts = copy.deepcopy(model.state_dict())

 print()

 time_elapsed = time.time() - since
 print('Training complete in :.0fm :.0fs'.format(
 time_elapsed // 60, time_elapsed % 60))
 print('Best val Acc: :4f'.format(best_acc))

 # load best model weights
 model.load_state_dict(best_model_wts)
 return model

def visualize_model(model, num_images=6):
 was_training = model.training
 model.eval()
 images_so_far = 0
 fig = plt.figure()

 with torch.no_grad():
 for i, (inputs, labels) in enumerate(dataloaders['val']):
 inputs = inputs.to(device)
 labels = labels.to(device)

 outputs = model(inputs)
 _, preds = torch.max(outputs, 1)

 for j in range(inputs.size()[0]):
 images_so_far += 1
 ax = plt.subplot(num_images//2, 2, images_so_far)
 ax.axis('off')
 ax.set_title('predicted: '.format(class_names[preds[j]]))
 imshow(inputs.cpu().data[j])

 if images_so_far == num_images:
 model.train(mode=was_training)
 return
 model.train(mode=was_training)

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 9)

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
 num_epochs=25)

visualize_model(model_ft)

Answer given by ptrblck of PyTorch community. Thanks a lot!

nb_classes = 9

confusion_matrix = torch.zeros(nb_classes, nb_classes)
with torch.no_grad():
 for i, (inputs, classes) in enumerate(dataloaders['val']):
 inputs = inputs.to(device)
 classes = classes.to(device)
 outputs = model_ft(inputs)
 _, preds = torch.max(outputs, 1)
 for t, p in zip(classes.view(-1), preds.view(-1)):
 confusion_matrix[t.long(), p.long()] += 1

print(confusion_matrix)

To get the per-class accuracy:

print(confusion_matrix.diag()/confusion_matrix.sum(1))