In [1]:
import os

import torch
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, Subset

from torchvision.transforms import v2

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
import random

random.seed(42)
torch.manual_seed(42)
np.random.seed(42)

import timm
from pprint import pprint
from collections import Counter
In [2]:
device = 'cuda'
In [3]:
DATA_PATH = '/net/travail/bformanek/MRI_dataset'
TRAIN_FOLDER = DATA_PATH + '/train'
VAL_FOLDER = DATA_PATH + '/val'
TEST_FOLDER = DATA_PATH + '/test'

train_categories = os.listdir(TRAIN_FOLDER)
val_categories = os.listdir(VAL_FOLDER)
test_categories = os.listdir(TEST_FOLDER)

print("Train image distribution: ")
class_num_in_train = []
for i in range(0, len(train_categories)):
  CLASS_FOLDER = TRAIN_FOLDER + '/' + train_categories[i]
  class_elements = os.listdir(CLASS_FOLDER)
  class_num_in_train.append(len(class_elements))
  print(f' {train_categories[i]}: {class_num_in_train[i]}')
  
print("Validation image distribution: ")
class_num_in_val = []
for i in range(0, len(val_categories)):
  CLASS_FOLDER = VAL_FOLDER + '/' + val_categories[i]
  class_elements = os.listdir(CLASS_FOLDER)
  class_num_in_val.append(len(class_elements))
  print(f' {val_categories[i]}: {class_num_in_val[i]}')
  
print("Test image distribution: ")
class_num_in_test = []
for i in range(0, len(test_categories)):
  CLASS_FOLDER = TEST_FOLDER + '/' + test_categories[i]
  class_elements = os.listdir(CLASS_FOLDER)
  class_num_in_test.append(len(class_elements))
  print(f' {test_categories[i]}: {class_num_in_test[i]}')
  
num_classes = len(class_num_in_train)
Train image distribution: 
 T2star: 25
 T2w: 1156
 FLAIRCE: 1126
 FLAIR: 5950
 T1w: 5881
 OTHER: 382
 T1wCE: 5947
Validation image distribution: 
 T2w: 160
 FLAIRCE: 157
 FLAIR: 844
 T1w: 838
 OTHER: 49
 T1wCE: 847
Test image distribution: 
 T2star: 4
 T2w: 325
 FLAIRCE: 319
 FLAIR: 1693
 T1w: 1678
 OTHER: 118
 T1wCE: 1696
In [4]:
def train_for_epoch_with_scaler(model, train_loader, optimizer, criterion, scaler, device):
    # set model to train
    model.train()
    
    train_losses = []
    train_accuracies = []
    counter = 0

    for batch, target in train_loader:

        # data to GPU
        batch = batch.to(device)
        target = target.to(device)

        # reset optimizer
        optimizer.zero_grad()

        # forward pass
        predictions = model(batch)

        # calculate accuracy
        accuracy = (torch.argmax(predictions, dim=1) == target).sum().item() / target.size(0)
        
        # calculate loss
        loss = criterion(predictions, target)

        # backward pass
        scaler.scale(loss).backward()

        # parameter update
        scaler.step(optimizer)
        scaler.update()

        # track loss
        train_losses.append(float(loss.item()))
        train_accuracies.append(accuracy)

        counter += 1
        if counter % 20 == 0:
          print('[{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                int(counter * len(batch)), len(train_loader.dataset),
                100. * counter / len(train_loader), loss.item()))

    train_loss = np.mean(np.array(train_losses))
    train_accuracy = np.mean(np.array(train_accuracies))
    
    print('\nTrain: Average loss: {:.4f}, Accuracy: {:.4f}\n'.format(
        train_loss, train_accuracy))
    
    return train_loss, train_accuracy

def validate(model, val_loader, criterion, device):
    model.eval()
    
    val_losses = []
    y_true, y_pred = [], []

    with torch.no_grad():
        for batch, target in val_loader:

            # move data to the device
            batch = batch.to(device)
            target = target.to(device)

            with torch.autocast(device_type=device, dtype=torch.float16):
              # make predictions
              predictions = model(batch)

              # calculate loss
              loss = criterion(predictions, target)

            # track losses and predictions
            val_losses.append(float(loss.item()))
            y_true.extend(target.cpu().numpy())
            y_pred.extend(predictions.argmax(dim=1).cpu().numpy())

    y_true = np.array(y_true)
    y_pred = np.array(y_pred)
    val_losses = np.array(val_losses)

    # calculate validation accuracy from y_true and y_pred
    val_accuracy = np.mean(y_true == y_pred)

    # calculate the mean validation loss
    val_loss = np.mean(val_losses)

    print('Validation: Average loss: {:.4f}, Accuracy: {:.4f}\n'.format(
        val_loss, val_accuracy))

    return val_loss, val_accuracy

def train_with_scaler(model, train_loader, val_loader, optimizer, criterion, epochs, scaler, device, checkpoints_foler = None, first_epoch=1):
    train_losses, val_losses = [],  []
    train_accuracies, val_accuracies = [], []
    max_val_acc = 0
    best_epoch = 0

    for epoch in range(first_epoch, epochs+first_epoch):

        print('Train Epoch: {}'.format(epoch))

        # train
        train_loss, train_acc = train_for_epoch_with_scaler(model, train_loader, optimizer, criterion, scaler, device)

        # validation
        valid_loss, valid_acc = validate(model, val_loader, criterion, device)

        train_losses.append(train_loss)
        val_losses.append(valid_loss)
        train_accuracies.append(train_acc)
        val_accuracies.append(valid_acc)

        # save checkpoint
        if checkpoints_foler != None and max_val_acc < valid_acc:
          max_val_acc = valid_acc
          best_epoch = epoch
          torch.save(model, checkpoints_foler+f'/avp_{epoch:03d}.pkl')

    return train_losses, val_losses, train_accuracies, val_accuracies, best_epoch
In [5]:
# define custom resample class to change image resolution without rescaling
class RandomResample:
    def __init__(self, scale_factor):
        self.scale_factor = random.uniform(0,scale_factor)
    
    def __call__(self, img):
        # Downsample
        width, height = img.size
        downscaled_size = (int(width / self.scale_factor), int(height / self.scale_factor))
        
        # Downsample the image
        img_downsampled = img.resize(downscaled_size)
        
        # Upsample back to the original size
        img_upsampled = img_downsampled.resize((width, height))
        
        return img_upsampled
In [6]:
train_transform = transforms.Compose([
    transforms.v2.Resize(224),
    
    # augmentations
    transforms.v2.RandomHorizontalFlip(p=0.5),
    transforms.v2.RandomVerticalFlip(p=0.5),
    transforms.v2.RandomRotation(degrees=360, expand=True),  # expand=True: esnure that the whole image is represented on the rotated image
    #transforms.v2.ColorJitter(contrast=0.1),
    #transforms.v2.GaussianBlur(7, sigma=2),
    #RandomResample(scale_factor=2),
    
    transforms.v2.Resize(224),
    transforms.ToTensor()
    
])
valid_transform = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor()
])
In [7]:
train_set = ImageFolder(TRAIN_FOLDER, transform = train_transform)
val_set = ImageFolder(VAL_FOLDER, transform = valid_transform)
test_set = ImageFolder(TEST_FOLDER, transform = valid_transform)

BATCH_SIZE = 64
WORKERS = 8
train_loader = DataLoader(train_set, batch_size = BATCH_SIZE, shuffle = True, num_workers=WORKERS)
val_loader = DataLoader(val_set, batch_size = BATCH_SIZE,  shuffle = False, num_workers=WORKERS)
test_loader = DataLoader(test_set, batch_size = BATCH_SIZE,  shuffle = False, num_workers=WORKERS)

# print(f'train samples: {len(train_set)}  validation samples: {len(val_set)}  test samples: {len(test_set)}')

#for image_batch, labels_batch in train_loader:
#  print("Batch sizes:", image_batch.shape, "(batch, channels, height, width)")
#  print("Label vector size:", labels_batch.shape)
#  break
In [8]:
num_in_class_dict = dict(Counter(train_set.targets[i] for i in range(len(train_set))))
num_in_class = np.zeros([1,len(num_in_class_dict)])
for i in range(0, len(num_in_class_dict)):
  num_in_class[0, i] = num_in_class_dict[i]

class_weights = 1-(num_in_class/num_in_class.sum()).squeeze()
class_weights_tensor = torch.Tensor(class_weights).to(device)

# print(num_in_class_dict)
# print(num_in_class)
In [9]:
MODEL_NAME = 'resnet18' ##resnet18, resnet50, efficientnet_b0
In [10]:
model = timm.create_model(MODEL_NAME, pretrained=True, num_classes=num_classes)
model.to(device)
Out[10]:
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (act1): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (drop_block): Identity()
      (act1): ReLU(inplace=True)
      (aa): Identity()
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (act2): ReLU(inplace=True)
    )
  )
  (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))
  (fc): Linear(in_features=512, out_features=7, bias=True)
)
In [11]:
criterion_balanced = nn.CrossEntropyLoss(weight = class_weights_tensor)
optimizer_Adam = optim.Adam(model.parameters(), 1e-3)
scaler = torch.cuda.amp.GradScaler()
/tmp/cache-bformanek/ipykernel_1725551/3247579378.py:3: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
  scaler = torch.cuda.amp.GradScaler()
In [12]:
RESULT_FOLDER_NAME = MODEL_NAME+"_flips_360"

checkpoints_foler = '/net/travail/bformanek/checkpoints/transfer_checkpoints_'+RESULT_FOLDER_NAME
if not os.path.exists(checkpoints_foler):
    os.mkdir(checkpoints_foler)
In [13]:
epochs = 30
train_losses, val_losses, train_accuracies, val_accuracies, best_epoch = train_with_scaler(model, train_loader, val_loader, optimizer_Adam, criterion_balanced, 
                                                                                           epochs, scaler, device, checkpoints_foler=checkpoints_foler)
Train Epoch: 1
[1280/20460 (6%)]	Loss: 0.999237
[2560/20460 (12%)]	Loss: 0.840347
[3840/20460 (19%)]	Loss: 0.486756
[5120/20460 (25%)]	Loss: 0.559186
[6400/20460 (31%)]	Loss: 0.458453
[7680/20460 (38%)]	Loss: 0.272512
[8960/20460 (44%)]	Loss: 0.324681
[10240/20460 (50%)]	Loss: 0.250867
[11520/20460 (56%)]	Loss: 0.180001
[12800/20460 (62%)]	Loss: 0.255491
[14080/20460 (69%)]	Loss: 0.443328
[15360/20460 (75%)]	Loss: 0.181067
[16640/20460 (81%)]	Loss: 0.198571
[17920/20460 (88%)]	Loss: 0.151553
[19200/20460 (94%)]	Loss: 0.178173
[14080/20460 (100%)]	Loss: 0.257576

Train: Average loss: 0.4285, Accuracy: 0.8485

Validation: Average loss: 1.2914, Accuracy: 0.6632

Train Epoch: 2
[1280/20460 (6%)]	Loss: 0.277554
[2560/20460 (12%)]	Loss: 0.357009
[3840/20460 (19%)]	Loss: 0.283155
[5120/20460 (25%)]	Loss: 0.209387
[6400/20460 (31%)]	Loss: 0.173001
[7680/20460 (38%)]	Loss: 0.174767
[8960/20460 (44%)]	Loss: 0.298894
[10240/20460 (50%)]	Loss: 0.210401
[11520/20460 (56%)]	Loss: 0.121789
[12800/20460 (62%)]	Loss: 0.149770
[14080/20460 (69%)]	Loss: 0.275387
[15360/20460 (75%)]	Loss: 0.094935
[16640/20460 (81%)]	Loss: 0.083764
[17920/20460 (88%)]	Loss: 0.129739
[19200/20460 (94%)]	Loss: 0.171136
[14080/20460 (100%)]	Loss: 0.029456

Train: Average loss: 0.2039, Accuracy: 0.9264

Validation: Average loss: 1.3303, Accuracy: 0.7283

Train Epoch: 3
[1280/20460 (6%)]	Loss: 0.116036
[2560/20460 (12%)]	Loss: 0.181494
[3840/20460 (19%)]	Loss: 0.157793
[5120/20460 (25%)]	Loss: 0.123821
[6400/20460 (31%)]	Loss: 0.152550
[7680/20460 (38%)]	Loss: 0.141733
[8960/20460 (44%)]	Loss: 0.122220
[10240/20460 (50%)]	Loss: 0.054822
[11520/20460 (56%)]	Loss: 0.069806
[12800/20460 (62%)]	Loss: 0.253814
[14080/20460 (69%)]	Loss: 0.062691
[15360/20460 (75%)]	Loss: 0.122664
[16640/20460 (81%)]	Loss: 0.105224
[17920/20460 (88%)]	Loss: 0.349258
[19200/20460 (94%)]	Loss: 0.246001
[14080/20460 (100%)]	Loss: 0.112209

Train: Average loss: 0.1521, Accuracy: 0.9467

Validation: Average loss: 1.1219, Accuracy: 0.8522

Train Epoch: 4
[1280/20460 (6%)]	Loss: 0.145345
[2560/20460 (12%)]	Loss: 0.208852
[3840/20460 (19%)]	Loss: 0.147174
[5120/20460 (25%)]	Loss: 0.046925
[6400/20460 (31%)]	Loss: 0.117533
[7680/20460 (38%)]	Loss: 0.093898
[8960/20460 (44%)]	Loss: 0.068285
[10240/20460 (50%)]	Loss: 0.174730
[11520/20460 (56%)]	Loss: 0.091854
[12800/20460 (62%)]	Loss: 0.261202
[14080/20460 (69%)]	Loss: 0.063772
[15360/20460 (75%)]	Loss: 0.183514
[16640/20460 (81%)]	Loss: 0.035941
[17920/20460 (88%)]	Loss: 0.081779
[19200/20460 (94%)]	Loss: 0.452818
[14080/20460 (100%)]	Loss: 0.279215

Train: Average loss: 0.1423, Accuracy: 0.9497

Validation: Average loss: 1.2911, Accuracy: 0.8858

Train Epoch: 5
[1280/20460 (6%)]	Loss: 0.162708
[2560/20460 (12%)]	Loss: 0.138465
[3840/20460 (19%)]	Loss: 0.150480
[5120/20460 (25%)]	Loss: 0.179126
[6400/20460 (31%)]	Loss: 0.074554
[7680/20460 (38%)]	Loss: 0.129655
[8960/20460 (44%)]	Loss: 0.028121
[10240/20460 (50%)]	Loss: 0.205477
[11520/20460 (56%)]	Loss: 0.053683
[12800/20460 (62%)]	Loss: 0.104494
[14080/20460 (69%)]	Loss: 0.042177
[15360/20460 (75%)]	Loss: 0.206843
[16640/20460 (81%)]	Loss: 0.180544
[17920/20460 (88%)]	Loss: 0.067690
[19200/20460 (94%)]	Loss: 0.131991
[14080/20460 (100%)]	Loss: 0.087955

Train: Average loss: 0.1191, Accuracy: 0.9574

Validation: Average loss: 1.4581, Accuracy: 0.8702

Train Epoch: 6
[1280/20460 (6%)]	Loss: 0.174448
[2560/20460 (12%)]	Loss: 0.124685
[3840/20460 (19%)]	Loss: 0.136886
[5120/20460 (25%)]	Loss: 0.115015
[6400/20460 (31%)]	Loss: 0.064571
[7680/20460 (38%)]	Loss: 0.083053
[8960/20460 (44%)]	Loss: 0.073907
[10240/20460 (50%)]	Loss: 0.079761
[11520/20460 (56%)]	Loss: 0.151325
[12800/20460 (62%)]	Loss: 0.070430
[14080/20460 (69%)]	Loss: 0.099623
[15360/20460 (75%)]	Loss: 0.086542
[16640/20460 (81%)]	Loss: 0.037445
[17920/20460 (88%)]	Loss: 0.145197
[19200/20460 (94%)]	Loss: 0.029267
[14080/20460 (100%)]	Loss: 0.132985

Train: Average loss: 0.1029, Accuracy: 0.9617

Validation: Average loss: 1.5689, Accuracy: 0.7698

Train Epoch: 7
[1280/20460 (6%)]	Loss: 0.092860
[2560/20460 (12%)]	Loss: 0.068341
[3840/20460 (19%)]	Loss: 0.078423
[5120/20460 (25%)]	Loss: 0.050065
[6400/20460 (31%)]	Loss: 0.059183
[7680/20460 (38%)]	Loss: 0.095126
[8960/20460 (44%)]	Loss: 0.080075
[10240/20460 (50%)]	Loss: 0.079809
[11520/20460 (56%)]	Loss: 0.100286
[12800/20460 (62%)]	Loss: 0.205798
[14080/20460 (69%)]	Loss: 0.157868
[15360/20460 (75%)]	Loss: 0.051074
[16640/20460 (81%)]	Loss: 0.093577
[17920/20460 (88%)]	Loss: 0.076363
[19200/20460 (94%)]	Loss: 0.152964
[14080/20460 (100%)]	Loss: 0.058098

Train: Average loss: 0.0981, Accuracy: 0.9644

Validation: Average loss: 1.8628, Accuracy: 0.8501

Train Epoch: 8
[1280/20460 (6%)]	Loss: 0.266822
[2560/20460 (12%)]	Loss: 0.054511
[3840/20460 (19%)]	Loss: 0.064749
[5120/20460 (25%)]	Loss: 0.067840
[6400/20460 (31%)]	Loss: 0.101704
[7680/20460 (38%)]	Loss: 0.041162
[8960/20460 (44%)]	Loss: 0.061517
[10240/20460 (50%)]	Loss: 0.093574
[11520/20460 (56%)]	Loss: 0.088456
[12800/20460 (62%)]	Loss: 0.124745
[14080/20460 (69%)]	Loss: 0.097899
[15360/20460 (75%)]	Loss: 0.124717
[16640/20460 (81%)]	Loss: 0.223142
[17920/20460 (88%)]	Loss: 0.163567
[19200/20460 (94%)]	Loss: 0.028378
[14080/20460 (100%)]	Loss: 0.009691

Train: Average loss: 0.0871, Accuracy: 0.9687

Validation: Average loss: 1.4028, Accuracy: 0.8352

Train Epoch: 9
[1280/20460 (6%)]	Loss: 0.103873
[2560/20460 (12%)]	Loss: 0.061105
[3840/20460 (19%)]	Loss: 0.050217
[5120/20460 (25%)]	Loss: 0.064988
[6400/20460 (31%)]	Loss: 0.054004
[7680/20460 (38%)]	Loss: 0.187985
[8960/20460 (44%)]	Loss: 0.100011
[10240/20460 (50%)]	Loss: 0.032275
[11520/20460 (56%)]	Loss: 0.095999
[12800/20460 (62%)]	Loss: 0.046272
[14080/20460 (69%)]	Loss: 0.108111
[15360/20460 (75%)]	Loss: 0.117144
[16640/20460 (81%)]	Loss: 0.039111
[17920/20460 (88%)]	Loss: 0.048137
[19200/20460 (94%)]	Loss: 0.116113
[14080/20460 (100%)]	Loss: 0.040787

Train: Average loss: 0.0807, Accuracy: 0.9713

Validation: Average loss: 1.6492, Accuracy: 0.7127

Train Epoch: 10
[1280/20460 (6%)]	Loss: 0.129111
[2560/20460 (12%)]	Loss: 0.160861
[3840/20460 (19%)]	Loss: 0.142680
[5120/20460 (25%)]	Loss: 0.043355
[6400/20460 (31%)]	Loss: 0.095755
[7680/20460 (38%)]	Loss: 0.093439
[8960/20460 (44%)]	Loss: 0.085926
[10240/20460 (50%)]	Loss: 0.108597
[11520/20460 (56%)]	Loss: 0.145176
[12800/20460 (62%)]	Loss: 0.058684
[14080/20460 (69%)]	Loss: 0.120203
[15360/20460 (75%)]	Loss: 0.053747
[16640/20460 (81%)]	Loss: 0.031906
[17920/20460 (88%)]	Loss: 0.092290
[19200/20460 (94%)]	Loss: 0.196654
[14080/20460 (100%)]	Loss: 0.110593

Train: Average loss: 0.0808, Accuracy: 0.9706

Validation: Average loss: 2.1011, Accuracy: 0.8214

Train Epoch: 11
[1280/20460 (6%)]	Loss: 0.043188
[2560/20460 (12%)]	Loss: 0.149980
[3840/20460 (19%)]	Loss: 0.063318
[5120/20460 (25%)]	Loss: 0.103241
[6400/20460 (31%)]	Loss: 0.077571
[7680/20460 (38%)]	Loss: 0.049932
[8960/20460 (44%)]	Loss: 0.037021
[10240/20460 (50%)]	Loss: 0.122454
[11520/20460 (56%)]	Loss: 0.037386
[12800/20460 (62%)]	Loss: 0.094418
[14080/20460 (69%)]	Loss: 0.150054
[15360/20460 (75%)]	Loss: 0.086483
[16640/20460 (81%)]	Loss: 0.010899
[17920/20460 (88%)]	Loss: 0.065263
[19200/20460 (94%)]	Loss: 0.060945
[14080/20460 (100%)]	Loss: 0.187086

Train: Average loss: 0.0749, Accuracy: 0.9733

Validation: Average loss: 2.1704, Accuracy: 0.8103

Train Epoch: 12
[1280/20460 (6%)]	Loss: 0.036053
[2560/20460 (12%)]	Loss: 0.078786
[3840/20460 (19%)]	Loss: 0.031719
[5120/20460 (25%)]	Loss: 0.023891
[6400/20460 (31%)]	Loss: 0.060088
[7680/20460 (38%)]	Loss: 0.073726
[8960/20460 (44%)]	Loss: 0.211413
[10240/20460 (50%)]	Loss: 0.031129
[11520/20460 (56%)]	Loss: 0.045819
[12800/20460 (62%)]	Loss: 0.008133
[14080/20460 (69%)]	Loss: 0.075346
[15360/20460 (75%)]	Loss: 0.032074
[16640/20460 (81%)]	Loss: 0.099488
[17920/20460 (88%)]	Loss: 0.128713
[19200/20460 (94%)]	Loss: 0.113352
[14080/20460 (100%)]	Loss: 0.078803

Train: Average loss: 0.0693, Accuracy: 0.9746

Validation: Average loss: 2.3092, Accuracy: 0.8280

Train Epoch: 13
[1280/20460 (6%)]	Loss: 0.021361
[2560/20460 (12%)]	Loss: 0.064092
[3840/20460 (19%)]	Loss: 0.007784
[5120/20460 (25%)]	Loss: 0.113468
[6400/20460 (31%)]	Loss: 0.023264
[7680/20460 (38%)]	Loss: 0.014301
[8960/20460 (44%)]	Loss: 0.042146
[10240/20460 (50%)]	Loss: 0.097107
[11520/20460 (56%)]	Loss: 0.150676
[12800/20460 (62%)]	Loss: 0.051004
[14080/20460 (69%)]	Loss: 0.057212
[15360/20460 (75%)]	Loss: 0.160044
[16640/20460 (81%)]	Loss: 0.047489
[17920/20460 (88%)]	Loss: 0.095587
[19200/20460 (94%)]	Loss: 0.060530
[14080/20460 (100%)]	Loss: 0.217007

Train: Average loss: 0.0684, Accuracy: 0.9756

Validation: Average loss: 2.4277, Accuracy: 0.8615

Train Epoch: 14
[1280/20460 (6%)]	Loss: 0.089016
[2560/20460 (12%)]	Loss: 0.032950
[3840/20460 (19%)]	Loss: 0.025786
[5120/20460 (25%)]	Loss: 0.109084
[6400/20460 (31%)]	Loss: 0.037234
[7680/20460 (38%)]	Loss: 0.018847
[8960/20460 (44%)]	Loss: 0.041286
[10240/20460 (50%)]	Loss: 0.035738
[11520/20460 (56%)]	Loss: 0.041484
[12800/20460 (62%)]	Loss: 0.067456
[14080/20460 (69%)]	Loss: 0.083951
[15360/20460 (75%)]	Loss: 0.167649
[16640/20460 (81%)]	Loss: 0.034117
[17920/20460 (88%)]	Loss: 0.022157
[19200/20460 (94%)]	Loss: 0.037026
[14080/20460 (100%)]	Loss: 0.083594

Train: Average loss: 0.0658, Accuracy: 0.9762

Validation: Average loss: 2.3616, Accuracy: 0.8577

Train Epoch: 15
[1280/20460 (6%)]	Loss: 0.065958
[2560/20460 (12%)]	Loss: 0.174801
[3840/20460 (19%)]	Loss: 0.098779
[5120/20460 (25%)]	Loss: 0.063341
[6400/20460 (31%)]	Loss: 0.098103
[7680/20460 (38%)]	Loss: 0.046305
[8960/20460 (44%)]	Loss: 0.073324
[10240/20460 (50%)]	Loss: 0.040650
[11520/20460 (56%)]	Loss: 0.065830
[12800/20460 (62%)]	Loss: 0.035708
[14080/20460 (69%)]	Loss: 0.120711
[15360/20460 (75%)]	Loss: 0.056538
[16640/20460 (81%)]	Loss: 0.036377
[17920/20460 (88%)]	Loss: 0.091699
[19200/20460 (94%)]	Loss: 0.012143
[14080/20460 (100%)]	Loss: 0.008556

Train: Average loss: 0.0574, Accuracy: 0.9800

Validation: Average loss: 3.1694, Accuracy: 0.7975

Train Epoch: 16
[1280/20460 (6%)]	Loss: 0.021537
[2560/20460 (12%)]	Loss: 0.074768
[3840/20460 (19%)]	Loss: 0.025322
[5120/20460 (25%)]	Loss: 0.030596
[6400/20460 (31%)]	Loss: 0.026692
[7680/20460 (38%)]	Loss: 0.117345
[8960/20460 (44%)]	Loss: 0.011465
[10240/20460 (50%)]	Loss: 0.059155
[11520/20460 (56%)]	Loss: 0.030028
[12800/20460 (62%)]	Loss: 0.036679
[14080/20460 (69%)]	Loss: 0.017885
[15360/20460 (75%)]	Loss: 0.021472
[16640/20460 (81%)]	Loss: 0.007105
[17920/20460 (88%)]	Loss: 0.169662
[19200/20460 (94%)]	Loss: 0.176929
[14080/20460 (100%)]	Loss: 0.127220

Train: Average loss: 0.0638, Accuracy: 0.9769

Validation: Average loss: 2.7536, Accuracy: 0.8477

Train Epoch: 17
[1280/20460 (6%)]	Loss: 0.045164
[2560/20460 (12%)]	Loss: 0.068306
[3840/20460 (19%)]	Loss: 0.114620
[5120/20460 (25%)]	Loss: 0.008024
[6400/20460 (31%)]	Loss: 0.081870
[7680/20460 (38%)]	Loss: 0.124366
[8960/20460 (44%)]	Loss: 0.004715
[10240/20460 (50%)]	Loss: 0.022715
[11520/20460 (56%)]	Loss: 0.064733
[12800/20460 (62%)]	Loss: 0.010231
[14080/20460 (69%)]	Loss: 0.080186
[15360/20460 (75%)]	Loss: 0.008757
[16640/20460 (81%)]	Loss: 0.095369
[17920/20460 (88%)]	Loss: 0.011833
[19200/20460 (94%)]	Loss: 0.027831
[14080/20460 (100%)]	Loss: 0.037789

Train: Average loss: 0.0557, Accuracy: 0.9805

Validation: Average loss: 2.5960, Accuracy: 0.7106

Train Epoch: 18
[1280/20460 (6%)]	Loss: 0.049802
[2560/20460 (12%)]	Loss: 0.011394
[3840/20460 (19%)]	Loss: 0.032899
[5120/20460 (25%)]	Loss: 0.086893
[6400/20460 (31%)]	Loss: 0.044579
[7680/20460 (38%)]	Loss: 0.040786
[8960/20460 (44%)]	Loss: 0.059407
[10240/20460 (50%)]	Loss: 0.042121
[11520/20460 (56%)]	Loss: 0.021799
[12800/20460 (62%)]	Loss: 0.106940
[14080/20460 (69%)]	Loss: 0.025000
[15360/20460 (75%)]	Loss: 0.167877
[16640/20460 (81%)]	Loss: 0.022861
[17920/20460 (88%)]	Loss: 0.021975
[19200/20460 (94%)]	Loss: 0.074256
[14080/20460 (100%)]	Loss: 0.138714

Train: Average loss: 0.0554, Accuracy: 0.9801

Validation: Average loss: 2.6120, Accuracy: 0.8342

Train Epoch: 19
[1280/20460 (6%)]	Loss: 0.004554
[2560/20460 (12%)]	Loss: 0.042295
[3840/20460 (19%)]	Loss: 0.028682
[5120/20460 (25%)]	Loss: 0.053749
[6400/20460 (31%)]	Loss: 0.079738
[7680/20460 (38%)]	Loss: 0.050336
[8960/20460 (44%)]	Loss: 0.073925
[10240/20460 (50%)]	Loss: 0.133525
[11520/20460 (56%)]	Loss: 0.042605
[12800/20460 (62%)]	Loss: 0.003682
[14080/20460 (69%)]	Loss: 0.018496
[15360/20460 (75%)]	Loss: 0.071872
[16640/20460 (81%)]	Loss: 0.193883
[17920/20460 (88%)]	Loss: 0.046756
[19200/20460 (94%)]	Loss: 0.037845
[14080/20460 (100%)]	Loss: 0.024763

Train: Average loss: 0.0518, Accuracy: 0.9816

Validation: Average loss: 3.2742, Accuracy: 0.8034

Train Epoch: 20
[1280/20460 (6%)]	Loss: 0.010771
[2560/20460 (12%)]	Loss: 0.125281
[3840/20460 (19%)]	Loss: 0.007049
[5120/20460 (25%)]	Loss: 0.016924
[6400/20460 (31%)]	Loss: 0.100763
[7680/20460 (38%)]	Loss: 0.085736
[8960/20460 (44%)]	Loss: 0.045819
[10240/20460 (50%)]	Loss: 0.065345
[11520/20460 (56%)]	Loss: 0.093369
[12800/20460 (62%)]	Loss: 0.017157
[14080/20460 (69%)]	Loss: 0.072407
[15360/20460 (75%)]	Loss: 0.108482
[16640/20460 (81%)]	Loss: 0.026168
[17920/20460 (88%)]	Loss: 0.060181
[19200/20460 (94%)]	Loss: 0.026590
[14080/20460 (100%)]	Loss: 0.092837

Train: Average loss: 0.0549, Accuracy: 0.9803

Validation: Average loss: 2.3883, Accuracy: 0.8591

Train Epoch: 21
[1280/20460 (6%)]	Loss: 0.021656
[2560/20460 (12%)]	Loss: 0.057478
[3840/20460 (19%)]	Loss: 0.005611
[5120/20460 (25%)]	Loss: 0.082956
[6400/20460 (31%)]	Loss: 0.036128
[7680/20460 (38%)]	Loss: 0.023281
[8960/20460 (44%)]	Loss: 0.022550
[10240/20460 (50%)]	Loss: 0.019933
[11520/20460 (56%)]	Loss: 0.053786
[12800/20460 (62%)]	Loss: 0.062733
[14080/20460 (69%)]	Loss: 0.020300
[15360/20460 (75%)]	Loss: 0.066334
[16640/20460 (81%)]	Loss: 0.005735
[17920/20460 (88%)]	Loss: 0.094226
[19200/20460 (94%)]	Loss: 0.074330
[14080/20460 (100%)]	Loss: 0.007086

Train: Average loss: 0.0461, Accuracy: 0.9840

Validation: Average loss: 1.7902, Accuracy: 0.8602

Train Epoch: 22
[1280/20460 (6%)]	Loss: 0.023283
[2560/20460 (12%)]	Loss: 0.022632
[3840/20460 (19%)]	Loss: 0.039984
[5120/20460 (25%)]	Loss: 0.097088
[6400/20460 (31%)]	Loss: 0.066772
[7680/20460 (38%)]	Loss: 0.045607
[8960/20460 (44%)]	Loss: 0.105642
[10240/20460 (50%)]	Loss: 0.073934
[11520/20460 (56%)]	Loss: 0.079578
[12800/20460 (62%)]	Loss: 0.119628
[14080/20460 (69%)]	Loss: 0.029313
[15360/20460 (75%)]	Loss: 0.048237
[16640/20460 (81%)]	Loss: 0.035918
[17920/20460 (88%)]	Loss: 0.156017
[19200/20460 (94%)]	Loss: 0.139338
[14080/20460 (100%)]	Loss: 0.103584

Train: Average loss: 0.0456, Accuracy: 0.9836

Validation: Average loss: 1.8767, Accuracy: 0.9058

Train Epoch: 23
[1280/20460 (6%)]	Loss: 0.104166
[2560/20460 (12%)]	Loss: 0.017932
[3840/20460 (19%)]	Loss: 0.053743
[5120/20460 (25%)]	Loss: 0.001651
[6400/20460 (31%)]	Loss: 0.056572
[7680/20460 (38%)]	Loss: 0.145369
[8960/20460 (44%)]	Loss: 0.072902
[10240/20460 (50%)]	Loss: 0.058727
[11520/20460 (56%)]	Loss: 0.022644
[12800/20460 (62%)]	Loss: 0.056106
[14080/20460 (69%)]	Loss: 0.036463
[15360/20460 (75%)]	Loss: 0.131182
[16640/20460 (81%)]	Loss: 0.029406
[17920/20460 (88%)]	Loss: 0.018448
[19200/20460 (94%)]	Loss: 0.056077
[14080/20460 (100%)]	Loss: 0.021559

Train: Average loss: 0.0463, Accuracy: 0.9829

Validation: Average loss: 2.4885, Accuracy: 0.7930

Train Epoch: 24
[1280/20460 (6%)]	Loss: 0.029782
[2560/20460 (12%)]	Loss: 0.024480
[3840/20460 (19%)]	Loss: 0.033176
[5120/20460 (25%)]	Loss: 0.014249
[6400/20460 (31%)]	Loss: 0.037911
[7680/20460 (38%)]	Loss: 0.023197
[8960/20460 (44%)]	Loss: 0.105220
[10240/20460 (50%)]	Loss: 0.003205
[11520/20460 (56%)]	Loss: 0.031969
[12800/20460 (62%)]	Loss: 0.032188
[14080/20460 (69%)]	Loss: 0.101091
[15360/20460 (75%)]	Loss: 0.007326
[16640/20460 (81%)]	Loss: 0.107772
[17920/20460 (88%)]	Loss: 0.076094
[19200/20460 (94%)]	Loss: 0.060503
[14080/20460 (100%)]	Loss: 0.026952

Train: Average loss: 0.0433, Accuracy: 0.9847

Validation: Average loss: 2.1807, Accuracy: 0.8872

Train Epoch: 25
[1280/20460 (6%)]	Loss: 0.001547
[2560/20460 (12%)]	Loss: 0.023376
[3840/20460 (19%)]	Loss: 0.046724
[5120/20460 (25%)]	Loss: 0.092519
[6400/20460 (31%)]	Loss: 0.027603
[7680/20460 (38%)]	Loss: 0.037492
[8960/20460 (44%)]	Loss: 0.013154
[10240/20460 (50%)]	Loss: 0.016246
[11520/20460 (56%)]	Loss: 0.081681
[12800/20460 (62%)]	Loss: 0.021114
[14080/20460 (69%)]	Loss: 0.012098
[15360/20460 (75%)]	Loss: 0.018630
[16640/20460 (81%)]	Loss: 0.053905
[17920/20460 (88%)]	Loss: 0.046273
[19200/20460 (94%)]	Loss: 0.030135
[14080/20460 (100%)]	Loss: 0.008063

Train: Average loss: 0.0443, Accuracy: 0.9842

Validation: Average loss: 1.5726, Accuracy: 0.8986

Train Epoch: 26
[1280/20460 (6%)]	Loss: 0.044958
[2560/20460 (12%)]	Loss: 0.005513
[3840/20460 (19%)]	Loss: 0.026505
[5120/20460 (25%)]	Loss: 0.051464
[6400/20460 (31%)]	Loss: 0.054007
[7680/20460 (38%)]	Loss: 0.047236
[8960/20460 (44%)]	Loss: 0.180607
[10240/20460 (50%)]	Loss: 0.015927
[11520/20460 (56%)]	Loss: 0.063853
[12800/20460 (62%)]	Loss: 0.056948
[14080/20460 (69%)]	Loss: 0.095953
[15360/20460 (75%)]	Loss: 0.087786
[16640/20460 (81%)]	Loss: 0.042396
[17920/20460 (88%)]	Loss: 0.056485
[19200/20460 (94%)]	Loss: 0.039375
[14080/20460 (100%)]	Loss: 0.013858

Train: Average loss: 0.0443, Accuracy: 0.9845

Validation: Average loss: 1.9030, Accuracy: 0.8854

Train Epoch: 27
[1280/20460 (6%)]	Loss: 0.014182
[2560/20460 (12%)]	Loss: 0.004076
[3840/20460 (19%)]	Loss: 0.057770
[5120/20460 (25%)]	Loss: 0.012988
[6400/20460 (31%)]	Loss: 0.080241
[7680/20460 (38%)]	Loss: 0.032667
[8960/20460 (44%)]	Loss: 0.020630
[10240/20460 (50%)]	Loss: 0.204567
[11520/20460 (56%)]	Loss: 0.025905
[12800/20460 (62%)]	Loss: 0.035793
[14080/20460 (69%)]	Loss: 0.003653
[15360/20460 (75%)]	Loss: 0.086452
[16640/20460 (81%)]	Loss: 0.006440
[17920/20460 (88%)]	Loss: 0.006412
[19200/20460 (94%)]	Loss: 0.053606
[14080/20460 (100%)]	Loss: 0.006571

Train: Average loss: 0.0395, Accuracy: 0.9860

Validation: Average loss: 2.2398, Accuracy: 0.9048

Train Epoch: 28
[1280/20460 (6%)]	Loss: 0.143015
[2560/20460 (12%)]	Loss: 0.002720
[3840/20460 (19%)]	Loss: 0.101957
[5120/20460 (25%)]	Loss: 0.076931
[6400/20460 (31%)]	Loss: 0.040592
[7680/20460 (38%)]	Loss: 0.012416
[8960/20460 (44%)]	Loss: 0.062311
[10240/20460 (50%)]	Loss: 0.039100
[11520/20460 (56%)]	Loss: 0.123086
[12800/20460 (62%)]	Loss: 0.012272
[14080/20460 (69%)]	Loss: 0.077870
[15360/20460 (75%)]	Loss: 0.097095
[16640/20460 (81%)]	Loss: 0.009405
[17920/20460 (88%)]	Loss: 0.018047
[19200/20460 (94%)]	Loss: 0.011251
[14080/20460 (100%)]	Loss: 0.089883

Train: Average loss: 0.0451, Accuracy: 0.9833

Validation: Average loss: 2.0306, Accuracy: 0.9003

Train Epoch: 29
[1280/20460 (6%)]	Loss: 0.049717
[2560/20460 (12%)]	Loss: 0.020472
[3840/20460 (19%)]	Loss: 0.038108
[5120/20460 (25%)]	Loss: 0.126613
[6400/20460 (31%)]	Loss: 0.026770
[7680/20460 (38%)]	Loss: 0.017383
[8960/20460 (44%)]	Loss: 0.014881
[10240/20460 (50%)]	Loss: 0.097292
[11520/20460 (56%)]	Loss: 0.060368
[12800/20460 (62%)]	Loss: 0.017599
[14080/20460 (69%)]	Loss: 0.011290
[15360/20460 (75%)]	Loss: 0.052744
[16640/20460 (81%)]	Loss: 0.011328
[17920/20460 (88%)]	Loss: 0.014417
[19200/20460 (94%)]	Loss: 0.009475
[14080/20460 (100%)]	Loss: 0.026258

Train: Average loss: 0.0350, Accuracy: 0.9879

Validation: Average loss: 1.7274, Accuracy: 0.8339

Train Epoch: 30
[1280/20460 (6%)]	Loss: 0.055909
[2560/20460 (12%)]	Loss: 0.012958
[3840/20460 (19%)]	Loss: 0.009414
[5120/20460 (25%)]	Loss: 0.028990
[6400/20460 (31%)]	Loss: 0.026297
[7680/20460 (38%)]	Loss: 0.014383
[8960/20460 (44%)]	Loss: 0.045923
[10240/20460 (50%)]	Loss: 0.003171
[11520/20460 (56%)]	Loss: 0.074503
[12800/20460 (62%)]	Loss: 0.034564
[14080/20460 (69%)]	Loss: 0.267748
[15360/20460 (75%)]	Loss: 0.021972
[16640/20460 (81%)]	Loss: 0.005628
[17920/20460 (88%)]	Loss: 0.024788
[19200/20460 (94%)]	Loss: 0.026827
[14080/20460 (100%)]	Loss: 0.074816

Train: Average loss: 0.0402, Accuracy: 0.9847

Validation: Average loss: 2.2622, Accuracy: 0.7979

In [14]:
epochs = range(1, len(train_losses) + 1)

plt.figure(figsize=(15,6))
plt.subplot(1,2,1)
plt.plot(epochs, train_losses, '-o', label='Training loss')
plt.plot(epochs, val_losses, '-o', label='Validation loss')
plt.legend()
plt.title('Learning curves')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.xticks(epochs)
plt.subplot(1,2,2)
plt.plot(epochs, train_accuracies, '-o', label='Training accuracy')
plt.plot(epochs, val_accuracies, '-o', label='Validation accuracy')
plt.legend()
plt.title('Learning curves')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.xticks(epochs)
plt.show()
In [15]:
# best_epoch = 32
model = torch.load(checkpoints_foler+f'/avp_{best_epoch:03d}.pkl')
/tmp/cache-bformanek/ipykernel_1725551/529002640.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  model = torch.load(checkpoints_foler+f'/avp_{best_epoch:03d}.pkl')
In [16]:
def predict(model, data_loader):
    model.eval()

    # save the predictions in this list
    y_pred = []

    # no gradient needed
    with torch.no_grad():

        # go over each batch in the loader. We can ignore the targets here
        for batch, _ in data_loader:

            # Move batch to the GPU
            batch = batch.to(device)

            # predict probabilities of each class
            predictions = model(batch)

            # apply a softmax to the predictions
            predictions = F.softmax(predictions, dim=1)

            # move to the cpu and convert to numpy
            predictions = predictions.cpu().numpy()

            # save
            y_pred.append(predictions)

    # stack predictions into a (num_samples, 10) array
    y_pred = np.vstack(y_pred)
    return y_pred
In [17]:
# compute predictions on the test set
y_pred = predict(model, test_loader)
# find the argmax of each of the predictions
y_pred = y_pred.argmax(axis=1)

# get the true labels and convert to numpy
y_true = np.array(test_set.targets)
In [18]:
num_errors = np.sum(y_true != y_pred)
print(f'Test errors {num_errors} (out of {len(test_set)})  {num_errors/len(test_set)*100:0.2f}%')
print(f'Test accuracy {100-num_errors/len(test_set)*100:0.2f}%')
Test errors 284 (out of 5826)  4.87%
Test accuracy 95.13%
In [19]:
from sklearn.metrics import confusion_matrix
import seaborn as sns

conf_matrix = confusion_matrix(y_true, y_pred)

plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',
            xticklabels=train_categories,
            yticklabels=train_categories)
plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.title('Confusion Matrix')
plt.show()
/usr/lib/python3/dist-packages/statsmodels/__init__.py:6: UserWarning: This appears to be an armel system, on which statsmodels is buggy (crashes and possibly wrong answers) - https://bugs.debian.org/968210
  warnings.warn("This appears to be an armel system, on which statsmodels is buggy (crashes and possibly wrong answers) - https://bugs.debian.org/968210")
In [20]:
TP = conf_matrix.diagonal()
P = conf_matrix.sum(axis=1)

# Calculate balanced accuracy
balanced_accuracy = sum(TP / P) / len(P)
print(f'Balanced accuracy {balanced_accuracy*100:0.2f}%')
Balanced accuracy 95.65%
In [ ]: