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 [ ]:
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=90, 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_1187671/3247579378.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_90"

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: 1.121783
[2560/20460 (12%)]	Loss: 0.946629
[3840/20460 (19%)]	Loss: 0.506714
[5120/20460 (25%)]	Loss: 0.533865
[6400/20460 (31%)]	Loss: 0.436800
[7680/20460 (38%)]	Loss: 0.262740
[8960/20460 (44%)]	Loss: 0.302465
[10240/20460 (50%)]	Loss: 0.297401
[11520/20460 (56%)]	Loss: 0.222155
[12800/20460 (62%)]	Loss: 0.380815
[14080/20460 (69%)]	Loss: 0.380882
[15360/20460 (75%)]	Loss: 0.246122
[16640/20460 (81%)]	Loss: 0.206862
[17920/20460 (88%)]	Loss: 0.156093
[19200/20460 (94%)]	Loss: 0.192158
[14080/20460 (100%)]	Loss: 0.278408

Train: Average loss: 0.4585, Accuracy: 0.8359

Validation: Average loss: 1.5108, Accuracy: 0.7027

Train Epoch: 2
[1280/20460 (6%)]	Loss: 0.281680
[2560/20460 (12%)]	Loss: 0.321837
[3840/20460 (19%)]	Loss: 0.238968
[5120/20460 (25%)]	Loss: 0.215485
[6400/20460 (31%)]	Loss: 0.297618
[7680/20460 (38%)]	Loss: 0.161287
[8960/20460 (44%)]	Loss: 0.262413
[10240/20460 (50%)]	Loss: 0.194598
[11520/20460 (56%)]	Loss: 0.066758
[12800/20460 (62%)]	Loss: 0.231040
[14080/20460 (69%)]	Loss: 0.313682
[15360/20460 (75%)]	Loss: 0.149096
[16640/20460 (81%)]	Loss: 0.218900
[17920/20460 (88%)]	Loss: 0.142537
[19200/20460 (94%)]	Loss: 0.157793
[14080/20460 (100%)]	Loss: 0.061833

Train: Average loss: 0.2134, Accuracy: 0.9217

Validation: Average loss: 1.2695, Accuracy: 0.8127

Train Epoch: 3
[1280/20460 (6%)]	Loss: 0.130062
[2560/20460 (12%)]	Loss: 0.238443
[3840/20460 (19%)]	Loss: 0.139244
[5120/20460 (25%)]	Loss: 0.170909
[6400/20460 (31%)]	Loss: 0.097468
[7680/20460 (38%)]	Loss: 0.112483
[8960/20460 (44%)]	Loss: 0.097386
[10240/20460 (50%)]	Loss: 0.049394
[11520/20460 (56%)]	Loss: 0.162956
[12800/20460 (62%)]	Loss: 0.351354
[14080/20460 (69%)]	Loss: 0.102100
[15360/20460 (75%)]	Loss: 0.136975
[16640/20460 (81%)]	Loss: 0.135995
[17920/20460 (88%)]	Loss: 0.308334
[19200/20460 (94%)]	Loss: 0.118473
[14080/20460 (100%)]	Loss: 0.105289

Train: Average loss: 0.1699, Accuracy: 0.9381

Validation: Average loss: 1.2912, Accuracy: 0.8287

Train Epoch: 4
[1280/20460 (6%)]	Loss: 0.146442
[2560/20460 (12%)]	Loss: 0.207035
[3840/20460 (19%)]	Loss: 0.177183
[5120/20460 (25%)]	Loss: 0.081921
[6400/20460 (31%)]	Loss: 0.124173
[7680/20460 (38%)]	Loss: 0.074887
[8960/20460 (44%)]	Loss: 0.074524
[10240/20460 (50%)]	Loss: 0.208884
[11520/20460 (56%)]	Loss: 0.045999
[12800/20460 (62%)]	Loss: 0.102252
[14080/20460 (69%)]	Loss: 0.039787
[15360/20460 (75%)]	Loss: 0.221526
[16640/20460 (81%)]	Loss: 0.087009
[17920/20460 (88%)]	Loss: 0.059424
[19200/20460 (94%)]	Loss: 0.266214
[14080/20460 (100%)]	Loss: 0.224559

Train: Average loss: 0.1398, Accuracy: 0.9497

Validation: Average loss: 1.1637, Accuracy: 0.8879

Train Epoch: 5
[1280/20460 (6%)]	Loss: 0.136847
[2560/20460 (12%)]	Loss: 0.113813
[3840/20460 (19%)]	Loss: 0.094093
[5120/20460 (25%)]	Loss: 0.129633
[6400/20460 (31%)]	Loss: 0.087359
[7680/20460 (38%)]	Loss: 0.123612
[8960/20460 (44%)]	Loss: 0.109611
[10240/20460 (50%)]	Loss: 0.135459
[11520/20460 (56%)]	Loss: 0.069905
[12800/20460 (62%)]	Loss: 0.086799
[14080/20460 (69%)]	Loss: 0.191349
[15360/20460 (75%)]	Loss: 0.206091
[16640/20460 (81%)]	Loss: 0.163483
[17920/20460 (88%)]	Loss: 0.138722
[19200/20460 (94%)]	Loss: 0.206998
[14080/20460 (100%)]	Loss: 0.119404

Train: Average loss: 0.1313, Accuracy: 0.9529

Validation: Average loss: 1.0963, Accuracy: 0.8702

Train Epoch: 6
[1280/20460 (6%)]	Loss: 0.187999
[2560/20460 (12%)]	Loss: 0.086030
[3840/20460 (19%)]	Loss: 0.144777
[5120/20460 (25%)]	Loss: 0.144760
[6400/20460 (31%)]	Loss: 0.124115
[7680/20460 (38%)]	Loss: 0.137852
[8960/20460 (44%)]	Loss: 0.027978
[10240/20460 (50%)]	Loss: 0.109399
[11520/20460 (56%)]	Loss: 0.055348
[12800/20460 (62%)]	Loss: 0.074755
[14080/20460 (69%)]	Loss: 0.131823
[15360/20460 (75%)]	Loss: 0.044131
[16640/20460 (81%)]	Loss: 0.047118
[17920/20460 (88%)]	Loss: 0.137253
[19200/20460 (94%)]	Loss: 0.203563
[14080/20460 (100%)]	Loss: 0.049812

Train: Average loss: 0.1150, Accuracy: 0.9585

Validation: Average loss: 1.4601, Accuracy: 0.8397

Train Epoch: 7
[1280/20460 (6%)]	Loss: 0.217276
[2560/20460 (12%)]	Loss: 0.090557
[3840/20460 (19%)]	Loss: 0.069154
[5120/20460 (25%)]	Loss: 0.047134
[6400/20460 (31%)]	Loss: 0.077934
[7680/20460 (38%)]	Loss: 0.045398
[8960/20460 (44%)]	Loss: 0.098383
[10240/20460 (50%)]	Loss: 0.115602
[11520/20460 (56%)]	Loss: 0.099869
[12800/20460 (62%)]	Loss: 0.135509
[14080/20460 (69%)]	Loss: 0.183088
[15360/20460 (75%)]	Loss: 0.063021
[16640/20460 (81%)]	Loss: 0.109143
[17920/20460 (88%)]	Loss: 0.147743
[19200/20460 (94%)]	Loss: 0.138264
[14080/20460 (100%)]	Loss: 0.061802

Train: Average loss: 0.1046, Accuracy: 0.9606

Validation: Average loss: 1.8557, Accuracy: 0.8429

Train Epoch: 8
[1280/20460 (6%)]	Loss: 0.201911
[2560/20460 (12%)]	Loss: 0.100179
[3840/20460 (19%)]	Loss: 0.068157
[5120/20460 (25%)]	Loss: 0.022204
[6400/20460 (31%)]	Loss: 0.087012
[7680/20460 (38%)]	Loss: 0.065616
[8960/20460 (44%)]	Loss: 0.043233
[10240/20460 (50%)]	Loss: 0.047198
[11520/20460 (56%)]	Loss: 0.044256
[12800/20460 (62%)]	Loss: 0.081848
[14080/20460 (69%)]	Loss: 0.070314
[15360/20460 (75%)]	Loss: 0.068294
[16640/20460 (81%)]	Loss: 0.197445
[17920/20460 (88%)]	Loss: 0.239804
[19200/20460 (94%)]	Loss: 0.038391
[14080/20460 (100%)]	Loss: 0.052701

Train: Average loss: 0.0914, Accuracy: 0.9663

Validation: Average loss: 2.1686, Accuracy: 0.6466

Train Epoch: 9
[1280/20460 (6%)]	Loss: 0.099265
[2560/20460 (12%)]	Loss: 0.108811
[3840/20460 (19%)]	Loss: 0.051881
[5120/20460 (25%)]	Loss: 0.063936
[6400/20460 (31%)]	Loss: 0.039354
[7680/20460 (38%)]	Loss: 0.108712
[8960/20460 (44%)]	Loss: 0.106972
[10240/20460 (50%)]	Loss: 0.059984
[11520/20460 (56%)]	Loss: 0.076435
[12800/20460 (62%)]	Loss: 0.083024
[14080/20460 (69%)]	Loss: 0.129473
[15360/20460 (75%)]	Loss: 0.068982
[16640/20460 (81%)]	Loss: 0.011408
[17920/20460 (88%)]	Loss: 0.122767
[19200/20460 (94%)]	Loss: 0.081746
[14080/20460 (100%)]	Loss: 0.053206

Train: Average loss: 0.0867, Accuracy: 0.9688

Validation: Average loss: 1.9094, Accuracy: 0.7480

Train Epoch: 10
[1280/20460 (6%)]	Loss: 0.050514
[2560/20460 (12%)]	Loss: 0.088662
[3840/20460 (19%)]	Loss: 0.034884
[5120/20460 (25%)]	Loss: 0.058542
[6400/20460 (31%)]	Loss: 0.062130
[7680/20460 (38%)]	Loss: 0.086473
[8960/20460 (44%)]	Loss: 0.038134
[10240/20460 (50%)]	Loss: 0.061116
[11520/20460 (56%)]	Loss: 0.051446
[12800/20460 (62%)]	Loss: 0.140623
[14080/20460 (69%)]	Loss: 0.223666
[15360/20460 (75%)]	Loss: 0.083736
[16640/20460 (81%)]	Loss: 0.025793
[17920/20460 (88%)]	Loss: 0.108333
[19200/20460 (94%)]	Loss: 0.211818
[14080/20460 (100%)]	Loss: 0.240535

Train: Average loss: 0.0854, Accuracy: 0.9692

Validation: Average loss: 1.6490, Accuracy: 0.8474

Train Epoch: 11
[1280/20460 (6%)]	Loss: 0.140121
[2560/20460 (12%)]	Loss: 0.114145
[3840/20460 (19%)]	Loss: 0.068639
[5120/20460 (25%)]	Loss: 0.170345
[6400/20460 (31%)]	Loss: 0.083817
[7680/20460 (38%)]	Loss: 0.035787
[8960/20460 (44%)]	Loss: 0.044273
[10240/20460 (50%)]	Loss: 0.224257
[11520/20460 (56%)]	Loss: 0.032594
[12800/20460 (62%)]	Loss: 0.032689
[14080/20460 (69%)]	Loss: 0.113383
[15360/20460 (75%)]	Loss: 0.070244
[16640/20460 (81%)]	Loss: 0.010874
[17920/20460 (88%)]	Loss: 0.075980
[19200/20460 (94%)]	Loss: 0.071065
[14080/20460 (100%)]	Loss: 0.091406

Train: Average loss: 0.0802, Accuracy: 0.9713

Validation: Average loss: 1.7442, Accuracy: 0.8515

Train Epoch: 12
[1280/20460 (6%)]	Loss: 0.038020
[2560/20460 (12%)]	Loss: 0.156714
[3840/20460 (19%)]	Loss: 0.023174
[5120/20460 (25%)]	Loss: 0.025744
[6400/20460 (31%)]	Loss: 0.048348
[7680/20460 (38%)]	Loss: 0.070500
[8960/20460 (44%)]	Loss: 0.167784
[10240/20460 (50%)]	Loss: 0.022081
[11520/20460 (56%)]	Loss: 0.048660
[12800/20460 (62%)]	Loss: 0.048582
[14080/20460 (69%)]	Loss: 0.108620
[15360/20460 (75%)]	Loss: 0.032134
[16640/20460 (81%)]	Loss: 0.109318
[17920/20460 (88%)]	Loss: 0.093002
[19200/20460 (94%)]	Loss: 0.098362
[14080/20460 (100%)]	Loss: 0.058102

Train: Average loss: 0.0703, Accuracy: 0.9738

Validation: Average loss: 1.8285, Accuracy: 0.8345

Train Epoch: 13
[1280/20460 (6%)]	Loss: 0.087391
[2560/20460 (12%)]	Loss: 0.030444
[3840/20460 (19%)]	Loss: 0.020562
[5120/20460 (25%)]	Loss: 0.173501
[6400/20460 (31%)]	Loss: 0.107384
[7680/20460 (38%)]	Loss: 0.040159
[8960/20460 (44%)]	Loss: 0.132579
[10240/20460 (50%)]	Loss: 0.043040
[11520/20460 (56%)]	Loss: 0.046314
[12800/20460 (62%)]	Loss: 0.034813
[14080/20460 (69%)]	Loss: 0.033003
[15360/20460 (75%)]	Loss: 0.069163
[16640/20460 (81%)]	Loss: 0.047007
[17920/20460 (88%)]	Loss: 0.057003
[19200/20460 (94%)]	Loss: 0.085094
[14080/20460 (100%)]	Loss: 0.162390

Train: Average loss: 0.0650, Accuracy: 0.9763

Validation: Average loss: 1.8287, Accuracy: 0.8924

Train Epoch: 14
[1280/20460 (6%)]	Loss: 0.133700
[2560/20460 (12%)]	Loss: 0.026024
[3840/20460 (19%)]	Loss: 0.024024
[5120/20460 (25%)]	Loss: 0.142325
[6400/20460 (31%)]	Loss: 0.050719
[7680/20460 (38%)]	Loss: 0.025884
[8960/20460 (44%)]	Loss: 0.050714
[10240/20460 (50%)]	Loss: 0.048575
[11520/20460 (56%)]	Loss: 0.008899
[12800/20460 (62%)]	Loss: 0.065333
[14080/20460 (69%)]	Loss: 0.020567
[15360/20460 (75%)]	Loss: 0.106526
[16640/20460 (81%)]	Loss: 0.030841
[17920/20460 (88%)]	Loss: 0.030987
[19200/20460 (94%)]	Loss: 0.073911
[14080/20460 (100%)]	Loss: 0.119375

Train: Average loss: 0.0684, Accuracy: 0.9761

Validation: Average loss: 2.0507, Accuracy: 0.8446

Train Epoch: 15
[1280/20460 (6%)]	Loss: 0.204143
[2560/20460 (12%)]	Loss: 0.113897
[3840/20460 (19%)]	Loss: 0.137349
[5120/20460 (25%)]	Loss: 0.102300
[6400/20460 (31%)]	Loss: 0.082263
[7680/20460 (38%)]	Loss: 0.103335
[8960/20460 (44%)]	Loss: 0.052692
[10240/20460 (50%)]	Loss: 0.043982
[11520/20460 (56%)]	Loss: 0.068718
[12800/20460 (62%)]	Loss: 0.034984
[14080/20460 (69%)]	Loss: 0.051655
[15360/20460 (75%)]	Loss: 0.025368
[16640/20460 (81%)]	Loss: 0.091609
[17920/20460 (88%)]	Loss: 0.033564
[19200/20460 (94%)]	Loss: 0.020121
[14080/20460 (100%)]	Loss: 0.025609

Train: Average loss: 0.0638, Accuracy: 0.9770

Validation: Average loss: 1.3940, Accuracy: 0.8629

Train Epoch: 16
[1280/20460 (6%)]	Loss: 0.021415
[2560/20460 (12%)]	Loss: 0.095036
[3840/20460 (19%)]	Loss: 0.040700
[5120/20460 (25%)]	Loss: 0.095202
[6400/20460 (31%)]	Loss: 0.077075
[7680/20460 (38%)]	Loss: 0.139535
[8960/20460 (44%)]	Loss: 0.109150
[10240/20460 (50%)]	Loss: 0.040185
[11520/20460 (56%)]	Loss: 0.033069
[12800/20460 (62%)]	Loss: 0.091608
[14080/20460 (69%)]	Loss: 0.027273
[15360/20460 (75%)]	Loss: 0.083298
[16640/20460 (81%)]	Loss: 0.037661
[17920/20460 (88%)]	Loss: 0.053975
[19200/20460 (94%)]	Loss: 0.054019
[14080/20460 (100%)]	Loss: 0.046255

Train: Average loss: 0.0634, Accuracy: 0.9770

Validation: Average loss: 2.0374, Accuracy: 0.8543

Train Epoch: 17
[1280/20460 (6%)]	Loss: 0.016832
[2560/20460 (12%)]	Loss: 0.035048
[3840/20460 (19%)]	Loss: 0.057127
[5120/20460 (25%)]	Loss: 0.004374
[6400/20460 (31%)]	Loss: 0.037183
[7680/20460 (38%)]	Loss: 0.102837
[8960/20460 (44%)]	Loss: 0.041151
[10240/20460 (50%)]	Loss: 0.054957
[11520/20460 (56%)]	Loss: 0.035360
[12800/20460 (62%)]	Loss: 0.018015
[14080/20460 (69%)]	Loss: 0.027054
[15360/20460 (75%)]	Loss: 0.016186
[16640/20460 (81%)]	Loss: 0.057146
[17920/20460 (88%)]	Loss: 0.020438
[19200/20460 (94%)]	Loss: 0.006786
[14080/20460 (100%)]	Loss: 0.042241

Train: Average loss: 0.0537, Accuracy: 0.9803

Validation: Average loss: 1.8400, Accuracy: 0.8861

Train Epoch: 18
[1280/20460 (6%)]	Loss: 0.005049
[2560/20460 (12%)]	Loss: 0.024975
[3840/20460 (19%)]	Loss: 0.004489
[5120/20460 (25%)]	Loss: 0.071913
[6400/20460 (31%)]	Loss: 0.058303
[7680/20460 (38%)]	Loss: 0.003656
[8960/20460 (44%)]	Loss: 0.014360
[10240/20460 (50%)]	Loss: 0.036314
[11520/20460 (56%)]	Loss: 0.070050
[12800/20460 (62%)]	Loss: 0.085967
[14080/20460 (69%)]	Loss: 0.016144
[15360/20460 (75%)]	Loss: 0.046558
[16640/20460 (81%)]	Loss: 0.053486
[17920/20460 (88%)]	Loss: 0.018694
[19200/20460 (94%)]	Loss: 0.033277
[14080/20460 (100%)]	Loss: 0.070492

Train: Average loss: 0.0524, Accuracy: 0.9799

Validation: Average loss: 1.5067, Accuracy: 0.8366

Train Epoch: 19
[1280/20460 (6%)]	Loss: 0.024740
[2560/20460 (12%)]	Loss: 0.063422
[3840/20460 (19%)]	Loss: 0.036730
[5120/20460 (25%)]	Loss: 0.076165
[6400/20460 (31%)]	Loss: 0.141040
[7680/20460 (38%)]	Loss: 0.084085
[8960/20460 (44%)]	Loss: 0.076674
[10240/20460 (50%)]	Loss: 0.205944
[11520/20460 (56%)]	Loss: 0.065567
[12800/20460 (62%)]	Loss: 0.025923
[14080/20460 (69%)]	Loss: 0.028149
[15360/20460 (75%)]	Loss: 0.025548
[16640/20460 (81%)]	Loss: 0.088734
[17920/20460 (88%)]	Loss: 0.028693
[19200/20460 (94%)]	Loss: 0.052938
[14080/20460 (100%)]	Loss: 0.017587

Train: Average loss: 0.0557, Accuracy: 0.9798

Validation: Average loss: 2.5894, Accuracy: 0.8117

Train Epoch: 20
[1280/20460 (6%)]	Loss: 0.019759
[2560/20460 (12%)]	Loss: 0.021306
[3840/20460 (19%)]	Loss: 0.005973
[5120/20460 (25%)]	Loss: 0.050559
[6400/20460 (31%)]	Loss: 0.153467
[7680/20460 (38%)]	Loss: 0.092604
[8960/20460 (44%)]	Loss: 0.017100
[10240/20460 (50%)]	Loss: 0.064931
[11520/20460 (56%)]	Loss: 0.041813
[12800/20460 (62%)]	Loss: 0.012860
[14080/20460 (69%)]	Loss: 0.085731
[15360/20460 (75%)]	Loss: 0.120585
[16640/20460 (81%)]	Loss: 0.023143
[17920/20460 (88%)]	Loss: 0.083957
[19200/20460 (94%)]	Loss: 0.022337
[14080/20460 (100%)]	Loss: 0.018483

Train: Average loss: 0.0546, Accuracy: 0.9809

Validation: Average loss: 2.5440, Accuracy: 0.8903

Train Epoch: 21
[1280/20460 (6%)]	Loss: 0.018003
[2560/20460 (12%)]	Loss: 0.046180
[3840/20460 (19%)]	Loss: 0.011885
[5120/20460 (25%)]	Loss: 0.016037
[6400/20460 (31%)]	Loss: 0.050253
[7680/20460 (38%)]	Loss: 0.029531
[8960/20460 (44%)]	Loss: 0.166838
[10240/20460 (50%)]	Loss: 0.021797
[11520/20460 (56%)]	Loss: 0.055100
[12800/20460 (62%)]	Loss: 0.037381
[14080/20460 (69%)]	Loss: 0.029021
[15360/20460 (75%)]	Loss: 0.025692
[16640/20460 (81%)]	Loss: 0.021525
[17920/20460 (88%)]	Loss: 0.054116
[19200/20460 (94%)]	Loss: 0.102765
[14080/20460 (100%)]	Loss: 0.006914

Train: Average loss: 0.0525, Accuracy: 0.9808

Validation: Average loss: 2.0844, Accuracy: 0.8851

Train Epoch: 22
[1280/20460 (6%)]	Loss: 0.013768
[2560/20460 (12%)]	Loss: 0.058332
[3840/20460 (19%)]	Loss: 0.018160
[5120/20460 (25%)]	Loss: 0.069345
[6400/20460 (31%)]	Loss: 0.016831
[7680/20460 (38%)]	Loss: 0.040255
[8960/20460 (44%)]	Loss: 0.023892
[10240/20460 (50%)]	Loss: 0.051529
[11520/20460 (56%)]	Loss: 0.039424
[12800/20460 (62%)]	Loss: 0.015601
[14080/20460 (69%)]	Loss: 0.080849
[15360/20460 (75%)]	Loss: 0.020875
[16640/20460 (81%)]	Loss: 0.018100
[17920/20460 (88%)]	Loss: 0.057401
[19200/20460 (94%)]	Loss: 0.028669
[14080/20460 (100%)]	Loss: 0.015214

Train: Average loss: 0.0454, Accuracy: 0.9839

Validation: Average loss: 2.8083, Accuracy: 0.8809

Train Epoch: 23
[1280/20460 (6%)]	Loss: 0.052872
[2560/20460 (12%)]	Loss: 0.027792
[3840/20460 (19%)]	Loss: 0.017103
[5120/20460 (25%)]	Loss: 0.011144
[6400/20460 (31%)]	Loss: 0.028261
[7680/20460 (38%)]	Loss: 0.233879
[8960/20460 (44%)]	Loss: 0.042307
[10240/20460 (50%)]	Loss: 0.045837
[11520/20460 (56%)]	Loss: 0.067601
[12800/20460 (62%)]	Loss: 0.033086
[14080/20460 (69%)]	Loss: 0.018354
[15360/20460 (75%)]	Loss: 0.046112
[16640/20460 (81%)]	Loss: 0.004897
[17920/20460 (88%)]	Loss: 0.023041
[19200/20460 (94%)]	Loss: 0.011263
[14080/20460 (100%)]	Loss: 0.111408

Train: Average loss: 0.0426, Accuracy: 0.9848

Validation: Average loss: 2.2658, Accuracy: 0.7934

Train Epoch: 24
[1280/20460 (6%)]	Loss: 0.016250
[2560/20460 (12%)]	Loss: 0.061643
[3840/20460 (19%)]	Loss: 0.015815
[5120/20460 (25%)]	Loss: 0.031169
[6400/20460 (31%)]	Loss: 0.026564
[7680/20460 (38%)]	Loss: 0.099475
[8960/20460 (44%)]	Loss: 0.049851
[10240/20460 (50%)]	Loss: 0.025703
[11520/20460 (56%)]	Loss: 0.021714
[12800/20460 (62%)]	Loss: 0.013830
[14080/20460 (69%)]	Loss: 0.152658
[15360/20460 (75%)]	Loss: 0.031056
[16640/20460 (81%)]	Loss: 0.014765
[17920/20460 (88%)]	Loss: 0.010391
[19200/20460 (94%)]	Loss: 0.106624
[14080/20460 (100%)]	Loss: 0.067084

Train: Average loss: 0.0477, Accuracy: 0.9827

Validation: Average loss: 2.6106, Accuracy: 0.8193

Train Epoch: 25
[1280/20460 (6%)]	Loss: 0.109922
[2560/20460 (12%)]	Loss: 0.024177
[3840/20460 (19%)]	Loss: 0.016365
[5120/20460 (25%)]	Loss: 0.020670
[6400/20460 (31%)]	Loss: 0.067381
[7680/20460 (38%)]	Loss: 0.006828
[8960/20460 (44%)]	Loss: 0.009186
[10240/20460 (50%)]	Loss: 0.011102
[11520/20460 (56%)]	Loss: 0.219380
[12800/20460 (62%)]	Loss: 0.060808
[14080/20460 (69%)]	Loss: 0.016508
[15360/20460 (75%)]	Loss: 0.037232
[16640/20460 (81%)]	Loss: 0.032674
[17920/20460 (88%)]	Loss: 0.046707
[19200/20460 (94%)]	Loss: 0.032258
[14080/20460 (100%)]	Loss: 0.029935

Train: Average loss: 0.0499, Accuracy: 0.9820

Validation: Average loss: 2.0060, Accuracy: 0.8872

Train Epoch: 26
[1280/20460 (6%)]	Loss: 0.054253
[2560/20460 (12%)]	Loss: 0.079039
[3840/20460 (19%)]	Loss: 0.184449
[5120/20460 (25%)]	Loss: 0.023214
[6400/20460 (31%)]	Loss: 0.014753
[7680/20460 (38%)]	Loss: 0.005708
[8960/20460 (44%)]	Loss: 0.198349
[10240/20460 (50%)]	Loss: 0.059076
[11520/20460 (56%)]	Loss: 0.006045
[12800/20460 (62%)]	Loss: 0.044515
[14080/20460 (69%)]	Loss: 0.007924
[15360/20460 (75%)]	Loss: 0.032671
[16640/20460 (81%)]	Loss: 0.090974
[17920/20460 (88%)]	Loss: 0.042169
[19200/20460 (94%)]	Loss: 0.036685
[14080/20460 (100%)]	Loss: 0.015652

Train: Average loss: 0.0471, Accuracy: 0.9836

Validation: Average loss: 2.7648, Accuracy: 0.8311

Train Epoch: 27
[1280/20460 (6%)]	Loss: 0.063548
[2560/20460 (12%)]	Loss: 0.035279
[3840/20460 (19%)]	Loss: 0.040248
[5120/20460 (25%)]	Loss: 0.051909
[6400/20460 (31%)]	Loss: 0.034989
[7680/20460 (38%)]	Loss: 0.002876
[8960/20460 (44%)]	Loss: 0.031894
[10240/20460 (50%)]	Loss: 0.079756
[11520/20460 (56%)]	Loss: 0.013293
[12800/20460 (62%)]	Loss: 0.036099
[14080/20460 (69%)]	Loss: 0.013924
[15360/20460 (75%)]	Loss: 0.021081
[16640/20460 (81%)]	Loss: 0.011268
[17920/20460 (88%)]	Loss: 0.021196
[19200/20460 (94%)]	Loss: 0.049185
[14080/20460 (100%)]	Loss: 0.005441

Train: Average loss: 0.0444, Accuracy: 0.9833

Validation: Average loss: 2.3483, Accuracy: 0.8664

Train Epoch: 28
[1280/20460 (6%)]	Loss: 0.051537
[2560/20460 (12%)]	Loss: 0.008787
[3840/20460 (19%)]	Loss: 0.004649
[5120/20460 (25%)]	Loss: 0.012945
[6400/20460 (31%)]	Loss: 0.013406
[7680/20460 (38%)]	Loss: 0.097199
[8960/20460 (44%)]	Loss: 0.013903
[10240/20460 (50%)]	Loss: 0.042764
[11520/20460 (56%)]	Loss: 0.017919
[12800/20460 (62%)]	Loss: 0.063798
[14080/20460 (69%)]	Loss: 0.011889
[15360/20460 (75%)]	Loss: 0.028720
[16640/20460 (81%)]	Loss: 0.028578
[17920/20460 (88%)]	Loss: 0.015394
[19200/20460 (94%)]	Loss: 0.030783
[14080/20460 (100%)]	Loss: 0.051448

Train: Average loss: 0.0383, Accuracy: 0.9861

Validation: Average loss: 2.2801, Accuracy: 0.8525

Train Epoch: 29
[1280/20460 (6%)]	Loss: 0.015476
[2560/20460 (12%)]	Loss: 0.016706
[3840/20460 (19%)]	Loss: 0.012022
[5120/20460 (25%)]	Loss: 0.036896
[6400/20460 (31%)]	Loss: 0.001162
[7680/20460 (38%)]	Loss: 0.039653
[8960/20460 (44%)]	Loss: 0.032841
[10240/20460 (50%)]	Loss: 0.112310
[11520/20460 (56%)]	Loss: 0.040032
[12800/20460 (62%)]	Loss: 0.020217
[14080/20460 (69%)]	Loss: 0.116125
[15360/20460 (75%)]	Loss: 0.048336
[16640/20460 (81%)]	Loss: 0.061584
[17920/20460 (88%)]	Loss: 0.009391
[19200/20460 (94%)]	Loss: 0.035957
[14080/20460 (100%)]	Loss: 0.142472

Train: Average loss: 0.0393, Accuracy: 0.9852

Validation: Average loss: 3.4516, Accuracy: 0.8494

Train Epoch: 30
[1280/20460 (6%)]	Loss: 0.021184
[2560/20460 (12%)]	Loss: 0.059198
[3840/20460 (19%)]	Loss: 0.009610
[5120/20460 (25%)]	Loss: 0.032773
[6400/20460 (31%)]	Loss: 0.020875
[7680/20460 (38%)]	Loss: 0.004248
[8960/20460 (44%)]	Loss: 0.050532
[10240/20460 (50%)]	Loss: 0.012212
[11520/20460 (56%)]	Loss: 0.009121
[12800/20460 (62%)]	Loss: 0.024016
[14080/20460 (69%)]	Loss: 0.036644
[15360/20460 (75%)]	Loss: 0.054126
[16640/20460 (81%)]	Loss: 0.008078
[17920/20460 (88%)]	Loss: 0.014535
[19200/20460 (94%)]	Loss: 0.008624
[14080/20460 (100%)]	Loss: 0.017956

Train: Average loss: 0.0401, Accuracy: 0.9866

Validation: Average loss: 2.9908, Accuracy: 0.8377

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_1187671/529002640.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 392 (out of 5826)  6.73%
Test accuracy 93.27%
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 95.00%
In [ ]: