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=180, 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_1444885/3247579378.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_180"

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: 1.154608
[2560/20460 (12%)]	Loss: 0.838899
[3840/20460 (19%)]	Loss: 0.485876
[5120/20460 (25%)]	Loss: 0.579889
[6400/20460 (31%)]	Loss: 0.437122
[7680/20460 (38%)]	Loss: 0.203284
[8960/20460 (44%)]	Loss: 0.307418
[10240/20460 (50%)]	Loss: 0.271076
[11520/20460 (56%)]	Loss: 0.123049
[12800/20460 (62%)]	Loss: 0.285132
[14080/20460 (69%)]	Loss: 0.528378
[15360/20460 (75%)]	Loss: 0.182749
[16640/20460 (81%)]	Loss: 0.245985
[17920/20460 (88%)]	Loss: 0.191815
[19200/20460 (94%)]	Loss: 0.233691
[14080/20460 (100%)]	Loss: 0.164780

Train: Average loss: 0.4413, Accuracy: 0.8430

Validation: Average loss: 1.0846, Accuracy: 0.7587

Train Epoch: 2
[1280/20460 (6%)]	Loss: 0.328148
[2560/20460 (12%)]	Loss: 0.399888
[3840/20460 (19%)]	Loss: 0.296892
[5120/20460 (25%)]	Loss: 0.236196
[6400/20460 (31%)]	Loss: 0.224795
[7680/20460 (38%)]	Loss: 0.237054
[8960/20460 (44%)]	Loss: 0.245093
[10240/20460 (50%)]	Loss: 0.234150
[11520/20460 (56%)]	Loss: 0.184884
[12800/20460 (62%)]	Loss: 0.172342
[14080/20460 (69%)]	Loss: 0.336267
[15360/20460 (75%)]	Loss: 0.117645
[16640/20460 (81%)]	Loss: 0.118147
[17920/20460 (88%)]	Loss: 0.104366
[19200/20460 (94%)]	Loss: 0.239917
[14080/20460 (100%)]	Loss: 0.081711

Train: Average loss: 0.2037, Accuracy: 0.9266

Validation: Average loss: 1.5448, Accuracy: 0.6954

Train Epoch: 3
[1280/20460 (6%)]	Loss: 0.079502
[2560/20460 (12%)]	Loss: 0.329850
[3840/20460 (19%)]	Loss: 0.119493
[5120/20460 (25%)]	Loss: 0.205317
[6400/20460 (31%)]	Loss: 0.075518
[7680/20460 (38%)]	Loss: 0.141399
[8960/20460 (44%)]	Loss: 0.067538
[10240/20460 (50%)]	Loss: 0.052563
[11520/20460 (56%)]	Loss: 0.168270
[12800/20460 (62%)]	Loss: 0.281087
[14080/20460 (69%)]	Loss: 0.102163
[15360/20460 (75%)]	Loss: 0.122213
[16640/20460 (81%)]	Loss: 0.101528
[17920/20460 (88%)]	Loss: 0.381102
[19200/20460 (94%)]	Loss: 0.156751
[14080/20460 (100%)]	Loss: 0.072861

Train: Average loss: 0.1577, Accuracy: 0.9413

Validation: Average loss: 1.2452, Accuracy: 0.8650

Train Epoch: 4
[1280/20460 (6%)]	Loss: 0.137309
[2560/20460 (12%)]	Loss: 0.233674
[3840/20460 (19%)]	Loss: 0.200537
[5120/20460 (25%)]	Loss: 0.044271
[6400/20460 (31%)]	Loss: 0.064060
[7680/20460 (38%)]	Loss: 0.081689
[8960/20460 (44%)]	Loss: 0.111413
[10240/20460 (50%)]	Loss: 0.122126
[11520/20460 (56%)]	Loss: 0.039462
[12800/20460 (62%)]	Loss: 0.157551
[14080/20460 (69%)]	Loss: 0.085240
[15360/20460 (75%)]	Loss: 0.282377
[16640/20460 (81%)]	Loss: 0.051879
[17920/20460 (88%)]	Loss: 0.137520
[19200/20460 (94%)]	Loss: 0.490313
[14080/20460 (100%)]	Loss: 0.123210

Train: Average loss: 0.1368, Accuracy: 0.9499

Validation: Average loss: 1.2225, Accuracy: 0.8830

Train Epoch: 5
[1280/20460 (6%)]	Loss: 0.073242
[2560/20460 (12%)]	Loss: 0.110220
[3840/20460 (19%)]	Loss: 0.197832
[5120/20460 (25%)]	Loss: 0.208767
[6400/20460 (31%)]	Loss: 0.085845
[7680/20460 (38%)]	Loss: 0.083905
[8960/20460 (44%)]	Loss: 0.034090
[10240/20460 (50%)]	Loss: 0.084934
[11520/20460 (56%)]	Loss: 0.061121
[12800/20460 (62%)]	Loss: 0.052989
[14080/20460 (69%)]	Loss: 0.133051
[15360/20460 (75%)]	Loss: 0.233461
[16640/20460 (81%)]	Loss: 0.186377
[17920/20460 (88%)]	Loss: 0.070991
[19200/20460 (94%)]	Loss: 0.119511
[14080/20460 (100%)]	Loss: 0.110713

Train: Average loss: 0.1166, Accuracy: 0.9576

Validation: Average loss: 1.1017, Accuracy: 0.8892

Train Epoch: 6
[1280/20460 (6%)]	Loss: 0.165688
[2560/20460 (12%)]	Loss: 0.085802
[3840/20460 (19%)]	Loss: 0.174651
[5120/20460 (25%)]	Loss: 0.102024
[6400/20460 (31%)]	Loss: 0.103382
[7680/20460 (38%)]	Loss: 0.232773
[8960/20460 (44%)]	Loss: 0.071463
[10240/20460 (50%)]	Loss: 0.069022
[11520/20460 (56%)]	Loss: 0.104528
[12800/20460 (62%)]	Loss: 0.056172
[14080/20460 (69%)]	Loss: 0.080539
[15360/20460 (75%)]	Loss: 0.034545
[16640/20460 (81%)]	Loss: 0.077291
[17920/20460 (88%)]	Loss: 0.120810
[19200/20460 (94%)]	Loss: 0.104904
[14080/20460 (100%)]	Loss: 0.047831

Train: Average loss: 0.1061, Accuracy: 0.9625

Validation: Average loss: 1.7595, Accuracy: 0.7792

Train Epoch: 7
[1280/20460 (6%)]	Loss: 0.083496
[2560/20460 (12%)]	Loss: 0.096981
[3840/20460 (19%)]	Loss: 0.118549
[5120/20460 (25%)]	Loss: 0.048066
[6400/20460 (31%)]	Loss: 0.105432
[7680/20460 (38%)]	Loss: 0.089801
[8960/20460 (44%)]	Loss: 0.169708
[10240/20460 (50%)]	Loss: 0.074856
[11520/20460 (56%)]	Loss: 0.107643
[12800/20460 (62%)]	Loss: 0.074542
[14080/20460 (69%)]	Loss: 0.128725
[15360/20460 (75%)]	Loss: 0.047379
[16640/20460 (81%)]	Loss: 0.081939
[17920/20460 (88%)]	Loss: 0.160932
[19200/20460 (94%)]	Loss: 0.173909
[14080/20460 (100%)]	Loss: 0.083087

Train: Average loss: 0.0967, Accuracy: 0.9655

Validation: Average loss: 1.6147, Accuracy: 0.8300

Train Epoch: 8
[1280/20460 (6%)]	Loss: 0.176052
[2560/20460 (12%)]	Loss: 0.069961
[3840/20460 (19%)]	Loss: 0.074433
[5120/20460 (25%)]	Loss: 0.015392
[6400/20460 (31%)]	Loss: 0.047652
[7680/20460 (38%)]	Loss: 0.050061
[8960/20460 (44%)]	Loss: 0.079029
[10240/20460 (50%)]	Loss: 0.071507
[11520/20460 (56%)]	Loss: 0.060875
[12800/20460 (62%)]	Loss: 0.078049
[14080/20460 (69%)]	Loss: 0.044981
[15360/20460 (75%)]	Loss: 0.153947
[16640/20460 (81%)]	Loss: 0.189049
[17920/20460 (88%)]	Loss: 0.308131
[19200/20460 (94%)]	Loss: 0.058292
[14080/20460 (100%)]	Loss: 0.024300

Train: Average loss: 0.0868, Accuracy: 0.9671

Validation: Average loss: 1.4454, Accuracy: 0.8190

Train Epoch: 9
[1280/20460 (6%)]	Loss: 0.034268
[2560/20460 (12%)]	Loss: 0.034598
[3840/20460 (19%)]	Loss: 0.050957
[5120/20460 (25%)]	Loss: 0.151066
[6400/20460 (31%)]	Loss: 0.027893
[7680/20460 (38%)]	Loss: 0.121117
[8960/20460 (44%)]	Loss: 0.096919
[10240/20460 (50%)]	Loss: 0.026340
[11520/20460 (56%)]	Loss: 0.129731
[12800/20460 (62%)]	Loss: 0.103561
[14080/20460 (69%)]	Loss: 0.112842
[15360/20460 (75%)]	Loss: 0.046268
[16640/20460 (81%)]	Loss: 0.038462
[17920/20460 (88%)]	Loss: 0.089703
[19200/20460 (94%)]	Loss: 0.123095
[14080/20460 (100%)]	Loss: 0.033711

Train: Average loss: 0.0839, Accuracy: 0.9692

Validation: Average loss: 1.4662, Accuracy: 0.8837

Train Epoch: 10
[1280/20460 (6%)]	Loss: 0.096313
[2560/20460 (12%)]	Loss: 0.116083
[3840/20460 (19%)]	Loss: 0.019745
[5120/20460 (25%)]	Loss: 0.037900
[6400/20460 (31%)]	Loss: 0.056467
[7680/20460 (38%)]	Loss: 0.121352
[8960/20460 (44%)]	Loss: 0.027698
[10240/20460 (50%)]	Loss: 0.037800
[11520/20460 (56%)]	Loss: 0.039997
[12800/20460 (62%)]	Loss: 0.041773
[14080/20460 (69%)]	Loss: 0.100383
[15360/20460 (75%)]	Loss: 0.065768
[16640/20460 (81%)]	Loss: 0.037660
[17920/20460 (88%)]	Loss: 0.146939
[19200/20460 (94%)]	Loss: 0.083759
[14080/20460 (100%)]	Loss: 0.107660

Train: Average loss: 0.0765, Accuracy: 0.9726

Validation: Average loss: 1.6286, Accuracy: 0.8834

Train Epoch: 11
[1280/20460 (6%)]	Loss: 0.101994
[2560/20460 (12%)]	Loss: 0.232339
[3840/20460 (19%)]	Loss: 0.082329
[5120/20460 (25%)]	Loss: 0.089559
[6400/20460 (31%)]	Loss: 0.075837
[7680/20460 (38%)]	Loss: 0.034033
[8960/20460 (44%)]	Loss: 0.106017
[10240/20460 (50%)]	Loss: 0.143096
[11520/20460 (56%)]	Loss: 0.017976
[12800/20460 (62%)]	Loss: 0.006701
[14080/20460 (69%)]	Loss: 0.160220
[15360/20460 (75%)]	Loss: 0.056265
[16640/20460 (81%)]	Loss: 0.002834
[17920/20460 (88%)]	Loss: 0.070355
[19200/20460 (94%)]	Loss: 0.110825
[14080/20460 (100%)]	Loss: 0.081801

Train: Average loss: 0.0703, Accuracy: 0.9739

Validation: Average loss: 1.7393, Accuracy: 0.8242

Train Epoch: 12
[1280/20460 (6%)]	Loss: 0.038984
[2560/20460 (12%)]	Loss: 0.076067
[3840/20460 (19%)]	Loss: 0.040983
[5120/20460 (25%)]	Loss: 0.036458
[6400/20460 (31%)]	Loss: 0.026381
[7680/20460 (38%)]	Loss: 0.090406
[8960/20460 (44%)]	Loss: 0.216269
[10240/20460 (50%)]	Loss: 0.031442
[11520/20460 (56%)]	Loss: 0.034816
[12800/20460 (62%)]	Loss: 0.008865
[14080/20460 (69%)]	Loss: 0.056212
[15360/20460 (75%)]	Loss: 0.044774
[16640/20460 (81%)]	Loss: 0.165942
[17920/20460 (88%)]	Loss: 0.096666
[19200/20460 (94%)]	Loss: 0.087762
[14080/20460 (100%)]	Loss: 0.041471

Train: Average loss: 0.0709, Accuracy: 0.9730

Validation: Average loss: 1.8173, Accuracy: 0.8809

Train Epoch: 13
[1280/20460 (6%)]	Loss: 0.027849
[2560/20460 (12%)]	Loss: 0.041348
[3840/20460 (19%)]	Loss: 0.009213
[5120/20460 (25%)]	Loss: 0.062436
[6400/20460 (31%)]	Loss: 0.020248
[7680/20460 (38%)]	Loss: 0.032236
[8960/20460 (44%)]	Loss: 0.027384
[10240/20460 (50%)]	Loss: 0.051286
[11520/20460 (56%)]	Loss: 0.061135
[12800/20460 (62%)]	Loss: 0.092660
[14080/20460 (69%)]	Loss: 0.101285
[15360/20460 (75%)]	Loss: 0.072427
[16640/20460 (81%)]	Loss: 0.102357
[17920/20460 (88%)]	Loss: 0.037063
[19200/20460 (94%)]	Loss: 0.025966
[14080/20460 (100%)]	Loss: 0.235572

Train: Average loss: 0.0634, Accuracy: 0.9766

Validation: Average loss: 1.5884, Accuracy: 0.8435

Train Epoch: 14
[1280/20460 (6%)]	Loss: 0.099768
[2560/20460 (12%)]	Loss: 0.008558
[3840/20460 (19%)]	Loss: 0.009383
[5120/20460 (25%)]	Loss: 0.076057
[6400/20460 (31%)]	Loss: 0.014432
[7680/20460 (38%)]	Loss: 0.019608
[8960/20460 (44%)]	Loss: 0.013267
[10240/20460 (50%)]	Loss: 0.019755
[11520/20460 (56%)]	Loss: 0.010128
[12800/20460 (62%)]	Loss: 0.040537
[14080/20460 (69%)]	Loss: 0.065339
[15360/20460 (75%)]	Loss: 0.176545
[16640/20460 (81%)]	Loss: 0.016213
[17920/20460 (88%)]	Loss: 0.026585
[19200/20460 (94%)]	Loss: 0.084198
[14080/20460 (100%)]	Loss: 0.125569

Train: Average loss: 0.0589, Accuracy: 0.9790

Validation: Average loss: 1.8937, Accuracy: 0.8674

Train Epoch: 15
[1280/20460 (6%)]	Loss: 0.123536
[2560/20460 (12%)]	Loss: 0.177383
[3840/20460 (19%)]	Loss: 0.106905
[5120/20460 (25%)]	Loss: 0.101723
[6400/20460 (31%)]	Loss: 0.096286
[7680/20460 (38%)]	Loss: 0.034977
[8960/20460 (44%)]	Loss: 0.048631
[10240/20460 (50%)]	Loss: 0.022317
[11520/20460 (56%)]	Loss: 0.061900
[12800/20460 (62%)]	Loss: 0.046843
[14080/20460 (69%)]	Loss: 0.029109
[15360/20460 (75%)]	Loss: 0.057164
[16640/20460 (81%)]	Loss: 0.020887
[17920/20460 (88%)]	Loss: 0.029551
[19200/20460 (94%)]	Loss: 0.009166
[14080/20460 (100%)]	Loss: 0.022743

Train: Average loss: 0.0594, Accuracy: 0.9792

Validation: Average loss: 1.4142, Accuracy: 0.8872

Train Epoch: 16
[1280/20460 (6%)]	Loss: 0.038064
[2560/20460 (12%)]	Loss: 0.109800
[3840/20460 (19%)]	Loss: 0.167969
[5120/20460 (25%)]	Loss: 0.102499
[6400/20460 (31%)]	Loss: 0.078386
[7680/20460 (38%)]	Loss: 0.015721
[8960/20460 (44%)]	Loss: 0.093845
[10240/20460 (50%)]	Loss: 0.064943
[11520/20460 (56%)]	Loss: 0.037591
[12800/20460 (62%)]	Loss: 0.037282
[14080/20460 (69%)]	Loss: 0.016141
[15360/20460 (75%)]	Loss: 0.097310
[16640/20460 (81%)]	Loss: 0.113463
[17920/20460 (88%)]	Loss: 0.069364
[19200/20460 (94%)]	Loss: 0.117187
[14080/20460 (100%)]	Loss: 0.154718

Train: Average loss: 0.0575, Accuracy: 0.9786

Validation: Average loss: 1.6305, Accuracy: 0.8380

Train Epoch: 17
[1280/20460 (6%)]	Loss: 0.017140
[2560/20460 (12%)]	Loss: 0.145840
[3840/20460 (19%)]	Loss: 0.031945
[5120/20460 (25%)]	Loss: 0.016463
[6400/20460 (31%)]	Loss: 0.086557
[7680/20460 (38%)]	Loss: 0.060283
[8960/20460 (44%)]	Loss: 0.043195
[10240/20460 (50%)]	Loss: 0.021971
[11520/20460 (56%)]	Loss: 0.069196
[12800/20460 (62%)]	Loss: 0.033842
[14080/20460 (69%)]	Loss: 0.093399
[15360/20460 (75%)]	Loss: 0.066984
[16640/20460 (81%)]	Loss: 0.085902
[17920/20460 (88%)]	Loss: 0.033242
[19200/20460 (94%)]	Loss: 0.053666
[14080/20460 (100%)]	Loss: 0.124028

Train: Average loss: 0.0628, Accuracy: 0.9789

Validation: Average loss: 2.1148, Accuracy: 0.8366

Train Epoch: 18
[1280/20460 (6%)]	Loss: 0.010938
[2560/20460 (12%)]	Loss: 0.135357
[3840/20460 (19%)]	Loss: 0.033848
[5120/20460 (25%)]	Loss: 0.032860
[6400/20460 (31%)]	Loss: 0.066463
[7680/20460 (38%)]	Loss: 0.008554
[8960/20460 (44%)]	Loss: 0.104291
[10240/20460 (50%)]	Loss: 0.030621
[11520/20460 (56%)]	Loss: 0.020883
[12800/20460 (62%)]	Loss: 0.178456
[14080/20460 (69%)]	Loss: 0.059583
[15360/20460 (75%)]	Loss: 0.149433
[16640/20460 (81%)]	Loss: 0.052170
[17920/20460 (88%)]	Loss: 0.026881
[19200/20460 (94%)]	Loss: 0.056583
[14080/20460 (100%)]	Loss: 0.056181

Train: Average loss: 0.0525, Accuracy: 0.9802

Validation: Average loss: 1.6125, Accuracy: 0.8612

Train Epoch: 19
[1280/20460 (6%)]	Loss: 0.007964
[2560/20460 (12%)]	Loss: 0.031899
[3840/20460 (19%)]	Loss: 0.024459
[5120/20460 (25%)]	Loss: 0.053967
[6400/20460 (31%)]	Loss: 0.251782
[7680/20460 (38%)]	Loss: 0.047591
[8960/20460 (44%)]	Loss: 0.080097
[10240/20460 (50%)]	Loss: 0.136145
[11520/20460 (56%)]	Loss: 0.061841
[12800/20460 (62%)]	Loss: 0.010178
[14080/20460 (69%)]	Loss: 0.019113
[15360/20460 (75%)]	Loss: 0.010566
[16640/20460 (81%)]	Loss: 0.156852
[17920/20460 (88%)]	Loss: 0.018930
[19200/20460 (94%)]	Loss: 0.012789
[14080/20460 (100%)]	Loss: 0.036572

Train: Average loss: 0.0552, Accuracy: 0.9804

Validation: Average loss: 2.5698, Accuracy: 0.8730

Train Epoch: 20
[1280/20460 (6%)]	Loss: 0.026344
[2560/20460 (12%)]	Loss: 0.117985
[3840/20460 (19%)]	Loss: 0.010154
[5120/20460 (25%)]	Loss: 0.016642
[6400/20460 (31%)]	Loss: 0.092467
[7680/20460 (38%)]	Loss: 0.045687
[8960/20460 (44%)]	Loss: 0.015743
[10240/20460 (50%)]	Loss: 0.039033
[11520/20460 (56%)]	Loss: 0.059726
[12800/20460 (62%)]	Loss: 0.021537
[14080/20460 (69%)]	Loss: 0.036110
[15360/20460 (75%)]	Loss: 0.014566
[16640/20460 (81%)]	Loss: 0.080196
[17920/20460 (88%)]	Loss: 0.113845
[19200/20460 (94%)]	Loss: 0.030796
[14080/20460 (100%)]	Loss: 0.026460

Train: Average loss: 0.0475, Accuracy: 0.9824

Validation: Average loss: 1.9205, Accuracy: 0.8678

Train Epoch: 21
[1280/20460 (6%)]	Loss: 0.018865
[2560/20460 (12%)]	Loss: 0.030405
[3840/20460 (19%)]	Loss: 0.007892
[5120/20460 (25%)]	Loss: 0.050218
[6400/20460 (31%)]	Loss: 0.022357
[7680/20460 (38%)]	Loss: 0.018565
[8960/20460 (44%)]	Loss: 0.012994
[10240/20460 (50%)]	Loss: 0.008311
[11520/20460 (56%)]	Loss: 0.018364
[12800/20460 (62%)]	Loss: 0.028620
[14080/20460 (69%)]	Loss: 0.014857
[15360/20460 (75%)]	Loss: 0.016304
[16640/20460 (81%)]	Loss: 0.062942
[17920/20460 (88%)]	Loss: 0.098809
[19200/20460 (94%)]	Loss: 0.042336
[14080/20460 (100%)]	Loss: 0.019796

Train: Average loss: 0.0488, Accuracy: 0.9823

Validation: Average loss: 1.9240, Accuracy: 0.8702

Train Epoch: 22
[1280/20460 (6%)]	Loss: 0.024643
[2560/20460 (12%)]	Loss: 0.040994
[3840/20460 (19%)]	Loss: 0.033839
[5120/20460 (25%)]	Loss: 0.072899
[6400/20460 (31%)]	Loss: 0.046923
[7680/20460 (38%)]	Loss: 0.064991
[8960/20460 (44%)]	Loss: 0.135172
[10240/20460 (50%)]	Loss: 0.044573
[11520/20460 (56%)]	Loss: 0.027280
[12800/20460 (62%)]	Loss: 0.036682
[14080/20460 (69%)]	Loss: 0.069262
[15360/20460 (75%)]	Loss: 0.080054
[16640/20460 (81%)]	Loss: 0.048438
[17920/20460 (88%)]	Loss: 0.039535
[19200/20460 (94%)]	Loss: 0.020977
[14080/20460 (100%)]	Loss: 0.069100

Train: Average loss: 0.0498, Accuracy: 0.9809

Validation: Average loss: 1.7595, Accuracy: 0.9017

Train Epoch: 23
[1280/20460 (6%)]	Loss: 0.020308
[2560/20460 (12%)]	Loss: 0.014758
[3840/20460 (19%)]	Loss: 0.023325
[5120/20460 (25%)]	Loss: 0.007724
[6400/20460 (31%)]	Loss: 0.015425
[7680/20460 (38%)]	Loss: 0.084390
[8960/20460 (44%)]	Loss: 0.032476
[10240/20460 (50%)]	Loss: 0.027792
[11520/20460 (56%)]	Loss: 0.071208
[12800/20460 (62%)]	Loss: 0.030492
[14080/20460 (69%)]	Loss: 0.048210
[15360/20460 (75%)]	Loss: 0.071811
[16640/20460 (81%)]	Loss: 0.037236
[17920/20460 (88%)]	Loss: 0.026675
[19200/20460 (94%)]	Loss: 0.022739
[14080/20460 (100%)]	Loss: 0.041387

Train: Average loss: 0.0468, Accuracy: 0.9829

Validation: Average loss: 2.2490, Accuracy: 0.8889

Train Epoch: 24
[1280/20460 (6%)]	Loss: 0.042783
[2560/20460 (12%)]	Loss: 0.032059
[3840/20460 (19%)]	Loss: 0.015904
[5120/20460 (25%)]	Loss: 0.027754
[6400/20460 (31%)]	Loss: 0.028237
[7680/20460 (38%)]	Loss: 0.042711
[8960/20460 (44%)]	Loss: 0.044785
[10240/20460 (50%)]	Loss: 0.006280
[11520/20460 (56%)]	Loss: 0.080609
[12800/20460 (62%)]	Loss: 0.012572
[14080/20460 (69%)]	Loss: 0.074517
[15360/20460 (75%)]	Loss: 0.033603
[16640/20460 (81%)]	Loss: 0.088048
[17920/20460 (88%)]	Loss: 0.017385
[19200/20460 (94%)]	Loss: 0.022218
[14080/20460 (100%)]	Loss: 0.081137

Train: Average loss: 0.0438, Accuracy: 0.9837

Validation: Average loss: 2.7997, Accuracy: 0.8300

Train Epoch: 25
[1280/20460 (6%)]	Loss: 0.013926
[2560/20460 (12%)]	Loss: 0.084272
[3840/20460 (19%)]	Loss: 0.030428
[5120/20460 (25%)]	Loss: 0.066782
[6400/20460 (31%)]	Loss: 0.022440
[7680/20460 (38%)]	Loss: 0.002669
[8960/20460 (44%)]	Loss: 0.042892
[10240/20460 (50%)]	Loss: 0.007015
[11520/20460 (56%)]	Loss: 0.120389
[12800/20460 (62%)]	Loss: 0.029422
[14080/20460 (69%)]	Loss: 0.023923
[15360/20460 (75%)]	Loss: 0.013669
[16640/20460 (81%)]	Loss: 0.030782
[17920/20460 (88%)]	Loss: 0.009424
[19200/20460 (94%)]	Loss: 0.020586
[14080/20460 (100%)]	Loss: 0.011779

Train: Average loss: 0.0402, Accuracy: 0.9849

Validation: Average loss: 2.3393, Accuracy: 0.8418

Train Epoch: 26
[1280/20460 (6%)]	Loss: 0.111363
[2560/20460 (12%)]	Loss: 0.060291
[3840/20460 (19%)]	Loss: 0.078664
[5120/20460 (25%)]	Loss: 0.033395
[6400/20460 (31%)]	Loss: 0.023525
[7680/20460 (38%)]	Loss: 0.030828
[8960/20460 (44%)]	Loss: 0.095568
[10240/20460 (50%)]	Loss: 0.045007
[11520/20460 (56%)]	Loss: 0.014352
[12800/20460 (62%)]	Loss: 0.089438
[14080/20460 (69%)]	Loss: 0.058843
[15360/20460 (75%)]	Loss: 0.065361
[16640/20460 (81%)]	Loss: 0.010174
[17920/20460 (88%)]	Loss: 0.004271
[19200/20460 (94%)]	Loss: 0.025217
[14080/20460 (100%)]	Loss: 0.236916

Train: Average loss: 0.0435, Accuracy: 0.9840

Validation: Average loss: 2.8346, Accuracy: 0.8913

Train Epoch: 27
[1280/20460 (6%)]	Loss: 0.005194
[2560/20460 (12%)]	Loss: 0.010395
[3840/20460 (19%)]	Loss: 0.043335
[5120/20460 (25%)]	Loss: 0.033927
[6400/20460 (31%)]	Loss: 0.031609
[7680/20460 (38%)]	Loss: 0.008762
[8960/20460 (44%)]	Loss: 0.083460
[10240/20460 (50%)]	Loss: 0.031592
[11520/20460 (56%)]	Loss: 0.012969
[12800/20460 (62%)]	Loss: 0.052551
[14080/20460 (69%)]	Loss: 0.033673
[15360/20460 (75%)]	Loss: 0.009864
[16640/20460 (81%)]	Loss: 0.019074
[17920/20460 (88%)]	Loss: 0.062375
[19200/20460 (94%)]	Loss: 0.004191
[14080/20460 (100%)]	Loss: 0.008334

Train: Average loss: 0.0457, Accuracy: 0.9838

Validation: Average loss: 2.9753, Accuracy: 0.8948

Train Epoch: 28
[1280/20460 (6%)]	Loss: 0.057985
[2560/20460 (12%)]	Loss: 0.018710
[3840/20460 (19%)]	Loss: 0.005535
[5120/20460 (25%)]	Loss: 0.012951
[6400/20460 (31%)]	Loss: 0.027756
[7680/20460 (38%)]	Loss: 0.075175
[8960/20460 (44%)]	Loss: 0.005305
[10240/20460 (50%)]	Loss: 0.029375
[11520/20460 (56%)]	Loss: 0.014121
[12800/20460 (62%)]	Loss: 0.076132
[14080/20460 (69%)]	Loss: 0.014897
[15360/20460 (75%)]	Loss: 0.037047
[16640/20460 (81%)]	Loss: 0.042828
[17920/20460 (88%)]	Loss: 0.033081
[19200/20460 (94%)]	Loss: 0.009244
[14080/20460 (100%)]	Loss: 0.058014

Train: Average loss: 0.0342, Accuracy: 0.9874

Validation: Average loss: 3.7855, Accuracy: 0.8892

Train Epoch: 29
[1280/20460 (6%)]	Loss: 0.017775
[2560/20460 (12%)]	Loss: 0.021924
[3840/20460 (19%)]	Loss: 0.020371
[5120/20460 (25%)]	Loss: 0.087809
[6400/20460 (31%)]	Loss: 0.026205
[7680/20460 (38%)]	Loss: 0.008068
[8960/20460 (44%)]	Loss: 0.094903
[10240/20460 (50%)]	Loss: 0.074380
[11520/20460 (56%)]	Loss: 0.041095
[12800/20460 (62%)]	Loss: 0.089401
[14080/20460 (69%)]	Loss: 0.012103
[15360/20460 (75%)]	Loss: 0.056116
[16640/20460 (81%)]	Loss: 0.102136
[17920/20460 (88%)]	Loss: 0.006600
[19200/20460 (94%)]	Loss: 0.045323
[14080/20460 (100%)]	Loss: 0.005971

Train: Average loss: 0.0392, Accuracy: 0.9864

Validation: Average loss: 3.1971, Accuracy: 0.8114

Train Epoch: 30
[1280/20460 (6%)]	Loss: 0.007091
[2560/20460 (12%)]	Loss: 0.010009
[3840/20460 (19%)]	Loss: 0.009125
[5120/20460 (25%)]	Loss: 0.049172
[6400/20460 (31%)]	Loss: 0.060266
[7680/20460 (38%)]	Loss: 0.039820
[8960/20460 (44%)]	Loss: 0.011549
[10240/20460 (50%)]	Loss: 0.004018
[11520/20460 (56%)]	Loss: 0.019896
[12800/20460 (62%)]	Loss: 0.042440
[14080/20460 (69%)]	Loss: 0.057332
[15360/20460 (75%)]	Loss: 0.042854
[16640/20460 (81%)]	Loss: 0.006532
[17920/20460 (88%)]	Loss: 0.013735
[19200/20460 (94%)]	Loss: 0.016025
[14080/20460 (100%)]	Loss: 0.014934

Train: Average loss: 0.0390, Accuracy: 0.9850

Validation: Average loss: 3.4696, Accuracy: 0.8764

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_1444885/529002640.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 236 (out of 5826)  4.05%
Test accuracy 95.95%
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 95.60%
In [ ]: