NumairSayed

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, random_split
import os
from PIL import Image, ImageOps
from sklearn.metrics import f1_score, recall_score

NumairSayed

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, random_split
import os
from PIL import Image, ImageOps
from sklearn.metrics import f1_score, recall_score
import numpy as np  

NumairSayed

"""
The inference transforms are available at GoogLeNet_Weights.IMAGENET1K_V1.transforms 
and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W)
and single (C, H, W) image torch.Tensor objects. 
The images are resized to resize_size=[256] using interpolation=InterpolationMode.
BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first
rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
"""

import os

NumairSayed

import torch
import torchvision.models as models

# Define your model architecture (ensure it matches AlexNet)
class MyAlexNet(torch.nn.Module):
    def __init__(self):
        super(MyAlexNet, self).__init__()
        self.features = torch.nn.Sequential(
            torch.nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            torch.nn.ReLU(inplace=True),