Test Score.txt

import torch
import time
import numpy as np
from torch import nn, optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
import torchvision
from collections import OrderedDict
from torch.autograd import Variable
from PIL import Image
import PIL
from torch.optim import lr_scheduler
import copy
import json
import os
from os.path import exists
import matplotlib.pyplot as plt

def load_checkpoint(filepath):
    checkpoint = torch.load(filepath, map_location='cpu')
    
    model = models.resnet152(pretrained=True)
    for param in model.parameters():
        param.requires_grad = False
        
    # Our input_size matches the in_features of pretrained model
    input_size = 2048
    output_size = 102
    
    classifier = nn.Sequential(nn.Dropout(p=0.5),
                           nn.Linear(2048,102))


    # Replacing the pretrained model classifier with our classifier
    model.fc = classifier
    
    return model

# get index to class mapping
model = load_checkpoint('Swati_Checkpoint.pt')

# If you used something other than 224x224 cropped images, set the correct size here
image_size = 224
# Values you used for normalizing the images. Default here are for 
# pretrained models from torchvision.
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]