script for loading the pytorch model, and processing incoming image and get the output for the skin cancer detection.

model_validating.py

import torch
import torchvision
from torchvision import datasets, transforms, models
import torch.nn as nn
from torch.autograd import Variable
from PIL import Image
import numpy as np

# >>> torch.__version__
# '0.2.0_4'
# >>> numpy.__version__
# '1.11.3'
# >>> PIL.__version__
# '4.0.0'
# have the pytorch model, in the same directory and images in the directory called 'validating', change them according to your  convenience.
if __name__ == "__main__":
    
	model = models.resnet50()
	model.fc = nn.Linear(2048, 2)

	model.load_state_dict(torch.load('./best_model.pth', map_location={'cuda:0': 'cpu'}))

	# Load image
	real = Image.open('./validating/ISIC_0012151.jpg')

	preprocess = transforms.Compose([
				transforms.Scale(256),
				transforms.CenterCrop(224),
				transforms.ToTensor(),
				transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
			])

	# Load input
	input_image = preprocess(real).unsqueeze_(0)

	# pass it through the model
	prediction = model(Variable(input_image))

	# get the result out and reshape it
	cpu_pred = prediction.cpu()
	result = cpu_pred.data.numpy()

	print(result)
	if (np.argmax(result) == 1): # there are two cases 0 position for being melanoma and 1 position for keratosis
		str_label = 'benign'
	else:
		str_label = 'malignant'
	print(str_label)