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Guess224x224SkinCancer_Resnet_Pytorch.py
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Guess224x224SkinCancer_Resnet_Pytorch.py
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# -*- coding: utf-8 -*-
"""
Alfonso Blanco García , Mar 2024
"""
######################################################################
# PARAMETERS
######################################################################
# https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
######################################################################
TabSkinCancerComplete =[
'akiec : Actinic keratoses and intraepithelial carcinoma / Bowen s disease',
'bkl : benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses',
'bcc : basal cell carcinoma',
'df : dermatofibroma',
'mel : melanoma',
'nv : melanocytic nevi',
'vasc : vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage,'
]
TabSkinCancer =[ 'akiec',
'bcc' ,
'bkl',
'df',
'mel',
'nv',
'vasc']
import torch
from torch import nn
import os
import re
import cv2
import numpy as np
import keras
import functools
import time
inicio=time.time()
from torchvision import datasets, transforms, models
import torchvision.models as models
from PIL import Image
#model = models.resnet34(pretrained=True)
model = models.resnet50(pretrained=True)
# https://stackoverflow.com/questions/53612835/size-mismatch-for-fc-bias-and-fc-weight-in-pytorch
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 7)
#TabCarBrand=[]
def load_checkpoint(filepath):
checkpoint = torch.load(filepath)
#model.load_state_dict(checkpoint['state_dict'])
model.load_state_dict(checkpoint['state_dict'], strict=False)
#model.class_to_idx = checkpoint['class_to_idx']
return model
#model_path= "my_checkpoint1.pth"
model_path= "checkpoint_SkinCancer_10epoch.pth"
model = load_checkpoint('checkpoint_SkinCancer_10epoch.pth')
# Checking model i.e. should have 43 output units in the classifier
#print(model)
#DataPath='C:\\archiveKaggle\\cars_train\\cars_train' + '\\'
def find_classes(dir):
classes = os.listdir(dir)
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
#classes, c_to_idx = find_classes(data_dir+"/train")
classes, c_to_idx = find_classes('Dir_SkinCancer_Resnet_Pytorch/train')
print(classes, c_to_idx)
def loadimagesTest():
imgpath = "Dir_SkinCancer_Resnet_Pytorch\\valid\\"
print("Reading imagenes from ",imgpath)
TabDirName=[]
for root, dirnames, filenames in os.walk(imgpath):
for dirname in dirnames:
print(dirname)
TabDirName.append(dirname)
TotImages=0
TotImagesValid=0
TabImagePath = []
NameImages=[]
Y=[]
for i in range(len(TabDirName)):
imgpath1=imgpath+ str(TabDirName[i])+"\\"
#print(imgpath1)
# https://stackoverflow.com/questions/62137343/how-to-get-full-path-with-os-walk-function-in-python
for root, dirnames, filenames in os.walk(imgpath1):
for filename in filenames:
#print(filename)
#if re.search("\.(jpg|jpeg|png|bmp|tiff)$", filename):
filepath = os.path.join(root, filename)
# https://stackoverflow.com/questions/51810407/convert-image-into-1d-array-in-python
#image = cv2.imread(filepath)
#cv2.imshow("image",image)
#cv2.waitKey(0)
TabImagePath.append(filepath)
NameImages.append(filename)
Y.append(TabDirName[i])
TotImages+=1
print( " Total images to test " + str(TotImages))
return TabImagePath, Y, NameImages
def process_image(image):
# Process a PIL image for use in a PyTorch model
# Converting image to PIL image using image file path
pil_im = Image.open(f'{image}')
"""
# Building image transform
"""
transform = transforms.Compose([transforms.Resize((224,224)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# Transforming image for use with network
pil_tfd = transform(pil_im)
# Converting to Numpy array
array_im_tfd = np.array(pil_tfd)
return array_im_tfd
def predict(image_path, model, topk=5):
# Implement the code to predict the class from an image file
# Loading model - using .cpu() for working with CPUs
loaded_model = load_checkpoint(model).cpu()
# Pre-processing image
img = process_image(image_path)
# Converting to torch tensor from Numpy array
img_tensor = torch.from_numpy(img).type(torch.FloatTensor)
# Adding dimension to image to comply with (B x C x W x H) input of model
img_add_dim = img_tensor.unsqueeze_(0)
# Setting model to evaluation mode and turning off gradients
loaded_model.eval()
with torch.no_grad():
# Running image through network
output = loaded_model.forward(img_add_dim)
#conf, predicted = torch.max(output.data, 1)
probs_top = output.topk(topk)[0]
predicted_top = output.topk(topk)[1]
# Converting probabilities and outputs to lists
conf = np.array(probs_top)[0]
predicted = np.array(predicted_top)[0]
#return probs_top_list, index_top_list
return conf, predicted
###########################################################
# MAIN
##########################################################
from tensorflow.keras.models import load_model
TabImagePath, Y_test, imageName_test=loadimagesTest()
#print(TabImagePath)
#print(Y_test)
TotalHits=0
TotalFailures=0
with open( "ModelsResults.txt" ,"w") as w:
for i in range(len(TabImagePath)):
TabP=[]
TabModel=[]
TabPredictions1=[]
conf, predicted1=predict(TabImagePath[i], model_path, topk=5)
NameSkinCancerPredicted=TabSkinCancer[predicted1[0]]
NameSkinCancerTrue=Y_test[i]
if NameSkinCancerPredicted!=NameSkinCancerTrue:
TotalFailures=TotalFailures + 1
print("ERROR " + imageName_test[i]+ " is assigned Model " + NameSkinCancerPredicted
+ " True Model " + NameSkinCancerTrue)
else:
print("HIT " + imageName_test[i]+ " is assigned model " + NameSkinCancerPredicted)
TotalHits=TotalHits+1
lineaw=[]
lineaw.append(imageName_test[i])
lineaw.append(Y_test[i])
lineaw.append(NameSkinCancerTrue)
lineaw.append(NameSkinCancerPredicted)
lineaWrite =','.join(lineaw)
lineaWrite=lineaWrite + "\n"
w.write(lineaWrite)
print("")
print("Total hits = " + str(TotalHits))
print("Total failures = " + str(TotalFailures) )
print("Accuracy = " + str(TotalHits*100/(TotalHits + TotalFailures)) + "%")