pre_post_data_gathering1_model_6.py

# read an image
from PIL import Image
from torch.autograd import Variable

loader = transforms.Compose([transforms.Resize(dim[0]), 
                             transforms.ToTensor(),
                            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

def image_loader(image_name):
    """load image, returns cuda tensor"""
    image = Image.open(image_name)
    image = loader(image).float()
    image = Variable(image, requires_grad=True)
    image = image.unsqueeze(0)  #this is for VGG, may not be needed for ResNet
    if torch.cuda.is_available():
        return image.cuda()  #assumes that you're using GPU
    else:
        return image
    
    
file_location="data/arranged/test/0/lower-puna-volcano_00000266_pre_disaster.png"
image = image_loader(file_location)
res=model_ft(image)
_, pred = torch.max(res, 1)
print("Prediction is ",pred)

file_location="data/arranged/test/1/nepal-flooding_00000332_post_disaster.png"
image = image_loader(file_location)
res=model_ft(image)
_, pred = torch.max(res, 1)
print("Prediction is ",pred)