Inference example

[sctrueew]

Hi,

Thanks for your work. I trained a model with my own data. How do I infer a single image? I wrote this script but I get wrong result

import torch
from models import ShuffleNet2

num_classes = 2 
input_size = 224
net_type = 1

model = ShuffleNet2(num_classes, input_size, net_type)
model_path = "./save/ShuffleNet21660477670.pkl"
model.load_state_dict(torch.load(model_path))
model.to('cuda')
model.eval()

from PIL import Image
from torchvision import transforms
input_image = Image.open('./dogvscat/train/cat.1.jpg')
preprocess = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

with torch.no_grad():
    output = model(input_batch)


print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
print(probabilities)

# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 2)
for i in range(top5_prob.size(0)):
    print(top5_catid[i], top5_prob[i].item())