To obtain predictions from a PyTorch model, perform the following steps:
torch.load()
or torch.load_state_dict()
functions.eval()
mode to disable dropout and batch normalization layers. This ensures that the model behaves consistently during inference.model.forward()
function.Here is some sample code to illustrate these steps:
import torch
from torchvision.transforms import ToTensor
from PIL import Image
# Step 1: Load the PyTorch model
model = torch.load('model.pth')
# Step 2: Set the model to eval mode
model.eval()
# Step 3: Convert the input data to the expected format
img = Image.open('input.png')
img = ToTensor()(img)
img = img.unsqueeze(0) # Add batch dimension
# Step 4: Pass the input data to the model to obtain the output tensor
output = model.forward(img)
# Step 5: Extract the required information from the output tensor
probs = torch.nn.functional.softmax(output, dim=1)
pred_class = torch.argmax(probs)
print(pred_class)
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Asked: 2021-10-26 11:00:00 +0000
Seen: 15 times
Last updated: Dec 21 '22
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