Total newbie here, I'm using this pytorch SegNet implementation with a '.pth' file containing weights from a 50 epochs training. How can I load a single test image and see the net prediction? I know this may sound like a stupid question but I'm stuck. What I've got is:
from segnet import SegNet
import torch
model = SegNet(2)
model.load_state_dict(torch.load('./model_segnet_epoch50.pth'))
How do I "use" the net on a single test picture?
I provide with an example of ResNet152
pre-trained model.
def image_loader(loader, image_name):
image = Image.open(image_name)
image = loader(image).float()
image = torch.tensor(image, requires_grad=True)
image = image.unsqueeze(0)
return image
data_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()
])
model_ft = models.resnet152(pretrained=True)
model_ft.eval()
print( np.argmax(model_ft(image_loader(data_transforms, $FILENAME)).detach().numpy()))
$FILENAME
is the path and name of your image to be loaded. I got necessary help from this post .
output = model(image)
.
Note that the image should be a Variable
object and that the output will be as well. If your image is, for example, a Numpy array, you can convert it like so:
var_image = Variable(torch.Tensor(image))
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