| from resnet import get_model | |
| import torch | |
| from PIL import Image | |
| from torchvision.transforms.functional import pil_to_tensor | |
| model = get_model("r100", dropout=0.0, fp16=True, num_features=512).cuda() | |
| model.load_state_dict(torch.load("model.pt")) | |
| model.eval() | |
| img = pil_to_tensor(Image.open("test.jpg").resize((112,112))).permute(0, 1, 2).to("cuda", torch.float16).unsqueeze(dim = 0) | |
| embeddings = model(img) |