Some images will be lost due to detection

[x12901]

The size of my picture is 1280*1024,I use the command streamlit run streamlit_app.py . The result is very good. But part of my picture is missing. The displayed result is not a complete picture. Can the cropping of the picture be changed? I tried to modify the code, but the result was not good.Can the detection speed be improved?Can I just load the model without training every time?

class SPADE(KNNExtractor):
    def __init__(
            self,
            k: int = 5,
            backbone_name: str = "resnet50",
    ):
        super().__init__(
            backbone_name=backbone_name,
            out_indices=(1, 2, 3),
            pool=True,
        )
        self.k = k
        self.image_size_x = 1280
        self.image_size_y = 1024
        self.z_lib = []
        self.feature_maps = []
        self.threshold_z = None
        self.threshold_fmaps = None
        self.blur = GaussianBlur(4)

    def predict(self, sample):
        feature_maps, z = self(sample)

        distances = torch.linalg.norm(self.z_lib - z, dim=1)
        values, indices = torch.topk(distances.squeeze(), self.k, largest=False)

        z_score = values.mean()

        # Build the feature gallery out of the k nearest neighbours.
        # The authors migh have concatenated all features maps first, then check the minimum norm per pixel.
        # Here, we check for the minimum norm first, then concatenate (sum) in the final layer.
        scaled_s_map = torch.zeros(1, 1, self.image_size_y, self.image_size_x)
        for idx, fmap in enumerate(feature_maps):
            nearest_fmaps = torch.index_select(self.feature_maps[idx], 0, indices)
            # min() because kappa=1 in the paper
            s_map, _ = torch.min(torch.linalg.norm(nearest_fmaps - fmap, dim=1), 0, keepdims=True)
            scaled_s_map += torch.nn.functional.interpolate(
                s_map.unsqueeze(0), size=(self.image_size_y, self.image_size_x), mode='bilinear'
            )

        scaled_s_map = self.blur(scaled_s_map)

        return z_score, scaled_s_map

[rvorias]

Hi, you should take a look at the transformations in data.py.
Code below will resize it without respecting the aspect ratio.
I would suggest to just upscale the output feature map to 1280*1024.

class StreamingDataset:
    """This dataset is made specifically for the streamlit app."""
    def __init__(self, size: int = 224):
        self.size = size
        self.transform=transforms.Compose([
---             transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC),
+++             transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
---             transforms.CenterCrop(size),
                transforms.ToTensor(),
                transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
            ])
        self.samples = []
    
    def add_pil_image(self, image : Image):
        image = image.convert('RGB')
        self.samples.append(image)

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, index):
        sample = self.samples[index]
        return (self.transform(sample), None)

[x12901]

This solved my problem. Thanks!