Adaptive resolution plus review #43
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henryruhs
commented
Mar 5, 2025
| @@ -169,9 +169,15 @@ def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tenso | |||
| return gaze_loss, weighted_gaze_loss | |||
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| def detect_gaze(self, input_tensor : Tensor) -> Gaze: | |||
| crop_tensor = input_tensor[:, :, 60: 224, 16: 205] | |||
| scale_factor = CONFIG.getint('training.losses', 'gaze_scale_factor') | |||
| y_min = int(60 * scale_factor) | |||
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I wonder where the magic numbers come from and if we could use a torchvision transform instead?
crop_tensor = F.interpolate(input_tensor, scale_factor = scale_factor, mode = 'bicubic')
crop_tensor = crop_tensor[:, :, 60:224, 16:205]
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The rest has been generated via ChatGPT.
Edit:
gaze_scale_factorwas probably not a good idea, but try it first on different resolutions.Finally, I used more self.properties and removed pointless passing to methods