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author | aktersnurra <gustaf.rydholm@gmail.com> | 2020-08-20 22:18:35 +0200 |
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committer | aktersnurra <gustaf.rydholm@gmail.com> | 2020-08-20 22:18:35 +0200 |
commit | 1f459ba19422593de325983040e176f97cf4ffc0 (patch) | |
tree | 89fef442d5dbe0c83253e9566d1762f0704f64e2 /src/text_recognizer/models/character_model.py | |
parent | 95cbdf5bc1cc9639febda23c28d8f464c998b214 (diff) |
A lot of stuff working :D. ResNet implemented!
Diffstat (limited to 'src/text_recognizer/models/character_model.py')
-rw-r--r-- | src/text_recognizer/models/character_model.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py index 0a0ab2d..0fd7afd 100644 --- a/src/text_recognizer/models/character_model.py +++ b/src/text_recognizer/models/character_model.py @@ -44,6 +44,7 @@ class CharacterModel(Model): self.tensor_transform = ToTensor() self.softmax = nn.Softmax(dim=0) + @torch.no_grad() def predict_on_image( self, image: Union[np.ndarray, torch.Tensor] ) -> Tuple[str, float]: @@ -64,10 +65,9 @@ class CharacterModel(Model): # If the image is an unscaled tensor. image = image.type("torch.FloatTensor") / 255 - with torch.no_grad(): - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - logits = self.network(image) + # Put the image tensor on the device the model weights are on. + image = image.to(self.device) + logits = self.network(image) prediction = self.softmax(logits.data.squeeze()) |