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patch: fix dimension.

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h-alice 2025-01-13 23:31:22 +08:00
parent fa4044adae
commit 910d6e1d80
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1 changed files with 7 additions and 10 deletions
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deepface/models/Demography.py
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@ -33,23 +33,20 @@ class Demography(ABC):
with n >= 1, x = image width, y = image height, c = channel with n >= 1, x = image width, y = image height, c = channel
Or Single image as np.ndarray (1, x, y, c) Or Single image as np.ndarray (1, x, y, c)
with x = image width, y = image height and c = channel with x = image width, y = image height and c = channel
The channel dimension may be omitted if the image is grayscale. (For emotion model) The channel dimension will be 1 if input is grayscale. (For emotion model)
""" """
if not self.model_name: # Check if called from derived class if not self.model_name: # Check if called from derived class
raise NotImplementedError("no model selected") raise NotImplementedError("no model selected")
assert img_batch.ndim == 4, "expected 4-dimensional tensor input" assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
if img_batch.shape[-1] != 3: # Handle grayscale image, check last dimension.
# Check if grayscale by checking last dimension, if not 3, it is grayscale.
img_batch = img_batch.squeeze(0) # Remove batch dimension
if img_batch.shape[0] == 1: # Single image if img_batch.shape[0] == 1: # Single image
img_batch = img_batch.squeeze(0) # Remove batch dimension
# Predict with legacy method. # Predict with legacy method.
return self.model(img_batch, training=False).numpy()[0, :] return self.model(img_batch, training=False).numpy()[0, :]
else:
# Batch of images # Batch of images
# Predict with batch prediction # Predict with batch prediction
return self.model.predict_on_batch(img_batch) return self.model.predict_on_batch(img_batch)
def _preprocess_batch_or_single_input( def _preprocess_batch_or_single_input(
self, self,