use pytorch to do resize and clip to reduce gpu memory usage.

#1
Files changed (1) hide show
  1. image_preprocessing_molmo.py +20 -20
image_preprocessing_molmo.py CHANGED
@@ -85,26 +85,26 @@ def resize_and_pad(
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  scaled_height = int(np.array(height, np.float32) * image_scale)
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  scaled_width = int(np.array(width, np.float32) * image_scale)
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- # if resize_method == "tensorflow":
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- # FIXME remove
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- import tensorflow as tf
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- image = tf.image.convert_image_dtype(tf.constant(image), dtype=tf.float32)
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- image = tf.image.resize(
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- image,
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- [scaled_height, scaled_width],
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- method=tf.image.ResizeMethod.BILINEAR,
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- antialias=True,
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- )
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- image = tf.clip_by_value(image, 0.0, 1.0)
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- image = image.numpy()
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- # else:
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- # image = torch.permute(torch.from_numpy(image), [2, 0, 1])
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- # image = convert_image_dtype(image) # resize in flaot32
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- # image = torchvision.transforms.Resize(
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- # [scaled_height, scaled_width], InterpolationMode.BILINEAR, antialias=True
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- # )(image)
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- # image = torch.clip(image, 0.0, 1.0)
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- # image = torch.permute(image, [1, 2, 0]).numpy()
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  top_pad = (desired_height - scaled_height) // 2
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  left_pad = (desired_width - scaled_width) // 2
 
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  scaled_height = int(np.array(height, np.float32) * image_scale)
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  scaled_width = int(np.array(width, np.float32) * image_scale)
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+ if resize_method == "tensorflow":
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+ # this option leads to large gpu mem increase likely due to how tensorflow handle memory allocation
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+ import tensorflow as tf
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+ image = tf.image.convert_image_dtype(tf.constant(image), dtype=tf.float32)
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+ image = tf.image.resize(
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+ image,
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+ [scaled_height, scaled_width],
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+ method=tf.image.ResizeMethod.BILINEAR,
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+ antialias=True,
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+ )
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+ image = tf.clip_by_value(image, 0.0, 1.0)
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+ image = image.numpy()
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+ else:
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+ image = torch.permute(torch.from_numpy(image), [2, 0, 1])
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+ image = convert_image_dtype(image) # resize in flaot32
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+ image = torchvision.transforms.Resize(
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+ [scaled_height, scaled_width], InterpolationMode.BILINEAR, antialias=True
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+ )(image)
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+ image = torch.clip(image, 0.0, 1.0)
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+ image = torch.permute(image, [1, 2, 0]).numpy()
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  top_pad = (desired_height - scaled_height) // 2
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  left_pad = (desired_width - scaled_width) // 2