model -> self.model.
Browse files- pipeline.py +2 -2
pipeline.py
CHANGED
@@ -24,7 +24,7 @@ class PreTrainedPipeline():
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im = tf.image.resize(img, (128, 128))
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im = tf.cast(im, tf.float32) / 255.0
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-
pred_mask = model.predict(im[tf.newaxis, ...])
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# take the best performing class for each pixel
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# the output of argmax looks like this [[1, 2, 0], ...]
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@@ -76,4 +76,4 @@ class PreTrainedPipeline():
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"mask": mask_codes[f"mask_{cls}"],
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"score": 1.0,
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})
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-
return labels
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im = tf.image.resize(img, (128, 128))
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im = tf.cast(im, tf.float32) / 255.0
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+
pred_mask = self.model.predict(im[tf.newaxis, ...])
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# take the best performing class for each pixel
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# the output of argmax looks like this [[1, 2, 0], ...]
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"mask": mask_codes[f"mask_{cls}"],
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"score": 1.0,
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})
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+
return labels
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