@@ -472,8 +472,6 @@ class FlavaSelfAttention(nn.Module):
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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@@ -1285,7 +1285,7 @@ class FlavaModelIntegrationTest(unittest.TestCase):
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# verify the embeddings
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self.assertAlmostEqual(outputs.image_embeddings.sum().item(), -1352.53540, places=4)
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self.assertAlmostEqual(outputs.text_embeddings.sum().item(), -198.98225, places=4)
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self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -4030.4602050, places=4)
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self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -4030.4604492, places=4)
|
||||
|
||||
|
||||
@require_vision
|
||||
|
||||
Reference in New Issue
Block a user