Additional image_embed metric (#75)

Added metric to track image_embed vs predicted_image_embed
This commit is contained in:
Nasir Khalid
2022-05-07 17:32:33 -04:00
committed by GitHub
parent 4010aec033
commit 2eac7996fa

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@@ -93,6 +93,8 @@ def report_cosine_sims(diffusion_prior, image_reader, text_reader, train_set_siz
text_embed, predicted_image_embeddings).cpu().numpy() text_embed, predicted_image_embeddings).cpu().numpy()
unrelated_similarity = cos( unrelated_similarity = cos(
text_embed, predicted_unrelated_embeddings).cpu().numpy() text_embed, predicted_unrelated_embeddings).cpu().numpy()
predicted_img_similarity = cos(
test_image_embeddings, predicted_image_embeddings).cpu().numpy()
wandb.log( wandb.log(
{"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity)}) {"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity)})
@@ -100,6 +102,8 @@ def report_cosine_sims(diffusion_prior, image_reader, text_reader, train_set_siz
predicted_similarity)}) predicted_similarity)})
wandb.log({"CosineSimilarity(text_embed,predicted_unrelated_embed)": np.mean( wandb.log({"CosineSimilarity(text_embed,predicted_unrelated_embed)": np.mean(
unrelated_similarity)}) unrelated_similarity)})
wandb.log({"CosineSimilarity(image_embed,predicted_image_embed)": np.mean(
predicted_img_similarity)})
return np.mean(predicted_similarity - original_similarity) return np.mean(predicted_similarity - original_similarity)