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因为一个图片对应的是五个caption,所以测试的是1K TEST 5折交叉验证用的是多少size的数据集, 5K TEST又用的是多少size的数据集? 在evalrank()函数中,有 for i in range(5): print(i) img_emb_new = img_embs[i * 5000 : int(i * 5000 + img_embs.size(0)/5):5] cap_emb_new = cap_embs[i * 5000 : int(i * 5000 + cap_embs.size(0)/5)] 这里明显,如果用1K张图扩展五倍构成的数据集,是一定会下标越界的.
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使用的同样的测试集,即5k张图片和25k条句子。分为5折交叉测试以及全部一起测试。 您提到的函数中。测试时图片为了和文本对齐,扩充成了25000张(每5张是一样的),每次测试1k张,比如说第一个1k就是下标0-4999的5k张每隔5张取一张,即: img_emb_new = img_embs[i * 5000 : int(i * 5000 + img_embs.size(0)/5):5]
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因为一个图片对应的是五个caption,所以测试的是1K TEST 5折交叉验证用的是多少size的数据集, 5K TEST又用的是多少size的数据集?
在evalrank()函数中,有
for i in range(5):
print(i)
img_emb_new = img_embs[i * 5000 : int(i * 5000 + img_embs.size(0)/5):5]
cap_emb_new = cap_embs[i * 5000 : int(i * 5000 + cap_embs.size(0)/5)]
这里明显,如果用1K张图扩展五倍构成的数据集,是一定会下标越界的.
The text was updated successfully, but these errors were encountered: