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63 lines
1.8 KiB
63 lines
1.8 KiB
2 years ago
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import numpy as np
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def i2t(sims, npts=None, return_ranks=False):
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"""
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Images->Text (Image Annotation)
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sims: (N, 5N) matrix of similarity im-cap
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"""
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npts = sims.shape[0]
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ranks = np.zeros(npts)
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top1 = np.zeros(npts)
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for index in range(npts):
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inds = np.argsort(sims[index])[::-1]
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# Score
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rank = 1e20
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for i in range(5 * index, 5 * index + 5, 1):
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tmp = np.where(inds == i)[0][0]
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if tmp < rank:
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rank = tmp
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ranks[index] = rank
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top1[index] = inds[0]
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# Compute metrics
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r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
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r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
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r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
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medr = np.floor(np.median(ranks)) + 1
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meanr = ranks.mean() + 1
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if return_ranks:
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return (r1, r5, r10, medr, meanr), (ranks, top1)
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else:
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return (r1, r5, r10, medr, meanr)
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def t2i(sims, npts=None, return_ranks=False):
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"""
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Text->Images (Image Search)
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sims: (N, 5N) matrix of similarity im-cap
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"""
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npts = sims.shape[0]
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ranks = np.zeros(5 * npts)
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top1 = np.zeros(5 * npts)
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# --> (5N(caption), N(image))
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sims = sims.T
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for index in range(npts):
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for i in range(5):
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inds = np.argsort(sims[5 * index + i])[::-1]
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ranks[5 * index + i] = np.where(inds == index)[0][0]
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top1[5 * index + i] = inds[0]
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# Compute metrics
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r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
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r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
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r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
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medr = np.floor(np.median(ranks)) + 1
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meanr = ranks.mean() + 1
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if return_ranks:
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return (r1, r5, r10, medr, meanr), (ranks, top1)
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else:
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return (r1, r5, r10, medr, meanr)
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