Update metrics.py
Bin Lu
GitHub
parent
3a2f97a911
commit
65780c29df
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@ -15,8 +15,6 @@
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import numpy as np
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import paddle
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import paddle.nn as nn
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from functools import lru_cache
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class TopkAcc(nn.Layer):
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def __init__(self, topk=(1, 5)):
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@ -36,35 +34,54 @@ class TopkAcc(nn.Layer):
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x, label, k=k)
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return metric_dict
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class mAP(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, similarities_matrix, query_img_id, gallery_img_id):
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def forward(self, similarities_matrix, query_labels, gallery_labels):
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metric_dict = dict()
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_, all_AP, _ = get_metrics(similarities_matrix, query_img_id,
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gallery_img_id)
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choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
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gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
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gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
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choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_labels)
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equal_flag = paddle.cast(equal_flag, 'float32')
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mAP = np.mean(all_AP)
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metric_dict["mAP"] = mAP
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acc_sum = paddle.cumsum(equal_flag, axis=1)
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div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
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precision = paddle.divide(acc_sum, div)
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#calc map
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precision_mask = paddle.multiply(equal_flag, precision)
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ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag, axis=1)
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metric_dict["mAP"] = paddle.mean(ap).numpy()[0]
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return metric_dict
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class mINP(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, similarities_matrix, query_img_id, gallery_img_id):
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def forward(self, similarities_matrix, query_labels, gallery_labels):
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metric_dict = dict()
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_, _, all_INP = get_metrics(similarities_matrix, query_img_id,
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gallery_img_id)
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choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
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gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
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gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
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choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
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tmp = paddle.equal(choosen_label, query_labels)
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tmp = paddle.cast(tmp, 'float64')
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mINP = np.mean(all_INP)
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metric_dict["mINP"] = mINP
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#do accumulative sum
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div = paddle.arange(tmp.shape[1]).astype("float64") + 2
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minus = paddle.divide(tmp, div)
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auxilary = paddle.subtract(tmp, minus)
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hard_index = paddle.argmax(auxilary, axis=1).astype("float64")
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all_INP = paddle.divide(paddle.sum(tmp, axis=1), hard_index)
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mINP = paddle.mean(all_INP)
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metric_dict["mINP"] = mINP.numpy()[0]
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return metric_dict
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class Recallk(nn.Layer):
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def __init__(self, topk=(1, 5)):
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super().__init__()
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@ -72,91 +89,26 @@ class Recallk(nn.Layer):
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if isinstance(topk, int):
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topk = [topk]
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self.topk = topk
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self.max_rank = max(self.topk) if max(self.topk) > 50 else 50
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def forward(self, similarities_matrix, query_img_id, gallery_img_id):
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def forward(self, similarities_matrix, query_labels, gallery_labels):
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metric_dict = dict()
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all_cmc, _, _ = get_metrics(similarities_matrix, query_img_id,
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gallery_img_id, self.max_rank)
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#get cmc
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choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
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gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
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gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
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choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_labels)
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equal_flag = paddle.cast(equal_flag, 'float32')
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acc_sum = paddle.cumsum(equal_flag, axis=1)
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mask = paddle.greater_than(acc_sum, paddle.to_tensor(0.)).astype("float32")
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all_cmc = paddle.mean(mask, axis=0).numpy()
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for k in self.topk:
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metric_dict["recall{}".format(k)] = all_cmc[k - 1]
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return metric_dict
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# retrieval metrics
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class RetriMetric(nn.Layer):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.max_rank = 50 #max(self.topk) if max(self.topk) > 50 else 50
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def forward(self, similarities_matrix, query_img_id, gallery_img_id):
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metric_dict = dict()
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all_cmc, all_AP, all_INP = get_metrics(
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similarities_matrix, query_img_id, gallery_img_id, self.max_rank)
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if "Recallk" in self.config.keys():
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topk = self.config['Recallk']['topk']
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assert isinstance(topk, (int, list, tuple))
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if isinstance(topk, int):
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topk = [topk]
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for k in topk:
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metric_dict["recall{}".format(k)] = all_cmc[k - 1]
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if "mAP" in self.config.keys():
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mAP = np.mean(all_AP)
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metric_dict["mAP"] = mAP
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if "mINP" in self.config.keys():
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mINP = np.mean(all_INP)
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metric_dict["mINP"] = mINP
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return metric_dict
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@lru_cache()
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def get_metrics(similarities_matrix, query_img_id, gallery_img_id,
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max_rank=50):
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num_q, num_g = similarities_matrix.shape
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q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
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g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
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if num_g < max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.format(
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num_g))
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indices = paddle.argsort(
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similarities_matrix, axis=1, descending=True).numpy()
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all_cmc = []
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all_AP = []
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all_INP = []
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num_valid_q = 0
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matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
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for q_idx in range(num_q):
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raw_cmc = matches[q_idx]
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if not np.any(raw_cmc):
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continue
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cmc = raw_cmc.cumsum()
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pos_idx = np.where(raw_cmc == 1)
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max_pos_idx = np.max(pos_idx)
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inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)
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all_INP.append(inp)
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cmc[cmc > 1] = 1
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all_cmc.append(cmc[:max_rank])
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num_valid_q += 1.
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num_rel = raw_cmc.sum()
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tmp_cmc = raw_cmc.cumsum()
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tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
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tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
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AP = tmp_cmc.sum() / num_rel
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all_AP.append(AP)
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assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
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all_cmc = np.asarray(all_cmc).astype(np.float32)
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all_cmc = all_cmc.sum(0) / num_valid_q
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return all_cmc, all_AP, all_INP
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class DistillationTopkAcc(TopkAcc):
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def __init__(self, model_key, feature_key=None, topk=(1, 5)):
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super().__init__(topk=topk)
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