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update metric
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ppcls/metric/__init__.py
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ppcls/metric/__init__.py
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ppcls/metric/metrics.py
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ppcls/metric/metrics.py
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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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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# TODO: fix the format
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class Topk(nn.Layer):
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def __init__(self, topk=(1, 5)):
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super().__init__()
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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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self.topk = topk
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def forward(self, x, label):
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if isinstance(x, dict):
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x = x["logits"]
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metric_dict = dict()
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for k in self.topk:
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metric_dict["top{}".format(k)] = paddle.metric.accuracy(
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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, max_rank=50):
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super().__init__()
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self.max_rank = max_rank
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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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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 < self.max_rank:
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self.max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.
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format(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_AP, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
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self.max_rank)
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mAP = np.mean(all_AP)
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metric_dict["mAP"] = mAP
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return metric_dict
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class mINP(nn.Layer):
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def __init__(self, max_rank=50):
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super().__init__()
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self.max_rank = max_rank
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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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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 < self.max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.
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format(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_INP = get_metrics(indices, num_q, num_g, q_pids, g_pids,
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self.max_rank)
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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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class Recallk(nn.Layer):
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def __init__(self, max_rank=50, topk=(1, 5)):
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super().__init__()
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self.max_rank = max_rank
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assert isinstance(topk, (int, list))
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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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def forward(self, similarities_matrix, query_img_id, gallery_img_id):
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metric_dict = dict()
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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 < self.max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.
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format(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, _, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
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self.max_rank)
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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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def get_metrics(indices, num_q, num_g, q_pids, g_pids, max_rank=50):
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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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q_pid = q_pids[q_idx]
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order = indices[q_idx]
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remove = g_pids[order] == q_pid
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keep = np.invert(remove)
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raw_cmc = matches[q_idx][keep]
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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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