diff --git a/ppcls/configs/reid/strong_baseline/baseline.yaml b/ppcls/configs/reid/strong_baseline/baseline.yaml index d49deeeb4d27f304ac8de76b5feb6d314ed18a48..35980206b19bab76f46df54e143adaecc1f4b566 100644 --- a/ppcls/configs/reid/strong_baseline/baseline.yaml +++ b/ppcls/configs/reid/strong_baseline/baseline.yaml @@ -12,6 +12,7 @@ Global: use_visualdl: False eval_mode: "retrieval" retrieval_feature_from: "backbone" # 'backbone' or 'neck' + re_ranking: False # used for static mode and model export image_shape: [3, 256, 128] save_inference_dir: "./inference" diff --git a/ppcls/configs/reid/strong_baseline/softmax_triplet.yaml b/ppcls/configs/reid/strong_baseline/softmax_triplet.yaml index c4d52cd1ef24745cd1d5fac7f7dec26fd98609e0..6f9cd955626316fe5267e3f9289b93b4317f736f 100644 --- a/ppcls/configs/reid/strong_baseline/softmax_triplet.yaml +++ b/ppcls/configs/reid/strong_baseline/softmax_triplet.yaml @@ -12,6 +12,7 @@ Global: use_visualdl: False eval_mode: "retrieval" retrieval_feature_from: "features" # 'backbone' or 'features' + re_ranking: False # used for static mode and model export image_shape: [3, 256, 128] save_inference_dir: "./inference" diff --git a/ppcls/configs/reid/strong_baseline/softmax_triplet_with_center.yaml b/ppcls/configs/reid/strong_baseline/softmax_triplet_with_center.yaml index 2d14c3f9ce83f231c5a664f8d24177b25543b421..22af5e516ca4b9945bc8413ed56c67c972b48609 100644 --- a/ppcls/configs/reid/strong_baseline/softmax_triplet_with_center.yaml +++ b/ppcls/configs/reid/strong_baseline/softmax_triplet_with_center.yaml @@ -12,6 +12,7 @@ Global: use_visualdl: False eval_mode: "retrieval" retrieval_feature_from: "features" # 'backbone' or 'features' + re_ranking: False # used for static mode and model export image_shape: [3, 256, 128] save_inference_dir: "./inference" diff --git a/ppcls/engine/evaluation/retrieval.py b/ppcls/engine/evaluation/retrieval.py index 05c5d0c35d0f6fdfcd0a8f1dc1a8a121026ede99..f68902285cae9896f76eca30cbabbbacaf5a2b3f 100644 --- a/ppcls/engine/evaluation/retrieval.py +++ b/ppcls/engine/evaluation/retrieval.py @@ -16,6 +16,9 @@ from __future__ import division from __future__ import print_function import platform +from typing import Optional + +import numpy as np import paddle from ppcls.utils import logger @@ -48,34 +51,67 @@ def retrieval_eval(engine, epoch_id=0): if engine.eval_loss_func is None: metric_dict = {metric_key: 0.} else: + reranking_flag = engine.config['Global'].get('re_ranking', False) + logger.info(f"re_ranking={reranking_flag}") metric_dict = dict() - for block_idx, block_fea in enumerate(fea_blocks): - similarity_matrix = paddle.matmul( - block_fea, gallery_feas, transpose_y=True) - if query_query_id is not None: - query_id_block = query_id_blocks[block_idx] - query_id_mask = (query_id_block != gallery_unique_id.t()) - - image_id_block = image_id_blocks[block_idx] - image_id_mask = (image_id_block != gallery_img_id.t()) - - keep_mask = paddle.logical_or(query_id_mask, image_id_mask) - similarity_matrix = similarity_matrix * keep_mask.astype( - "float32") - else: - keep_mask = None - - metric_tmp = engine.eval_metric_func(similarity_matrix, - image_id_blocks[block_idx], - gallery_img_id, keep_mask) + if reranking_flag: + # set the order from small to large + for i in range(len(engine.eval_metric_func.metric_func_list)): + if hasattr(engine.eval_metric_func.metric_func_list[i], 'descending') \ + and engine.eval_metric_func.metric_func_list[i].descending is True: + engine.eval_metric_func.metric_func_list[ + i].descending = False + logger.warning( + f"re_ranking=True,{engine.eval_metric_func.metric_func_list[i].__class__.__name__}.descending has been set to False" + ) + + # compute distance matrix(The smaller the value, the more similar) + distmat = re_ranking( + query_feas, gallery_feas, k1=20, k2=6, lambda_value=0.3) + # compute keep mask + query_id_mask = (query_query_id != gallery_unique_id.t()) + image_id_mask = (query_img_id != gallery_img_id.t()) + keep_mask = paddle.logical_or(query_id_mask, image_id_mask) + + # set inf(1e9) distance to those exist in gallery + distmat = distmat * keep_mask.astype("float32") + inf_mat = (paddle.logical_not(keep_mask).astype("float32")) * 1e20 + distmat = distmat + inf_mat + + # compute metric + metric_tmp = engine.eval_metric_func(distmat, query_img_id, + gallery_img_id, keep_mask) for key in metric_tmp: - if key not in metric_dict: - metric_dict[key] = metric_tmp[key] * block_fea.shape[ - 0] / len(query_feas) + metric_dict[key] = metric_tmp[key] + else: + for block_idx, block_fea in enumerate(fea_blocks): + similarity_matrix = paddle.matmul( + block_fea, gallery_feas, transpose_y=True) # [n,m] + if query_query_id is not None: + query_id_block = query_id_blocks[block_idx] + query_id_mask = (query_id_block != gallery_unique_id.t()) + + image_id_block = image_id_blocks[block_idx] + image_id_mask = (image_id_block != gallery_img_id.t()) + + keep_mask = paddle.logical_or(query_id_mask, image_id_mask) + similarity_matrix = similarity_matrix * keep_mask.astype( + "float32") else: - metric_dict[key] += metric_tmp[key] * block_fea.shape[ - 0] / len(query_feas) + keep_mask = None + + metric_tmp = engine.eval_metric_func( + similarity_matrix, image_id_blocks[block_idx], + gallery_img_id, keep_mask) + + for key in metric_tmp: + if key not in metric_dict: + metric_dict[key] = metric_tmp[key] * block_fea.shape[ + 0] / len(query_feas) + else: + metric_dict[key] += metric_tmp[key] * block_fea.shape[ + 0] / len(query_feas) metric_info_list = [] for key in metric_dict: @@ -185,3 +221,109 @@ def cal_feature(engine, name='gallery'): logger.info("Build {} done, all feat shape: {}, begin to eval..".format( name, all_feas.shape)) return all_feas, all_img_id, all_unique_id + + +def re_ranking(query_feas: paddle.Tensor, + gallery_feas: paddle.Tensor, + k1: int=20, + k2: int=6, + lambda_value: int=0.5, + local_distmat: Optional[np.ndarray]=None, + only_local: bool=False) -> paddle.Tensor: + """re-ranking, most computed with numpy + + code heavily based on + https://github.com/michuanhaohao/reid-strong-baseline/blob/3da7e6f03164a92e696cb6da059b1cd771b0346d/utils/reid_metric.py + + Args: + query_feas (paddle.Tensor): query features, [num_query, num_features] + gallery_feas (paddle.Tensor): gallery features, [num_gallery, num_features] + k1 (int, optional): k1. Defaults to 20. + k2 (int, optional): k2. Defaults to 6. + lambda_value (int, optional): lambda. Defaults to 0.5. + local_distmat (Optional[np.ndarray], optional): local_distmat. Defaults to None. + only_local (bool, optional): only_local. Defaults to False. + + Returns: + paddle.Tensor: final_dist matrix after re-ranking, [num_query, num_gallery] + """ + query_num = query_feas.shape[0] + all_num = query_num + gallery_feas.shape[0] + if only_local: + original_dist = local_distmat + else: + feat = paddle.concat([query_feas, gallery_feas]) + logger.info('using GPU to compute original distance') + + # L2 distance + distmat = paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]) + \ + paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]).t() + distmat = distmat.addmm(x=feat, y=feat.t(), alpha=-2.0, beta=1.0) + + original_dist = distmat.cpu().numpy() + del feat + if local_distmat is not None: + original_dist = original_dist + local_distmat + + gallery_num = original_dist.shape[0] + original_dist = np.transpose(original_dist / np.max(original_dist, axis=0)) + V = np.zeros_like(original_dist).astype(np.float16) + initial_rank = np.argsort(original_dist).astype(np.int32) + logger.info('starting re_ranking') + for i in range(all_num): + # k-reciprocal neighbors + forward_k_neigh_index = initial_rank[i, :k1 + 1] + backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1] + fi = np.where(backward_k_neigh_index == i)[0] + k_reciprocal_index = forward_k_neigh_index[fi] + k_reciprocal_expansion_index = k_reciprocal_index + for j in range(len(k_reciprocal_index)): + candidate = k_reciprocal_index[j] + candidate_forward_k_neigh_index = initial_rank[candidate, :int( + np.around(k1 / 2)) + 1] + candidate_backward_k_neigh_index = initial_rank[ + candidate_forward_k_neigh_index, :int(np.around(k1 / 2)) + 1] + fi_candidate = np.where( + candidate_backward_k_neigh_index == candidate)[0] + candidate_k_reciprocal_index = candidate_forward_k_neigh_index[ + fi_candidate] + if len( + np.intersect1d(candidate_k_reciprocal_index, + k_reciprocal_index)) > 2 / 3 * len( + candidate_k_reciprocal_index): + k_reciprocal_expansion_index = np.append( + k_reciprocal_expansion_index, candidate_k_reciprocal_index) + + k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index) + weight = np.exp(-original_dist[i, k_reciprocal_expansion_index]) + V[i, k_reciprocal_expansion_index] = weight / np.sum(weight) + original_dist = original_dist[:query_num, ] + if k2 != 1: + V_qe = np.zeros_like(V, dtype=np.float16) + for i in range(all_num): + V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0) + V = V_qe + del V_qe + del initial_rank + invIndex = [] + for i in range(gallery_num): + invIndex.append(np.where(V[:, i] != 0)[0]) + + jaccard_dist = np.zeros_like(original_dist, dtype=np.float16) + for i in range(query_num): + temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16) + indNonZero = np.where(V[i, :] != 0)[0] + indImages = [invIndex[ind] for ind in indNonZero] + for j in range(len(indNonZero)): + temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum( + V[i, indNonZero[j]], V[indImages[j], indNonZero[j]]) + jaccard_dist[i] = 1 - temp_min / (2 - temp_min) + + final_dist = jaccard_dist * (1 - lambda_value + ) + original_dist * lambda_value + del original_dist + del V + del jaccard_dist + final_dist = final_dist[:query_num, query_num:] + final_dist = paddle.to_tensor(final_dist) + return final_dist diff --git a/ppcls/metric/metrics.py b/ppcls/metric/metrics.py index 03e742082b57439227746d21695379b498e7f1d8..27b04a9ee0b2881ad7e3ee9e36ca911f30137979 100644 --- a/ppcls/metric/metrics.py +++ b/ppcls/metric/metrics.py @@ -43,15 +43,16 @@ class TopkAcc(nn.Layer): class mAP(nn.Layer): - def __init__(self): + def __init__(self, descending=True): super().__init__() + self.descending = descending def forward(self, similarities_matrix, query_img_id, gallery_img_id, keep_mask): metric_dict = dict() choosen_indices = paddle.argsort( - similarities_matrix, axis=1, descending=True) + similarities_matrix, axis=1, descending=self.descending) gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0]) gallery_labels_transpose = paddle.broadcast_to( gallery_labels_transpose, @@ -87,15 +88,16 @@ class mAP(nn.Layer): class mINP(nn.Layer): - def __init__(self): + def __init__(self, descending=True): super().__init__() + self.descending = descending def forward(self, similarities_matrix, query_img_id, gallery_img_id, keep_mask): metric_dict = dict() choosen_indices = paddle.argsort( - similarities_matrix, axis=1, descending=True) + similarities_matrix, axis=1, descending=self.descending) gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0]) gallery_labels_transpose = paddle.broadcast_to( gallery_labels_transpose, @@ -130,12 +132,13 @@ class mINP(nn.Layer): class Recallk(nn.Layer): - def __init__(self, topk=(1, 5)): + def __init__(self, topk=(1, 5), descending=True): super().__init__() assert isinstance(topk, (int, list, tuple)) if isinstance(topk, int): topk = [topk] self.topk = topk + self.descending = descending def forward(self, similarities_matrix, query_img_id, gallery_img_id, keep_mask): @@ -143,7 +146,7 @@ class Recallk(nn.Layer): #get cmc choosen_indices = paddle.argsort( - similarities_matrix, axis=1, descending=True) + similarities_matrix, axis=1, descending=self.descending) gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0]) gallery_labels_transpose = paddle.broadcast_to( gallery_labels_transpose, @@ -175,12 +178,13 @@ class Recallk(nn.Layer): class Precisionk(nn.Layer): - def __init__(self, topk=(1, 5)): + def __init__(self, topk=(1, 5), descending=True): super().__init__() assert isinstance(topk, (int, list, tuple)) if isinstance(topk, int): topk = [topk] self.topk = topk + self.descending = descending def forward(self, similarities_matrix, query_img_id, gallery_img_id, keep_mask): @@ -188,7 +192,7 @@ class Precisionk(nn.Layer): #get cmc choosen_indices = paddle.argsort( - similarities_matrix, axis=1, descending=True) + similarities_matrix, axis=1, descending=self.descending) gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0]) gallery_labels_transpose = paddle.broadcast_to( gallery_labels_transpose,