add re-ranking code
HydrogenSulfate
parent
283ae9b327
commit
88295413f5
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@ -12,6 +12,7 @@ Global:
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use_visualdl: False
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eval_mode: "retrieval"
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retrieval_feature_from: "backbone" # 'backbone' or 'neck'
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re_ranking: False
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# used for static mode and model export
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image_shape: [3, 256, 128]
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save_inference_dir: "./inference"
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@ -12,6 +12,7 @@ Global:
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use_visualdl: False
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eval_mode: "retrieval"
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retrieval_feature_from: "features" # 'backbone' or 'features'
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re_ranking: False
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# used for static mode and model export
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image_shape: [3, 256, 128]
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save_inference_dir: "./inference"
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@ -12,6 +12,7 @@ Global:
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use_visualdl: False
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eval_mode: "retrieval"
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retrieval_feature_from: "features" # 'backbone' or 'features'
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re_ranking: False
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# used for static mode and model export
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image_shape: [3, 256, 128]
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save_inference_dir: "./inference"
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@ -16,6 +16,9 @@ from __future__ import division
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from __future__ import print_function
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import platform
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from typing import Optional
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import numpy as np
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import paddle
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from ppcls.utils import logger
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@ -48,34 +51,68 @@ def retrieval_eval(engine, epoch_id=0):
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if engine.eval_loss_func is None:
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metric_dict = {metric_key: 0.}
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else:
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reranking_flag = engine.config['Global'].get('re_ranking', False)
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logger.info(f"re_ranking={reranking_flag}")
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metric_dict = dict()
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for block_idx, block_fea in enumerate(fea_blocks):
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similarity_matrix = paddle.matmul(
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block_fea, gallery_feas, transpose_y=True)
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if query_query_id is not None:
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query_id_block = query_id_blocks[block_idx]
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query_id_mask = (query_id_block != gallery_unique_id.t())
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if reranking_flag:
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# set the order from small to large
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for i in range(len(engine.eval_metric_func.metric_func_list)):
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if hasattr(engine.eval_metric_func.metric_func_list[i], 'descending') \
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and engine.eval_metric_func.metric_func_list[i].descending is True:
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engine.eval_metric_func.metric_func_list[
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i].descending = False
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logger.info(
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f"set {engine.eval_metric_func.metric_func_list[i].__class__.__name__}.descending to False when re_ranking=True"
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)
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image_id_block = image_id_blocks[block_idx]
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image_id_mask = (image_id_block != gallery_img_id.t())
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# compute distance matrix(The smaller the value, the more similar)
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distmat = re_ranking(
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query_feas, gallery_feas, k1=20, k2=6, lambda_value=0.3)
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distmat = paddle.to_tensor(distmat)
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keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
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similarity_matrix = similarity_matrix * keep_mask.astype(
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"float32")
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else:
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keep_mask = None
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# compute keep mask
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query_id_mask = (query_query_id != gallery_unique_id.t())
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image_id_mask = (query_img_id != gallery_img_id.t())
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keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
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metric_tmp = engine.eval_metric_func(similarity_matrix,
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image_id_blocks[block_idx],
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# set inf(1e9) distance to those exist in gallery
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distmat = distmat * keep_mask.astype("float32")
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inf_mat = (paddle.logical_not(keep_mask).astype("float32")) * 1e20
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distmat = distmat + inf_mat
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# compute metric
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metric_tmp = engine.eval_metric_func(distmat, query_img_id,
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gallery_img_id, keep_mask)
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for key in metric_tmp:
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if key not in metric_dict:
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metric_dict[key] = metric_tmp[key] * block_fea.shape[
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0] / len(query_feas)
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metric_dict[key] = metric_tmp[key]
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else:
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for block_idx, block_fea in enumerate(fea_blocks):
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similarity_matrix = paddle.matmul(
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block_fea, gallery_feas, transpose_y=True) # [n,m]
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if query_query_id is not None:
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query_id_block = query_id_blocks[block_idx]
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query_id_mask = (query_id_block != gallery_unique_id.t())
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image_id_block = image_id_blocks[block_idx]
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image_id_mask = (image_id_block != gallery_img_id.t())
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keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
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similarity_matrix = similarity_matrix * keep_mask.astype(
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"float32")
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else:
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metric_dict[key] += metric_tmp[key] * block_fea.shape[
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0] / len(query_feas)
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keep_mask = None
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metric_tmp = engine.eval_metric_func(
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similarity_matrix, image_id_blocks[block_idx],
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gallery_img_id, keep_mask)
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for key in metric_tmp:
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if key not in metric_dict:
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metric_dict[key] = metric_tmp[key] * block_fea.shape[
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0] / len(query_feas)
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else:
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metric_dict[key] += metric_tmp[key] * block_fea.shape[
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0] / len(query_feas)
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metric_info_list = []
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for key in metric_dict:
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@ -185,3 +222,109 @@ def cal_feature(engine, name='gallery'):
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logger.info("Build {} done, all feat shape: {}, begin to eval..".format(
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name, all_feas.shape))
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return all_feas, all_img_id, all_unique_id
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def re_ranking(query_feas: paddle.Tensor,
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gallery_feas: paddle.Tensor,
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k1: int=20,
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k2: int=6,
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lambda_value: int=0.5,
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local_distmat: Optional[np.ndarray]=None,
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only_local: bool=False) -> paddle.Tensor:
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"""re-ranking, most computed with numpy
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code heavily based on
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https://github.com/michuanhaohao/reid-strong-baseline/blob/3da7e6f03164a92e696cb6da059b1cd771b0346d/utils/reid_metric.py
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Args:
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query_feas (paddle.Tensor): query features, [num_query, num_features]
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gallery_feas (paddle.Tensor): gallery features, [num_gallery, num_features]
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k1 (int, optional): k1. Defaults to 20.
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k2 (int, optional): k2. Defaults to 6.
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lambda_value (int, optional): lambda. Defaults to 0.5.
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local_distmat (Optional[np.ndarray], optional): local_distmat. Defaults to None.
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only_local (bool, optional): only_local. Defaults to False.
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Returns:
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paddle.Tensor: final_dist matrix after re-ranking, [num_query, num_gallery]
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"""
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query_num = query_feas.shape[0]
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all_num = query_num + gallery_feas.shape[0]
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if only_local:
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original_dist = local_distmat
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else:
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feat = paddle.concat([query_feas, gallery_feas])
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logger.info('using GPU to compute original distance')
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# L2 distance
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distmat = paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]) + \
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paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]).t()
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distmat = distmat.addmm(x=feat, y=feat.t(), alpha=-2.0, beta=1.0)
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original_dist = distmat.cpu().numpy()
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del feat
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if local_distmat is not None:
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original_dist = original_dist + local_distmat
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gallery_num = original_dist.shape[0]
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original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
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V = np.zeros_like(original_dist).astype(np.float16)
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initial_rank = np.argsort(original_dist).astype(np.int32)
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logger.info('starting re_ranking')
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for i in range(all_num):
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# k-reciprocal neighbors
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forward_k_neigh_index = initial_rank[i, :k1 + 1]
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backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
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fi = np.where(backward_k_neigh_index == i)[0]
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k_reciprocal_index = forward_k_neigh_index[fi]
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k_reciprocal_expansion_index = k_reciprocal_index
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for j in range(len(k_reciprocal_index)):
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candidate = k_reciprocal_index[j]
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candidate_forward_k_neigh_index = initial_rank[candidate, :int(
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np.around(k1 / 2)) + 1]
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candidate_backward_k_neigh_index = initial_rank[
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candidate_forward_k_neigh_index, :int(np.around(k1 / 2)) + 1]
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fi_candidate = np.where(
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candidate_backward_k_neigh_index == candidate)[0]
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candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
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fi_candidate]
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if len(
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np.intersect1d(candidate_k_reciprocal_index,
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k_reciprocal_index)) > 2 / 3 * len(
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candidate_k_reciprocal_index):
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k_reciprocal_expansion_index = np.append(
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k_reciprocal_expansion_index, candidate_k_reciprocal_index)
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k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
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weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
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V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
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original_dist = original_dist[:query_num, ]
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if k2 != 1:
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V_qe = np.zeros_like(V, dtype=np.float16)
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for i in range(all_num):
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V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
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V = V_qe
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del V_qe
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del initial_rank
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invIndex = []
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for i in range(gallery_num):
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invIndex.append(np.where(V[:, i] != 0)[0])
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jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
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for i in range(query_num):
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temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
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indNonZero = np.where(V[i, :] != 0)[0]
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indImages = [invIndex[ind] for ind in indNonZero]
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for j in range(len(indNonZero)):
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temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
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V[i, indNonZero[j]], V[indImages[j], indNonZero[j]])
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jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
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final_dist = jaccard_dist * (1 - lambda_value
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) + original_dist * lambda_value
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del original_dist
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del V
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del jaccard_dist
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final_dist = final_dist[:query_num, query_num:]
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final_dist = paddle.to_tensor(final_dist)
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return final_dist
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@ -43,15 +43,16 @@ class TopkAcc(nn.Layer):
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class mAP(nn.Layer):
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def __init__(self):
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def __init__(self, descending=True):
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super().__init__()
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=True)
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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@ -87,15 +88,16 @@ class mAP(nn.Layer):
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class mINP(nn.Layer):
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def __init__(self):
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def __init__(self, descending=True):
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super().__init__()
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=True)
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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@ -130,12 +132,13 @@ class mINP(nn.Layer):
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class Recallk(nn.Layer):
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def __init__(self, topk=(1, 5)):
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def __init__(self, topk=(1, 5), descending=True):
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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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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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@ -143,7 +146,7 @@ class Recallk(nn.Layer):
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#get cmc
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=True)
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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@ -175,12 +178,13 @@ class Recallk(nn.Layer):
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class Precisionk(nn.Layer):
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def __init__(self, topk=(1, 5)):
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def __init__(self, topk=(1, 5), descending=True):
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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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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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@ -188,7 +192,7 @@ class Precisionk(nn.Layer):
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#get cmc
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=True)
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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