209 lines
7.9 KiB
Python
209 lines
7.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
import os
|
|
import sys
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from .trainer import Trainer
|
|
from ppcls.utils import logger
|
|
from ppcls.data import build_dataloader
|
|
|
|
|
|
class TrainerReID(Trainer):
|
|
def __init__(self, config, mode="train"):
|
|
super().__init__(config, mode)
|
|
|
|
self.gallery_dataloader = build_dataloader(self.config["DataLoader"],
|
|
"Gallery", self.device)
|
|
|
|
self.query_dataloader = build_dataloader(self.config["DataLoader"],
|
|
"Query", self.device)
|
|
|
|
@paddle.no_grad()
|
|
def eval(self, epoch_id=0):
|
|
output_info = dict()
|
|
self.model.eval()
|
|
print_batch_step = self.config["Global"]["print_batch_step"]
|
|
|
|
# step1. build gallery
|
|
gallery_feas, gallery_img_id, gallery_camera_id = self._cal_feature(
|
|
name='gallery')
|
|
query_feas, query_img_id, query_camera_id = self._cal_feature(
|
|
name='query')
|
|
|
|
# step2. do evaluation
|
|
if "num_split" in self.config["Global"]:
|
|
num_split = self.config["Global"]["num_split"]
|
|
else:
|
|
num_split = 1
|
|
fea_blocks = paddle.split(query_feas, num_or_sections=1)
|
|
|
|
total_similarities_matrix = None
|
|
|
|
for block_fea in fea_blocks:
|
|
similarities_matrix = paddle.matmul(
|
|
block_fea, gallery_feas, transpose_y=True)
|
|
if total_similarities_matrix is None:
|
|
total_similarities_matrix = similarities_matrix
|
|
else:
|
|
total_similarities_matrix = paddle.concat(
|
|
[total_similarities_matrix, similarities_matrix])
|
|
|
|
# distmat = (1 - total_similarities_matrix).numpy()
|
|
q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
|
|
g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
|
|
if query_camera_id is not None and gallery_camera_id is not None:
|
|
q_camids = query_camera_id.numpy().reshape(
|
|
(query_camera_id.shape[0]))
|
|
g_camids = gallery_camera_id.numpy().reshape(
|
|
(gallery_camera_id.shape[0]))
|
|
max_rank = 50
|
|
|
|
num_q, num_g = total_similarities_matrix.shape
|
|
if num_g < max_rank:
|
|
max_rank = num_g
|
|
print('Note: number of gallery samples is quite small, got {}'.
|
|
format(num_g))
|
|
|
|
# indices = np.argsort(distmat, axis=1)
|
|
indices = paddle.argsort(
|
|
total_similarities_matrix, axis=1, descending=True).numpy()
|
|
|
|
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
|
|
|
|
# compute cmc curve for each query
|
|
all_cmc = []
|
|
all_AP = []
|
|
all_INP = []
|
|
num_valid_q = 0. # number of valid query
|
|
for q_idx in range(num_q):
|
|
# get query pid and camid
|
|
q_pid = q_pids[q_idx]
|
|
q_camid = q_camids[q_idx]
|
|
|
|
# remove gallery samples that have the same pid and camid with query
|
|
order = indices[q_idx]
|
|
if query_camera_id is not None and gallery_camera_id is not None:
|
|
remove = (g_pids[order] == q_pid) & (
|
|
g_camids[order] == q_camid)
|
|
else:
|
|
remove = g_pids[order] == q_pid
|
|
keep = np.invert(remove)
|
|
|
|
# compute cmc curve
|
|
raw_cmc = matches[q_idx][
|
|
keep] # binary vector, positions with value 1 are correct matches
|
|
if not np.any(raw_cmc):
|
|
# this condition is true when query identity does not appear in gallery
|
|
continue
|
|
|
|
cmc = raw_cmc.cumsum()
|
|
|
|
pos_idx = np.where(raw_cmc == 1)
|
|
max_pos_idx = np.max(pos_idx)
|
|
inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)
|
|
all_INP.append(inp)
|
|
|
|
cmc[cmc > 1] = 1
|
|
|
|
all_cmc.append(cmc[:max_rank])
|
|
num_valid_q += 1.
|
|
|
|
# compute average precision
|
|
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
|
|
num_rel = raw_cmc.sum()
|
|
tmp_cmc = raw_cmc.cumsum()
|
|
tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
|
|
tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
|
|
AP = tmp_cmc.sum() / num_rel
|
|
all_AP.append(AP)
|
|
assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
|
|
|
|
all_cmc = np.asarray(all_cmc).astype(np.float32)
|
|
all_cmc = all_cmc.sum(0) / num_valid_q
|
|
|
|
mAP = np.mean(all_AP)
|
|
mINP = np.mean(all_INP)
|
|
logger.info(
|
|
"[Eval][Epoch {}]: mAP: {:.5f}, mINP: {:.5f},rank_1: {:.5f}, rank_5: {:.5f}"
|
|
.format(epoch_id, mAP, mINP, all_cmc[0], all_cmc[4]))
|
|
return mAP
|
|
|
|
def _cal_feature(self, name='gallery'):
|
|
all_feas = None
|
|
all_image_id = None
|
|
all_camera_id = None
|
|
if name == 'gallery':
|
|
dataloader = self.gallery_dataloader
|
|
elif name == 'query':
|
|
dataloader = self.query_dataloader
|
|
else:
|
|
raise RuntimeError("Only support gallery or query dataset")
|
|
|
|
has_cam_id = False
|
|
for idx, batch in enumerate(dataloader(
|
|
)): # load is very time-consuming
|
|
batch = [paddle.to_tensor(x) for x in batch]
|
|
batch[1] = batch[1].reshape([-1, 1])
|
|
if len(batch) == 3:
|
|
has_cam_id = True
|
|
batch[2] = batch[2].reshape([-1, 1])
|
|
out = self.model(batch[0], batch[1])
|
|
batch_feas = out["features"]
|
|
|
|
# do norm
|
|
if self.config["Global"].get("feature_normalize", True):
|
|
feas_norm = paddle.sqrt(
|
|
paddle.sum(paddle.square(batch_feas), axis=1,
|
|
keepdim=True))
|
|
batch_feas = paddle.divide(batch_feas, feas_norm)
|
|
|
|
batch_feas = batch_feas
|
|
batch_image_labels = batch[1]
|
|
if has_cam_id:
|
|
batch_camera_labels = batch[2]
|
|
|
|
if all_feas is None:
|
|
all_feas = batch_feas
|
|
if has_cam_id:
|
|
all_camera_id = batch[2]
|
|
all_image_id = batch[1]
|
|
else:
|
|
all_feas = paddle.concat([all_feas, batch_feas])
|
|
all_image_id = paddle.concat([all_image_id, batch[1]])
|
|
if has_cam_id:
|
|
all_camera_id = paddle.concat([all_camera_id, batch[2]])
|
|
|
|
if paddle.distributed.get_world_size() > 1:
|
|
feat_list = []
|
|
img_id_list = []
|
|
cam_id_list = []
|
|
paddle.distributed.all_gather(feat_list, all_feas)
|
|
paddle.distributed.all_gather(img_id_list, all_image_id)
|
|
all_feas = paddle.concat(feat_list, axis=0)
|
|
all_image_id = paddle.concat(img_id_list, axis=0)
|
|
if has_cam_id:
|
|
paddle.distributed.all_gather(cam_id_list, all_camera_id)
|
|
all_camera_id = paddle.concat(cam_id_list, axis=0)
|
|
|
|
logger.info("Build {} done, all feat shape: {}, begin to eval..".
|
|
format(name, all_feas.shape))
|
|
return all_feas, all_image_id, all_camera_id
|