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mAP_test.py
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mAP_test.py
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"""
trp
epoch train test
399 45.97
499 55.39 26.88
599 31.73
799 61.06 32.7
1999 70.01 40.29
2200 74.23 40.56
2400 73.63 38.75
3000 76.26 38.69
6000 42.10
trp-fc (no eval)
400 64.04 36.45
1600 77.31 40.30
2200 40.26
3000 40.33
4000 40.71
5000 43.41
adv-fc-0
3-92 33.47
4-52 31.74
5-1 49.36 33.1
adv-fc (pretrained on fc no eval, train with eval)
eval no eval
0-90 40.28 45.65
=================================================
trp-fc
3999 42.02
adv-0
4-49 26.20
7-76 26
=================================================
trp-fc-new (new net struct, with margin)
3099 55.95
4599 56.77
"""
import torch
from torch.autograd.variable import Variable
import numpy as np
from model.discriminator import Discriminator
from model.gv2model import Inceptionv2
from model.selector import Selector
from data_input.read_D import Dataset_origin, DatasetFeatureHHH
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import os
import time
import torch.nn.functional as F
os.environ['CUDA_VISIBLE_DEVICES'] = '7'
time.sleep(1.5)
def cos_similarity(q, d):
return F.cosine_similarity(q, d, dim=1)
def AP(q_id, doc_id_list, doc_score_tensor):
sorted_score, sorted_index = torch.sort(doc_score_tensor, dim=0, descending=True)
eql = np.array([q_id] * len(doc_id_list)) == np.array(doc_id_list)
eql = np.array(eql).astype(np.float32)
eql_tensor = torch.FloatTensor(eql).cuda()
ranked_eql_tensor = torch.gather(eql_tensor, 0, sorted_index)
numerator = torch.cumsum(ranked_eql_tensor, 0)
denominator = torch.cumsum(torch.ones(ranked_eql_tensor.size()).cuda(), 0)
AP = torch.sum(numerator / denominator * ranked_eql_tensor) / torch.sum(ranked_eql_tensor)
# if sorted_score.is_cuda:
# sorted_score = sorted_score.cpu()
# sorted_score = sorted_score.numpy()
#
# [length, ] = sorted_score.shape
# hit_cnt = 0
# total_hit = doc_id_list.count(q_id)
# img_index = 0
# point = []
# for i in range(length):
# img_index += 1
# if q_id == doc_id_list[sorted_index[i]]:
# hit_cnt += 1
# point.append(hit_cnt / img_index)
# if hit_cnt >= total_hit:
# break
#
# AP = np.sum(np.array(point)) / total_hit
return AP
def mAP(dis: Discriminator, dataset_fea: DatasetFeatureHHH, batch_size):
AP_list = []
for idx in range(len(dataset_fea.query)):
[q_id, q_fea] = dataset_fea.get_query(idx) # prop person
doc_id_list = []
doc_score_tensor = torch.FloatTensor().cuda()
# test all persons
dl = DataLoader(dataset_fea, batch_size, shuffle=False)
for doc_ids, doc in dl:
# query
query = torch.unsqueeze(q_fea, 0)
query = Variable(query.cuda(), volatile=True)
# doc
doc = Variable(doc.cuda(), volatile=True)
doc_scores = dis(query, doc) # metric learning similarity
# doc_scores = cos_similarity(query, doc) # cosine similarity
doc_scores = doc_scores.data
doc_id_list.extend(doc_ids)
doc_score_tensor = torch.cat([doc_score_tensor, doc_scores])
# doc_score_tensor.append(doc_scores.detach().cpu().data.numpy())
# print(len(doc_id_list))
AP_ = AP(q_id, doc_id_list, doc_score_tensor)
AP_list.append(AP_)
print(idx, AP_ * 100)
AP_list = np.array(AP_list)
mAP = np.mean(AP_list)
print(mAP * 100)
return mAP
def extract_all_feature(batch_size, feature_extractor):
# train_or_test = 'train'
train_or_test = 'test'
dataset = Dataset_origin(data_dir="/home/nhli/SharedSSD/PersonReID/MARS/bbox_%s/" % train_or_test)
dl = DataLoader(dataset, batch_size, shuffle=False, num_workers=1, drop_last=False)
dataset_fea = DatasetFeatureHHH()
for doc_ids, doc in dl:
doc = Variable(doc.cuda(), volatile=True)
feature = feature_extractor(doc)
dataset_fea.insert(doc_ids, feature.data)
dataset_fea.build()
return dataset_fea
# class Net(torch.nn.Module):
# def __init__(self, fea_ex, dis):
# super(Net, self).__init__()
# self.fea_ex = fea_ex
# self.dis = dis
#
# def forward(self, query_: torch.FloatTensor, doc_: torch.FloatTensor):
# query_ = Variable(query_, volatile=True)
# doc_ = Variable(doc_, volatile=True)
# fea_q = self.fea_ex(query_) # bs =1
# fea_d = self.fea_ex(doc_) # bs>1
#
# res = self.dis(fea_q, fea_d) # expand fea_q inside
# return res.data # return Tensor
if __name__ == '__main__':
# config
batch_size = 512
is_cuda = True
save_step = 100
gv2_model_path = "/home/nhli/PycharmProj/ReIDGAN_/params/record-step-12685-model.pkl"
# trp-fc-new
dis_model_path = "/home/nhli/PycharmProj/ReIDGAN_/workdir/trp-fc-new/save-dis-4599"
# adv-0
# dis_model_path = "/home/nhli/PycharmProj/ReIDGAN_/workdir/adv-0/save-sel_4-49"
# build graph
feature_extractor = Inceptionv2()
fea_ext_dict = torch.load(gv2_model_path)
fea_ext_dict.pop('classifier.weight')
fea_ext_dict.pop('classifier.bias')
fea_ext_dict.pop('criterion2.center_feature')
fea_ext_dict.pop('criterion2.all_labels')
feature_extractor.load_state_dict(fea_ext_dict)
dis = Discriminator()
# dis = Selector()
dis.load_state_dict(torch.load(dis_model_path))
# eval
dis.eval()
feature_extractor.eval()
if is_cuda:
dis.cuda()
feature_extractor.cuda()
dataset_feature = extract_all_feature(batch_size, feature_extractor)
mAP = mAP(dis, dataset_feature, batch_size)