def on_epoch_begin(self):
self.batch_nb = 0
self.current_epoch += 1
self.t0 = time.time()
self.running_loss.reset()
self.tng_prefetcher = data_prefetcher(self.tng_dataloader)
def inference_with_distmat(
cfg,
test_dataloader,
num_query,
distmat,
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
pids, camids = [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
# query
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def get_distmat(self):
m = self.model.eval()
feats = []
pids = []
camids = []
val_prefetcher = data_prefetcher(self.val_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = m(img.cuda())
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats)
qf = feats[:self.num_query]
gf = feats[self.num_query:]
self.q_pids = np.asarray(pids[:self.num_query])
self.g_pids = np.asarray(pids[self.num_query:])
self.q_camids = np.asarray(camids[:self.num_query])
self.g_camids = np.asarray(camids[self.num_query:])
# Cosine distance
distmat = torch.mm(qf, gf.t())
self.distmat = distmat.cpu().numpy()
self.indices = np.argsort(-self.distmat, axis=1)
self.matches = (
self.g_pids[self.indices] == self.q_pids[:, np.newaxis]).astype(
np.int32)
def test(self):
# convert to eval mode
self.model.eval()
feats, pids, camids = [], [], []
val_prefetcher = data_prefetcher(self.val_dataloader, self.cfg)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = self.model(img)
if isinstance(feat, tuple):
feats.append(feat[0])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
####
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:self.num_query]
q_pids = np.asarray(pids[:self.num_query])
q_camids = np.asarray(camids[:self.num_query])
# gallery
gf = feats[self.num_query:]
g_pids = np.asarray(pids[self.num_query:])
g_camids = np.asarray(camids[self.num_query:])
# TODO: 添加rerank的测评结果
# m, n = qf.shape[0], gf.shape[0]
distmat = -torch.mm(qf, gf.t()).cpu().numpy()
# distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
# torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
# distmat.addmm_(1, -2, qf, gf.t())
# distmat = distmat.numpy()
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
self.logger.info(f"Test Results - Epoch: {self.current_epoch}")
self.logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self.logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
self.writer.add_scalar('rank1', cmc[0], self.global_step)
self.writer.add_scalar('mAP', mAP, self.global_step)
metric_dict = {'rank1': cmc[0], 'mAP': mAP}
# convert to train mode
self.model.train()
return metric_dict
def on_epoch_begin(self):
self.batch_nb = 0
self.current_epoch += 1
self.t0 = time.time()
self.running_loss.reset()
if 'InsDis' in list(self.cfg.SOLVER.LOSSTYPE):
self.tng_prefetcher = instance_prefetcher(self.tng_dataloader)
else:
self.tng_prefetcher = data_prefetcher(self.tng_dataloader)
def inference_no_rerank(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
#local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
distmat = -torch.mm(qf, gf.t()).cpu().numpy()
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
index = np.argsort(distmat, axis=1) # from small to large
new_gallery_index = np.unique(index[:, :top_k].reshape(-1))
print('new_gallery_index', len(new_gallery_index))
return new_gallery_index
def test(self):
# convert to eval mode
self.model.eval()
metric_dict = list()
for val_dataset_name, val_dataloader, num_query in zip(self.cfg.DATASETS.TEST_NAMES, self.val_dataloader_collection, self.num_query_len_collection):
feats,pids,camids = [],[],[]
val_prefetcher = data_prefetcher(val_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
_, feat = self.model(img)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
feats = torch.cat(feats, dim=0)
if self.cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# m, n = qf.shape[0], gf.shape[0]
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
# torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
# distmat.addmm_(1, -2, qf, gf.t())
# distmat = distmat.numpy()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
self.logger.info(f"Test Results on {val_dataset_name} - Epoch: {self.current_epoch}")
self.logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
self.logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
self.writer.add_scalar('rank1', cmc[0], self.global_step)
self.writer.add_scalar('mAP', mAP, self.global_step)
metric_dict.append({'rank1': cmc[0], 'mAP': mAP})
# convert to train mode
self.model.train()
return metric_dict[0]
def inference(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# euclidean distance
# distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
# torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
# distmat.addmm_(1, -2, qf, gf.t())
# distmat = distmat.numpy()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
def inference_flipped_deprecated(
cfg,
model,
test_dataloader,
num_query,
theta=0.95,
use_local_feature=False # 是否使用local特征
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
g_feats, l_feats, gf_feats, lf_feats, pids, camids = [], [], [], [], [], []
val_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
g_feat, bn_gf, lf, l_feat = model(img)
gf_feat, bn_gff, lff, lf_feat = model(torch.flip(img, [3]))
g_feats.append(g_feat.data.cpu())
l_feats.append(l_feat.data.cpu())
gf_feats.append(gf_feat.data.cpu())
lf_feats.append(lf_feat.data.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
g_feats = torch.cat(g_feats, dim=0)
l_feats = torch.cat(l_feats, dim=0)
gf_feats = torch.cat(gf_feats, dim=0)
lf_feats = torch.cat(lf_feats, dim=0)
if cfg.TEST.NORM:
g_feats = F.normalize(g_feats, p=2, dim=1)
gf_feats = F.normalize(gf_feats, p=2, dim=1)
# query
qf = g_feats[:num_query]
lqf = l_feats[:num_query]
qff = gf_feats[:num_query]
lqff = lf_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = g_feats[num_query:]
lgf = l_feats[num_query:]
gff = gf_feats[num_query:]
lgff = lf_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# calculate the global distance
scores, indices, dist_mats = [], [], []
#use_local_feature = True
if use_local_feature:
logger.info("--------computing local features ...--------")
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.numpy(),
lqf.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.numpy(),
lgf.numpy(),
aligned=True)
local_dist = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
logger.info("--------computing flipped local features ...--------")
lqff = lqff.permute(0, 2, 1)
lgff = lgff.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqff.numpy(),
lgff.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqff.numpy(),
lqff.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgff.numpy(),
lgff.numpy(),
aligned=True)
local_dist_flip = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_dist = None
local_dist_flip = None
logger.info("use reranking")
#for theta in thetas:
if True:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist,
theta_value=theta,
only_local=False)
distmat_flip = re_ranking(qff,
gff,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist_flip,
theta_value=theta,
only_local=False)
# 合并距离
distmat = (distmat + distmat_flip) / 2
score = distmat
index = np.argsort(score, axis=1) # from small to large
scores.append(score)
indices.append(index)
dist_mats.append(distmat)
return scores, indices, dist_mats
def inference_aligned_flipped(cfg, model, test_dataloader, num_query,
use_local_feature, use_rerank,
use_cross_feature):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing aligned with flipping")
model.eval()
pids, camids = [], []
gfs, bn_gfs, lfs, bn_lfs = [], [], [], []
gfs_flipped, bn_gfs_flipped, lfs_flipped, bn_lfs_flipped = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
ret = model(img)
ret_flip = model(torch.flip(img, [3]))
if len(ret) == 4:
gf, bn_gf, lf, bn_lf = ret
gff, bn_gff, lff, bn_lff = ret_flip
elif len(ret) == 2:
gf, bn_gf = ret
gff, bn_gff = ret_flip
lf, bn_lf = None, None
lff, bn_lff = None, None
elif ret is not tuple:
gf = bn_gf = ret
gff = bn_gff = ret_flip
lf, bn_lf = None, None
lff, bn_lff = None, None
else:
#print('ret', ret.size())
raise Exception("Unknown model returns, length = ", len(ret))
# 4 features
gfs.append(gf.cpu())
bn_gfs.append(bn_gf.cpu())
if use_local_feature:
lfs.append(lf.cpu())
bn_lfs.append(bn_lf.cpu())
# 4 features flipped
gfs_flipped.append(gff.cpu())
bn_gfs_flipped.append(bn_gff.cpu())
if use_local_feature:
lfs_flipped.append(lff.cpu())
bn_lfs_flipped.append(bn_lff.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
distmat1 = distmat2 = None
logger.info(
f"use_cross_feature = {use_cross_feature}, use_local_feature = {use_local_feature}, use_rerank = {use_rerank}"
)
if use_cross_feature:
logger.info("Computing distmat with bn_gf (+ lf)")
distmat2 = compute_distmat(cfg,
num_query,
bn_gfs,
bn_gfs_flipped,
lfs,
lfs_flipped,
theta=0.45,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat2
#distmat = (distmat1 + distmat2) / 2
else:
logger.info("Computing distmat with gf (+ bn_lf)")
distmat1 = compute_distmat(cfg,
num_query,
gfs,
gfs_flipped,
bn_lfs,
bn_lfs_flipped,
theta=0.95,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat1
score = distmat
index = np.argsort(score, axis=1) # from small to large
#index = mem_saving_argsort(score) # better for large matrix ?
return distmat, index, distmat1, distmat2
def inference(
cfg,
model,
test_dataloader_collection,
num_query_collection,
is_vis=False,
test_collection=None,
use_mask=True,
num_parts=10,
mask_image=False
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
adj = torch.from_numpy(coarse_adj_npy).float()
idx = -1
for test_dataset_name, test_dataloader, num_query in zip(cfg.DATASETS.TEST_NAMES, test_dataloader_collection, num_query_collection):
idx += 1
feats, pids, camids = [], [], []
if use_mask:
test_prefetcher = data_prefetcher_mask(test_dataloader)
else:
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
if use_mask:
img, mask, pid, camid = batch
adj_batch = adj.repeat(img.size(0), 1, 1)
with torch.no_grad():
output = model(img, img, mask, adj_batch)
# feat = output[1]
# feat = output[3]
feat = torch.cat([output[1], output[3]], dim=1)
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
distmat = torch.mm(qf, gf.t()).cpu().numpy()
# euclidean distance
# distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
# torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
# distmat.addmm_(1, -2, qf, gf.t())
# distmat = distmat.numpy()
cmc, mAP = evaluate(-distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"Results on {test_dataset_name} : ")
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
if is_vis:
query_rand = 10
topK = 10
is_save_all = True
query_rand_idx = range(0, num_query) if is_save_all else random.sample(range(0, num_query), query_rand)
print(f'|-------- Randomly saving top-{topK} results of {len(query_rand_idx)} queries for {test_dataset_name} --------|')
qf_rand = qf[query_rand_idx]
q_pids_rand = q_pids[query_rand_idx]
q_camids_rand = q_camids[query_rand_idx]
q_items = test_collection[idx][:num_query]
q_items_rand = list()
for i in query_rand_idx:
q_items_rand.append(q_items[i])
g_items = test_collection[idx][num_query:]
distmat_rand = torch.mm(qf_rand, gf.t()).cpu().numpy()
distmat_rand = -distmat_rand
indices = np.argsort(distmat_rand, axis=1)
matches = (g_pids[indices] == q_pids_rand[:, np.newaxis]).astype(np.int32)
save_img_size = (256, 256)
if test_dataset_name == 'market1501':
save_img_size = (128, 256)
for q_idx in range(len(query_rand_idx)):
savefilename = ''
# get query pid and camid
q_path = q_items_rand[q_idx][0]
q_pid = q_items_rand[q_idx][1]
q_camid = q_items_rand[q_idx][2]
savefilename += 'q-'+q_path.split('/')[-1]+'_g'
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
keep = np.invert(remove)
print('Query Path : ', q_path)
print('Result idx : ', order[:topK])
img_list = list()
q_img = cv2.imread(q_path)
q_img = cv2.resize(q_img, save_img_size)
cv2.rectangle(q_img, (0,0), save_img_size, (255,0,0), 4)
img_list.append(q_img)
for g_idx in order[:topK]:
g_img = cv2.imread(g_items[g_idx][0])
g_img = cv2.resize(g_img, save_img_size)
if q_pid == g_items[g_idx][1] and q_camid == g_items[g_idx][2]:
cv2.rectangle(g_img, (0,0), save_img_size, (255,255,0), 4)
elif q_pid == g_items[g_idx][1] and q_camid != g_items[g_idx][2]:
cv2.rectangle(g_img, (0,0), save_img_size, (0,255,0), 4)
else:
cv2.rectangle(g_img, (0,0), save_img_size, (0,0,255), 4)
img_list.append(g_img)
savefilename += '-'+str(g_items[g_idx][1])
pic = np.concatenate(img_list, 1)
picsavedir = os.path.join(cfg.OUTPUT_DIR, '-'.join(cfg.DATASETS.TEST_NAMES), 'examples', test_dataset_name)
if not os.path.exists(picsavedir): os.makedirs(picsavedir)
savefilepath = os.path.join(picsavedir, savefilename+'.jpg')
cv2.imwrite(savefilepath, pic)
print('Save example picture to ', savefilepath)
def update_bn_stats(model, data_loader, num_iters: int = 200):
"""
Recompute and update the batch norm stats to make them more precise. During
training both BN stats and the weight are changing after every iteration, so
the running average can not precisely reflect the actual stats of the
current model.
In this function, the BN stats are recomputed with fixed weights, to make
the running average more precise. Specifically, it computes the true average
of per-batch mean/variance instead of the running average.
Args:
model (nn.Module): the model whose bn stats will be recomputed.
Note that:
1. This function will not alter the training mode of the given model.
Users are responsible for setting the layers that needs
precise-BN to training mode, prior to calling this function.
2. Be careful if your models contain other stateful layers in
addition to BN, i.e. layers whose state can change in forward
iterations. This function will alter their state. If you wish
them unchanged, you need to either pass in a submodule without
those layers, or backup the states.
data_loader (iterator): an iterator. Produce data as inputs to the model.
num_iters (int): number of iterations to compute the stats.
"""
bn_layers = get_bn_modules(model)
if len(bn_layers) == 0:
return
# In order to make the running stats only reflect the current batch, the
# momentum is disabled.
# bn.running_mean = (1 - momentum) * bn.running_mean + momentum * batch_mean
# Setting the momentum to 1.0 to compute the stats without momentum.
momentum_actual = [bn.momentum for bn in bn_layers]
for bn in bn_layers:
bn.momentum = 1.0
# Note that running_var actually means "running average of variance"
running_mean = [torch.zeros_like(bn.running_mean) for bn in bn_layers]
running_var = [torch.zeros_like(bn.running_var) for bn in bn_layers]
ind = 0
num_epoch = num_iters // len(data_loader) + 1
for _ in range(num_epoch):
prefetcher = data_prefetcher(data_loader)
batch = prefetcher.next()
while batch[0] is not None:
model(batch[0], batch[1])
for i, bn in enumerate(bn_layers):
# Accumulates the bn stats.
running_mean[i] += (bn.running_mean - running_mean[i]) / (ind +
1)
running_var[i] += (bn.running_var - running_var[i]) / (ind + 1)
# We compute the "average of variance" across iterations.
if ind == (num_iters - 1):
print(
f"update_bn_stats is running for {num_iters} iterations.")
break
ind += 1
batch = prefetcher.next()
for i, bn in enumerate(bn_layers):
# Sets the precise bn stats.
bn.running_mean = running_mean[i]
bn.running_var = running_var[i]
bn.momentum = momentum_actual[i]
def inference_flipped(
cfg,
model,
test_dataloader,
num_query,
thetas,
use_local_feature=True # 是否使用local特征
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, feats_flipped, pids, camids = [], [], [], []
local_feats, local_feats_flipped = [], []
local_feats2, local_feats2_flipped = [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
feat_flipped = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
feats_flipped.append(feat_flipped[0])
if len(feat[1]) > 1:
local_feats.append(feat[1][0])
local_feats2.append(feat[1][1])
local_feats_flipped.append(feat_flipped[1][0])
local_feats2_flipped.append(feat_flipped[1][1])
else:
local_feats.append(feat[1])
local_feats_flipped.append(feat_flipped[1])
else:
feats.append(feat)
feats_flipped.append(feat_flipped)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
feats_flipped = torch.cat(feats_flipped, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
local_feats_flipped = torch.cat(local_feats_flipped, dim=0)
if len(local_feats2) > 0:
local_feats2 = torch.cat(local_feats2, dim=0)
local_feats2_flipped = torch.cat(local_feats2_flipped, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
feats_flipped = F.normalize(feats_flipped, p=2, dim=1)
# query
qf = feats[:num_query]
qf_flipped = feats_flipped[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
lqf_flipped = local_feats_flipped[:num_query]
if len(local_feats2) > 0:
lqf2 = local_feats2[:num_query]
lqf2_flipped = local_feats2_flipped[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
gf_flipped = feats_flipped[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
lgf_flipped = local_feats_flipped[num_query:]
if len(local_feats2) > 0:
lgf2 = local_feats2[num_query:]
lgf2_flipped = local_feats2_flipped[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
# distmat = torch.mm(qf, gf.t()).cpu().numpy()
if len(local_feats) > 0:
local_distmat = compute_local_distmat(lqf, lgf)
local_distmat_flipped = compute_local_distmat(lqf_flipped, lgf_flipped)
else:
local_distmat = None
local_distmat_flipped = None
if len(local_feats2) > 0:
local_distmat2 = compute_local_distmat(lqf2, lgf2)
local_distmat2_flipped = compute_local_distmat(lqf2_flipped,
lgf2_flipped)
else:
local_distmat2 = None
local_distmat2_flipped = None
# use reranking
logger.info("use reranking")
theta = 0.95
scores, indices, dist_mats = [], [], []
for theta2 in thetas:
distmat = re_ranking_v3(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta,
local_distmat2=local_distmat2,
theta_value2=theta2,
only_local=False)
distmat_flip = re_ranking_v3(qf_flipped,
gf_flipped,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat_flipped,
theta_value=theta,
local_distmat2=local_distmat2_flipped,
theta_value2=theta2,
only_local=False)
# 合并距离
distmat = (distmat + distmat_flip) / 2
score = distmat
index = np.argsort(score, axis=1) # from small to large
scores.append(score)
indices.append(index)
dist_mats.append(distmat)
return scores, indices, dist_mats
def inference_aligned_flipped(cfg, model, test_dataloader, num_query,
use_local_feature, use_rerank,
use_cross_feature):
"""
inference an aligned net with flipping and two pairs of global feature and local feature
:param cfg:
:param model:
:param test_dataloader:
:param num_query:
:return:
"""
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing aligned with flipping")
model.eval()
pids, camids = [], []
gfs, bn_gfs, lfs, bn_lfs = [], [], [], []
gfs_flipped, bn_gfs_flipped, lfs_flipped, bn_lfs_flipped = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
ret = model(img)
ret_flip = model(torch.flip(img, [3]))
if len(ret) == 4:
gf, bn_gf, lf, bn_lf = ret
gff, bn_gff, lff, bn_lff = ret_flip
elif len(ret) == 2:
gf, bn_gf = ret
gff, bn_gff = ret_flip
lf, bn_lf = None, None
lff, bn_lff = None, None
elif ret is not tuple:
bn_gf = ret[:, :2048]
gf = ret[:, 2048:]
gff = ret_flip[:, :2048]
bn_gff = ret_flip[:, 2048:]
lf, bn_lf = None, None
lff, bn_lff = None, None
else:
# print('ret', ret.size())
raise Exception("Unknown model returns, length = ", len(ret))
# 4 features
gfs.append(gf.cpu())
bn_gfs.append(bn_gf.cpu())
if use_local_feature:
if use_cross_feature:
lfs.append(lf.cpu())
else:
bn_lfs.append(bn_lf.cpu())
# 4 features flipped
gfs_flipped.append(gff.cpu())
bn_gfs_flipped.append(bn_gff.cpu())
if use_local_feature:
if use_cross_feature:
lfs_flipped.append(lff.cpu())
else:
bn_lfs_flipped.append(bn_lff.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
logger.info(
f"use_local_feature = {use_local_feature}, use_rerank = {use_rerank}")
logger.info("Computing distmat with bn_gf")
distmat2 = compute_distmat(cfg,
num_query,
bn_gfs,
bn_gfs_flipped,
lfs,
lfs_flipped,
theta=0.45,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
logger.info("Computing distmat with gf + bn_lf")
distmat1 = compute_distmat(cfg,
num_query,
gfs,
gfs_flipped,
bn_lfs,
bn_lfs_flipped,
theta=0.95,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
for theta in np.linspace(0, 1, 21):
#theta = 0.55
distmat = distmat1 * (1 - theta) + distmat2 * theta
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"theta: {theta:.2%} mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_flipped(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, feats_flipped, pids, camids = [], [], [], []
local_feats, local_feats_flipped = [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
feat_flipped = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
local_feats.append(feat[1])
feats_flipped.append(feat_flipped[0])
local_feats_flipped.append(feat_flipped[1])
else:
feats.append(feat)
feats_flipped.append(feat_flipped)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
feats_flipped = torch.cat(feats_flipped, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
local_feats_flipped = torch.cat(local_feats_flipped, dim=0)
#print('feats_flipped', len(feats_flipped), feats_flipped[0])
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
feats_flipped = F.normalize(feats_flipped, p=2, dim=1)
# query
qf = feats[:num_query]
qf_flipped = feats_flipped[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
lqf_flipped = local_feats_flipped[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
gf_flipped = feats_flipped[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
lgf_flipped = local_feats_flipped[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
#distmat = torch.mm(qf, gf.t()).cpu().numpy()
if len(local_feats) > 0:
#if True:
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
# flipped
lqf = lqf_flipped.permute(0, 2, 1)
lgf = lgf_flipped.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat_flipped = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_theta = True
if search_theta:
best_score = 0
#for theta in np.linspace(0.9, 1.0, 11):
for theta in np.linspace(0, 1.0, 21):
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta,
only_local=False) # (current best)
distmat_flipped = re_ranking(qf_flipped,
gf_flipped,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat_flipped,
theta_value=theta,
only_local=False) # (current best)
distmat = (distmat + distmat_flipped) / 2
#cmc, mAP = evaluate(distmat + distmat_flipped, q_pids, g_pids, q_camids, g_camids)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
score = (cmc[0] + mAP) / 2
print('theta', np.around(theta, 2), 'r1, mAP, score',
np.around(cmc[0], 4), np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = theta
best_distmat = distmat
# saving
strtime = time.strftime("%Y%m%d_%H%M%S", time.localtime())
if abs(theta - 0.95) < 1e-4:
# saving dist_mats
f = h5py.File(
'dist_mats/val_%s_%s_t0.95_flip.h5' %
(cfg.MODEL.NAME, strtime), 'w')
f.create_dataset('dist_mat', data=distmat, compression='gzip')
f.close()
print('Best Param', best_param)
distmat = best_distmat
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.95) #(current best)
distmat_flipped = re_ranking(qf_flipped,
gf_flipped,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat_flipped,
theta_value=0.95,
only_local=False) # (current best)
distmat = (distmat + distmat_flipped) / 2
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_aligned(
cfg,
model,
test_dataloader,
num_query,
):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
g_feats, l_feats, pids, camids = [], [], [], []
val_prefetcher = data_prefetcher(test_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
g_feat, l_feat = model(img)
#g_feat, l_feat = model(torch.flip(img, [3])) # better
g_feats.append(g_feat.data.cpu())
l_feats.append(l_feat.data.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
g_feats = torch.cat(g_feats, dim=0)
l_feats = torch.cat(l_feats, dim=0)
if cfg.TEST.NORM:
g_feats = F.normalize(g_feats, p=2, dim=1)
# query
qf = g_feats[:num_query]
lqf = l_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = g_feats[num_query:]
lgf = l_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# calculate the global distance
if True:
logger.info("--------use re-ranking--------")
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.numpy(),
lqf.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.numpy(),
lgf.numpy(),
aligned=True)
local_dist = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist,
theta_value=0.5,
only_local=False)
## theta hyer-patameters
# for theta in np.arange(0,1.1,0.1):
# distmat = re_ranking(qf,gf,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist,theta=theta,only_local=False)
# cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
# logger.info(f"mAP: {mAP:.1%}")
# for r in [1, 5, 10]:
# logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
# logger.info("Theta:{}; Score: {}".format(theta, (mAP+cmc[0])/2.))
#score = distmat
#index = np.argsort(score, axis=1) # from small to large
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference_flipped(cfg,
model,
test_dataloader,
num_query,
use_re_ranking=True,
distance_metric='global_local'):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
g_feats, l_feats, gf_feats, lf_feats, pids, camids = [], [], [], [], [], []
val_prefetcher = data_prefetcher(test_dataloader)
batch = val_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
g_feat, l_feat = model(img)
gf_feat, lf_feat = model(torch.flip(img, [3]))
g_feats.append(g_feat.data.cpu())
l_feats.append(l_feat.data.cpu())
gf_feats.append(gf_feat.data.cpu())
lf_feats.append(lf_feat.data.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = val_prefetcher.next()
g_feats = torch.cat(g_feats, dim=0)
l_feats = torch.cat(l_feats, dim=0)
gf_feats = torch.cat(gf_feats, dim=0)
lf_feats = torch.cat(lf_feats, dim=0)
if cfg.TEST.NORM:
g_feats = F.normalize(g_feats, p=2, dim=1)
gf_feats = F.normalize(gf_feats, p=2, dim=1)
# query
qf = g_feats[:num_query]
lqf = l_feats[:num_query]
qff = gf_feats[:num_query]
lqff = lf_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = g_feats[num_query:]
lgf = l_feats[num_query:]
gff = gf_feats[num_query:]
lgff = lf_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# calculate the global distance
if not use_re_ranking:
m, n = qf.shape[0], gf.shape[0]
global_distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
global_distmat.addmm_(1, -2, qf, gf.t())
global_distmat = global_distmat.numpy()
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
if distance_metric == 'global':
logger.info("--------use global features--------")
distmat = global_distmat
elif distance_metric == 'local':
logger.info("--------use local features--------")
distmat = local_distmat
elif distance_metric == 'global_local':
logger.info("--------use global and local features--------")
distmat = global_distmat + local_distmat
else:
logger.info("--------use re-ranking--------")
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.numpy(),
lqf.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.numpy(),
lgf.numpy(),
aligned=True)
local_dist = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
logger.info("--------use re-ranking flipped--------")
lqff = lqff.permute(0, 2, 1)
lgff = lgff.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqff.numpy(),
lgff.numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqff.numpy(),
lqff.numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgff.numpy(),
lgff.numpy(),
aligned=True)
local_dist_flipped = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
# for theta in np.arange(0.0,1.0,0.05):
# distmat = re_ranking(qf,gf,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist,theta=theta,only_local=False)
# distmat_flip = re_ranking(qff,gff,k1=6,k2=2,lambda_value=0.3,local_distmat=local_dist_flipped,theta=theta,only_local=False)
# distmat = distmat + distmat_flip
# cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
# logger.info(f"mAP: {mAP:.1%}")
# for r in [1, 5, 10]:
# logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
# logger.info("Theta:{}; Score: {}".format(theta, (mAP+cmc[0])/2.))
theta = 0.45
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist,
theta_value=theta,
only_local=False)
distmat_flip = re_ranking(qff,
gff,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_dist_flipped,
theta_value=theta,
only_local=False)
distmat = (distmat + distmat_flip) / 2
score = distmat
index = np.argsort(score, axis=1) # from small to large
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference(cfg, model, test_dataloader, num_query, thetas):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
local_feats = []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
if isinstance(feat, tuple):
feats.append(feat[0])
local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
if len(local_feats) > 0:
local_feats = F.normalize(local_feats, p=2, dim=1)
# query
qf = feats[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
# gallery
gf = feats[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
if len(local_feats) > 0:
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
#logger.info('Computing local_qg_distmat ...')
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
#logger.info('Computing local_qq_distmat ...')
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
#logger.info('Computing local_gg_distmat ...')
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_distmat = None
use_rerank = True
if use_rerank:
#thetas = [0.4, 0.5, 0.9, 0.95, 1.0]
scores, indices, dist_mats = [], [], []
logger.info("use reranking")
for theta in thetas:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta)
score = distmat
index = np.argsort(score, axis=1) # from small to large
scores.append(score)
indices.append(index)
dist_mats.append(distmat)
return scores, indices, dist_mats
else:
logger.info("No reranking")
distmat = -torch.mm(qf, gf.t()).cpu().numpy()
score = distmat
index = np.argsort(score, axis=1) # from small to large
return score, index
def inference_aligned(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
local_feats = []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if len(local_feats) > 0:
local_feats = torch.cat(local_feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# 局部特征是三维的，不做归一化 (对结果没影响)
#if len(local_feats) > 0:
# local_feats = F.normalize(local_feats, p=2, dim=1)
# query
qf = feats[:num_query]
if len(local_feats) > 0:
lqf = local_feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
if len(local_feats) > 0:
lgf = local_feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
if len(local_feats) > 0:
#if True:
# calculate the local distance
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_qg_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_qq_distmat = low_memory_local_dist(lqf.cpu().numpy(),
lqf.cpu().numpy(),
aligned=True)
local_gg_distmat = low_memory_local_dist(lgf.cpu().numpy(),
lgf.cpu().numpy(),
aligned=True)
local_distmat = np.concatenate([
np.concatenate([local_qq_distmat, local_qg_distmat], axis=1),
np.concatenate([local_qg_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
else:
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_param = False
search_theta = True
if search_param:
best_score = 0
best_param = []
for k1 in range(5, 9):
for k2 in range(1, k1):
for l in np.linspace(0, 0.5, 11):
distmat = re_ranking(qf, gf, k1=k1, k2=k2, lambda_value=l)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids,
g_camids)
score = (cmc[0] + mAP) / 2
#logger.info(f"mAP: {mAP:.1%}")
print('k1, k2, l', k1, k2, np.around(l, 2),
'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = [k1, k2, l]
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=best_param[0],
k2=best_param[1],
lambda_value=best_param[2],
local_distmat=local_distmat,
only_local=False)
elif search_theta:
best_score = 0
for theta in np.linspace(0, 1.0, 11):
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=theta,
only_local=False) # (current best)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
score = (cmc[0] + mAP) / 2
print('theta', theta, 'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = theta
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
theta_value=best_param,
only_local=False) # (current best)
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.9) #(current best)
#distmat = re_ranking(qf, gf, k1=6, k2=2, lambda_value=0.4) # try
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def estimate_distance(
cfg,
model,
data_loader,
sample_number=None
):
model.eval()
savedir = os.path.join(cfg.OUTPUT_DIR, '-'.join(cfg.DATASETS.TEST_NAMES), 'estimate_dist')
if not os.path.exists(savedir): os.makedirs(savedir)
if len(data_loader) > 10:
print('Estimate distance on train data...')
outfile = open(os.path.join(savedir, cfg.DATASETS.TEST_NAMES[0]+'_train.txt'), 'w')
feats, pids, camids, pos_dists, mean_dists, neg_dists = [], [], [], [], [], []
prefetcher = data_prefetcher(data_loader)
batch = prefetcher.next()
batchi = 0
while batch[0] is not None:
print(f'Extracting feature for batch {batchi}')
img, pid, camid = batch
with torch.no_grad():
logit, feat = model(img)
feats.append(feat)
pids.extend(pid.cpu().numpy().tolist())
camids.extend(np.asarray(camid))
batch = prefetcher.next()
batchi += 1
feats = torch.cat(feats, dim=0)
pids = np.asarray(pids)
camids = np.asarray(camids)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
batchN = feats.shape[0]//cfg.TEST.IMS_PER_BATCH
print('batchN : ', batchN+1)
for batch_i in range(batchN+1):
print(f'Processing batch : {batch_i}')
if batch_i != batchN:
feats_split = feats[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
pids_split = pids[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
camids_split = camids[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
else:
feats_split = feats[batch_i*cfg.TEST.IMS_PER_BATCH:]
pids_split = pids[batch_i*cfg.TEST.IMS_PER_BATCH:]
camids_split = camids[batch_i*cfg.TEST.IMS_PER_BATCH:]
# cosine distance
distmat_split = torch.mm(feats_split, feats.t()).cpu().numpy()
distmat_split = -distmat_split
for sample_i in range(len(pids_split)):
dist = -distmat_split[sample_i]
pid = pids_split[sample_i]
camid = camids_split[sample_i]
# remove gallery samples that have the same pid and camid with query
positive = (pids == pid) & (camids != camid)
negtive = (pids != pid)
if not np.any(positive):
# this condition is true when query identity does not appear in gallery
continue
dist_pos = dist[positive]
dist_neg = dist[negtive]
pos_dists.append(np.max(dist_pos))
neg_dists.append(np.max(dist_neg))
mean_dists.append(np.mean(dist_pos))
item = 0
max_sample = 1000000
if len(pids) > max_sample:
sample_rand = random.sample(range(len(pids)), max_sample)
for sample_i in sample_rand:
outfile.write(f'Item {item:6d} : id = {pids[sample_i]:6d} , pos_dist = {pos_dists[sample_i]:.6f}, mean_dist = {mean_dists[sample_i]:.6f}, neg_dist = {neg_dists[sample_i]:.6f}\n')
item += 1
else:
for a, b, c, d in zip(pids, pos_dists, mean_dists, neg_dists):
outfile.write(f'Item {item:6d} : id = {a:6d} , pos_dist = {b:.6f} , mean_dist = {c:.6f}, neg_dist = {d:.6f}\n')
item += 1
else:
idx = -1
for test_dataset_name, dataloader, num_query in zip(cfg.DATASETS.TEST_NAMES, data_loader, sample_number):
print(f'Estimate distance on test data of {test_dataset_name}')
idx += 1
outfile = open(os.path.join(savedir, test_dataset_name+'_test.txt'), 'w')
feats, pids, camids, pos_dists, mean_dists, neg_dists = [], [], [], [], [], []
prefetcher = data_prefetcher(dataloader)
batch = prefetcher.next()
batchi = 0
while batch[0] is not None:
print(f'Extracting feature for batch {batchi}')
img, pid, camid = batch
with torch.no_grad():
logit, feat = model(img)
feats.append(feat)
pids.extend(pid.cpu().numpy().tolist())
camids.extend(np.asarray(camid))
batch = prefetcher.next()
batchi += 1
feats = torch.cat(feats, dim=0)
pids = np.asarray(pids)
camids = np.asarray(camids)
if 'veri' == test_dataset_name:
feats = feats[1678:]
pids = pids[1678:]
camids = camids[1678:]
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
batchN = feats.shape[0]//cfg.TEST.IMS_PER_BATCH
print('batchN : ', batchN+1)
for batch_i in range(batchN+1):
print(f'Processing batch : {batch_i}')
if batch_i != batchN:
feats_split = feats[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
pids_split = pids[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
camids_split = camids[batch_i*cfg.TEST.IMS_PER_BATCH:(batch_i+1)*cfg.TEST.IMS_PER_BATCH]
else:
feats_split = feats[batch_i*cfg.TEST.IMS_PER_BATCH:]
pids_split = pids[batch_i*cfg.TEST.IMS_PER_BATCH:]
camids_split = camids[batch_i*cfg.TEST.IMS_PER_BATCH:]
# cosine distance
distmat_split = torch.mm(feats_split, feats.t()).cpu().numpy()
distmat_split = -distmat_split
for sample_i in range(len(pids_split)):
dist = -distmat_split[sample_i]
pid = pids_split[sample_i]
camid = camids_split[sample_i]
# remove gallery samples that have the same pid and camid with query
positive = (pids == pid) & (camids != camid)
negtive = (pids != pid)
if not np.any(positive):
# this condition is true when query identity does not appear in gallery
continue
dist_pos = dist[positive]
dist_neg = dist[negtive]
pos_dists.append(np.max(dist_pos))
neg_dists.append(np.max(dist_neg))
mean_dists.append(np.mean(dist_pos))
item = 0
max_sample = 1000000
if len(pids) > max_sample:
sample_rand = random.sample(range(len(pids)), max_sample)
for sample_i in sample_rand:
outfile.write(f'Item {item:6d} : id = {pids[sample_i]:6d} , pos_dist = {pos_dists[sample_i]:.6f}, mean_dist = {mean_dists[sample_i]:.6f}, neg_dist = {neg_dists[sample_i]:.6f}\n')
item += 1
else:
for a, b, c, d in zip(pids, pos_dists, mean_dists, neg_dists):
outfile.write(f'Item {item:6d} : id = {a:6d} , pos_dist = {b:.6f} , mean_dist = {c:.6f}, neg_dist = {d:.6f}\n')
item += 1
def inference_aligned_flipped(cfg, model, test_dataloader, num_query,
use_local_feature, use_rerank,
use_cross_feature):
"""
inference an aligned net with flipping and two pairs of global feature and local feature
:param cfg:
:param model:
:param test_dataloader:
:param num_query:
:return:
"""
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing aligned with flipping")
model.eval()
pids, camids = [], []
gfs, bn_gfs, lfs, bn_lfs = [], [], [], []
gfs_flipped, bn_gfs_flipped, lfs_flipped, bn_lfs_flipped = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
gf, bn_gf, lf, bn_lf = model(img)
gff, bn_gff, lff, bn_lff = model(torch.flip(img, [3]))
# 4 features
gfs.append(gf.cpu())
bn_gfs.append(bn_gf.cpu())
if use_local_feature:
lfs.append(lf.cpu())
bn_lfs.append(bn_lf.cpu())
# 4 features flipped
gfs_flipped.append(gff.cpu())
bn_gfs_flipped.append(bn_gff.cpu())
if use_local_feature:
lfs_flipped.append(lff.cpu())
bn_lfs_flipped.append(bn_lff.cpu())
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
logger.info(
f"use_cross_feature = {use_cross_feature}, use_local_feature = {use_local_feature}, use_rerank = {use_rerank}"
)
if use_cross_feature:
logger.info("Computing distmat with bn_gf (+ lf)")
distmat2 = compute_distmat(cfg,
num_query,
bn_gfs,
bn_gfs_flipped,
lfs,
lfs_flipped,
theta=0.45,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat2
#distmat = (distmat1 + distmat2) / 2
else:
logger.info("Computing distmat with gf + bn_lf")
distmat1 = compute_distmat(cfg,
num_query,
gfs,
gfs_flipped,
bn_lfs,
bn_lfs_flipped,
theta=0.95,
use_local_feature=use_local_feature,
use_rerank=use_rerank)
distmat = distmat1
#distmat1 = None
#distmat2 = None
#distmat = original_distmat
#distmat[:, new_gallery_index] = distmat1 - 100
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def estimate_logits(
cfg,
model,
data_loader,
sample_number=None
):
model.eval()
savedir = os.path.join(cfg.OUTPUT_DIR, '-'.join(cfg.DATASETS.TEST_NAMES), 'estimate_logits')
if not os.path.exists(savedir): os.makedirs(savedir)
if len(data_loader) > 10:
print('Estimate logits on train data...')
outfile = open(os.path.join(savedir, cfg.DATASETS.TEST_NAMES[0]+'_train.txt'), 'w')
logits, feats, pids, camids, probs, max_probs, pred_ids = [], [], [], [], [], [], []
prefetcher = data_prefetcher(data_loader)
batch = prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
logit, feat = model(img)
# logits.append(logit)
prob = F.softmax(logit, dim=1)
# probs.append(prob)
max_prob, pred_id = torch.max(prob, dim=1)
pred_ids.extend(pred_id.cpu().numpy().tolist())
max_probs.extend(max_prob.cpu().numpy().tolist())
pids.extend(pid.cpu().numpy().tolist())
# camids.extend(np.asarray(camid))
batch = prefetcher.next()
item = 0
max_sample = 1000000
if len(pids) > max_sample:
sample_rand = random.sample(range(len(pids)), max_sample)
for sample_i in sample_rand:
outfile.write(f'Item {item:6d} : id = {pids[sample_i]:6d} , pred = {pred_ids[sample_i]:6d} , prob = {max_probs[sample_i]:.4f}\n')
item += 1
else:
for a, b, c in zip(pids, pred_ids, max_probs):
# print(f'Item {item:6d} : id = {a:4d} , pred = {b:4d} , prob = {c:.4f}')
outfile.write(f'Item {item:6d} : id = {a:6d} , pred = {b:6d} , prob = {c:.4f}\n')
item += 1
else:
idx = -1
for test_dataset_name, dataloader, num_query in zip(cfg.DATASETS.TEST_NAMES, data_loader, sample_number):
print(f'Estimate logits on test data of {test_dataset_name}')
idx += 1
outfile = open(os.path.join(savedir, test_dataset_name+'_test.txt'), 'w')
logits, feats, pids, camids, probs, max_probs, pred_ids = [], [], [], [], [], [], []
prefetcher = data_prefetcher(dataloader)
batch = prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
logit, feat = model(img)
# logits.append(logit)
prob = F.softmax(logit, dim=1)
# probs.append(prob)
max_prob, pred_id = torch.max(prob, dim=1)
pred_ids.extend(pred_id.cpu().numpy().tolist())
max_probs.extend(max_prob.cpu().numpy().tolist())
pids.extend(pid.cpu().numpy().tolist())
# camids.extend(np.asarray(camid))
batch = prefetcher.next()
if 'veri' == test_dataset_name:
pred_ids = pred_ids[1678:]
pids = pids[1678:]
max_probs = max_probs[1678:]
item = 0
max_sample = 1000000
if len(pids) > max_sample:
sample_rand = random.sample(range(len(pids)), max_sample)
for sample_i in sample_rand:
outfile.write(f'Item {item:6d} : id = {pids[sample_i]:6d} , pred = {pred_ids[sample_i]:6d} , prob = {max_probs[sample_i]:.4f}\n')
item += 1
else:
for a, b, c in zip(pids, pred_ids, max_probs):
# print(f'Item {item:6d} : id = {a:4d} , pred = {b:4d} , prob = {c:.4f}')
outfile.write(f'Item {item:6d} : id = {a:6d} , pred = {b:6d} , prob = {c:.4f}\n')
item += 1
def inference(cfg, model, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
model.eval()
feats, pids, camids = [], [], []
#local_feats = []
test_prefetcher = data_prefetcher(test_dataloader, cfg)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
with torch.no_grad():
feat = model(img)
#feat = model(torch.flip(img, [3]))
if isinstance(feat, tuple):
feats.append(feat[0])
#local_feats.append(feat[1])
else:
feats.append(feat)
pids.extend(pid.cpu().numpy())
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
# query
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
local_distmat = None
# use reranking
logger.info("use reranking")
#distmat = re_ranking(qf, gf, k1=14, k2=4, lambda_value=0.4)
search_param = False
if search_param:
best_score = 0
best_param = []
for k1 in range(5, 9):
for k2 in range(1, k1):
for l in np.linspace(0, 0.5, 11):
distmat = re_ranking(qf, gf, k1=k1, k2=k2, lambda_value=l)
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids,
g_camids)
score = (cmc[0] + mAP) / 2
#logger.info(f"mAP: {mAP:.1%}")
print('k1, k2, l', k1, k2, np.around(l, 2),
'r1, mAP, score', np.around(cmc[0], 4),
np.around(mAP, 4), np.around(score, 4))
if score > best_score:
best_score = score
best_param = [k1, k2, l]
print('Best Param', best_param)
distmat = re_ranking(qf,
gf,
k1=best_param[0],
k2=best_param[1],
lambda_value=best_param[2],
local_distmat=local_distmat,
only_local=False)
else:
distmat = re_ranking(qf,
gf,
k1=6,
k2=2,
lambda_value=0.3,
local_distmat=local_distmat,
only_local=False,
theta_value=0.9) #(current best)
#distmat = re_ranking(qf, gf, k1=6, k2=2, lambda_value=0.4) # try
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
logger.info(f"Score: {(mAP + cmc[0]) / 2.:.1%}")
def inference(cfg, model, train_dataloader, test_dataloader, num_query):
logger = logging.getLogger("reid_baseline.inference")
logger.info("Start inferencing")
logger.info("compute precise batchnorm ...")
# model.train()
# update_bn_stats(model, train_dataloader, num_iters=300)
model.eval()
cat_feats, feats, pids, camids = [], [], [], []
test_prefetcher = data_prefetcher(test_dataloader)
batch = test_prefetcher.next()
while batch[0] is not None:
img, pid, camid = batch
cat_feat, feat = model(img)
cat_feats.append(cat_feat.cpu())
feats.append(feat.cpu())
pids.extend(np.asarray(pid.cpu().numpy()))
camids.extend(np.asarray(camid))
batch = test_prefetcher.next()
feats = torch.cat(feats, dim=0)
cat_feats = torch.cat(cat_feats, dim=0)
if cfg.TEST.NORM:
feats = F.normalize(feats, p=2, dim=1)
cat_feats = F.normalize(cat_feats, p=2, dim=1)
# query
cat_qf = cat_feats[:num_query]
qf = feats[:num_query]
q_pids = np.asarray(pids[:num_query])
q_camids = np.asarray(camids[:num_query])
# gallery
cat_gf = cat_feats[num_query:]
gf = feats[num_query:]
g_pids = np.asarray(pids[num_query:])
g_camids = np.asarray(camids[num_query:])
# cosine distance
cat_dist = torch.mm(cat_qf, cat_gf.t())
distmat = torch.mm(qf, gf.t())
# IIA post fusion strategy for all query and gallery
# qf = qf
# gf = gf
# m = qf.shape[0]
# n = gf.shape[0]
# distmat = torch.zeros((m, n)).to(qf)
# for i, q_f in enumerate(qf):
# print(i)
# D = torch.cat([q_f[None, :], gf], dim=0) # [1+g, 2048]
# S = torch.mm(D, D.t()) # [1+g, 1+g]
# for _ in range(5):
# S = S - torch.eye(S.shape[0]).to(S)
# s_v, s_i = torch.topk(S, 10, dim=1)
# s_v = F.softmax(s_v, dim=1)
# s = torch.zeros((S.size()[0], S.size()[0])).to(qf) # [1+g, 1+g]
# for j in range(s_i.shape[0]):
# s[j, s_i[j]] = s_v[j]
# u = 0.8 * torch.eye(S.size()[0]).to(s) + 0.2 * s
# D = torch.mm(u, D)
# S = torch.mm(D, D.t())
# distmat[i] = S[0][1:]
cmc, mAP = evaluate(1 - distmat.numpy(), q_pids, g_pids, q_camids,
g_camids)
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")
cmc, mAP = evaluate(1 - cat_dist.numpy(), q_pids, g_pids, q_camids,
g_camids)
logger.info('cat feature')
logger.info(f"mAP: {mAP:.1%}")
for r in [1, 5, 10]:
logger.info(f"CMC curve, Rank-{r:<3}:{cmc[r - 1]:.1%}")