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test_with_TensorRT.py
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test_with_TensorRT.py
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# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import torch
from torch2trt import torch2trt
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import time
import os
import scipy.io
import yaml
import math
from model import ft_net, ft_net_dense, ft_net_hr, ft_net_swin, ft_net_efficient, ft_net_NAS, PCB, PCB_test
#fp16
try:
from apex.fp16_utils import *
except ImportError: # will be 3.x series
print('This is not an error. If you want to use low precision, i.e., fp16, please install the apex with cuda support (https://github.com/NVIDIA/apex) and update pytorch to 1.0')
######################################################################
# Options
# --------
parser = argparse.ArgumentParser(description='Test')
parser.add_argument('--gpu_ids',default='0', type=str,help='gpu_ids: e.g. 0 0,1,2 0,2')
parser.add_argument('--which_epoch',default='last', type=str, help='0,1,2,3...or last')
parser.add_argument('--test_dir',default='../Market/pytorch',type=str, help='./test_data')
parser.add_argument('--name', default='ft_ResNet50', type=str, help='save model path')
parser.add_argument('--batchsize', default=256, type=int, help='batchsize')
parser.add_argument('--linear_num', default=512, type=int, help='feature dimension: 512 or default or 0 (linear=False)')
parser.add_argument('--use_dense', action='store_true', help='use densenet121' )
parser.add_argument('--use_efficient', action='store_true', help='use efficient-b4' )
parser.add_argument('--use_hr', action='store_true', help='use hr18 net' )
parser.add_argument('--PCB', action='store_true', help='use PCB' )
parser.add_argument('--multi', action='store_true', help='use multiple query' )
parser.add_argument('--fp16', action='store_true', help='use fp16.' )
parser.add_argument('--ibn', action='store_true', help='use ibn.' )
parser.add_argument('--ms',default='1', type=str,help='multiple_scale: e.g. 1 1,1.1 1,1.1,1.2')
opt = parser.parse_args()
###load config###
# load the training config
config_path = os.path.join('./model',opt.name,'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream, Loader=yaml.FullLoader) # for the new pyyaml via 'conda install pyyaml'
opt.fp16 = config['fp16']
opt.PCB = config['PCB']
opt.use_dense = config['use_dense']
opt.use_NAS = config['use_NAS']
opt.stride = config['stride']
if 'use_swin' in config:
opt.use_swin = config['use_swin']
if 'use_efficient' in config:
opt.use_efficient = config['use_efficient']
if 'use_hr' in config:
opt.use_hr = config['use_hr']
if 'nclasses' in config: # tp compatible with old config files
opt.nclasses = config['nclasses']
else:
opt.nclasses = 751
if 'ibn' in config:
opt.ibn = config['ibn']
if 'linear_num' in config:
opt.linear_num = config['linear_num']
str_ids = opt.gpu_ids.split(',')
#which_epoch = opt.which_epoch
name = opt.name
test_dir = opt.test_dir
gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >=0:
gpu_ids.append(id)
print('We use the scale: %s'%opt.ms)
str_ms = opt.ms.split(',')
ms = []
for s in str_ms:
s_f = float(s)
ms.append(math.sqrt(s_f))
# set gpu ids
if len(gpu_ids)>0:
torch.cuda.set_device(gpu_ids[0])
cudnn.benchmark = True
######################################################################
# Load Data
# ---------
#
# We will use torchvision and torch.utils.data packages for loading the
# data.
#
if opt.use_swin:
h, w = 224, 224
else:
h, w = 256, 128
data_transforms = transforms.Compose([
transforms.Resize((h, w), interpolation=3),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
############### Ten Crop
#transforms.TenCrop(224),
#transforms.Lambda(lambda crops: torch.stack(
# [transforms.ToTensor()(crop)
# for crop in crops]
# )),
#transforms.Lambda(lambda crops: torch.stack(
# [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop)
# for crop in crops]
# ))
])
if opt.PCB:
data_transforms = transforms.Compose([
transforms.Resize((384,192), interpolation=3),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
h, w = 384, 192
data_dir = test_dir
if opt.multi:
image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery','query','multi-query']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
shuffle=False, num_workers=16) for x in ['gallery','query','multi-query']}
else:
image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery','query']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
shuffle=False, num_workers=16) for x in ['gallery','query']}
class_names = image_datasets['query'].classes
use_gpu = torch.cuda.is_available()
######################################################################
# Load model
#---------------------------
def load_network(network):
save_path = os.path.join('./model',name,'net_%s.pth'%opt.which_epoch)
network.load_state_dict(torch.load(save_path))
return network
######################################################################
# Extract feature
# ----------------------
#
# Extract feature from a trained model.
#
def fliplr(img):
'''flip horizontal'''
inv_idx = torch.arange(img.size(3)-1,-1,-1).long() # N x C x H x W
img_flip = img.index_select(3,inv_idx)
return img_flip
def extract_feature(model,dataloaders):
#features = torch.FloatTensor()
count = 0
if opt.linear_num <= 0:
if opt.use_swin or opt.use_dense:
opt.linear_num = 1024
elif opt.use_efficient:
opt.linear_num = 1792
elif opt.use_NAS:
opt.linear_num = 4032
else:
opt.linear_num = 2048
for iter, data in enumerate(dataloaders):
img, label = data
n, c, h, w = img.size()
count += n
print(count)
ff = torch.FloatTensor(n,opt.linear_num).zero_().cuda()
if opt.PCB:
ff = torch.FloatTensor(n,2048,6).zero_().cuda() # we have six parts
for i in range(2):
if(i==1):
img = fliplr(img)
input_img = Variable(img.cuda())
for scale in ms:
if scale != 1:
# bicubic is only available in pytorch>= 1.1
input_img = nn.functional.interpolate(input_img, scale_factor=scale, mode='bicubic', align_corners=False)
outputs = model(input_img)
ff += outputs
# norm feature
if opt.PCB:
# feature size (n,2048,6)
# 1. To treat every part equally, I calculate the norm for every 2048-dim part feature.
# 2. To keep the cosine score==1, sqrt(6) is added to norm the whole feature (2048*6).
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True) * np.sqrt(6)
ff = ff.div(fnorm.expand_as(ff))
ff = ff.view(ff.size(0), -1)
else:
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)
ff = ff.div(fnorm.expand_as(ff))
if iter == 0:
features = torch.FloatTensor( len(dataloaders.dataset), ff.shape[1])
#features = torch.cat((features,ff.data.cpu()), 0)
start = iter*opt.batchsize
end = min( (iter+1)*opt.batchsize, len(dataloaders.dataset))
features[ start:end, :] = ff
return features
def get_id(img_path):
camera_id = []
labels = []
for path, v in img_path:
#filename = path.split('/')[-1]
filename = os.path.basename(path)
label = filename[0:4]
camera = filename.split('c')[1]
if label[0:2]=='-1':
labels.append(-1)
else:
labels.append(int(label))
camera_id.append(int(camera[0]))
return camera_id, labels
gallery_path = image_datasets['gallery'].imgs
query_path = image_datasets['query'].imgs
gallery_cam,gallery_label = get_id(gallery_path)
query_cam,query_label = get_id(query_path)
if opt.multi:
mquery_path = image_datasets['multi-query'].imgs
mquery_cam,mquery_label = get_id(mquery_path)
######################################################################
# Load Collected data Trained model
if opt.use_dense:
model_structure = ft_net_dense(opt.nclasses, linear_num=opt.linear_num)
elif opt.use_NAS:
model_structure = ft_net_NAS(opt.nclasses, linear_num=opt.linear_num)
elif opt.use_swin:
model_structure = ft_net_swin(opt.nclasses, linear_num=opt.linear_num)
elif opt.use_efficient:
model_structure = ft_net_efficient(opt.nclasses, linear_num=opt.linear_num)
elif opt.use_hr:
model_structure = ft_net_hr(opt.nclasses, linear_num=opt.linear_num)
else:
model_structure = ft_net(opt.nclasses, stride = opt.stride, ibn = opt.ibn, linear_num=opt.linear_num)
if opt.PCB:
model_structure = PCB(opt.nclasses)
#if opt.fp16:
# model_structure = network_to_half(model_structure)
model = load_network(model_structure)
# Remove the final fc layer and classifier layer
if opt.PCB:
#if opt.fp16:
# model = PCB_test(model[1])
#else:
model = PCB_test(model)
else:
#if opt.fp16:
#model[1].model.fc = nn.Sequential()
#model[1].classifier = nn.Sequential()
#else:
model.classifier.classifier = nn.Sequential()
# Change to test mode
model = model.eval()
if use_gpu:
model = model.cuda()
# create example data
# batchsize= 1
x = torch.ones((opt.batchsize, 3, h, w)).cuda()
# convert to TensorRT feeding sample data as input
print('Please pip install nvidia-pyindex; pip install nvidia-tensorrt; git clone https://github.com/NVIDIA-AI-IOT/torch2trt; cd torch2trt; python setup.py install')
model = torch2trt(model, [x], fp16_mode=True, max_batch_size=opt.batchsize)
print('-------test-----------')
# Extract feature
since = time.time()
with torch.no_grad():
gallery_feature = extract_feature(model,dataloaders['gallery'])
query_feature = extract_feature(model,dataloaders['query'])
if opt.multi:
mquery_feature = extract_feature(model,dataloaders['multi-query'])
time_elapsed = time.time() - since
print('Testing complete in {:.0f}m {:.2f}s'.format(
time_elapsed // 60, time_elapsed % 60))
# Save to Matlab for check
result = {'gallery_f':gallery_feature.numpy(),'gallery_label':gallery_label,'gallery_cam':gallery_cam,'query_f':query_feature.numpy(),'query_label':query_label,'query_cam':query_cam}
scipy.io.savemat('pytorch_result.mat',result)
print(opt.name)
result = './model/%s/result.txt'%opt.name
os.system('python evaluate_gpu.py | tee -a %s'%result)
if opt.multi:
result = {'mquery_f':mquery_feature.numpy(),'mquery_label':mquery_label,'mquery_cam':mquery_cam}
scipy.io.savemat('multi_query.mat',result)