New test.py

[Lamborghini1709]

#coding=utf-8
import os
import json
import csv
import argparse
import pandas as pd
import numpy as np
from math import ceil
from tqdm import tqdm
import pickle
import shutil

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import CrossEntropyLoss
from torchvision import datasets, models
import torch.backends.cudnn as cudnn
import torch.nn.functional as F

from transforms import transforms
from models.LoadModel import MainModel
from utils.dataset_DCL import collate_fn4train, collate_fn4test, collate_fn4val, dataset
from config import LoadConfig, load_data_transformers
from utils.test_tool import set_text, save_multi_img, cls_base_acc

import pdb

os.environ['CUDA_DEVICE_ORDRE'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'

def parse_args():
parser = argparse.ArgumentParser(description='dcl parameters')
parser.add_argument('--data', dest='dataset',
default='CUB', type=str)
parser.add_argument('--backbone', dest='backbone',
default='resnet50', type=str)
parser.add_argument('--b', dest='batch_size',
default=16, type=int)
parser.add_argument('--nw', dest='num_workers',
default=16, type=int)
parser.add_argument('--ver', dest='version',
default='test', type=str)
parser.add_argument('--save', dest='resume',
default=None, type=str)
parser.add_argument('--size', dest='resize_resolution',
default=512, type=int)
parser.add_argument('--crop', dest='crop_resolution',
default=448, type=int)
parser.add_argument('--ss', dest='save_suffix',
default=None, type=str)
parser.add_argument('--acc_report', dest='acc_report',
action='store_true')
parser.add_argument('--swap_num', default=[7, 7],
nargs=2, metavar=('swap1', 'swap2'),
type=int, help='specify a range')
args = parser.parse_args()
return args

if name == 'main':
args = parse_args()
print(args)
# if args.submit:
# args.version = 'test'
# if args.save_suffix == '':
# raise Exception('**** miss --ss save suffix is needed. ')
args.version = 'test'
Config = LoadConfig(args, args.version)
transformers = load_data_transformers(args.resize_resolution, args.crop_resolution, args.swap_num)
data_set = dataset(Config,
anno=Config.val_anno if args.version == 'val' else Config.test_anno ,
# unswap=transformers["None"],
swap=transformers["None"],
totensor=transformers['test_totensor'],
test=True)

dataloader = torch.utils.data.DataLoader(data_set,\
                                         batch_size=args.batch_size,\
                                         shuffle=False,\
                                         num_workers=args.num_workers,\
                                         collate_fn=collate_fn4test)

setattr(dataloader, 'total_item_len', len(data_set))

cudnn.benchmark = True
print('****************')
Config.cls_2xmul = True
print(Config.cls_2xmul)
model = MainModel(Config)
model_dict=model.state_dict()
pretrained_dict=torch.load(args.resume)
pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model.cuda()
model = nn.DataParallel(model)

model.train(False)
with torch.no_grad():
    val_corrects1 = 0
    val_corrects2 = 0
    val_corrects3 = 0
    val_size = ceil(len(data_set) / dataloader.batch_size)
    result_gather = {}
    count_bar = tqdm(total=dataloader.__len__())
    for batch_cnt_val, data_val in enumerate(dataloader):
        count_bar.update(1)
        inputs, labels, img_name = data_val
        inputs = Variable(inputs.cuda())
        labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())

        outputs = model(inputs)
        # print('outputs:', outputs)
        outputs_pred = outputs[0] + outputs[1][:,0:Config.numcls] + outputs[1][:,Config.numcls:2*Config.numcls]
        print('outputs_pred:', outputs_pred)

        top3_val, top3_pos = torch.topk(outputs_pred, 3)

        if args.version == 'val':
            batch_corrects1 = torch.sum((top3_pos[:, 0] == labels)).data.item()
            val_corrects1 += batch_corrects1
            batch_corrects2 = torch.sum((top3_pos[:, 1] == labels)).data.item()
            val_corrects2 += (batch_corrects2 + batch_corrects1)
            batch_corrects3 = torch.sum((top3_pos[:, 2] == labels)).data.item()
            val_corrects3 += (batch_corrects3 + batch_corrects2 + batch_corrects1)

        if args.acc_report:
            for sub_name, sub_cat, sub_val, sub_label in zip(img_name, top3_pos.tolist(), top3_val.tolist(), labels.tolist()):
                result_gather[sub_name] = {'top1_cat': sub_cat[0], 'top2_cat': sub_cat[1], 'top3_cat': sub_cat[2],
                                           'top1_val': sub_val[0], 'top2_val': sub_val[1], 'top3_val': sub_val[2],
                                           'label': sub_label}
if args.acc_report:
    result_gather_json = open('result_gather_%s'%args.resume.split('/')[-1][:-4]+ '.json', 'w')
    json.dump(result_gather, result_gather_json)
    result_gather_json.close()
    torch.save(result_gather, 'result_gather_%s'%args.resume.split('/')[-1][:-4]+ '.pt')

count_bar.close()
print(args.acc_report)
if args.acc_report:

    val_acc1 = val_corrects1 / len(data_set)
    val_acc2 = val_corrects2 / len(data_set)
    val_acc3 = val_corrects3 / len(data_set)
    print('%sacc1 %f%s\n%sacc2 %f%s\n%sacc3 %f%s\n'%(8*'-', val_acc1, 8*'-', 8*'-', val_acc2, 8*'-', 8*'-',  val_acc3, 8*'-'))

    cls_top1, cls_top3, cls_count = cls_base_acc(result_gather)

    acc_report_io = open('acc_report_%s_%s.json'%(args.save_suffix, args.resume.split('/')[-1]), 'w')
    json.dump({'val_acc1':val_acc1,
               'val_acc2':val_acc2,
               'val_acc3':val_acc3,
               'cls_top1':cls_top1,
               'cls_top3':cls_top3,
               'cls_count':cls_count}, acc_report_io)
    acc_report_io.close()

run test : python test.py --save ./net_model/training_descibe_72721_CUB/weights_36_4999_0.8608_0.9998.pth --acc_report

[yunchangxiaoguan]

hello, i use the code ,but error:

`python new_test.py --save net_model/_8514_CUB/weights_20_0_1.0000_1.0000.pth --acc_report
Namespace(acc_report=True, backbone='resnet50', batch_size=16, crop_resolution=448, dataset='CUB', num_workers=16, resize_resolution=512, resume='net_model/_8514_CUB/weights_20_0_1.0000_1.0000.pth', save_suffix=None, swap_num=[7, 7], version='test')

---

True
resnet50
Traceback (most recent call last):
File "new_test.py", line 95, in
model.load_state_dict(model_dict)
File "/home/guanxiao/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 847, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for MainModel:
size mismatch for classifier_swap.weight: copying a param with shape torch.Size([2, 2048]) from checkpoint, the shape in current model is torch.Size([6, 2048]).
`
how to make it?thanks

[Lamborghini1709]

check out your num_classes

[yunchangxiaoguan]

check out your num_classes

thanks，i have solve it

[BaofengZan]

@Lamborghini1709 @yunchangxiaoguan 你好，能不能分享下训练好的cub模型，这个模型是真的需要硬件。小batch训练太慢。

[JiCheng12138]

@yunchangxiaoguan 您好请问这个size mismatch for classifier_swap.weight:问题您如何解决的呢，谢谢

[Lamborghini1709]

@yunchangxiaoguan 您好请问这个size mismatch for classifier_swap.weight:问题您如何解决的呢，谢谢

检查你的输出类别数 num_classes