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eval.py
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eval.py
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# coding:utf-8
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import datetime
from utils import *
import cfgs.cfgs_eval as cfgs
from collections import OrderedDict
import time
import sys
def flatten_label(target):
label_flatten = []
label_length = []
for i in range(0, target.size()[0]):
cur_label = target[i].tolist()
label_flatten += cur_label[:cur_label.index(0) + 1]
label_length.append(cur_label.index(0) + 1)
label_flatten = torch.LongTensor(label_flatten)
label_length = torch.IntTensor(label_length)
return (label_flatten, label_length)
def Train_or_Eval(model, state='Train'):
if state == 'Train':
model.train()
else:
model.eval()
def load_dataset():
train_data_set = cfgs.dataset_cfgs['dataset_train'](**cfgs.dataset_cfgs['dataset_train_args'])
train_loader = DataLoader(train_data_set, **cfgs.dataset_cfgs['dataloader_train'])
test_data_all = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_all'])
test_loader_all = DataLoader(test_data_all, **cfgs.dataset_cfgs['dataloader_test'])
test_data_set = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_args'])
test_loader = DataLoader(test_data_set, **cfgs.dataset_cfgs['dataloader_test'])
test_data_setIC13 = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_argsIC13'])
test_loaderIC13 = DataLoader(test_data_setIC13, **cfgs.dataset_cfgs['dataloader_test'])
test_data_setIC15 = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_argsIC15'])
test_loaderIC15 = DataLoader(test_data_setIC15, **cfgs.dataset_cfgs['dataloader_test'])
test_data_setSVT = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_argsSVT'])
test_loaderSVT = DataLoader(test_data_setSVT, **cfgs.dataset_cfgs['dataloader_test'])
test_data_setSVTP = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_argsSVTP'])
test_loaderSVTP = DataLoader(test_data_setSVTP, **cfgs.dataset_cfgs['dataloader_test'])
test_data_setCUTE = cfgs.dataset_cfgs['dataset_test'](**cfgs.dataset_cfgs['dataset_test_argsCUTE'])
test_loaderCUTE = DataLoader(test_data_setCUTE, **cfgs.dataset_cfgs['dataloader_test'])
# pdb.set_trace()
return (train_loader, test_loader_all, test_loader, test_loaderIC13, test_loaderIC15, test_loaderSVT, test_loaderSVTP, test_loaderCUTE)
def load_network():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_VL = cfgs.net_cfgs['VisualLAN'](**cfgs.net_cfgs['args'])
model_VL = model_VL.to(device)
model_VL = torch.nn.DataParallel(model_VL)
if cfgs.net_cfgs['init_state_dict'] != None:
fe_state_dict_ori = torch.load(cfgs.net_cfgs['init_state_dict'])
fe_state_dict = OrderedDict()
for k, v in fe_state_dict_ori.items():
# if 'MLM' in k:
# print()
if 'module' not in k:
k = 'module.' + k
else:
k = k.replace('features.module.', 'module.features.')
fe_state_dict[k] = v
model_dict_fe = model_VL.state_dict()
state_dict_fe = {k: v for k, v in fe_state_dict.items() if k in model_dict_fe.keys()}
model_dict_fe.update(state_dict_fe)
model_VL.load_state_dict(model_dict_fe)
return model_VL
def test(test_loader, model, tools, best_acc, string_name):
Train_or_Eval(model, 'Eval')
print('------' + string_name + '--------')
for sample_batched in test_loader:
data = sample_batched['image']
label = sample_batched['label']
target = tools[0].encode(label)
data = data.cuda()
target = target
label_flatten, length = tools[1](target)
target, label_flatten = target.cuda(), label_flatten.cuda()
output, out_length = model(data, target, '', False)
tools[2].add_iter(output, out_length, length, label)
best_acc, change = tools[2].show_test(best_acc)
Train_or_Eval(model, 'Train')
return best_acc, change
if __name__ == '__main__':
model = load_network()
train_loader, test_loader_all, test_loader, test_loaderIC13, test_loaderIC15, test_loaderSVT, test_loaderSVTP, test_loaderCUTE = load_dataset()
test_acc_counter = Attention_AR_counter('\ntest accuracy: ', cfgs.dataset_cfgs['dict_dir'],
cfgs.dataset_cfgs['case_sensitive'])
encdec = cha_encdec(cfgs.dataset_cfgs['dict_dir'], cfgs.dataset_cfgs['case_sensitive'])
if cfgs.global_cfgs['state'] == 'Test':
test((test_loader_all),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='Average on 6 benchmarks')
test((test_loader),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='IIIT')
test((test_loaderIC13),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='IC13')
test((test_loaderIC15),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='IC15')
test((test_loaderSVT),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='SVT')
test((test_loaderSVTP),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='SVTP')
test((test_loaderCUTE),
model,
[encdec,
flatten_label,
test_acc_counter], best_acc=0, string_name='CUTE')
exit()