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test.py
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test.py
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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import cv2
import os
from STA.loss import KLDLoss
from utils.args_config import get_parser
from torch.utils.tensorboard import SummaryWriter
from utils.DataLoader import get_dataLoader
from datetime import datetime
from utils.model_tool import get_model
from utils.DataFromtxt import id_category
args = get_parser()
def test(model, STA_mode, Pic_path, H5_path, GT_path, is_val, save_index, batch_size,
input_size, dataset_name, Summary_Writer, test_re_dir):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.eval()
if is_val:
save_path_hh = os.path.join(test_re_dir, str(save_index))
else:
save_path_hh = test_re_dir
test_loader = get_dataLoader(Pic_path=Pic_path, H5_path=H5_path, GT_path=GT_path,
train_mode="test", STA_mode=STA_mode,
batch_size=batch_size, input_size=input_size) # 获取测试集
# !!
for idx_test, dat_test in enumerate(test_loader):
with torch.no_grad():
if STA_mode == "S":
img_name, img1, class_id, onehot_label = dat_test
img1 = img1.to(device)
class_id = class_id.to(device)
x11, x22, map1, map2 = model(img1)
loss_t = F.cross_entropy(x11, class_id) + F.cross_entropy(x22, class_id)
elif STA_mode == "SA":
img_name, img1, aud1, class_id, onehot_label = dat_test
img1 = img1.to(device)
aud1 = aud1.to(device)
class_id = class_id.to(device)
x11, x22, map1, map2 = model(img1, aud1)
loss_t = F.cross_entropy(x11, class_id) + F.cross_entropy(x22, class_id)
elif STA_mode == "ST":
img_name, img_bef, img_now, img_aft, class_id, onehot_label = dat_test
img_bef = img_bef.to(device)
img_now = img_now.to(device)
img_aft = img_aft.to(device)
class_id = class_id.to(device)
x11, x1, x22, x2, x33, x3, map1, map2 = model(img_bef, img_now, img_aft)
loss_t = 0.4 * (F.cross_entropy(x11, class_id) + F.cross_entropy(x22, class_id)
+ F.cross_entropy(x33, class_id)) \
+ 0.6 * (F.cross_entropy(x1, class_id) + F.cross_entropy(x2, class_id)
+ F.cross_entropy(x3, class_id))
else:
img_name, img_bef, aud_bef, gt_bef, img_1, aud_now, gt_now, \
img_aft, aud_aft, gt_aft, class_id, onehot_label = dat_test
img_bef = img_bef.to(device)
img_now = img_1.to(device)
img_aft = img_aft.to(device)
aud_bef = aud_aft.to(device)
aud_now = aud_now.to(device)
aud_aft = aud_aft.to(device)
gt_bef = gt_bef.to(device)
gt_now = gt_now.to(device)
gt_aft = gt_aft.to(device)
audiocls = torch.load(r'STA/AudioSwitch.pt')
audiocls.cuda().eval()
with torch.no_grad():
switch_bef = audiocls(aud_bef, img_bef)
switch_now = audiocls(aud_now, img_now)
switch_aft = audiocls(aud_aft, img_aft)
loss2 = nn.BCEWithLogitsLoss().to(device)
loss1 = KLDLoss().to(device)
p04, p03, p02, p14, p13, p12, p24, p23, p22 = \
model(img_bef, img_now, img_aft, aud_bef, aud_now, aud_aft,
switch_bef, switch_now, switch_aft)
loss_t = loss2(p04, gt_bef) + loss2(p14, gt_now) + loss2(p24, gt_aft) + \
loss2(p03, gt_bef) + loss2(p13, gt_now) + loss2(p23, gt_aft) + \
loss2(p02, gt_bef) + loss2(p12, gt_now) + loss2(p22, gt_aft) + \
loss1(F.sigmoid(p04), gt_bef) + loss1(F.sigmoid(p14), gt_now) + \
loss1(F.sigmoid(p24), gt_aft) + loss1(F.sigmoid(p03), gt_bef) + \
loss1(F.sigmoid(p13), gt_now) + loss1(F.sigmoid(p23), gt_aft) + \
loss1(F.sigmoid(p02), gt_bef) + loss1(F.sigmoid(p12), gt_now) + \
loss1(F.sigmoid(p22), gt_aft)
result_show_list = []
if is_val:
Summary_Writer.add_scalars(dataset_name + "_" + STA_mode, {"val_loss": loss_t.data.item()},
(save_index * len(test_loader) + idx_test) * 46)
else:
Summary_Writer.add_scalar(dataset_name + "_" + STA_mode + "_test_loss", loss_t.data.item(),
save_index * len(test_loader) + idx_test)
dt = datetime.now().strftime("%y-%m-%d %H:%M:%S")
print('time:{}\t'
'Batch: [{:4d}/{:4d}]\t'
'Loss {:.4f})\t'.format(dt, idx_test, len(test_loader), loss_t.data.item()))
if not is_val or args.need_val_repic_save or (idx_test % (len(test_loader) // 4) == 0):
# is_test or you need to save all val_repic
# or (idx_test % (len(test_loader)//4)==0 which need to generate tensorboard pic)
h_x = F.softmax(x11, dim=1).data.squeeze() # softmax 转化为概率
probs, index_of_pic = h_x.sort(1, True) # 1行排序
probs = probs[:, 0] # 排序后最大数值
index_of_pic = index_of_pic[:, 0] # 排序后最大值索引
ind = torch.nonzero(onehot_label) # [10, 28] -> 非0元素的行列索引
for i in range(ind.shape[0]): # 非0元素的个数
batch_index, la = ind[i] # 帧索引，类别索引
save_accu_map_folder = os.path.join(save_path_hh, "%02d_%s" % (la, id_category[la]), img_name[i][:-3])
if not os.path.exists(save_accu_map_folder):
os.makedirs(save_accu_map_folder)
save_accu_map_path = os.path.join(save_accu_map_folder, img_name[i][-2:])
if la != index_of_pic[i]:
save_accu_map_path += r"(wrong_%02d_%s)" % (index_of_pic[i], id_category[index_of_pic[i]])
atts = (map1[i] + map2[i]) / 2 # 计算两幅图的平均值
atts[atts < 0] = 0
att = atts[la].cpu().data.numpy() # 转为numpy数组
att = np.clip(att, 0, 255)
att = np.array(att, np.uint8)
att = cv2.resize(att, (356, 356)) # 修改分辨率
heatmap = cv2.applyColorMap(att, cv2.COLORMAP_JET)
img = cv2.imread(os.path.join(Pic_path, img_name[i] + ".jpg"))
img = cv2.resize(img, (356, 356))
result = heatmap * 0.3 + img * 0.5
if (not is_val) or args.need_val_repic_save:
cv2.imwrite(save_accu_map_path + '.png', att) # 保存图片
cv2.imwrite(save_accu_map_path + ".jpg", result)
if is_val:
# if True:
cv2.imwrite(save_accu_map_path + ".jpg", result)
img = cv2.imread(save_accu_map_path + ".jpg")
img1 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
result_show_list.append(img1)
if len(result_show_list) > 0:
Summary_Writer.add_images("result batch:" + str(idx_test), np.stack(result_show_list, 0),
save_index, dataformats="NHWC")
def load_model_weight_bef_test(test_weight_id=-1, STA_mode="S", fname=r'test_result'):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net, _ = get_model(STA_mode)
save_weight_fold = os.path.join(args.save_dir, STA_mode, './model_weight/')
if test_weight_id == -1:
test_epoch = input(r"please input your test weight of your mode! Maybe is 10/20/.../best: ")
else:
test_epoch = str(test_weight_id)
if test_epoch == "best":
best_pth = os.path.join(save_weight_fold, '%s_%s_model_best.pth.tar' % (args.dataset_name, STA_mode))
if os.path.exists(best_pth):
print("-----> find pretrained model weight in", best_pth)
state = torch.load(best_pth)
net.load_state_dict(state['state_dict'])
test_epoch = str(state['epoch'] + 1) + "_best"
else:
print("Error! There is not pretrained weight --" + test_epoch, "in", best_pth)
exit()
else:
num = int(test_epoch)
best_pth = os.path.join(save_weight_fold, "%s_%s_%03d" % (args.dataset_name, STA_mode, num) + '.pth')
if os.path.exists(best_pth):
print("-----> find pretrained model weight in", best_pth)
state = torch.load(best_pth)
net.load_state_dict(state)
else:
print("Error! There is not pretrained weight --" + test_epoch, "in", best_pth)
exit()
print("-----> success load pretrained weight form ", best_pth)
print("-----> let's test! -------------->")
net.to(device)
test_result_dir = os.path.join(args.save_dir, STA_mode, r"pic_result",
r'./%s_%s_%s/' % (STA_mode, fname, test_epoch)) # 结果保存文件夹
if not os.path.exists(test_result_dir):
os.makedirs(test_result_dir)
writer = SummaryWriter(os.path.join(args.save_dir, STA_mode, r'./test_log/',
r'./%s_%s_%s/' % (STA_mode, fname, test_epoch)))
test(model=net, STA_mode=STA_mode, Pic_path=args.Pic_path, H5_path=args.H5_path, GT_path=args.GT_path, is_val=False,
save_index=0, batch_size=args.batch_size, input_size=args.input_size,
dataset_name=args.dataset_name, Summary_Writer=writer, test_re_dir=test_result_dir)
writer.close()
# ========================================================================
if __name__ == '__main__':
args = get_parser()
load_model_weight_bef_test(test_weight_id=-1, STA_mode="ST")
# for i in range(31, 32):
# print("now let's test weight", i)
# load_model_weight_bef_test(i)
# tensorboard.exe --logdir ./ --samples_per_plugin images=100