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test_model.py
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import torch.optim
from Load_Dataset import ValGenerator, ImageToImage2D
from torch.utils.data import DataLoader
import warnings
warnings.filterwarnings("ignore")
import Config as config
import matplotlib.pyplot as plt
from tqdm import tqdm
import os
from nets.LViT import LViT
from utils import *
import cv2
def show_image_with_dice(predict_save, labs, save_path):
tmp_lbl = (labs).astype(np.float32)
tmp_3dunet = (predict_save).astype(np.float32)
dice_pred = 2 * np.sum(tmp_lbl * tmp_3dunet) / (np.sum(tmp_lbl) + np.sum(tmp_3dunet) + 1e-5)
# dice_show = "%.3f" % (dice_pred)
iou_pred = jaccard_score(tmp_lbl.reshape(-1), tmp_3dunet.reshape(-1))
# fig, ax = plt.subplots()
# plt.gca().add_patch(patches.Rectangle(xy=(4, 4),width=120,height=20,color="white",linewidth=1))
if config.task_name == "MoNuSeg":
predict_save = cv2.pyrUp(predict_save, (448, 448))
predict_save = cv2.resize(predict_save, (2000, 2000))
# kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32) #定义一个核
# predict_save = cv2.filter2D(predict_save, -1, kernel=kernel)
cv2.imwrite(save_path, predict_save * 255)
else:
cv2.imwrite(save_path, predict_save * 255)
# plt.imshow(predict_save * 255,cmap='gray')
# plt.text(x=10, y=24, s="Dice:" + str(dice_show), fontsize=5)
# plt.axis("off")
# remove the white borders
# height, width = predict_save.shape
# fig.set_size_inches(width / 100.0 / 3.0, height / 100.0 / 3.0)
# plt.gca().xaxis.set_major_locator(plt.NullLocator())
# plt.gca().yaxis.set_major_locator(plt.NullLocator())
# plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0)
# plt.margins(0, 0)
# plt.savefig(save_path, dpi=2000)
# plt.close()
return dice_pred, iou_pred
def vis_and_save_heatmap(model, input_img, text, img_RGB, labs, vis_save_path, dice_pred, dice_ens):
model.eval()
output = model(input_img.cuda(), text.cuda())
pred_class = torch.where(output > 0.5, torch.ones_like(output), torch.zeros_like(output))
predict_save = pred_class[0].cpu().data.numpy()
predict_save = np.reshape(predict_save, (config.img_size, config.img_size))
dice_pred_tmp, iou_tmp = show_image_with_dice(predict_save, labs,
save_path=vis_save_path + '_predict' + model_type + '.jpg')
return dice_pred_tmp, iou_tmp
if __name__ == '__main__':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
test_session = config.test_session
if config.task_name == "MoNuSeg":
test_num = 14
model_type = config.model_name
model_path = "./MoNuSeg/" + model_type + "/" + test_session + "/models/best_model-" + model_type + ".pth.tar"
elif config.task_name == "Covid19":
test_num = 2113
model_type = config.model_name
model_path = "./Covid19/" + model_type + "/" + test_session + "/models/best_model-" + model_type + ".pth.tar"
save_path = config.task_name + '/' + model_type + '/' + test_session + '/'
vis_path = "./" + config.task_name + '_visualize_test/'
if not os.path.exists(vis_path):
os.makedirs(vis_path)
checkpoint = torch.load(model_path, map_location='cuda')
if model_type == 'LViT':
config_vit = config.get_CTranS_config()
model = LViT(config_vit, n_channels=config.n_channels, n_classes=config.n_labels)
elif model_type == 'LViT_pretrain':
config_vit = config.get_CTranS_config()
model = LViT(config_vit, n_channels=config.n_channels, n_classes=config.n_labels)
else:
raise TypeError('Please enter a valid name for the model type')
model = model.cuda()
if torch.cuda.device_count() > 1:
print("Let's use {0} GPUs!".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model.load_state_dict(checkpoint['state_dict'], strict=False)
print('Model loaded !')
tf_test = ValGenerator(output_size=[config.img_size, config.img_size])
test_text = read_text(config.test_dataset + 'Test_text.xlsx')
test_dataset = ImageToImage2D(config.test_dataset, config.task_name, test_text, tf_test, image_size=config.img_size)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)
dice_pred = 0.0
iou_pred = 0.0
dice_ens = 0.0
with tqdm(total=test_num, desc='Test visualize', unit='img', ncols=70, leave=True) as pbar:
for i, (sampled_batch, names) in enumerate(test_loader, 1):
# print(names)
test_data, test_label, test_text = sampled_batch['image'], sampled_batch['label'], sampled_batch['text']
arr = test_data.numpy()
arr = arr.astype(np.float32())
lab = test_label.data.numpy()
img_lab = np.reshape(lab, (lab.shape[1], lab.shape[2])) * 255
fig, ax = plt.subplots()
plt.imshow(img_lab, cmap='gray')
plt.axis("off")
height, width = config.img_size, config.img_size
fig.set_size_inches(width / 100.0 / 3.0, height / 100.0 / 3.0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0)
plt.margins(0, 0)
plt.savefig(vis_path + str(names) + "_lab.jpg", dpi=300)
plt.close()
input_img = torch.from_numpy(arr)
dice_pred_t, iou_pred_t = vis_and_save_heatmap(model, input_img, test_text, None, lab,
vis_path + str(names),
dice_pred=dice_pred, dice_ens=dice_ens)
dice_pred += dice_pred_t
iou_pred += iou_pred_t
torch.cuda.empty_cache()
pbar.update()
print("dice_pred", dice_pred / test_num)
print("iou_pred", iou_pred / test_num)