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test.py
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test.py
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r""" JTFN training code """
import argparse
import os
import torch.nn as nn
import torch
from common.logger import AverageMeter
from common.evaluation import Evaluator
from common import config
from common import utils
from common.show import show_compare
from data.dataset import CSDataset
from models import create_model
import PIL.Image as Image
import csv
import numpy as np
def get_parser():
parser = argparse.ArgumentParser(description='JTFN for Curvilinear Structure Segmentation')
parser.add_argument('--config', type=str, default='config/UNet_DRIVE.yaml', help='Model config file')
args = parser.parse_args()
assert args.config is not None
cfg = config.load_cfg_from_cfg_file(args.config)
return cfg
def create_csv(path, csv_head):
# path = "aa.csv"
with open(path, 'w', newline='') as f:
csv_write = csv.writer(f)
# csv_head = ["good","bad"]
csv_write.writerow(csv_head)
def write_csv(path, data_row):
# path = "aa.csv"
with open(path, 'a+', newline='') as f:
csv_write = csv.writer(f)
# data_row = ["1","2"]
csv_write.writerow(data_row)
def main():
global args
args = get_parser()
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.train_gpu)
# Model initialization
model = create_model(args)
print("=> creating model ...")
print("Classes: {}".format(args.classes))
# Device setup
print('# available GPUs: %d' % torch.cuda.device_count())
if torch.cuda.device_count() > 1:
model = model.cuda()
model = nn.DataParallel(model)
print('Use GPU Parallel.')
elif torch.cuda.is_available():
model = model.cuda()
else:
model = model
print("args.weight", args.weight)
if args.weight:
if os.path.isfile(args.weight):
print("=> loading weight '{}'".format(args.weight))
checkpoint = torch.load(args.weight)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded weight '{}'".format(args.weight))
else:
print("=> no weight found at '{}'".format(args.weight))
else:
raise RuntimeError("Please support weight.")
Evaluator.initialize()
# Dataset initialization
CSDataset.initialize(datapath=args.datapath)
dataloader_val = CSDataset.build_dataloader(args.benchmark,
args.batch_size_val,
args.nworker,
'val',
'same',
None)
# show_weight(model, dataloader_val)
# threshold_list = np.arange(0.1, 1, 0.01).tolist()
#print(threshold_list)
#threshold_list = [0.4, 0.5]
best_th = 0
best_f1, best_pr, best_r, best_quality, best_cor, best_com = 0, 0, 0, 0, 0, 0
with torch.no_grad():
for threshold in range(15, 26):
thresh = threshold / 100
val_loss_dict, val_f1, val_pr, val_r, val_quality, val_cor, val_com = evaluate_threshold(model, dataloader_val, thresh)
if val_f1 > best_f1:
best_th = thresh
best_f1, best_pr, best_r, best_quality, best_cor, best_com \
= val_f1, val_pr, val_r, val_quality, val_cor, val_com
print(best_th, best_f1)
# val_loss_dict, val_f1, val_pr, val_r, val_quality, val_cor, val_com = evaluate(model, dataloader_val)
# val_loss_dict, val_f1, val_pr, val_r, val_quality, val_cor, val_com = evaluate_threshold(model, dataloader_val, 0.16)
# print('F1: {:.2f} Precision: {:.2f} Recall: {:.2f}'.format(val_f1.item(), val_pr.item(), val_r.item()))
# print('Quality: {:.2f} Correctness: {:.2f} Completeness: {:.2f}'.format(val_quality.item(), val_cor.item(),
# val_com.item()))
print(best_th)
print('F1: {:.2f} Precision: {:.2f} Recall: {:.2f}'.format(best_f1.item(), best_pr.item(), best_r.item()))
print('Quality: {:.2f} Correctness: {:.2f} Completeness: {:.2f}'.format(best_quality.item(), best_cor.item(),
best_com.item()))
print('==================== Finished Testing ====================')
def show_weight(model, dataloader):
path = "save_image/weight/"
for idx, batch in enumerate(dataloader):
# 1. Forward pass
# batch = utils.to_cuda(batch) if torch.cuda.is_available() else batch
img = batch['img']
gt = batch['anno_mask']
weight = np.load("weight_scale.npy")
print(gt.shape, weight.shape)
gt_weight_1 = gt * weight[:, 0, :, :]
gt_weight_2 = gt * weight[:, 1, :, :]
gt_weight_3 = gt * weight[:, 2, :, :]
gt_weight_1 = gt_weight_1[0, 0, :, :].cpu().detach().numpy()
gt_weight_2 = gt_weight_2[0, 0, :, :].cpu().detach().numpy()
gt_weight_3 = gt_weight_3[0, 0, :, :].cpu().detach().numpy()
print(gt_weight_1.shape, gt_weight_2.shape, gt_weight_3.shape)
im = Image.fromarray(gt_weight_1 * 255.).convert("L")
im_name = path + '{}_weight_1.png'.format(str(idx))
im.save(im_name)
im = Image.fromarray(gt_weight_2 * 255.).convert("L")
im_name = path + '{}_weight_2.png'.format(str(idx))
im.save(im_name)
im = Image.fromarray(gt_weight_3 * 255.).convert("L")
im_name = path + '{}_weight_3.png'.format(str(idx))
im.save(im_name)
# print(gt_weight_1.shape, gt_weight_2.shape, gt_weight_3.shape)
def save_image(prob, affinity, out_affinity_1, idx):
N = prob.shape[0]
image_path = 'save_image/affuse/prob/'
aff_path = 'save_image/affuse/affinity/'
for num in range(0, N):
image = prob[num, 0, :, :].cpu().detach().numpy()
# print("image",image.shape)
# print("unique",np.unique(image))
im = Image.fromarray(image * 255.).convert("L")
im_name = image_path + '{}.png'.format(str(idx))
im.save(im_name)
for num in range(0, N):
affinity_image = affinity[num, :, :, :].cpu().detach().numpy()
Affinity_num = affinity_image.shape[0]
for direct in range(0, Affinity_num):
image = affinity_image[direct, :, :]
# print("image", image.shape)
print("image_0", np.unique(image))
im = Image.fromarray(image * 255.).convert("L")
im_name = aff_path + '{}_aff_{}.png'.format(str(idx), str(direct))
im.save(im_name)
for num in range(0, N):
affinity_image = out_affinity_1[num, :, :, :].cpu().detach().numpy()
Affinity_num = affinity_image.shape[0]
for direct in range(0, Affinity_num):
image = affinity_image[direct, :, :]
# print("image", image.shape)
im = Image.fromarray(image * 255.).convert("L")
im_name = aff_path + '{}_aff_{}_1.png'.format(str(idx), str(direct))
im.save(im_name)
def evaluate(model, dataloader):
r""" Eval JTFN """
global args
args = get_parser()
# Force randomness during training / freeze randomness during testing
if torch.cuda.device_count() > 1:
model.module.eval()
else:
model.eval()
average_meter = AverageMeter(dataloader.dataset)
val_score_path = os.path.join('logs', args.logname + '.log') + '/' + 'single_image_val.csv'
csv_head = ["image_name", "f1", "pr", "recall", "quality", "cor", "com"]
create_csv(val_score_path, csv_head)
for idx, batch in enumerate(dataloader):
# 1. Forward pass
batch = utils.to_cuda(batch) if torch.cuda.is_available() else batch
output_dict = model(batch)
out = output_dict['output']
pred_mask = torch.where(out >= 0.5, 1, 0)
# out_affinity = output_dict['step_' + '2' + '_output_affinity'][0]
# out_affinity_1 = output_dict['step_' + '2' + '_output_affinity'][1]
# print(out_affinity.shape, out_affinity_1.shape)
# save_image(out, out_affinity, out_affinity_1, idx)
# 2. Compute loss & update model parameters
loss_dict = model.module.compute_objective(output_dict,
batch) if torch.cuda.device_count() > 1 else model.compute_objective(
output_dict, batch_dict=batch)
# 3. Evaluate prediction
f1, pr, r, quality, cor, com = Evaluator.classify_prediction(pred_mask.clone(), batch)
img_name = batch.get('img_name')
data_row_f1score = [str(img_name), str(f1), str(pr), str(r),
str(quality), str(cor), str(com)]
write_csv(val_score_path, data_row_f1score)
average_meter.update(f1, pr, r, quality, cor, com, loss_dict)
# print(f1, pr, r, quality, cor, com)
#img = batch.get("img")[0, 0, :, :]
##pred_mask = pred_mask[0,0,:,:]
#print(img_name)
#rgb_img = torch.stack([pred_mask, pred_mask, pred_mask], dim=-1).cpu().numpy()*255.
##rgb_img = torch.stack([img, img, img], dim=-1).cpu().numpy()*255.
#print(rgb_img.shape)
# print(rgb_img)
#rgb_img = show_compare(rgb_img, pred_mask, batch.get('anno_mask'),
# batch.get('ignore_mask') if 'ignore_mask' in batch.keys() else None)
#im = Image.fromarray(np.uint8(rgb_img))
#im_name = "save_image/topoloss/drive_image/" + '{}.png'.format(str(img_name[0]))
#im.save(im_name)
avg_loss_dict = dict()
for key in average_meter.loss_buf.keys():
avg_loss_dict[key] = utils.mean(average_meter.loss_buf[key])
f1 = average_meter.compute_f1()
pr = average_meter.compute_precision()
r = average_meter.compute_recall()
quality = average_meter.compute_quality()
cor = average_meter.compute_correctness()
com = average_meter.compute_completeness()
return avg_loss_dict, f1, pr, r, quality, cor, com
def evaluate_threshold(model, dataloader, threshold=0.5):
r""" Eval JTFN """
# Force randomness during training / freeze randomness during testing
if torch.cuda.device_count() > 1:
model.module.eval()
else:
model.eval()
average_meter = AverageMeter(dataloader.dataset)
val_score_path = os.path.join('logs', args.logname + '.log') + '/' + 'single_image_val.csv'
csv_head = ["image_name", "f1", "pr", "recall", "quality", "cor", "com"]
create_csv(val_score_path, csv_head)
for idx, batch in enumerate(dataloader):
# 1. Forward pass
batch = utils.to_cuda(batch) if torch.cuda.is_available() else batch
# print(batch['img'].shape)
output_dict = model(batch)
out = output_dict['output']
pred_mask = torch.where(out >= threshold, 1, 0)
#out_affinity = output_dict['step_' + '2' + '_output_affinity'][0]
#out_affinity_1 = output_dict['step_' + '2' + '_output_affinity'][1]
#print(out_affinity.shape, out_affinity_1.shape)
# save_image(out, out_affinity, out_affinity_1, idx)
# 2. Compute loss & update model parameters
loss_dict = model.module.compute_objective(output_dict,
batch) if torch.cuda.device_count() > 1 else model.compute_objective(
output_dict, batch_dict=batch)
# 3. Evaluate prediction
f1, pr, r, quality, cor, com = Evaluator.classify_prediction(pred_mask.clone(), batch)
img_name = batch.get('img_name')
data_row_f1score = [str(img_name), str(f1), str(pr), str(r),
str(quality), str(cor), str(com)]
write_csv(val_score_path, data_row_f1score)
average_meter.update(f1, pr, r, quality, cor, com, loss_dict)
#
# img_name = batch.get('img_name')
#
# img = batch.get("img")[0, 0, :, :]
# #pred_mask = pred_mask[0,0,:,:]
# print(img_name)
# #rgb_img = torch.stack([pred_mask, pred_mask, pred_mask], dim=-1).cpu().numpy()*255.
# rgb_img = torch.stack([img, img, img], dim=-1).cpu().numpy()*255.
# #print(rgb_img.shape)
# # print(rgb_img)
# rgb_img = show_compare(rgb_img, pred_mask, batch.get('anno_mask'),
# batch.get('ignore_mask') if 'ignore_mask' in batch.keys() else None)
#
# im = Image.fromarray(np.uint8(rgb_img))
# im_name = "save_image/jtfn/dsa_image/" + '{}.png'.format(str(img_name[0]))
# im.save(im_name)
avg_loss_dict = dict()
for key in average_meter.loss_buf.keys():
avg_loss_dict[key] = utils.mean(average_meter.loss_buf[key])
f1 = average_meter.compute_f1()
pr = average_meter.compute_precision()
r = average_meter.compute_recall()
quality = average_meter.compute_quality()
cor = average_meter.compute_correctness()
com = average_meter.compute_completeness()
return avg_loss_dict, f1, pr, r, quality, cor, com
if __name__ == '__main__':
main()