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test_cd.py
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test_cd.py
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import torch
import torch.optim as optim
import data as Data
import models as Model
import torch.nn as nn
import argparse
import logging
import core.logger as Logger
import os
import numpy as np
from misc.metric_tools import ConfuseMatrixMeter
from models.loss import *
from collections import OrderedDict
import core.metrics as Metrics
from misc.torchutils import get_scheduler, save_network
if __name__ == '__main__':
parser =argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='/data/zht/PycharmProiect/cd_mamba/config/whu_mamaba/whu_test_vis_cdmamba.json',
help='JSON file for configuration')
parser.add_argument('--phase', type=str, default='test',
choices=['train', 'test'], help='Run either train(training + validation) or testing',)
parser.add_argument('--gpu_ids', type=str, default=None)
parser.add_argument('-log_eval', action='store_true')
#paser config
args =parser.parse_args()
opt = Logger.parse(args)
#Convert to NoneDict, which return None for missing key.
opt = Logger.dict_to_nonedict(opt)
#logging
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
Logger.setup_logger(logger_name=None, root=opt['path_cd']['log'], phase='train',
level=logging.INFO, screen=True)
Logger.setup_logger(logger_name='test', root=opt['path_cd']['log'], phase='test',
level=logging.INFO)
logger = logging.getLogger('base')
logger.info(Logger.dict2str(opt))
#dataset
for phase, dataset_opt in opt['datasets'].items(): #train train{}
#print(" phase is {}, dataopt is {}".format(phase, dataset_opt))
if phase == 'train' and args.phase != 'test':
print("Creat [train] change-detection dataloader")
train_set = Data.create_cd_dataset(dataset_opt=dataset_opt, phase=phase)
train_loader = Data.create_cd_dataloader(train_set, dataset_opt, phase)
opt['len_train_dataloader'] = len(train_loader)
elif phase == 'val' and args.phase != 'test':
print("Creat [val] change-detection dataloader")
val_set = Data.create_cd_dataset(dataset_opt=dataset_opt, phase=phase)
val_loader = Data.create_cd_dataloader(val_set, dataset_opt, phase)
opt['len_val_dataloader'] = len(val_loader)
elif phase == 'test' and args.phase == 'test':
print("Creat [test] change-detection dataloader")
test_set = Data.create_cd_dataset(dataset_opt=dataset_opt, phase=phase)
test_loader = Data.create_cd_dataloader(test_set, dataset_opt, phase)
opt['len_test_dataloader'] = len(test_loader)
logger.info('Initial Dataset Finished')
#Create cd model
cd_model = Model.create_CD_model(opt)
#Create criterion
if opt['model']['loss'] == 'ce_dice':
loss_fun = ce_dice
elif opt['model']['loss'] == 'ce':
loss_fun = cross_entropy
#loss_fun.to(opt["gpu_ids"])
#Create optimer
if opt['train']["optimizer"]["type"] == 'adam':
optimer = optim.Adam(cd_model.parameters(), lr=opt['train']["optimizer"]["lr"])
elif opt['train']["optimizer"]["type"] == 'adamw':
optimer = optim.AdamW(cd_model.parameters(), lr=opt['train']["optimizer"]["lr"])
device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
cd_model.to(device)
if len(opt['gpu_ids']) > 0:
cd_model = nn.DataParallel(cd_model)
metric = ConfuseMatrixMeter(n_class=2)
log_dict = OrderedDict()
#################
# Training loop #
#################
if opt['phase'] == 'train':
best_mF1 = 0.0
for current_epoch in range(0, opt['train']['n_epoch']):
print("......Begin Training......")
metric.clear()
cd_model.train()
train_result_path = '{}/train/{}'.format(opt['path_cd']['result'], current_epoch)
os.makedirs(train_result_path, exist_ok=True)
#################
# Training #
#################
message = 'lr: %0.7f\n \n' % optimer.param_groups[0]['lr']
logger.info(message)
for current_step, train_data in enumerate(train_loader):
train_im1 = train_data['A'].to(device)
train_im2 = train_data['B'].to(device)
pred_img = cd_model(train_im1, train_im2)
gt = train_data['L'].to(device).long()
train_loss = loss_fun(pred_img, gt)
optimer.zero_grad()
train_loss.backward()
optimer.step()
log_dict['loss'] = train_loss.item()
#pred score
G_pred = pred_img.detach()
G_pred = torch.argmax(G_pred, dim=1)
current_score = metric.update_cm(pr=G_pred.cpu().numpy(), gt=gt.detach().cpu().numpy())
log_dict['running_acc'] = current_score.item()
# log running batch status
if current_step % opt['train']['train_print_iter'] == 0:
# message
logs = log_dict
message = '[Training CD]. epoch: [%d/%d]. Itter: [%d/%d], CD_loss: %.5f, running_mf1: %.5f\n' % \
(current_epoch, opt['train']['n_epoch'] - 1, current_step, len(train_loader), logs['loss'],
logs['running_acc'])
logger.info(message)
#vis
out_dict = OrderedDict()
out_dict['pred_cm'] = torch.argmax(pred_img, dim=1, keepdim=False)
out_dict['gt_cm'] = gt
visuals = out_dict
img_mode = "grid"
if img_mode == "single":
# Converting to uint8
img_A = Metrics.tensor2img(train_data['A'], out_type=np.uint8, min_max=(-1, 1)) # uint8
img_B = Metrics.tensor2img(train_data['B'], out_type=np.uint8, min_max=(-1, 1)) # uint8
gt_cm = Metrics.tensor2img(visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1), out_type=np.uint8,
min_max=(0, 1)) # uint8
pred_cm = Metrics.tensor2img(visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1), out_type=np.uint8,
min_max=(0, 1)) # uint8
# save imgs
Metrics.save_img(
img_A, '{}/img_A_e{}_b{}.png'.format(train_result_path, current_epoch, current_step))
Metrics.save_img(
img_B, '{}/img_B_e{}_b{}.png'.format(train_result_path, current_epoch, current_step))
Metrics.save_img(
pred_cm, '{}/img_pred_e{}_b{}.png'.format(train_result_path, current_epoch, current_step))
Metrics.save_img(
gt_cm, '{}/img_gt_e{}_b{}.png'.format(train_result_path, current_epoch, current_step))
else:
# grid img
visuals['pred_cm'] = visuals['pred_cm'] * 2.0 - 1.0
visuals['gt_cm'] = visuals['gt_cm'] * 2.0 - 1.0
grid_img = torch.cat((train_data['A'].to(device),
train_data['B'].to(device),
visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1)),
dim=0)
grid_img = Metrics.tensor2img(grid_img) # uint8
Metrics.save_img(
grid_img,
'{}/img_A_B_pred_gt_e{}_b{}.png'.format(train_result_path, current_epoch, current_step))
### log epoch status ###
scores = metric.get_scores()
epoch_acc = scores['mf1']
log_dict['epoch_acc'] = epoch_acc.item()
for k, v in scores.items():
log_dict[k] = v
logs =log_dict
message = '[Training CD (epoch summary)]: epoch: [%d/%d]. epoch_mF1=%.5f \n' % \
(current_epoch, opt['train']['n_epoch'] - 1, logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
message += '\n'
logger.info(message)
metric.clear()
##################
### validation ###
##################
cd_model.eval()
with torch.no_grad():
if current_epoch % opt['train']['val_freq'] == 0:
val_result_path = '{}/val/{}'.format(opt['path_cd']['result'], current_epoch)
os.makedirs(val_result_path, exist_ok=True)
for current_step, val_data in enumerate(val_loader):
val_img1 = val_data['A'].to(device)
val_img2 = val_data['B'].to(device)
pred_img = cd_model(val_img1, val_img2)
gt = val_data['L'].to(device).long()
val_loss = loss_fun(pred_img, gt)
log_dict['loss'] = val_loss.item()
#pred score
G_pred = pred_img.detach()
G_pred = torch.argmax(G_pred, dim=1)
current_score = metric.update_cm(pr=G_pred.cpu().numpy(), gt=gt.detach().cpu().numpy())
log_dict['running_acc'] = current_score.item()
# log running batch status for val data
if current_step % opt['train']['val_print_iter'] == 0:
# message
logs = log_dict
message = '[Validation CD]. epoch: [%d/%d]. Itter: [%d/%d], running_mf1: %.5f\n' % \
(current_epoch, opt['train']['n_epoch'] - 1, current_step, len(val_loader), logs['running_acc'])
logger.info(message)
#visual
out_dict = OrderedDict()
out_dict['pred_cm'] = torch.argmax(pred_img, dim=1, keepdim=False)
out_dict['gt_cm'] = gt
visuals = out_dict
img_mode = "grid"
if img_mode == "single":
# Converting to uint8
img_A = Metrics.tensor2img(val_data['A'], out_type=np.uint8, min_max=(-1, 1)) # uint8
img_B = Metrics.tensor2img(val_data['B'], out_type=np.uint8, min_max=(-1, 1)) # uint8
gt_cm = Metrics.tensor2img(visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
out_type=np.uint8, min_max=(0, 1)) # uint8
pred_cm = Metrics.tensor2img(visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
out_type=np.uint8, min_max=(0, 1)) # uint8
# save imgs
Metrics.save_img(
img_A, '{}/img_A_e{}_b{}.png'.format(val_result_path, current_epoch, current_step))
Metrics.save_img(
img_B, '{}/img_B_e{}_b{}.png'.format(val_result_path, current_epoch, current_step))
Metrics.save_img(
pred_cm, '{}/img_pred_e{}_b{}.png'.format(val_result_path, current_epoch, current_step))
Metrics.save_img(
gt_cm, '{}/img_gt_e{}_b{}.png'.format(val_result_path, current_epoch, current_step))
else:
# grid img
visuals['pred_cm'] = visuals['pred_cm'] * 2.0 - 1.0
visuals['gt_cm'] = visuals['gt_cm'] * 2.0 - 1.0
grid_img = torch.cat((val_data['A'].to(device),
val_data['B'].to(device),
visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1)),
dim=0)
grid_img = Metrics.tensor2img(grid_img) # uint8
Metrics.save_img(
grid_img,'{}/img_A_B_pred_gt_e{}_b{}.png'.format(val_result_path, current_epoch, current_step))
### log epoch status ###
scores = metric.get_scores()
epoch_acc = scores['mf1']
log_dict['epoch_acc'] = epoch_acc.item()
for k, v in scores.items():
log_dict[k] = v
logs = log_dict
message = '[Validation CD (epoch summary)]: epoch: [%d/%d]. epoch_mF1=%.5f \n' % \
(current_epoch, opt['train']['n_epoch'], logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
message += '\n'
logger.info(message)
#best model
if logs['epoch_acc'] > best_mF1:
is_best_model = True
best_mF1 = logs['epoch_acc']
logger.info('[Validation CD] Best model updated. Saving the models (current + best) and training states.')
else:
is_best_model = False
logger.info('[Validation CD]Saving the current cd model and training states.')
logger.info('--- Proceed To The Next Epoch ----\n \n')
#save model
save_network(opt, current_epoch, cd_model, optimer, is_best_model)
metric.clear()
get_scheduler(optimizer=optimer, args=opt['train']).step()
logger.info('End of training.')
else:
logger.info('Begin Model Evaluation (testing).')
test_result_path = '{}/test/'.format(opt['path_cd']['result'])
os.makedirs(test_result_path, exist_ok=True)
logger_test = logging.getLogger('test') # test logger
##load network
load_path = opt["path_cd"]["resume_state"]
print(load_path)
if load_path is not None:
logger.info(
'Loading pretrained model for CD model [{:s}] ...'.format(load_path))
gen_path = '{}_gen.pth'.format(load_path)
opt_path = '{}_opt.pth'.format(load_path)
# change detection model
cd_model = Model.create_CD_model(opt)
cpkt_state = torch.load(gen_path)
missing_keys, unexpected_keys = cd_model.load_state_dict(cpkt_state, strict=False) #True
print(missing_keys)
# cd_model.load_state_dict(cpkt_state, strict=False) #True
cd_model.to(device)
metric.clear()
cd_model.eval()
with torch.no_grad():
for current_step, test_data in enumerate(test_loader):
test_img1 = test_data['A'].to(device)
test_img2 = test_data['B'].to(device)
pred_img = cd_model(test_img1, test_img2)
gt = test_data['L'].to(device).long()
# pred score
G_pred = pred_img.detach()
G_pred = torch.argmax(G_pred, dim=1)
current_score = metric.update_cm(pr=G_pred.cpu().numpy(), gt=gt.detach().cpu().numpy())
log_dict['running_acc'] = current_score.item()
logs = log_dict
message = '[Testing CD]. Itter: [%d/%d], running_mf1: %.5f\n' % \
(current_step, len(test_loader), logs['running_acc'])
logger_test.info(message)
# Vissuals
out_dict = OrderedDict()
out_dict['pred_cm'] = torch.argmax(pred_img, dim=1, keepdim=False)
out_dict['gt_cm'] = gt
visuals = out_dict
img_mode = 'single'
if img_mode == 'single':
# Converting to uint8
visuals['pred_cm'] = visuals['pred_cm'] * 2.0 - 1.0
visuals['gt_cm'] = visuals['gt_cm'] * 2.0 - 1.0
img_A = Metrics.tensor2img(test_data['A'], out_type=np.uint8, min_max=(-1, 1)) # uint8
img_B = Metrics.tensor2img(test_data['B'], out_type=np.uint8, min_max=(-1, 1)) # uint8
gt_cm = Metrics.tensor2img(visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1), out_type=np.uint8,
min_max=(0, 1)) # uint8
pred_cm = Metrics.tensor2img(visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
out_type=np.uint8, min_max=(0, 1)) # uint8
# Save imgs
Metrics.save_img(
img_A, '{}/img_A_{}.png'.format(test_result_path, current_step))
Metrics.save_img(
img_B, '{}/img_B_{}.png'.format(test_result_path, current_step))
Metrics.save_img(
pred_cm, '{}/img_pred_cm{}.png'.format(test_result_path, current_step))
Metrics.save_img(
gt_cm, '{}/img_gt_cm{}.png'.format(test_result_path, current_step))
else:
# grid img
visuals['pred_cm'] = visuals['pred_cm'] * 2.0 - 1.0
visuals['gt_cm'] = visuals['gt_cm'] * 2.0 - 1.0
grid_img = torch.cat((test_data['A'],
test_data['B'],
visuals['pred_cm'].unsqueeze(1).repeat(1, 3, 1, 1),
visuals['gt_cm'].unsqueeze(1).repeat(1, 3, 1, 1)),
dim=0)
grid_img = Metrics.tensor2img(grid_img) # uint8
Metrics.save_img(
grid_img, '{}/img_A_B_pred_gt_{}.png'.format(test_result_path, current_step))
### log epoch status ###
scores = metric.get_scores()
epoch_acc = scores['mf1']
log_dict['epoch_acc'] = epoch_acc.item()
for k, v in scores.items():
log_dict[k] = v
logs = log_dict
message = '[Test CD summary]: Test mF1=%.5f \n' % \
(logs['epoch_acc'])
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
message += '\n'
logger_test.info(message)
logger.info('End of testing...')