Datasets:
jblitzar
/
github-python-corpus

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g = GraphBuilder(cfg) # build fresh compute graph
g.restore(restore_snap=snap, select_list=['model/.*'])
tester = ModelTester(cfg)
if 'val' in cfg.mode:
log_file = os.path.join(cfg.saving_path, f'log_validation.txt_{step}')
with redirect_io(log_file, cfg.debug):
log_config(cfg)
print('using restored model, chosen_snap =', snap, flush=True)
tester.val_vote(g.sess, g.ops, g.dataset, g.model, num_votes=cfg.num_votes) # fresh voting
print(flush=True)
print_mem('>>> finished val', check_time=True)
if 'test' in cfg.mode:
log_file = os.path.join(cfg.saving_path, f'log_test.txt_{step}')
test_path = os.path.join(cfg.saving_path, f'test_{step}')
with redirect_io(log_file, cfg.debug):
log_config(cfg)
tester.test_vote(g.sess, g.ops, g.dataset, g.model, num_votes=cfg.num_votes, test_path=test_path)
print(flush=True)
print_mem('>>> finished test', check_time=True)
for snap in snap_list:
val_test(snap)
# cleanup
for child in mp.active_children():
child.terminate()
parent = psutil.Process(os.getpid())
children = parent.children(recursive=True)
for child in children:
child.kill()
# <FILESEP>
from dataset import *
from torchvision import transforms
import copy
import time
import datetime
from torchsummary import summary
from fvcore.nn import FlopCountAnalysis, ActivationCountAnalysis
from models.H2former import *
from dataset import *
from utils import *
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
num_classes = 2
batch_size = 1
image_size = (512,512)
save_dir='./result/'
base_dir = './data/skin/test/' # polyp, idrid, skin
dataset = 'skin'
db_val = testBaseDataSets(base_dir, 'test.txt',image_size,dataset,transform=transforms.Compose([RandomGenerator()]))
valloader = DataLoader(db_val, batch_size=batch_size, shuffle=False,num_workers=0)
model_name='res34_swin_MS_skin'
model = res34_swin_MS(image_size[0],2)
for k in range(75,76,3):
print('./new/'+model_name+str(k)+'.pth')
model.load_state_dict(torch.load('./new/'+model_name+str(k)+'.pth'))
model.cuda()
model.eval()
j = 0
evaluator = Evaluator()
start_time = time.time()
with torch.no_grad():
for sampled_batch in valloader:
images, labels = sampled_batch['image'], sampled_batch['label']
images, labels = images.cuda(),labels.cuda()
predictions = model(images)
pred = predictions[0,1,:,:]
evaluator.update(pred, labels[0,:,:].float())
for i in range(batch_size):
labels = labels.cpu().numpy()
images = images[i].cpu().numpy()
label = (labels[i]*255)
pred = pred.cpu().numpy()
#total_img = np.concatenate((label,pred[:,:]*255),axis=1)
#cv2.imwrite(save_dir+'Pre'+str(j)+'.jpg',pred[:,:]*255)
#cv2.imwrite(save_dir+'GT'+str(j)+'.jpg',label)
#cv2.imwrite(save_dir+'image'+str(j)+'.jpg',images.transpose(1, 2, 0)[:,:,::-1])
j=j+1
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Test time {}'.format(total_time_str))
evaluator.show()
# <FILESEP>
#!/usr/bin/env -S uv run --quiet --script
# /// script
# dependencies = [