if '--inherit' in sys.argv:
i = sys.argv.index('--inherit') + 1
if sys.argv[i].endswith(('.yml', '.yaml')):
sys.argv[i] = os.path.join(config_path, sys.argv[i])
else:
sys.argv[i] = os.path.join(experiments_path, sys.argv[i])
if '--update' in sys.argv:
i = sys.argv.index('--update') + 1
sys.argv[i] = os.path.join(config_path, sys.argv[i])
i = 0
while True:
if f'--update{i}' in sys.argv:
ind = sys.argv.index(f'--update{i}') + 1
sys.argv[ind] = os.path.join(config_path, sys.argv[ind])
i += 1
else:
break
cls = BaseCremiExperiment
trainer = Trainer()
trainer.load(from_directory='../../runs/cremi/speedrun/run_0', best=False)
loader = BaseCremiExperiment().build_train_loader()
trainer.cuda()
target, loss = trainer.apply_model_and_loss(
loader.dataset[0][0].unsqueeze(0).unsqueeze(0),
loader.dataset[0][1].unsqueeze(0).unsqueeze(0))
print('hi')
path='./val-volume.h5',
path_in_h5_dataset='data',
transforms=trans,
**yaml2dict('config_val.yml')['slicing_config'])
labelset_val = HDF5VolumeLoader(
path='./stardistance_val.h5',
path_in_h5_dataset='data',
transforms=trans2,
**yaml2dict('config_val.yml')['slicing_config_truth'])
trainer = Trainer()
trainer.load(from_directory='checkpoints', map_location='cpu', best=False)
result, loss = trainer.apply_model_and_loss(
imageset_val[5].unsqueeze(0).unsqueeze(0), #.to('cuda'),
labelset_val[5].unsqueeze(0).unsqueeze(0)) #.to('cuda'))
print(loss)
print(result)
plt.figure()
plt.imshow(result.squeeze().detach().numpy()[0])
#plt.imshow(result[0].detach().squeeze().cpu().numpy())
plt.title('result')
plt.figure()
plt.imshow(imageset_val[5].detach().squeeze().cpu().numpy())
plt.title('image')
plt.figure()
plt.imshow(labelset_val[5].detach().squeeze().cpu().numpy()[0])
