def expo(args):
def filename_fn(args):
rs = 'N({}, {})'.format(args.radius, args.sigma)
return rs
def fpath(fname):
_fpath = os.path.join(args.output_dir, fname)
return _fpath
length = 5 * args.radius
linspace, data = SyntheticDataset.grid_data(args.num_points, length=length)
# loader = dataset[args.dataset](args)
# trainData = loader.train
# for batch_idx, samples in enumerate(trainData):
# data,labels = samples[DatasetType.InD]
plt.xlim(-1 * length, length)
plt.ylim(-1 * length, length)
for scale in tqdm([1, 2, 3, 4]):
sigma = scale * args.sigma
scale_args = deepcopy(args)
scale_args.sigma = sigma
fname = filename_fn(scale_args)
checkpoint_dir = os.path.join(args.work_dir, 'checkpoints')
saver = Saver(checkpoint_dir) # makes directory if already not present
payload = saver.load(hash_args(
scale_args)) #hash_args(scale_args) generates the hex string
def run_and_save(scale_args):
export = main(scale_args) #Model creation??
payload = export['model']
saver.save(hash_args(scale_args), payload)
return payload
export = payload or run_and_save(scale_args)
with torch.no_grad():
scores = inference(export, data)
np_x = data.cpu().numpy()
for key in scores:
score = scores[key].cpu().numpy()
plot_pcolormesh(np_x, linspace, score)
score_fname = '{}_{}'.format(fname, key)
plt.title(score_fname)
flush_plot(plt, fpath(score_fname) + '.png')
def restore(args, model):
# load
saver = Saver(args)
return saver.load(model)
