def run(self):
dataset_names = ["mnist", "cifar10"]
cudas = [False, True]
r, s, t, combined = config.common_transformations_combined
transformation_sets = [r, s, t, combined]
n_images = 4
p = profiler.Profiler()
p.event("start")
for transformations, dataset_name, use_cuda in itertools.product(
transformation_sets, dataset_names, cudas):
print(f"### Loading dataset {dataset_name} ....")
folderpath = self.folderpath / f"{dataset_name}"
folderpath.mkdir(exist_ok=True, parents=True)
dataset = datasets.get_classification(dataset_name)
dataset.normalize_features()
adapter = tm.NumpyPytorchImageTransformationAdapter(
use_cuda=use_cuda)
numpy_dataset = NumpyDataset(dataset.x_test, dataset.y_test)
x, y = numpy_dataset.get_batch(range(n_images))
if use_cuda:
x = x.cuda()
n_t = len(transformations)
print(
f"Dataset {dataset_name}, Transformations: {n_t} ({transformations})"
)
for i in range(n_images):
print(f"Generating plots for image {i}")
original_torch = x[i, :]
# transformed_images = []
transformed_torch = torch.zeros((n_t, *original_torch.shape))
original_torch = original_torch.unsqueeze(0)
for j, t in enumerate(transformations):
transformed_torch[j, :] = t(original_torch)
transformed_numpy = adapter.post_adapt(transformed_torch)
cuda_str = "_cuda" if use_cuda else ""
filepath = folderpath / f"samples_first_{i}_{transformations}{cuda_str}.png"
util.plot_image_grid(transformed_numpy,
samples=n_t,
grid_cols=16,
show=False,
save=filepath)
p.event("end")
print(p.summary(human=True))
folderpath=config.testing_path()/ "numpy_iterator"
folderpath.mkdir(parents=True,exist_ok=True)
images= 32
preprocessing=True
print(f"Using datasets: {datasets.names}")
for dataset_name,preprocessing,normalize in itertools.product(datasets.names,[True,False],[True,False]):
print(dataset_name,preprocessing,normalize)
dataset = datasets.get_classification(dataset_name)
# print(dataset.summary())
pre_str = 'preprocessing_' if preprocessing else ""
normalize_str = 'normalized_' if preprocessing else ""
filepath = folderpath / f"{pre_str}{normalize_str}{dataset_name}.png"
numpy_dataset=NumpyDataset(dataset.x_test,dataset.y_test)
if preprocessing:
numpy_dataset= ImageClassificationDataset(numpy_dataset)
x,y= numpy_dataset.get_batch(list(range(images)))
if not preprocessing:
x = x.float()/255
# print(x.shape,y.shape)
# permute to NHWC order
x = x.permute(0, 2, 3, 1)
x = x.numpy()
plot_image_grid(x,show=False,save=filepath,normalize=normalize)