x = x[:, :, :, 0]
x = x[0, :, :]
skimage.io.imsave(str(filepath), (x * 255).astype("uint8"))
def get_image(path):
image = skimage.io.imread(path)
original_shape = image.shape
if len(original_shape) == 2:
image = image[:, :, np.newaxis]
image = (image.astype(np.float32)) / 255
image = image[np.newaxis, ]
return image
results_path = config.testing_path() / "affine_generator/"
results_path.mkdir(parents=True, exist_ok=True)
samples = {
"cifar1": Path("testing/samples/cifar1.png"),
"cifar2": Path("testing/samples/cifar2.png"),
"mnist": Path("testing/samples/mnist.png"),
}
for sample_id, sample_path in samples.items():
image = get_image(sample_path)
transformations_sets = config.common_transformations()
for transformation_set in transformations_sets:
n, h, w, c = image.shape
transformation_set.set_input_shape((h, w, c))
transformation_set.set_pytorch(False)
import skimage.io
import transformational_measures as tm
import numpy as np
import matplotlib.pyplot as plt
import os
import config
results_path = config.testing_path() / "affine_transformation/"
results_path.mkdir(parents=True, exist_ok=True)
use_cuda = True
source_path = "testing/samples/mnist.png"
input_shape = np.array((28, 28, 1))
def apply_transformation_numpy(t, image_name):
x = skimage.io.imread(source_path)
x = x[np.newaxis, :, :, np.newaxis]
x = t(x)
filepath = results_path / f"{image_name}_numpy.png"
skimage.io.imsave(filepath, x)
def apply_transformation_pytorch(t, image_name):
adapter = tm.NumpyPytorchImageTransformationAdapter(use_cuda=use_cuda)
x = skimage.io.imread(source_path) / 255.0
x = x[np.newaxis, :, :, np.newaxis].astype(np.float32)
x = adapter.pre_adapt(x)
x = t(x)
model = model_config.make_model(dataset.input_shape, dataset.num_classes, use_cuda)
p= profiler.Profiler()
p.event("start")
#transformations=tm.SimpleAffineTransformationGenerator(r=360, s=4, t=3)
transformation.set_input_shape(dataset.input_shape)
transformation.set_pytorch(True)
transformation.set_cuda(use_cuda)
iterator = iterator_class(model, image_dataset, transformation, batch_size=batch_size, num_workers=0, adapter=None,use_cuda=use_cuda)
adapter = tm.PytorchNumpyImageTransformationAdapter(use_cuda=use_cuda)
folderpath = config.testing_path() / f"{iterator.__class__.__name__}"
folderpath.mkdir(exist_ok=True,parents=True)
i=0
for original_x,activations_iterator in iterator.samples_first():
for v in activations_iterator:
if iterator_class == tm.BothPytorchActivationsIterator:
x_transformed, pre_transformed_activations, post_transformed_activations = v
layers=np.concatenate([pre_transformed_activations[0], post_transformed_activations[0]], axis=0)
else:
x_transformed, activations = v
layers=activations[0]
layers= layers.transpose((0, 2, 3, 1))
# grab only the first feature map
p.event("start")
#transformations=tm.SimpleAffineTransformationGenerator(r=360, s=4, t=3)
transformations = tm.SimpleAffineTransformationGenerator(r=360, n_rotations=4)
transformations.set_input_shape(dataset.input_shape)
transformations.set_pytorch(True)
transformations.set_cuda(use_cuda)
iterator = tm.NormalPytorchActivationsIterator(model,
image_dataset,
transformations,
batch_size=64,
num_workers=0,
use_cuda=use_cuda)
adapter = tm.PytorchNumpyImageTransformationAdapter(use_cuda=use_cuda)
folderpath = config.testing_path() / f"activations_iterator"
folderpath.mkdir(exist_ok=True, parents=True)
batch_size = 64
i = 0
for original_x, activations_iterator in iterator.samples_first():
for x_transformed, activations in activations_iterator:
x = adapter.pre_adapt(x_transformed)
filepath = folderpath / f"{dataset}_samples_first.png"
util.plot_image_grid(x, x.shape[0], show=False, save=filepath)
i = i + 1
if i == 10:
break
break
p.event("end")
from testing.util import plot_image_grid
import datasets
import config
from pytorch.pytorch_image_dataset import ImageClassificationDataset
from pytorch.numpy_dataset import NumpyDataset
import itertools
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