def showRandomImages(data, targets, predictions=None, classes=None, count=20, muSigmaPair=None):
randImages = Utility.pickRandomElements(data, count)
images = data[randImages]
if (muSigmaPair is not None):
images = Utility.unnormalize(images, muSigmaPair[0], muSigmaPair[1])
images = images.permute(0, 2, 3, 1)
targets = __getLabels(targets, randImages, classes)
if predictions is not None:
predictions = __getLabels(predictions, randImages, classes)
showImages(images.numpy(), targets, predictions, cols=5)
def visualize(self,
data,
data_targets,
classes,
count=5,
muSigPair=None,
figSize=(15, 15)):
heatmaps, cam_pred = self.gradCam(data)
randIndices = Utility.pickRandomElements(data, count)
rand_data, rand_targets, rand_cam_pred = data[
randIndices], data_targets[randIndices], cam_pred[randIndices]
rand_superImposedImages = {}
for layer in heatmaps:
rand_superImposedImages[layer] = self.superImpose(
rand_data, heatmaps[layer], muSigPair)
self.plot(Utility.toImages(rand_data, muSigPair), rand_targets,
rand_cam_pred, rand_superImposedImages, classes, figSize)
def showLoaderImages(loader, classes=None, count=20, muSigmaPair=None):
"""
Takes random images from the loader and shows the images.
Optionally Mean and Sigma pair can be passed to unnormalize data before showing the image.
:param muSigmaPair: Default is (0, 1)
"""
d, l = iter(loader).next()
randImages = Utility.pickRandomElements(d, count)
images = d[randImages]
if (muSigmaPair is not None):
images = Utility.unnormalize(images, muSigmaPair[0], muSigmaPair[1])
# Loader has the channel at 1 index. But the show images need channel at the end.
images = images.permute(0, 2, 3, 1)
labels = __getLabels(l, randImages, classes)
showImages(images.numpy(), labels, cols=5)