random_outputs = outputs_.data.cpu().numpy()[0,logging_frames,:,:,:]
else:
random_inputs = inputs_.data.numpy()[0,logging_frames,:,:,:]
random_outputs = outputs_.data.numpy()[0,logging_frames,:,:,:]
for imageIndex in range(10):
inputImage = random_inputs[imageIndex, :, :, :]
inputImage = np.transpose(inputImage, (1, 2, 0))
inputImage = inputImage[:,:,::-1]
images_list.append(inputImage)
mask = random_outputs[imageIndex, 0, :, :]
heatMap = getHeatMapFrom2DArray(mask)
images_list.append(heatMap)
mask = 1 / (1 + np.exp(-mask))
print('max: ' + str(np.max(mask)) + 'min: ' + str(np.min(mask)))
mask_ = np.greater(mask, 0.5).astype(np.float32)
overlayedImage = helpers.overlay_mask(inputImage, mask_)
overlayedImage = overlayedImage * 255
images_list.append(overlayedImage)
tboardLogger.image_summary('image_{}'.format(epoch), images_list, epoch)
# Compute the losses, side outputs and fuse
losses = [0] * len(outputs)
for i in range(0, len(outputs)):
losses[i] = class_balanced_cross_entropy_loss(outputs[i], gts, size_average=True)
running_loss_tr[i] += losses[i].data[0]
loss = (1 - epoch / nEpochs)*sum(losses[:-1]) + losses[-1]
# Print stuff
if ii % num_img_tr == num_img_tr - 1:
running_loss_tr = [x / num_img_tr for x in running_loss_tr]
if __name__ == '__main__':
import matplotlib.pyplot as plt
import dataloaders.helpers as helpers
import torch
import dataloaders.custom_transforms as tr
from torchvision import transforms
transform = transforms.Compose([tr.ToTensor()])
dataset = VOCSegmentation(split=['train', 'val'],
transform=transform,
retname=True)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=0)
for i, sample in enumerate(dataloader):
plt.figure()
overlay = helpers.overlay_mask(
helpers.tens2image(sample["image"]) / 255.,
np.squeeze(helpers.tens2image(sample["gt"])))
plt.imshow(overlay)
plt.title(dataset.category_names[sample["meta"]["category"][0]])
if i == 3:
break
plt.show(block=True)