def evaluate_segmentation(net_segmentation):
net_segmentation.eval()
hist = np.zeros((nClasses, nClasses))
val_seg_loader = torch.utils.data.DataLoader(segmentation_data_loader(
img_root=val_img_root,
gt_root=val_gt_root,
image_list=val_image_list,
suffix=dataset,
out=out,
crop=False,
mirror=False),
batch_size=1,
num_workers=8,
shuffle=False)
progbar = tqdm(total=len(val_seg_loader), desc='Eval')
hist = np.zeros((nClasses, nClasses))
for batch_idx, (inputs_, targets) in enumerate(val_seg_loader):
inputs_, targets = Variable(inputs_.to(device)), Variable(
targets.to(device))
outputs = net_segmentation(inputs_)
_, predicted = torch.max(outputs.data, 1)
correctLabel = targets.view(-1, targets.size()[1], targets.size()[2])
hist += fast_hist(
correctLabel.view(correctLabel.size(0), -1).cpu().numpy(),
predicted.view(predicted.size(0), -1).cpu().numpy(), nClasses)
miou, p_acc, fwacc = performMetrics(hist)
progbar.set_description('Eval (mIoU=%.4f)' % (miou))
progbar.update(1)
miou, p_acc, fwacc = performMetrics(hist)
print('\n mIoU: ', miou)
print('\n Pixel accuracy: ', p_acc)
print('\n Frequency Weighted Pixel accuracy: ', fwacc)
def visualize_segmentation(net_segmentation):
val_seg_loader = torch.utils.data.DataLoader(segmentation_data_loader(
img_root=val_img_root,
gt_root=val_gt_root,
image_list=val_image_list,
suffix=dataset,
out=out,
crop=False,
mirror=False),
batch_size=1,
num_workers=8,
shuffle=False)
fig, axs = plt.subplots(nrows=4, ncols=3, figsize=(9, 9))
for batch_idx, (inputs_, targets) in enumerate(val_seg_loader):
inputs_, targets = Variable(inputs_.to(device)), Variable(
targets.to(device))
outputs = net_segmentation(inputs_)
_, predicted = torch.max(outputs.data, 1)
input_ = np.asarray(inputs_[0].cpu().numpy().transpose(1, 2, 0) +
mean_bgr[np.newaxis, np.newaxis, :],
dtype=np.uint8)[:, :, ::-1]
axs[batch_idx, 0].imshow(input_)
axs[batch_idx, 1].imshow(apply_color_map(targets[0].cpu().data, c_map))
axs[batch_idx,
2].imshow(apply_color_map(predicted[0].cpu().data, c_map))
if batch_idx == 3:
break
axs[0, 0].set_title('input', fontsize=18)
axs[0, 1].set_title('GT', fontsize=18)
axs[0, 2].set_title('Pred', fontsize=18)
fig.tight_layout()
plt.show()
from loss import soft_iou
from metric import fast_hist, performMetrics
from utils.dataloaders import segmentation_data_loader
train_seg_loss = []
val_seg_loss = []
train_seg_iou = []
val_seg_iou = []
ITER_SIZE = 2 ### accumulate gradients over ITER_SIZE iterations
best_iou = 0.
train_seg_loader = torch.utils.data.DataLoader(segmentation_data_loader(
img_root=train_img_root,
gt_root=train_gt_root,
image_list=train_image_list_path + supervised_split + '.txt',
suffix=dataset,
out=out,
crop=True,
crop_shape=[256, 256],
mirror=True),
batch_size=32,
num_workers=8,
shuffle=True)
val_seg_loader = torch.utils.data.DataLoader(segmentation_data_loader(
img_root=val_img_root,
gt_root=val_gt_root,
image_list=val_image_list,
suffix=dataset,
out=out,
crop=False,