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import torch
import random
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
def untransform(img, lt):
img = (img + 1) * 127.5
lt = lt * 128
return img, lt
def recursive_glob(rootdir='.', suffix=''):
"""Performs recursive glob with given suffix and rootdir
:param rootdir is the root directory
:param suffix is the suffix to be searched
"""
return [os.path.join(looproot, filename)
for looproot, _, filenames in os.walk(rootdir)
for filename in filenames if filename.endswith(suffix)]
def get_cityscapes_labels():
return np.array([
# [ 0, 0, 0],
[128, 64, 128],
[244, 35, 232],
[70, 70, 70],
[102, 102, 156],
[190, 153, 153],
[153, 153, 153],
[250, 170, 30],
[220, 220, 0],
[107, 142, 35],
[152, 251, 152],
[0, 130, 180],
[220, 20, 60],
[255, 0, 0],
[0, 0, 142],
[0, 0, 70],
[0, 60, 100],
[0, 80, 100],
[0, 0, 230],
[119, 11, 32]])
def get_pascal_labels():
"""Load the mapping that associates pascal classes with label colors
Returns:
np.ndarray with dimensions (21, 3)
"""
return np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
[0, 64, 128]])
def encode_segmap(mask):
"""Encode segmentation label images as pascal classes
Args:
mask (np.ndarray): raw segmentation label image of dimension
(M, N, 3), in which the Pascal classes are encoded as colours.
Returns:
(np.ndarray): class map with dimensions (M,N), where the value at
a given location is the integer denoting the class index.
"""
mask = mask.astype(int)
label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16)
for ii, label in enumerate(get_pascal_labels()):
label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii
label_mask = label_mask.astype(int)
return label_mask
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2]))
return rgb_masks
def decode_segmap(label_mask, dataset, plot=False):
"""Decode segmentation class labels into a color image
Args:
label_mask (np.ndarray): an (M,N) array of integer values denoting
the class label at each spatial location.
plot (bool, optional): whether to show the resulting color image
in a figure.
Returns:
(np.ndarray, optional): the resulting decoded color image.
"""
if dataset == 'pascal':
n_classes = 21
label_colours = get_pascal_labels()
elif dataset == 'cityscapes':
n_classes = 19
label_colours = get_cityscapes_labels()
else:
raise NotImplementedError
r = label_mask.copy()
g = label_mask.copy()
b = label_mask.copy()
for ll in range(0, n_classes):
r[label_mask == ll] = label_colours[ll, 0]
g[label_mask == ll] = label_colours[ll, 1]
b[label_mask == ll] = label_colours[ll, 2]
rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
rgb[:, :, 0] = r / 255.0
rgb[:, :, 1] = g / 255.0
rgb[:, :, 2] = b / 255.0
if plot:
plt.imshow(rgb)
plt.show()
else:
return rgb
def generate_param_report(logfile, param):
log_file = open(logfile, 'w')
for key, val in param.items():
log_file.write(key + ':' + str(val) + '\n')
log_file.close()
def cross_entropy2d(logit, target, ignore_index=255, weight=None, size_average=True, batch_average=True):
n, c, h, w = logit.size()
# logit = logit.permute(0, 2, 3, 1)
target = target.squeeze(1)
if weight is None:
criterion = nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_index, size_average=False)
else:
criterion = nn.CrossEntropyLoss(weight=torch.from_numpy(np.array(weight)).float().cuda(), ignore_index=ignore_index, size_average=False)
loss = criterion(logit, target.long())
if size_average:
loss /= (h * w)
if batch_average:
loss /= n
return loss
def lr_poly(base_lr, iter_, max_iter=100, power=0.9):
return base_lr * ((1 - float(iter_) / max_iter) ** power)
def get_iou(pred, gt, n_classes=21):
total_iou = 0.0
for i in range(len(pred)):
pred_tmp = pred[i]
gt_tmp = gt[i]
intersect = [0] * n_classes
union = [0] * n_classes
for j in range(n_classes):
match = (pred_tmp == j) + (gt_tmp == j)
it = torch.sum(match == 2).item()
un = torch.sum(match > 0).item()
intersect[j] += it
union[j] += un
iou = []
for k in range(n_classes):
if union[k] == 0:
continue
iou.append(intersect[k] / union[k])
img_iou = (sum(iou) / len(iou))
total_iou += img_iou
return total_iou
def get_dice(pred, gt):
total_dice = 0.0
pred = pred.long()
gt = gt.long()
for i in range(len(pred)):
pred_tmp = pred[i]
gt_tmp = gt[i]
dice = 2.0*torch.sum(pred_tmp*gt_tmp).item()/(torch.sum(pred_tmp**2)+torch.sum(gt_tmp**2)).item()
total_dice += dice
return total_dice
from skimage import measure
import scipy.ndimage as nd
def post_processing(prediction):
prediction = nd.binary_fill_holes(prediction)
label_cc, num_cc = measure.label(prediction,return_num=True)
total_cc = np.sum(prediction)
measure.regionprops(label_cc)
for cc in range(1,num_cc+1):
single_cc = (label_cc==cc)
single_vol = np.sum(single_cc)
if single_vol/total_cc<0.2:
prediction[single_cc]=0
return prediction
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