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import torchvision.transforms as transforms
import os
from torch.utils.tensorboard import SummaryWriter
# import git
import warnings
def tensor_to_numpy(image):
image_np = (image.numpy() * 255).astype('uint8')
return image_np
def detach_to_cpu(x):
return x.detach().cpu()
def fix_width_trunc(x):
return ('{:.9s}'.format('{:0.9f}'.format(x)))
class BoardLogger:
def __init__(self, id):
if id is None:
self.no_log = True
warnings.warn('Logging has been disbaled.')
else:
self.no_log = False
self.inv_im_trans = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
std=[1/0.229, 1/0.224, 1/0.225])
self.inv_seg_trans = transforms.Normalize(
mean=[-0.5/0.5],
std=[1/0.5])
log_path = os.path.join('.', 'log', '%s' % id)
self.logger = SummaryWriter(log_path)
# repo = git.Repo(".")
# self.log_string('git', str(repo.active_branch) + ' ' + str(repo.head.commit.hexsha))
def log_scalar(self, tag, x, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
self.logger.add_scalar(tag, x, step)
def log_metrics(self, l1_tag, l2_tag, val, step, f=None):
tag = l1_tag + '/' + l2_tag
text = 'It {:8d} [{:5s}] [{:19s}]: {:s}'.format(step, l1_tag.upper(), l2_tag, fix_width_trunc(val))
print(text)
if f is not None:
f.write(text + '\n')
f.flush()
self.log_scalar(tag, val, step)
def log_im(self, tag, x, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = detach_to_cpu(x)
x = self.inv_im_trans(x)
x = tensor_to_numpy(x)
self.logger.add_image(tag, x, step)
def log_cv2(self, tag, x, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = x.transpose((2, 0, 1))
self.logger.add_image(tag, x, step)
def log_seg(self, tag, x, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = detach_to_cpu(x)
x = self.inv_seg_trans(x)
x = tensor_to_numpy(x)
self.logger.add_image(tag, x, step)
def log_gray(self, tag, x, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
x = detach_to_cpu(x)
x = tensor_to_numpy(x)
self.logger.add_image(tag, x, step)
def log_string(self, tag, x):
print(tag, x)
if self.no_log:
warnings.warn('Logging has been disabled.')
return
self.logger.add_text(tag, x)
def log_total(self, tag, im, gt, seg, pred, step):
if self.no_log:
warnings.warn('Logging has been disabled.')
return
row_cnt = min(10, im.shape[0])
w = im.shape[2]
h = im.shape[3]
output_image = np.zeros([3, w*row_cnt, h*5], dtype=np.uint8)
for i in range(row_cnt):
im_ = tensor_to_numpy(self.inv_im_trans(detach_to_cpu(im[i])))
gt_ = tensor_to_numpy(detach_to_cpu(gt[i]))
seg_ = tensor_to_numpy(self.inv_seg_trans(detach_to_cpu(seg[i])))
pred_ = tensor_to_numpy(detach_to_cpu(pred[i]))
output_image[:, i * w : (i+1) * w, 0 : h] = im_
output_image[:, i * w : (i+1) * w, h : 2*h] = gt_
output_image[:, i * w : (i+1) * w, 2*h : 3*h] = seg_
output_image[:, i * w : (i+1) * w, 3*h : 4*h] = pred_
output_image[:, i * w : (i+1) * w, 4*h : 5*h] = im_*0.5 + 0.5 * (im_ * (1-(pred_/255)) + (pred_/255) * (np.array([255,0,0],dtype=np.uint8).reshape([1,3,1,1])))
self.logger.add_image(tag, output_image, step)
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