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