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A10G
Running
on
A10G
| import torch | |
| from utils.flow_viz import flow_tensor_to_image | |
| class Logger: | |
| def __init__(self, lr_scheduler, | |
| summary_writer, | |
| summary_freq=100, | |
| start_step=0, | |
| ): | |
| self.lr_scheduler = lr_scheduler | |
| self.total_steps = start_step | |
| self.running_loss = {} | |
| self.summary_writer = summary_writer | |
| self.summary_freq = summary_freq | |
| def print_training_status(self, mode='train'): | |
| print('step: %06d \t epe: %.3f' % (self.total_steps, self.running_loss['epe'] / self.summary_freq)) | |
| for k in self.running_loss: | |
| self.summary_writer.add_scalar(mode + '/' + k, | |
| self.running_loss[k] / self.summary_freq, self.total_steps) | |
| self.running_loss[k] = 0.0 | |
| def lr_summary(self): | |
| lr = self.lr_scheduler.get_last_lr()[0] | |
| self.summary_writer.add_scalar('lr', lr, self.total_steps) | |
| def add_image_summary(self, img1, img2, flow_preds, flow_gt, mode='train', | |
| ): | |
| if self.total_steps % self.summary_freq == 0: | |
| img_concat = torch.cat((img1[0].detach().cpu(), img2[0].detach().cpu()), dim=-1) | |
| img_concat = img_concat.type(torch.uint8) # convert to uint8 to visualize in tensorboard | |
| flow_pred = flow_tensor_to_image(flow_preds[-1][0]) | |
| forward_flow_gt = flow_tensor_to_image(flow_gt[0]) | |
| flow_concat = torch.cat((torch.from_numpy(flow_pred), | |
| torch.from_numpy(forward_flow_gt)), dim=-1) | |
| concat = torch.cat((img_concat, flow_concat), dim=-2) | |
| self.summary_writer.add_image(mode + '/img_pred_gt', concat, self.total_steps) | |
| def push(self, metrics, mode='train'): | |
| self.total_steps += 1 | |
| self.lr_summary() | |
| for key in metrics: | |
| if key not in self.running_loss: | |
| self.running_loss[key] = 0.0 | |
| self.running_loss[key] += metrics[key] | |
| if self.total_steps % self.summary_freq == 0: | |
| self.print_training_status(mode) | |
| self.running_loss = {} | |
| def write_dict(self, results): | |
| for key in results: | |
| tag = key.split('_')[0] | |
| tag = tag + '/' + key | |
| self.summary_writer.add_scalar(tag, results[key], self.total_steps) | |
| def close(self): | |
| self.summary_writer.close() | |