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A10G
Running
on
A10G
File size: 2,510 Bytes
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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()
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