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Added Wandb For Better Training Log Visualization #661

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Added Wandb For Better Training Log Visualization #661

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8d6f6b5
added wandb support to train script
Jun 21, 2021
e39ae87
updated readme for wandb installation
Jun 21, 2021
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2 changes: 1 addition & 1 deletion readme.md
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Original file line number Diff line number Diff line change
Expand Up @@ -82,7 +82,7 @@ The speed/FPS test includes the time of post-processing with no jit/data precisi
## Demo

# install requirements
pip install pycocotools numpy opencv-python tqdm tensorboard tensorboardX pyyaml webcolors
pip install pycocotools numpy opencv-python tqdm tensorboard tensorboardX pyyaml webcolors wandb
pip install torch==1.4.0
pip install torchvision==0.5.0

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16 changes: 15 additions & 1 deletion train.py
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Expand Up @@ -10,6 +10,7 @@
import numpy as np
import torch
import yaml
import wandb
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
Expand Down Expand Up @@ -173,6 +174,7 @@ def freeze_backbone(m):
else:
use_sync_bn = False

wandb.init() # initialize a wandb run
writer = SummaryWriter(opt.log_path + f'/{datetime.datetime.now().strftime("%Y%m%d-%H%M%S")}/')

# warp the model with loss function, to reduce the memory usage on gpu0 and speedup
Expand Down Expand Up @@ -248,7 +250,13 @@ def freeze_backbone(m):
# log learning_rate
current_lr = optimizer.param_groups[0]['lr']
writer.add_scalar('learning_rate', current_lr, step)

wandb.log({"step" : step,
"epoch": epoch,
"train-loss": loss,
"train-reg-loss": reg_loss,
"train-cls-loss" : cls_loss,
"lr" : current_lr
})
step += 1

if step % opt.save_interval == 0 and step > 0:
Expand Down Expand Up @@ -296,6 +304,12 @@ def freeze_backbone(m):
writer.add_scalars('Regression_loss', {'val': reg_loss}, step)
writer.add_scalars('Classfication_loss', {'val': cls_loss}, step)

wandb.log({"step" : step,
"val-loss" : loss,
"val-reg-loss" : reg_loss,
"val-cls-loss" : cls_loss,
"epoch" : epoch})

if loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
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