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import torch
import torch.nn.functional as F
from torch import nn
from model import ResNet18
from preprocessing import PreprocessedImageFolder, augmentations, make_dls
from trainer import (
LRFinderCB,
ActivationStatsCB,
AugmentCB,
DeviceCB,
MultiClassAccuracyCB,
ProgressCB,
Trainer,
WandBCB,
)
device = "cuda" if torch.cuda.is_available() else "cpu"
train_ds = PreprocessedImageFolder("./dataset/train", None)
valid_ds = PreprocessedImageFolder("./dataset/test", None)
dls = make_dls(train_ds, valid_ds, batch_size=32, num_workers=2)
model = ResNet18(in_channels=1, num_classes=len(train_ds.classes))
# lr_find = LRFinderCB(min_lr=1e-4, max_lr=0.1, max_mult=3)
# act_stats = ActivationStatsCB(mod_filter=lambda x: isinstance(x, nn.Conv2d) or isinstance(x, nn.Linear), with_wandb=True) # for debugging purposes
progress = ProgressCB(in_notebook=False)
wandb_cb = WandBCB(proj_name="test", model_path="./model.pth")
augment = AugmentCB(device=device, transform=augmentations)
acc_cb = MultiClassAccuracyCB(with_wandb=True)
trainer = Trainer(
model,
dls,
F.cross_entropy,
torch.optim.SGD,
lr=1e-4,
cbs=[DeviceCB(device), augment, progress, wandb_cb, acc_cb],
) # act_stats, lr_find
trainer.fit(5, True, True)
# TODO: saving plots to wandb
progress.plot_losses(save=True)
# act_stats.plot_stats(save=True)
# act_stats.color_dim(save=True)
# act_stats.dead_chart(save=True)
# torch.save(trainer.model.state_dict(), "./model.pth") # done by WandBCB
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