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train_shot.py
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train_shot.py
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from pathlib import Path
import pytorch_lightning as pl
from torch.optim import Adam
import torch
import numpy as np
from itertools import combinations
import torch.nn.functional as F
import hydra
from dataset import ShapeNetDirectDataset
import torch.nn as nn
import time
from multiprocessing import cpu_count
from pytorch_lightning import loggers as pl_loggers
from torch import optim
from pytorch_lightning.callbacks import ModelCheckpoint
from utils.util import real2prob, prob2real
class ResLayer(torch.nn.Module):
def __init__(self, dim_in, dim_out, bn=False, dropout=False) -> None:
super().__init__()
# assert(bn is False)
self.fc1 = torch.nn.Linear(dim_in, dim_out)
if bn:
self.bn1 = torch.nn.BatchNorm1d(dim_out)
else:
self.bn1 = lambda x: x
self.fc2 = torch.nn.Linear(dim_out, dim_out)
if bn:
self.bn2 = torch.nn.BatchNorm1d(dim_out)
else:
self.bn2 = lambda x: x
if dim_in != dim_out:
self.fc0 = torch.nn.Linear(dim_in, dim_out)
else:
self.fc0 = None
self.dropout = nn.Dropout(0.2) if dropout else nn.Identity()
def forward(self, x):
x_res = x if self.fc0 is None else self.fc0(x)
x = F.relu(self.bn1(self.fc1(x)))
x = self.bn2(self.fc2(x))
return self.dropout(x + x_res)
class BeyondCPPF(pl.LightningModule):
def __init__(self, cfg) -> None:
super().__init__()
self.cfg = cfg
# self.vis = Visdom(port=12345)
fcs_shot = [352,] + [128] * 5 + [64,]
self.shot_encoder = nn.Sequential(
*[ResLayer(fcs_shot[i], fcs_shot[i + 1], False) for i in range(len(fcs_shot) - 1)]
)
input_dim = len(list(combinations(np.arange(cfg.num_more + 2), 2))) * 4 + (cfg.num_more + 2) * 64
output_dim = 256 # 3 for scale
fcs = [input_dim] + [128] * 5 + [output_dim]
self.tuple_encoder = nn.Sequential(
*[ResLayer(fcs[i], fcs[i + 1], False) for i in range(len(fcs) - 1)]
)
self.logit_encoder = nn.Sequential(
ResLayer(256, 256, False),
ResLayer(256, 256, False),
ResLayer(256, 64 * 3, False),
)
self.scale_encoder = nn.Sequential(
ResLayer(256, 128, False),
ResLayer(128, 64, False),
ResLayer(64, 3, False),
)
def prepare_tuple_inputs(self, points, point_idxs_all, shot_feat, normal):
shot_inputs = torch.cat([shot_feat[point_idxs_all[:, i]] for i in range(0, self.cfg.num_more + 2)], -1)
normal_inputs = torch.cat([torch.max(torch.sum(normal[point_idxs_all[:, i]] * normal[point_idxs_all[:, j]], dim=-1, keepdim=True),
torch.sum(-normal[point_idxs_all[:, i]] * normal[point_idxs_all[:, j]], dim=-1, keepdim=True))
for (i, j) in combinations(np.arange(point_idxs_all.shape[-1]), 2)], -1)
coord_inputs = torch.cat([points[point_idxs_all[:, i]] - points[point_idxs_all[:, j]] for (i, j) in combinations(np.arange(point_idxs_all.shape[-1]), 2)], -1)
inputs = torch.cat([coord_inputs, normal_inputs, shot_inputs], -1)
return inputs
def training_step(self, batch, batch_idx):
pc_canon = batch['pc_canon'][0]
point_idxs_all = batch['point_idxs_all'][0]
points = batch['pc'][0]
shot_feat = self.shot_encoder(batch['shot'][0])
# shot_feat.fill_(0)
normal = batch['normal'][0]
inputs = self.prepare_tuple_inputs(points, point_idxs_all, shot_feat, normal)
# pca_quat = matrix_to_quaternion(pca_basis)
feat = self.tuple_encoder(inputs)
preds_cls = self.logit_encoder(feat).reshape(feat.shape[0], 6, -1)
with torch.no_grad():
target_cls = real2prob(torch.clamp(pc_canon[point_idxs_all[:, :2]], -0.5, 0.5) + 0.5, 1., preds_cls.shape[-1]).reshape(feat.shape[0], 6, -1) # 2NP x 3 x NBIN
loss_cls = F.kl_div(F.log_softmax(preds_cls, dim=-1), target_cls, reduction='batchmean')
preds_scale = self.scale_encoder(feat)
target_scale = batch['bound'][0]
loss_scale = F.mse_loss(preds_scale, target_scale[None].expand_as(preds_scale))
loss = loss_cls + loss_scale
# self.log('loss', loss, prog_bar=True)
self.log('cls', loss_cls, prog_bar=True)
self.log('scale', loss_scale, prog_bar=True)
# vis_weights = torch_scatter.scatter_add(preds_weight[:, 1:2].expand_as(point_idxs_all).reshape(-1), point_idxs_all.reshape(-1), dim_size=points.shape[0]).detach()
# vis_weights /= vis_weights.max()
# if batch_idx % 10 == 0:
# # print(vis_weights.max(), vis_weights.mean(), (vis_weights > 0).sum())
# cmap = cm.get_cmap('jet')
# self.vis.scatter(points.cpu().numpy(), win=4, opts=dict(markersize=3, markercolor=(np.array([cmap(w)[:3] for w in vis_weights.cpu().numpy()]) * 255.).astype(np.uint8)))
return loss
def forward(self, points, point_idxs_all, shot_feat, normal):
inputs = self.prepare_tuple_inputs(points, point_idxs_all, self.shot_encoder(shot_feat), normal)
feat = self.tuple_encoder(inputs)
preds_scale = self.scale_encoder(feat)
preds_cls = self.logit_encoder(feat).reshape(feat.shape[0], 6, -1)
return preds_cls, preds_scale
def configure_optimizers(self):
opt = Adam([*self.shot_encoder.parameters(),
*self.tuple_encoder.parameters(),
*self.logit_encoder.parameters(),
*self.scale_encoder.parameters()],
lr=self.cfg.opt.lr, weight_decay=self.cfg.opt.weight_decay)
return [opt], [optim.lr_scheduler.StepLR(opt, 25, 0.5)]
import wandb
@hydra.main(config_path='./config', config_name='config', version_base='1.2')
def train(cfg):
hydra_cfg = hydra.core.hydra_config.HydraConfig.get()
output_dir = hydra_cfg['runtime']['output_dir']
model_ckpt = ModelCheckpoint(save_last=True, save_top_k=-1, every_n_epochs=10, filename='{epoch}')
pl_module = BeyondCPPF(cfg)
trainer = pl.Trainer(max_epochs=101, accelerator='auto', callbacks=[model_ckpt],
logger=pl_loggers.TensorBoardLogger(save_dir=output_dir),
detect_anomaly=False) # check_val_every_n_epoch=10)
def init_fn(i):
return np.random.seed(round(time.time() * 1000) % (2 ** 32) + i)
ds = ShapeNetDirectDataset(cfg)
df = torch.utils.data.DataLoader(ds, pin_memory=True, batch_size=1, shuffle=True, num_workers=cpu_count() // 2, worker_init_fn=init_fn)
trainer.fit(pl_module, df)
if __name__ == '__main__':
train()