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inst_trainer.py
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inst_trainer.py
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import os
import time
from tqdm import tqdm
import torch
from torch import nn
# import torch._dynamo
import numpy as np
from torch_ema import ExponentialMovingAverage
from torchmetrics.classification import (
MulticlassAccuracy,
BinaryAccuracy,
BinaryF1Score,
BinaryJaccardIndex,
MulticlassJaccardIndex)
import wandb
from dataloader.mfinstseg import MFInstSegDataset
from models.inst_segmentors import AAGNetSegmentor
from utils.misc import seed_torch, init_logger, print_num_params
if __name__ == '__main__':
torch.set_float32_matmul_precision("high") # may be faster if GPU support TF32
os.environ["WANDB_API_KEY"] = '##################'
os.environ["WANDB_MODE"] = "offline"
# start a new wandb run to track this script
time_str = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
wandb.init(
# set the wandb project where this run will be logged
project="aagnet" + "MFInstSeg",
# track hyperparameters and run metadata
config={
"edge_attr_dim": 12,
"node_attr_dim": 10,
"edge_attr_emb": 64, # recommend: 64
"node_attr_emb": 64, # recommend: 64
"edge_grid_dim": 0,
"node_grid_dim": 7,
"edge_grid_emb": 0,
"node_grid_emb": 64, # recommend: 64
"num_layers": 3, # recommend: 3
"delta": 2, # obsolete
"mlp_ratio": 2,
"drop": 0.25,
"drop_path": 0.25,
"head_hidden_dim": 64,
"conv_on_edge": False,
"use_uv_gird": True,
"use_edge_attr": True,
"use_face_attr": True,
"seed": 42,
"device": 'cuda',
"architecture": "AAGNetGraphEncoder", # recommend: AAGNetGraphEncoder option: GCN SAGE GIN GAT GATv2 DeeperGCN AAGNetGraphEncoder AAGNetGraphEncoderV2
"dataset_type": "full",
"dataset": "../traning_data/data2",
"epochs": 100,
"lr": 1e-2,
"weight_decay": 1e-2,
"batch_size": 256,
"ema_decay_per_epoch": 1. / 2.,
"seg_a": 1.,
"inst_a": 1.,
"bottom_a": 1.,
}
)
print(wandb.config)
seed_torch(wandb.config['seed'])
device = wandb.config['device']
dataset = wandb.config['dataset']
dataset_type = wandb.config['dataset_type']
n_classes = MFInstSegDataset.num_classes(dataset_type)
model = AAGNetSegmentor(num_classes=n_classes,
arch=wandb.config['architecture'],
edge_attr_dim=wandb.config['edge_attr_dim'],
node_attr_dim=wandb.config['node_attr_dim'],
edge_attr_emb=wandb.config['edge_attr_emb'],
node_attr_emb=wandb.config['node_attr_emb'],
edge_grid_dim=wandb.config['edge_grid_dim'],
node_grid_dim=wandb.config['node_grid_dim'],
edge_grid_emb=wandb.config['edge_grid_emb'],
node_grid_emb=wandb.config['node_grid_emb'],
num_layers=wandb.config['num_layers'],
delta=wandb.config['delta'],
mlp_ratio=wandb.config['mlp_ratio'],
drop=wandb.config['drop'],
drop_path=wandb.config['drop_path'],
head_hidden_dim=wandb.config['head_hidden_dim'],
conv_on_edge=wandb.config['conv_on_edge'],
use_uv_gird=wandb.config['use_uv_gird'],
use_edge_attr=wandb.config['use_edge_attr'],
use_face_attr=wandb.config['use_face_attr'],)
model = model.to(device)
# Reset since we are using a different mode.
# torch._dynamo.reset()
# model = torch.compile(model, mode="reduce-overhead")
total_params = print_num_params(model)
wandb.config['total_params'] = total_params
# model_param = torch.load("E:\\AAGNet\\outpout\\weight_38-epoch.pth", map_location=device)
# model.load_state_dict(model_param)
train_dataset = MFInstSegDataset(root_dir=dataset, split='train',
center_and_scale=False, normalize=True, random_rotate=False,
dataset_type=dataset_type, num_threads=8)
graphs = train_dataset.graphs() # no need to load graphs again !
val_dataset = MFInstSegDataset(root_dir=dataset, graphs=graphs, split='val',
center_and_scale=False, normalize=True,
dataset_type=dataset_type, num_threads=8)
train_loader = train_dataset.get_dataloader(batch_size=wandb.config['batch_size'], pin_memory=True)
val_loader = val_dataset.get_dataloader(batch_size=wandb.config['batch_size'], shuffle=False, drop_last=False, pin_memory=True)
seg_loss = nn.CrossEntropyLoss()
instance_loss = nn.BCEWithLogitsLoss()
bottom_loss = nn.BCEWithLogitsLoss()
opt = torch.optim.AdamW(model.parameters(), lr=wandb.config['lr'], weight_decay=wandb.config['weight_decay'])
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=wandb.config['epochs'], eta_min=0)
train_seg_acc = MulticlassAccuracy(num_classes=n_classes).to(device)
train_inst_acc = BinaryAccuracy().to(device)
train_bottom_acc = BinaryAccuracy().to(device)
train_seg_iou = MulticlassJaccardIndex(num_classes=n_classes).to(device)
train_inst_f1 = BinaryF1Score().to(device)
# train_inst_ap = BinaryAveragePrecision().to(device)
train_bottom_iou = BinaryJaccardIndex().to(device)
val_seg_acc = MulticlassAccuracy(num_classes=n_classes).to(device)
val_inst_acc = BinaryAccuracy().to(device)
val_bottom_acc = BinaryAccuracy().to(device)
val_seg_iou = MulticlassJaccardIndex(num_classes=n_classes).to(device)
val_inst_f1 = BinaryF1Score().to(device)
# val_inst_ap = BinaryAveragePrecision().to(device)
val_bottom_iou = BinaryJaccardIndex().to(device)
iters = len(train_loader)
ema_decay = wandb.config['ema_decay_per_epoch']**(1/iters)
print(f'EMA decay: {ema_decay}')
ema = ExponentialMovingAverage(model.parameters(), decay=ema_decay)
best_acc = 0.
save_path = 'output'
if not os.path.exists(save_path):
os.mkdir(save_path)
save_path = os.path.join(save_path, time_str)
if not os.path.exists(save_path):
os.mkdir(save_path)
logger = init_logger(os.path.join(save_path, 'log.txt'))
for epoch in range(wandb.config['epochs']):
logger.info(f'------------- Now start epoch {epoch}------------- ')
model.train()
# train_per_inst_acc = []
train_losses = []
train_bar = tqdm(train_loader)
for data in train_bar:
graphs = data["graph"].to(device, non_blocking=True)
inst_label = data["inst_labels"].to(device, non_blocking=True)
seg_label = graphs.ndata["seg_y"]
bottom_label = graphs.ndata["bottom_y"]
# Zero the gradients
opt.zero_grad(set_to_none=True)
# Forward pass
seg_pred, inst_pred, bottom_pred = model(graphs)
loss_seg = seg_loss(seg_pred, seg_label)
loss_inst = instance_loss(inst_pred, inst_label)
loss_bottom = bottom_loss(bottom_pred, bottom_label)
loss = wandb.config['seg_a'] * loss_seg + \
wandb.config['inst_a'] * loss_inst + \
wandb.config['bottom_a'] * loss_bottom
train_losses.append(loss.item())
lr = opt.param_groups[0]["lr"]
info = "Epoch:%d LR:%f Seg:%f Inst:%f Bottom:%f Total:%f" % (
epoch, lr, loss_seg, loss_inst, loss_bottom, loss)
train_bar.set_description(info)
# # Backward pass
loss.backward()
opt.step()
# Update the moving average with the new parameters from the last optimizer step
ema.update()
train_seg_acc.update(seg_pred, seg_label)
train_seg_iou.update(seg_pred, seg_label)
train_inst_acc.update(inst_pred, inst_label)
train_inst_f1.update(inst_pred, inst_label)
train_bottom_acc.update(bottom_pred, bottom_label)
train_bottom_iou.update(bottom_pred, bottom_label)
scheduler.step()
# batch end
mean_train_loss = np.mean(train_losses).item()
mean_train_seg_acc = train_seg_acc.compute().item()
mean_train_seg_iou = train_seg_iou.compute().item()
mean_train_inst_acc = train_inst_acc.compute().item()
mean_train_inst_f1 = train_inst_f1.compute().item()
mean_train_bottom_acc = train_bottom_acc.compute().item()
mean_train_bottom_iou = train_bottom_iou.compute().item()
logger.info(f'train_loss : {mean_train_loss}, \
train_seg_acc: {mean_train_seg_acc}, \
train_seg_iou: {mean_train_seg_iou}, \
train_inst_acc: {mean_train_inst_acc}, \
train_inst_f1: {mean_train_inst_f1}, \
train_bottom_acc: {mean_train_bottom_acc}, \
train_bottom_iou: {mean_train_bottom_iou}')
wandb.log({'epoch': epoch,
'train_loss': mean_train_loss,
'train_seg_acc': mean_train_seg_acc,
'train_seg_iou': mean_train_seg_iou,
'train_inst_acc': mean_train_inst_acc,
'train_inst_f1': mean_train_inst_f1,
'train_bottom_acc': mean_train_bottom_acc,
'train_bottom_iou': mean_train_bottom_iou
})
train_seg_acc.reset()
train_inst_acc.reset()
train_bottom_acc.reset()
train_seg_iou.reset()
train_inst_f1.reset()
# train_inst_ap.reset()
train_bottom_iou.reset()
# eval
with torch.no_grad():
with ema.average_parameters():
model.eval()
# val_per_inst_acc = []
val_losses = []
for data in tqdm(val_loader):
graphs = data["graph"].to(device)
inst_label = data["inst_labels"].to(device)
seg_label = graphs.ndata["seg_y"]
bottom_label = graphs.ndata["bottom_y"]
with torch.autocast(device_type=device, dtype=torch.float16):
seg_pred, inst_pred, bottom_pred = model(graphs)
loss_seg = seg_loss(seg_pred, seg_label)
loss_inst = instance_loss(inst_pred, inst_label)
loss_bottom = bottom_loss(bottom_pred, bottom_label)
loss = wandb.config['seg_a'] * loss_seg + \
wandb.config['inst_a'] * loss_inst + \
wandb.config['bottom_a'] * loss_bottom
val_losses.append(loss.item())
val_seg_acc.update(seg_pred, seg_label)
val_seg_iou.update(seg_pred, seg_label)
val_inst_acc.update(inst_pred, inst_label)
val_inst_f1.update(inst_pred, inst_label)
val_bottom_acc.update(bottom_pred, bottom_label)
val_bottom_iou.update(bottom_pred, bottom_label)
# val end
mean_val_loss = np.mean(val_losses).item()
mean_val_seg_acc = val_seg_acc.compute().item()
mean_val_seg_iou = val_seg_iou.compute().item()
mean_val_inst_acc = val_inst_acc.compute().item()
mean_val_inst_f1 = val_inst_f1.compute().item()
mean_val_bottom_acc = val_bottom_acc.compute().item()
mean_val_bottom_iou = val_bottom_iou.compute().item()
logger.info(f'val_loss : {mean_val_loss}, \
val_seg_acc: {mean_val_seg_acc}, \
val_seg_iou: {mean_val_seg_iou}, \
val_inst_acc: {mean_val_inst_acc}, \
val_inst_f1: {mean_val_inst_f1}, \
val_bottom_acc: {mean_val_bottom_acc}, \
val_bottom_iou: {mean_val_bottom_iou}')
wandb.log({'epoch': epoch,
'val_loss': mean_val_loss,
'val_seg_acc': mean_val_seg_acc,
'val_seg_iou': mean_val_seg_iou,
'val_inst_acc': mean_val_inst_acc,
'val_inst_f1': mean_val_inst_f1,
'val_bottom_acc': mean_val_bottom_acc,
'val_bottom_iou': mean_val_bottom_iou
})
val_seg_acc.reset()
val_seg_iou.reset()
val_inst_acc.reset()
val_inst_f1.reset()
# val_inst_ap.reset()
val_bottom_acc.reset()
val_bottom_iou.reset()
cur_acc = mean_val_seg_iou + mean_val_inst_f1 + mean_val_bottom_iou
if cur_acc > best_acc:
best_acc = cur_acc
logger.info(f'best metric: {cur_acc}, model saved')
torch.save(model.state_dict(), os.path.join(save_path, "weight_%d-epoch.pth"%(epoch)))
# epoch end
# training end test
graphs = train_dataset.graphs() # no need to load graphs again !
test_dataset = MFInstSegDataset(root_dir=dataset, graphs=graphs, split='test',
center_and_scale=False, normalize=True, random_rotate=False,
dataset_type=dataset_type, num_threads=8)
test_loader = test_dataset.get_dataloader(batch_size=wandb.config['batch_size'], pin_memory=True)
test_seg_acc = MulticlassAccuracy(num_classes=n_classes).to(device)
test_inst_acc = BinaryAccuracy().to(device)
test_bottom_acc = BinaryAccuracy().to(device)
test_seg_iou = MulticlassJaccardIndex(num_classes=n_classes).to(device)
test_inst_f1 = BinaryF1Score().to(device)
# test_inst_ap = BinaryAveragePrecision().to(device)
test_bottom_iou = BinaryJaccardIndex().to(device)
with torch.no_grad():
logger.info(f'------------- Now start testing ------------- ')
model.eval()
# test_per_inst_acc = []
test_losses = []
for data in tqdm(test_loader):
graphs = data["graph"].to(device, non_blocking=True)
inst_label = data["inst_labels"].to(device, non_blocking=True)
seg_label = graphs.ndata["seg_y"]
bottom_label = graphs.ndata["bottom_y"]
# Forward pass
seg_pred, inst_pred, bottom_pred = model(graphs)
loss_seg = seg_loss(seg_pred, seg_label)
loss_inst = instance_loss(inst_pred, inst_label)
loss_bottom = bottom_loss(bottom_pred, bottom_label)
loss = wandb.config['seg_a'] * loss_seg + \
wandb.config['inst_a'] * loss_inst + \
wandb.config['bottom_a'] * loss_bottom
test_losses.append(loss.item())
test_seg_acc.update(seg_pred, seg_label)
test_seg_iou.update(seg_pred, seg_label)
test_inst_acc.update(inst_pred, inst_label)
test_inst_f1.update(inst_pred, inst_label)
test_bottom_acc.update(bottom_pred, bottom_label)
test_bottom_iou.update(bottom_pred, bottom_label)
# batch end
mean_test_loss = np.mean(test_losses).item()
mean_test_seg_acc = test_seg_acc.compute().item()
mean_test_seg_iou = test_seg_iou.compute().item()
mean_test_inst_acc = test_inst_acc.compute().item()
mean_test_inst_f1 = test_inst_f1.compute().item()
mean_test_bottom_acc = test_bottom_acc.compute().item()
mean_test_bottom_iou = test_bottom_iou.compute().item()
logger.info(f'test_loss : {mean_test_loss}, \
test_seg_acc: {mean_test_seg_acc}, \
test_seg_iou: {mean_test_seg_iou}, \
test_inst_acc: {mean_test_inst_acc}, \
test_inst_f1: {mean_test_inst_f1}, \
test_bottom_acc: {mean_test_bottom_acc}, \
test_bottom_iou: {mean_test_bottom_iou}')
wandb.log({'test_loss': mean_test_loss,
'test_seg_acc': mean_test_seg_acc,
'test_seg_iou': mean_test_seg_iou,
'test_inst_acc': mean_test_inst_acc,
'test_inst_f1': mean_test_inst_f1,
'test_bottom_acc': mean_test_bottom_acc,
'test_bottom_iou': mean_test_bottom_iou
})