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convert_checkpoint.py
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convert_checkpoint.py
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import os
import cv2
import time
import json
import random
import inspect
import argparse
import numpy as np
from tqdm import tqdm
from dataloaders import make_data_loader
from models.sync_batchnorm.replicate import patch_replication_callback
from models.vs_net import *
from utils.loss import loss_dict
from utils.lr_scheduler import LR_Scheduler
from utils.saver import Saver
from utils.summaries import TensorboardSummary
from utils.metrics import Evaluator
from utils import utils
from torch.autograd import Variable
import os.path as osp
from configs import *
import warnings
warnings.filterwarnings("ignore")
class Trainer(object):
def __init__(self, cfg):
self.cfg = cfg
# Define Saver
self.saver = Saver(cfg)
# Define Tensorboard Summary
self.summary = TensorboardSummary(self.cfg["log_tb_dir"])
self.summary.create_summary()
# Define Dataloader
kwargs = {"num_workers": cfg["num_workers"], "pin_memory": True}
self.train_loader, self.val_loader, self.test_loader, dset = make_data_loader(
cfg, **kwargs)
# read landmark centers
self.id2center = np.array(json.load(
open(osp.join(cfg["data_dir"], "id2centers.json")))).astype(np.float64)
self.coding_book = torch.zeros(
(len(self.id2center), cfg["seg_channel"]), dtype=torch.float32).cuda()
torch.nn.init.xavier_uniform(self.coding_book)
print("coding book size = {}".format(self.coding_book.shape))
# generate color map
unique_label = np.arange(len(self.id2center))
unique_label = unique_label.astype(
np.int64) * 6364136223846793005 + 1442695040888963407
color_map = np.zeros((unique_label.shape[0], 3), np.uint8)
color_map[:, 0] = np.bitwise_and(unique_label, 0xff)
color_map[:, 1] = np.bitwise_and(np.right_shift(unique_label, 4), 0xff)
color_map[:, 2] = np.bitwise_and(np.right_shift(unique_label, 8), 0xff)
self.color_map = np.array(color_map)
self.coding_book = Variable(self.coding_book, requires_grad=True)
# Define network
model = VSNet(backbone=cfg["backbone"],
seg_decoder=cfg["seg_decoder"],
vertex_decoder=cfg["vertex_decoder"],
seg_channel=cfg["seg_channel"],
vertex_channel=cfg["vertex_channel"],
output_stride=cfg["out_stride"],
sync_bn=cfg["sync_bn"])
train_params = [{"params": model.get_1x_lr_params(), "lr": cfg["lr"]},
{"params": model.get_10x_lr_params(),
"lr": cfg["lr"] * 10},
{"params": self.coding_book, "lr": cfg["lr"] * 10}
]
# Define Optimizer
if cfg["optimizer"] == "SGD":
optimizer = torch.optim.SGD(train_params, momentum=cfg["momentum"],
weight_decay=cfg["weight_decay"], nesterov=cfg["nesterov"])
elif cfg["optimizer"] == "Adam":
optimizer = torch.optim.Adam(train_params, lr=cfg["lr"],
weight_decay=cfg["weight_decay"], amsgrad=True)
else:
raise NotImplementedError
# Define Criterion
self.seg_criterion = loss_dict[cfg["seg_loss_type"]]
self.vertex_criterion = loss_dict[cfg["vertex_loss_type"]]
self.model, self.optimizer = model, optimizer
# Define Evaluator
self.evaluator = Evaluator(
self.coding_book.shape[0], cfg["vertex_channel"])
# Define lr scheduler
self.scheduler = LR_Scheduler(mode=cfg["lr_scheduler"], base_lr=cfg["lr"],
num_epochs=cfg["epochs"], iters_per_epoch=len(
self.train_loader),
lr_step=cfg["lr_step"])
self.model = torch.nn.DataParallel(self.model)
patch_replication_callback(self.model)
self.model = self.model.cuda()
# Resuming checkpoint
self.best_pred = {"mIoU": 0.0, "Acc": 0.0, "Acc": 0.0,
"FWIoU": 0.0, "translation_median": 1000}
if cfg["resume"] is not None and cfg["resume"] == True:
print(os.path.isfile(cfg["resume_checkpoint"]))
if not os.path.isfile(cfg["resume_checkpoint"]):
raise RuntimeError("=> no checkpoint found at {}" .format(
cfg["resume_checkpoint"]))
checkpoint = torch.load(cfg["resume_checkpoint"])
cfg.opt["start_epoch"] = checkpoint["epoch"] - 1
state_dict = checkpoint["state_dict"]
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if "mask_decoder" in k: continue
new_state_dict[k] = v
self.model.module.load_state_dict(new_state_dict)
# self.coding_book.load_state_dict(checkpoint["coding_book"])
if not cfg["ft"]:
self.optimizer.load_state_dict(checkpoint["optimizer"])
self.best_pred = checkpoint["best_pred"]
print("coding book shape:", checkpoint["coding_book"].shape)
if "coding_book" in checkpoint.keys():
self.coding_book = checkpoint["coding_book"]
else:
print("Alert! coding book does not exist in the checkpoint")
print("=> loaded checkpoint {} (epoch {})"
.format(cfg["resume"], checkpoint["epoch"]))
# self.saver.save_checkpoint({
# "epoch": checkpoint["epoch"],
# "state_dict": self.model.module.state_dict(),
# "optimizer": self.optimizer.state_dict(),
# "best_pred": self.best_pred,
# "coding_book": self.coding_book
# }, False, filename="checkpoint-backup.pth.tar", save_model=self.cfg["save_model"])
def main():
parser = argparse.ArgumentParser(
description="PyTorch Landmark Segmentation Training")
parser.add_argument("--dataset", type=str,
choices=["7scenes_loc", "cambridge_loc"], help="experiment config file")
parser.add_argument("--scene", type=str, default="",
help="experiment scene")
parser.add_argument("--gpu-id", type=str, default="",
help="experiment gpu id")
parser.add_argument("--use-aug", type=str, default="",
choices=["", "true", "false"], help="experiment use aug")
parser.add_argument("--config", type=str, default=None,
help="experiment config file")
parser.add_argument("--debug", type=str, default="",
choices=["", "true", "false"], help="debug")
parser.add_argument("--resume", type=str, default="true",
choices=["", "true", "false"], help="resume")
args = parser.parse_args()
debug = None
if args.debug != "":
debug = (args.debug == "true")
if args.dataset == "7scenes_loc":
cfg = SevenScenesLocConfig(args.config, debug)
elif args.dataset == "cambridge_loc":
cfg = CambridgeLocConfig(args.config, debug)
if args.scene != "":
cfg.opt["scene"] = args.scene
if args.gpu_id != "":
cfg.opt["devices"] = args.gpu_id
if args.use_aug == "true":
cfg.opt["use_aug"] = True
if args.resume == "true":
cfg.opt["resume"] = True
cfg.opt["resume_checkpoint"] = cfg["export_dir"] + \
'/ckpts/checkpoint-backup.pth.tar'
cfg.print_opt()
cfg.set_environmental_variables()
torch.manual_seed(cfg["seed"])
torch.cuda.manual_seed(cfg["seed"])
np.random.seed(cfg["seed"])
random.seed(cfg["seed"])
trainer = Trainer(cfg)
print("Starting Epoch:", trainer.cfg["start_epoch"])
print("Total Epoches:", trainer.cfg["epochs"])
if __name__ == "__main__":
main()