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train.py
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train.py
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import argparse
import os
import random
import time
from pathlib import Path
import CBR
import numpy as np
import copy
import pdb
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch import autograd
from torch.optim.lr_scheduler import ExponentialLR
# from torch.optim.lr_scheduler import StepLR
from torch.optim.lr_scheduler import MultiStepLR
# from torch.optim.lr_scheduler import CosineAnnealingLR
from tensorboardX import SummaryWriter
from PIL import Image
import matplotlib.pyplot as PLT
import matplotlib.cm as mpl_color_map
from opt import get_args
import tqdm
from losses import compute_losses
from utils import mean_IU, mean_precision, BatchCollator
from CBR.utils.evaluation import generate_kitti_3d_detection, evaluate_python
from CBR.utils.vis_utils import show_image_with_boxes
def readlines(filename):
"""Read all the lines in a text file and return as a list
"""
with open(filename, 'r') as f:
lines = f.read().splitlines()
return lines
class Trainer:
def __init__(self):
self.opt = get_args()
if not os.path.isdir(self.opt.out_dir):
os.makedirs(self.opt.out_dir)
os.makedirs(os.path.join(self.opt.out_dir, 'det'))
self.device = "cuda"
self.seed = self.opt.global_seed
if self.seed != 0:
self.set_seed() # set seed
self.models = {}
self.inputs = {}
self.parameters_to_train = []
self.transform_parameters_to_train = []
self.detection_parameters_to_train = []
self.base_parameters_to_train = []
self.parameters_to_train = []
self.parameters_to_train_D = []
self.weight = self.opt.weight
self.criterion_d = nn.BCEWithLogitsLoss()
self.criterion = compute_losses(self.device)
self.create_time = time.strftime("%Y-%m-%d-%H-%M", time.localtime())
self.epoch = 0
self.start_epoch = 0
self.scheduler = 0
# Save log and models path
self.opt.log_root = self.opt.log_root
self.opt.save_path = self.opt.save_path
self.writer = SummaryWriter(os.path.join(self.opt.log_root, self.opt.model_name, self.create_time))
self.log = open(os.path.join(self.opt.log_root, self.opt.model_name, self.create_time, '%s.csv' % self.opt.model_name), 'w')
self.det_output = os.path.join(self.opt.out_dir, 'det')
# Initializing models
self.models["encoder"] = CBR.Encoder(18, self.opt.height, self.opt.width, True)
self.models['DecoupleViewProjection'] = CBR.DecoupleViewProjection(in_dim=16)
self.models["CrossViewEnhancement"] = CBR.CrossViewEnhancement(64)
self.models["bev_decoder"] = CBR.Decoder(self.models["encoder"].resnet_encoder.num_ch_enc, self.opt.num_class, "bev_decoder")
self.models["fv_decoder"] = CBR.Decoder(self.models["encoder"].resnet_encoder.num_ch_enc, self.opt.num_class, "fv_decoder")
self.models["det_heads"] = CBR.Bev_predictor(self.opt.num_class, 64)
self.det_infer = CBR.DetInfer(self.device)
for key in self.models.keys():
self.models[key].to(self.device)
if "transform" in key:
self.transform_parameters_to_train += list(self.models[key].parameters())
else:
self.base_parameters_to_train += list(self.models[key].parameters())
self.parameters_to_train = [
{"params": self.transform_parameters_to_train, "lr": self.opt.lr_transform},
{"params": self.base_parameters_to_train, "lr": self.opt.lr},]
# Optimization
self.model_optimizer = optim.Adam(self.parameters_to_train)
# self.scheduler = ExponentialLR(self.model_optimizer, gamma=0.99)
# self.scheduler = StepLR(self.model_optimizer, step_size=step_size, gamma=0.65)
# self.scheduler = MultiStepLR(self.model_optimizer, milestones=self.opt.lr_steps, gamma=0.1)
# self.scheduler = CosineAnnealingLR(self.model_optimizer, T_max=15) # iou 35.55
# Data Loaders
self.dataset = CBR.KITTIObject
self.fpath = os.path.join(self.opt.data_path, "splits", "{}_files.txt")
train_filenames = readlines(self.fpath.format("train"))
val_filenames = readlines(self.fpath.format("val"))
self.val_filenames = val_filenames
self.train_filenames = train_filenames
train_dataset = self.dataset(self.opt, train_filenames)
val_dataset = self.dataset(self.opt, val_filenames, is_train=False)
collator = BatchCollator()
self.train_loader = DataLoader(
train_dataset,
self.opt.batch_size,
True,
num_workers=self.opt.num_workers,
collate_fn=collator,
pin_memory=True,
drop_last=True)
self.val_loader = DataLoader(
val_dataset,
1,
True,
num_workers=self.opt.num_workers,
collate_fn=collator,
pin_memory=True,
drop_last=True)
# Load weights
if self.opt.load_weights_folder != "":
self.load_model()
print("There are {:d} training items and {:d} validation items\n".format(len(train_dataset), len(val_dataset)))
def train(self):
if not os.path.isdir(self.opt.log_root):
os.mkdir(self.opt.log_root)
for self.epoch in range(self.start_epoch, self.opt.num_epochs + 1):
self.adjust_learning_rate(self.model_optimizer, self.epoch, self.opt.lr_steps)
loss = self.run_epoch()
output = ("Epoch: %d | lr:%.7f | Loss: %.4f | bev seg Loss: %.4f | fv seg Loss: %.4f || det map Loss: %.4f | det reg Loss: %.4f | det ori Loss: %.4f"
% (self.epoch, self.model_optimizer.param_groups[-1]['lr'], loss["loss"], loss["bev_seg_loss"], loss["fv_seg_loss"], loss["det_map_loss"], loss["det_reg_loss"], loss["det_ori_loss"]))
print(output)
self.log.write(output + '\n')
self.log.flush()
for loss_name in loss:
self.writer.add_scalar(loss_name, loss[loss_name], global_step=self.epoch)
if self.epoch % self.opt.log_frequency == 0:
self.validation(self.log)
if self.opt.model_split_save:
self.save_model()
self.save_model()
def process_batch(self, inputs, validation=False):
outputs = {}
self.inputs['color'] = torch.stack([t["color"] for t in inputs]).to(self.device)
self.inputs['bev_seg'] = torch.stack([t["bev_seg"] for t in inputs]).to(self.device)
self.inputs['fv_seg'] = torch.stack([t["fv_seg"] for t in inputs]).to(self.device)
self.inputs['filename'] = [t["filename"] for t in inputs]
self.inputs['bev_map'] = torch.stack([t["bev_map"] for t in inputs]).to(self.device)
self.inputs['bev_inds'] = torch.stack([t["bev_inds"] for t in inputs]).to(self.device)
self.inputs['bev_masks'] = torch.stack([t["bev_masks"] for t in inputs]).to(self.device)
self.inputs['bev_boxes'] = torch.stack([t["bev_boxes"] for t in inputs]).to(self.device)
self.inputs['bev_ori'] = torch.stack([t["bev_ori"] for t in inputs]).to(self.device)
self.inputs['cls_ids'] = torch.stack([t["cls_ids"] for t in inputs]).to(self.device)
self.inputs['cv_seg'] = torch.stack([t["cv_seg"] for t in inputs]).to(self.device)
features = self.models["encoder"](self.inputs["color"])
bev_features, fv_features = self.models["DecoupleViewProjection"](features)
if validation:
outputs["bev_seg"], bev_features = self.models["bev_decoder"](bev_features, False)
outputs["fv_seg"], fv_features = self.models["fv_decoder"](fv_features, False)
bev_features = self.models["CrossViewEnhancement"](fv_features, bev_features)
else:
outputs["bev_seg"], bev_features = self.models["bev_decoder"](bev_features)
outputs["fv_seg"], fv_features = self.models["fv_decoder"](fv_features)
bev_features = self.models["CrossViewEnhancement"](fv_features, bev_features)
outputs["det_cls"], outputs["det_reg"] = self.models["det_heads"](bev_features)
if validation:
return outputs
losses = self.criterion(self.opt, self.weight, self.inputs, outputs)
return outputs, losses
def run_epoch(self):
self.model_optimizer.step()
loss = {
"loss": 0.0,
"bev_seg_loss": 0.0,
"fv_seg_loss": 0.0,
"det_map_loss": 0.0,
"det_reg_loss": 0.0,
"det_ori_loss": 0.0,
}
accumulation_steps = 1
for batch_idx, inputs in tqdm.tqdm(enumerate(self.train_loader)):
outputs, losses = self.process_batch(inputs)
self.model_optimizer.zero_grad()
losses["loss"] = losses["loss"] / accumulation_steps
losses["loss"].backward()
self.model_optimizer.step()
for loss_name in losses:
loss[loss_name] += losses[loss_name].item()
# self.scheduler.step()
for loss_name in loss:
loss[loss_name] /= len(self.train_loader)
return loss
def validation(self, log):
bev_iou, bev_mAP = np.array([0., 0.]), np.array([0., 0.])
fv_iou, fv_mAP = np.array([0., 0.]), np.array([0., 0.])
for batch_idx, inputs in tqdm.tqdm(enumerate(self.val_loader)):
with torch.no_grad():
outputs = self.process_batch(inputs, True)
# Segmentation
bev_pred = np.squeeze(torch.argmax(outputs["bev_seg"].detach(), 1).cpu().numpy())
bev_gt = np.squeeze(self.inputs["bev_seg"].detach().cpu().numpy())
bev_iou += mean_IU(bev_pred, bev_gt)
bev_mAP += mean_precision(bev_pred, bev_gt)
fv_pred = np.squeeze(torch.argmax(outputs["fv_seg"].detach(), 1).cpu().numpy())
fv_gt = np.squeeze(self.inputs["fv_seg"].detach().cpu().numpy())
fv_iou += mean_IU(fv_pred, fv_gt)
fv_mAP += mean_precision(fv_pred, fv_gt)
# Detection
det_pred = self.det_infer(outputs, inputs)
det_pred = det_pred.to(torch.device("cpu"))
predict_txt = inputs[0]['filename'] + '.txt'
predict_txt = os.path.join(self.det_output, predict_txt)
generate_kitti_3d_detection(det_pred, predict_txt)
# show_image_with_boxes(det_pred, inputs)
bev_iou /= len(self.val_loader)
bev_mAP /= len(self.val_loader)
fv_iou /= len(self.val_loader)
fv_mAP /= len(self.val_loader)
det_results, ret_dict = evaluate_python(label_path=os.path.join(self.opt.data_path, 'label_2'),
result_path=os.path.join(self.opt.out_dir, 'det'),
label_split_file=self.fpath.format("val"),
current_class=0,
metric='R40')
print ('\n' + det_results)
output = ("Epoch: %d | bev: mIOU: %.4f mAP: %.4f | fv: mIOU: %.4f mAP: %.4f " % (self.epoch, bev_iou[1], bev_mAP[1], fv_iou[1], fv_mAP[1]))
print(output)
log.write(output + '\n')
log.write(det_results + '\n')
log.flush()
def save_model(self):
save_path = os.path.join(
self.opt.save_path,
self.opt.model_name,
"weights_{}".format(
self.epoch)
)
if not os.path.exists(save_path):
os.makedirs(save_path)
for model_name, model in self.models.items():
model_path = os.path.join(save_path, "{}.pth".format(model_name))
state_dict = model.state_dict()
state_dict['epoch'] = self.epoch
if model_name == "encoder":
state_dict["height"] = self.opt.height
state_dict["width"] = self.opt.width
torch.save(state_dict, model_path)
optim_path = os.path.join(save_path, "{}.pth".format("adam"))
torch.save(self.model_optimizer.state_dict(), optim_path)
def load_model(self):
"""
Load model(s) from disk
"""
self.opt.load_weights_folder = os.path.expanduser(self.opt.load_weights_folder)
assert os.path.isdir(self.opt.load_weights_folder), \
"Cannot find folder {}".format(self.opt.load_weights_folder)
print("loading model from folder {}".format(self.opt.load_weights_folder))
for key in self.models.keys():
print("Loading {} weights...".format(key))
path = os.path.join(self.opt.load_weights_folder,"{}.pth".format(key))
model_dict = self.models[key].state_dict()
pretrained_dict = torch.load(path)
if 'epoch' in pretrained_dict:
self.start_epoch = pretrained_dict['epoch']
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
self.models[key].load_state_dict(model_dict)
# loading adam state
optimizer_load_path = os.path.join(self.opt.load_weights_folder, "adam.pth")
if os.path.isfile(optimizer_load_path):
print("Loading Adam weights")
optimizer_dict = torch.load(optimizer_load_path)
self.model_optimizer.load_state_dict(optimizer_dict)
else:
print("Cannot find Adam weights so Adam is randomly initialized")
def adjust_learning_rate(self, optimizer, epoch, lr_steps):
"""Sets the learning rate to the initial LR decayed by 10 every 25 epochs"""
decay = 0.1 ** (sum(epoch >= np.array(lr_steps)))
decay = round(decay, 2)
lr = self.opt.lr * decay
lr_transform = self.opt.lr_transform * decay
decay = self.opt.weight_decay
optimizer.param_groups[0]['lr'] = lr_transform
optimizer.param_groups[1]['lr'] = lr
optimizer.param_groups[0]['weight_decay'] = decay
optimizer.param_groups[1]['weight_decay'] = decay
def set_seed(self):
seed = self.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if __name__ == "__main__":
start_time = time.ctime()
print(start_time)
trainer = Trainer()
trainer.train()
end_time = time.ctime()
print(end_time)