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train.py
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import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
import numpy as np
import os, json, cv2, random
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
from facemask_dataset import register_facemask_dataset, get_facemask_1_dicts
from detectron2.engine.hooks import HookBase
from detectron2.evaluation import inference_context
from detectron2.utils.logger import log_every_n_seconds
from detectron2.data import DatasetMapper, build_detection_test_loader
import detectron2.utils.comm as comm
import torch
import time
import datetime
import logging
import matplotlib.pyplot as plt
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='pass argument to train model')
parser.add_argument('--gpu', type=str, default='0', help='GPU')
parser.add_argument('--resume', action='store_true')
parser.add_argument('--output', type=str, default='./output/facemask_train')
parser.add_argument('--plot_only', action='store_true')
parser.add_argument('--max_iter', type=int, default=2000)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
return args
class LossEvalHook(HookBase):
def __init__(self, eval_period, model, data_loader):
self._model = model
self._period = eval_period
self._data_loader = data_loader
def _do_loss_eval(self):
# Copying inference_on_dataset from evaluator.py
total = len(self._data_loader)
num_warmup = min(5, total - 1)
start_time = time.perf_counter()
total_compute_time = 0
losses = []
for idx, inputs in enumerate(self._data_loader):
if idx == num_warmup:
start_time = time.perf_counter()
total_compute_time = 0
start_compute_time = time.perf_counter()
if torch.cuda.is_available():
torch.cuda.synchronize()
total_compute_time += time.perf_counter() - start_compute_time
iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
seconds_per_img = total_compute_time / iters_after_start
if idx >= num_warmup * 2 or seconds_per_img > 5:
total_seconds_per_img = (time.perf_counter() - start_time) / iters_after_start
eta = datetime.timedelta(seconds=int(total_seconds_per_img * (total - idx - 1)))
log_every_n_seconds(
logging.INFO,
"Loss on Validation done {}/{}. {:.4f} s / img. ETA={}".format(
idx + 1, total, seconds_per_img, str(eta)
),
n=5,
)
loss_batch = self._get_loss(inputs)
losses.append(loss_batch)
mean_loss = np.mean(losses)
self.trainer.storage.put_scalar('validation_loss', mean_loss)
comm.synchronize()
return losses
def _get_loss(self, data):
# How loss is calculated on train_loop
metrics_dict = self._model(data)
# print(metrics_dict)
metrics_dict = {
k: v.detach().cpu().item() if isinstance(v, torch.Tensor) else float(v)
for k, v in metrics_dict.items()
}
# print(merics)
total_losses_reduced = sum(loss for loss in metrics_dict.values())
return total_losses_reduced
def after_step(self):
next_iter = self.trainer.iter + 1
is_final = next_iter == self.trainer.max_iter
if is_final or (self._period > 0 and next_iter % self._period == 0):
self._do_loss_eval()
self.trainer.storage.put_scalars(timetest=12)
class MyTrainer(DefaultTrainer):
def build_hooks(self):
hooks = super().build_hooks()
hooks.insert(-1,LossEvalHook(
cfg.TEST.EVAL_PERIOD,
self.model,
build_detection_test_loader(
self.cfg,
self.cfg.DATASETS.TEST[0],
DatasetMapper(self.cfg,True)
)
))
return hooks
#modify the config file
def modify_cfg(args, cfg_filepath = "COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml"):
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(cfg_filepath))
cfg.DATASETS.TRAIN = ("facemask_1_train",)
cfg.DATASETS.TEST = ("facemask_1_val",)
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_filepath) # Let training initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025 # pick a good LR
cfg.SOLVER.MAX_ITER = args.max_iter # 300 iterations seems good enough for this toy dataset; you will need to train longer for a practical dataset
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # faster, and good enough for this toy dataset (default: 512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3 # (see https://detectron2.readthedocs.io/tutorials/datasets.html#update-the-config-for-new-datasets)
cfg.OUTPUT_DIR = args.output
cfg.TEST.EVAL_PERIOD = 200
return cfg
def plot_loss(cfg):
experiment_folder = cfg.OUTPUT_DIR
def load_json_arr(json_path):
lines = []
with open(json_path, 'r') as f:
for line in f:
lines.append(json.loads(line))
return lines
experiment_metrics = load_json_arr(experiment_folder + '/metrics.json')
# print(experiment_metrics)
plt.plot([x['iteration'] for x in experiment_metrics if 'total_loss' in x],
[x['total_loss'] for x in experiment_metrics if 'total_loss' in x])
plt.plot([x['iteration'] for x in experiment_metrics if 'validation_loss' in x],
[x['validation_loss'] for x in experiment_metrics if 'validation_loss' in x])
plt.legend(['total_loss', 'validation_loss'], loc='upper left')
plt.show()
#train model
def train_model(args, cfg):
trainer = MyTrainer(cfg)
trainer.resume_or_load(resume=args.resume)
trainer.train()
if __name__=='__main__':
args = parse_args()
# _, _ = register_facemask_dataset()
_, _ = register_facemask_dataset(split='val')
cfg = modify_cfg(args)
if args.plot_only:
plot_loss(cfg)
else:
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
train_model(args, cfg)