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train_and_eval.py
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train_and_eval.py
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# coding: utf-8
import torch
import os
import random
from tqdm import tqdm
import time
from util.loaders import VOCDataLoader, read_classes, get_color_dict
from models import YOLO
import argparse
class Classifier:
def __init__(self, args):
print("args = {")
for k in args:
print("\t{} = {}".format(k, args[k]))
print("}")
self.args = args.copy()
if self.args["dataset_path"] != "":
self.data_loader = VOCDataLoader(self.args["dataset_path"], num_processes=4, preload=self.args["preload"])
self.classes = read_classes(self.args["class_path"])
self.color_dict = get_color_dict(self.classes, self.args["color_path"])
if self.args["model_save_dir"] != "" and not os.path.exists(self.args["model_save_dir"]):
os.makedirs(self.args["model_save_dir"])
if self.args["graph_save_dir"] != "" and not os.path.exists(self.args["graph_save_dir"]):
os.makedirs(self.args["graph_save_dir"])
self.yolo = YOLO()
def run(self):
if self.args["image_detect_path"] != "":
self.yolo.detect_image_and_show(
self.args["image_detect_path"],
self.color_dict,
0
)
if self.args["video_detect_path"] != "":
self.yolo.detect_video_and_show(
self.args["video_detect_path"],
self.color_dict,
)
if not any([self.args["do_train"], self.args["do_eval"], self.args["do_test"]]):
return None
print('-' * 20 + 'Reading data' + '-' * 20, flush=True)
data_train = self.data_loader.get_data_train() if self.args["do_train"] else []
data_eval = self.data_loader.get_data_eval() if self.args["do_eval"] else []
data_test = self.data_loader.get_data_test() if self.args["do_test"] else []
print('-' * 20 + 'Preprocessing data' + '-' * 20, flush=True)
for data_id in range(len(data_train)):
data_train[data_id][0] = self.yolo.preprocess_image(*(data_train[data_id]), cvt_RGB=True)[0]
for data_id in range(len(data_eval)):
data_eval[data_id][0] = self.yolo.preprocess_image(*(data_eval[data_id]), cvt_RGB=True)[0]
for data_id in range(len(data_test)):
data_test[data_id][0] = self.yolo.preprocess_image(*(data_test[data_id]), cvt_RGB=True)[0]
if self.args["graph_save_dir"] != "":
self.yolo.save_graph(self.args["graph_save_dir"])
for epoch in range(self.args["num_epochs"]):
if self.args["do_train"]:
"""Train"""
print('-' * 20 + 'Training epoch %d' % epoch + '-' * 20, flush=True)
time.sleep(0.5)
random.shuffle(data_train) # 打乱训练数据
for start in tqdm(
range(0, len(data_train), self.args["train_batch_size"]),
desc='Training batch: '
):
end = min(start + self.args["train_batch_size"], len(data_train))
loss = self.yolo.train(data_train[start:end])
print(loss)
"""Save current model"""
if self.args["model_save_dir"] != "":
self.yolo.save(os.path.join(
self.args["model_save_dir"],
time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime()) + "_" + str(epoch) + ".pth"
))
if self.args["do_eval"]:
"""Evaluate"""
print('-' * 20 + 'Evaluating epoch %d' % epoch + '-' * 20, flush=True)
time.sleep(0.5)
pred_results = []
for start in tqdm(
range(0, len(data_eval), self.args["eval_batch_size"]),
desc='Evaluating batch: '
):
end = min(start + self.args["eval_batch_size"], len(data_eval))
pred_results += self.yolo.predict([data[0] for data in data_eval[start:end]], num_processes=0)
mmAP = self.yolo.get_mmAP(data_eval, pred_results)
print("mmAP =", mmAP)
if not self.args["do_train"]:
break
if self.args["do_test"]:
pass
# """Test"""
# print('-' * 20 + 'Testing epoch %d' % epoch + '-' * 20, flush=True)
# time.sleep(0.1)
# m = metrics.Metrics(self.labels)
# for start in tqdm(range(0, len(self.data_test), self.args.test_batch_size),
# desc='Testing batch: '):
# images = [d[0] for d in self.data_test[start:start + self.args.test_batch_size]]
# actual_labels = [d[1] for d in self.data_test[start:start + self.args.test_batch_size]]
# """forward"""
# batch_images = torch.tensor(images, dtype=torch.float32)
# outputs = self.model(batch_images)
# """update confusion matrix"""
# pred_labels = outputs.softmax(1).argmax(1).tolist()
# m.update(actual_labels, pred_labels)
# """testing"""
# print(m.get_accuracy())
if not self.args["do_train"]:
break
print()
def parse_args():
parser = argparse.ArgumentParser(description="Train and evaluate.")
parser.add_argument('--image_detect_path', type=str, default='',
help='Image path for detection. '
'If empty, the detection will not perform.')
parser.add_argument('--video_detect_path', type=str, default='',
help='Image path for detection. '
'If zero, OpenCV will predict through camera. '
'If empty, the detection will not perform.')
parser.add_argument('--dataset_path', type=str, default='',
help='Dataset path.')
parser.add_argument('--preload', action='store_true', default=False,
help="Whether to preload the dataset.")
parser.add_argument('--model_load_path', type=str, default='',
help='Input path for models.')
parser.add_argument('--model_name', type=str, default='yolov1',
help='Model type. '
'Not required when the loading path of the model is specified.',
choices=['yolov1', 'yolov1-tiny', 'yolov3'])
parser.add_argument('--class_path', type=str, default='data/voc.names',
help='Path to a file which stores names of the classes.')
parser.add_argument('--color_path', type=str, default='data/colors',
help='Path to a file which stores colors.')
parser.add_argument('--graph_save_dir', type=str, default='',
help='Output directory for the graph of the model. '
'If empty, graph will not be saved.')
parser.add_argument('--device_ids', type=str, default='-1',
help="Device ids. "
"Should be seperated by commas. "
"-1 means cpu.")
"""Arguments for training"""
parser.add_argument('--do_train', action='store_true', default=False,
help="Whether to train the model on dataset.")
parser.add_argument('--train_batch_size', type=int, default=4,
help='Batch size of train set.')
parser.add_argument('--num_epochs', type=int, default=200,
help='Number of epochs.')
parser.add_argument('--lr', type=float, default=0.0005,
help='Learning rate.')
parser.add_argument('--momentum', type=float, default=0.9,
help='Momentum of optimizer.')
parser.add_argument('--lambda_coord', type=float, default=5,
help='Lambda of coordinates.')
parser.add_argument('--lambda_noobj', type=float, default=0.5,
help='Lambda with no objects.')
parser.add_argument('--clip_max_norm', type=float, default=0,
help='Max norm of the gradients. '
'If zero, the gradients will not be clipped.')
parser.add_argument('--model_save_dir', type=str, default='',
help='Output directory for the model. '
'When empty, the model will not be saved')
"""Arguments for evaluation"""
parser.add_argument('--do_eval', action='store_true', default=False,
help="Whether to evaluate the model on dataset.")
parser.add_argument('--eval_batch_size', type=int, default=4,
help='Batch size of evaluation set.')
parser.add_argument('--score_threshold', type=float, default=0.1,
help='Threshold of score(IOU * P(Object)).')
parser.add_argument('--iou_threshold', type=float, default=0.4,
help='Threshold of IOU used for calculation of NMS.')
parser.add_argument('--iou_thresholds_mmAP', type=list,
default=[0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95],
help='Thresholds of IOU used for calculation of mmAP.')
"""Arguments for test"""
parser.add_argument('--do_test', action='store_true', default=False,
help="Whether to test the model.")
parser.add_argument('--test_batch_size', type=int, default=16,
help='Batch size of test set.')
return parser.parse_args().__dict__
if __name__ == '__main__':
classifier = Classifier(parse_args())
classifier.run()