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| #!/usr/bin/env python3 | |
| # -*- coding:utf-8 -*- | |
| # Copyright (c) Megvii, Inc. and its affiliates. | |
| import os | |
| import torch.nn as nn | |
| from yolox.exp import Exp as MyExp | |
| class Exp(MyExp): | |
| def __init__(self): | |
| super(Exp, self).__init__() | |
| self.depth = 0.33 | |
| self.width = 0.25 | |
| self.input_size = (416, 416) | |
| self.mosaic_scale = (0.5, 1.5) | |
| self.random_size = (10, 20) | |
| self.test_size = (416, 416) | |
| self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0] | |
| self.enable_mixup = False | |
| # Define yourself dataset path | |
| self.data_dir = "datasets/coco128" | |
| self.train_ann = "instances_train2017.json" | |
| self.val_ann = "instances_val2017.json" | |
| self.num_classes = 71 | |
| def get_model(self, sublinear=False): | |
| def init_yolo(M): | |
| for m in M.modules(): | |
| if isinstance(m, nn.BatchNorm2d): | |
| m.eps = 1e-3 | |
| m.momentum = 0.03 | |
| if "model" not in self.__dict__: | |
| from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead | |
| in_channels = [256, 512, 1024] | |
| # NANO model use depthwise = True, which is main difference. | |
| backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, depthwise=True) | |
| head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, depthwise=True) | |
| self.model = YOLOX(backbone, head) | |
| self.model.apply(init_yolo) | |
| self.model.head.initialize_biases(1e-2) | |
| return self.model | |