import torch import torch.nn as nn try: from .yolox_basic import Conv, CSPBlock from .yolox_neck import SPPF except: from yolox_basic import Conv, CSPBlock from yolox_neck import SPPF # ImageNet-1K pretrained weight model_urls = { "cspdarknet_n": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/cspdarknet_nano.pth", "cspdarknet_s": "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/cspdarknet_small.pth", "cspdarknet_m": None, # For Medium-level, it is not necessary to load pretrained weight. "cspdarknet_l": None, # For Large-level, it is not necessary to load pretrained weight. "cspdarknet_x": None, # For Huge-level, it is not necessary to load pretrained weight. } # CSPDarkNet class CSPDarkNet(nn.Module): def __init__(self, depth=1.0, width=1.0, act_type='silu', norm_type='BN', depthwise=False): super(CSPDarkNet, self).__init__() self.feat_dims = [round(64 * width), round(128 * width), round(256 * width), round(512 * width), round(1024 * width)] # P1/2 self.layer_1 = Conv(3, self.feat_dims[0], k=6, p=2, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise) # P2/4 self.layer_2 = nn.Sequential( Conv(self.feat_dims[0], self.feat_dims[1], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise), CSPBlock(in_dim = self.feat_dims[1], out_dim = self.feat_dims[1], expand_ratio = 0.5, nblocks = round(3*depth), shortcut = True, act_type = act_type, norm_type = norm_type, depthwise = depthwise) ) # P3/8 self.layer_3 = nn.Sequential( Conv(self.feat_dims[1], self.feat_dims[2], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise), CSPBlock(in_dim = self.feat_dims[2], out_dim = self.feat_dims[2], expand_ratio = 0.5, nblocks = round(9*depth), shortcut = True, act_type = act_type, norm_type = norm_type, depthwise = depthwise) ) # P4/16 self.layer_4 = nn.Sequential( Conv(self.feat_dims[2], self.feat_dims[3], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise), CSPBlock(in_dim = self.feat_dims[3], out_dim = self.feat_dims[3], expand_ratio = 0.5, nblocks = round(9*depth), shortcut = True, act_type = act_type, norm_type = norm_type, depthwise = depthwise) ) # P5/32 self.layer_5 = nn.Sequential( Conv(self.feat_dims[3], self.feat_dims[4], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise), SPPF(self.feat_dims[4], self.feat_dims[4], expand_ratio=0.5), CSPBlock(in_dim = self.feat_dims[4], out_dim = self.feat_dims[4], expand_ratio = 0.5, nblocks = round(3*depth), shortcut = True, act_type = act_type, norm_type = norm_type, depthwise = depthwise) ) def forward(self, x): c1 = self.layer_1(x) c2 = self.layer_2(c1) c3 = self.layer_3(c2) c4 = self.layer_4(c3) c5 = self.layer_5(c4) outputs = [c3, c4, c5] return outputs # ---------------------------- Functions ---------------------------- ## load pretrained weight def load_weight(model, model_name): # load weight print('Loading pretrained weight ...') url = model_urls[model_name] if url is not None: checkpoint = torch.hub.load_state_dict_from_url( url=url, map_location="cpu", check_hash=True) # checkpoint state dict checkpoint_state_dict = checkpoint.pop("model") # model state dict model_state_dict = model.state_dict() # check for k in list(checkpoint_state_dict.keys()): if k in model_state_dict: shape_model = tuple(model_state_dict[k].shape) shape_checkpoint = tuple(checkpoint_state_dict[k].shape) if shape_model != shape_checkpoint: checkpoint_state_dict.pop(k) else: checkpoint_state_dict.pop(k) print('Unused key: ', k) model.load_state_dict(checkpoint_state_dict) else: print('No pretrained for {}'.format(model_name)) return model ## build CSPDarkNet def build_backbone(cfg, pretrained=False): # Build backbone backbone = CSPDarkNet(cfg['depth'], cfg['width'], cfg['bk_act'], cfg['bk_norm'], cfg['bk_dpw']) feat_dims = backbone.feat_dims[-3:] # Load pretrained weight if pretrained: if cfg['width'] == 0.25 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='cspdarknet_n') elif cfg['width'] == 0.5 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='cspdarknet_s') elif cfg['width'] == 0.75 and cfg['depth'] == 0.67: backbone = load_weight(backbone, model_name='cspdarknet_m') elif cfg['width'] == 1.0 and cfg['depth'] == 1.0: backbone = load_weight(backbone, model_name='cspdarknet_l') elif cfg['width'] == 1.25 and cfg['depth'] == 1.34: backbone = load_weight(backbone, model_name='cspdarknet_x') return backbone, feat_dims if __name__ == '__main__': import time from thop import profile cfg = { 'bk_pretrained': True, 'bk_act': 'lrelu', 'bk_norm': 'BN', 'bk_dpw': False, 'p6_feat': False, 'p7_feat': False, 'width': 1.0, 'depth': 1.0, } model, feats = build_backbone(cfg, pretrained=cfg['bk_pretrained']) x = torch.randn(1, 3, 640, 640) t0 = time.time() outputs = model(x) t1 = time.time() print('Time: ', t1 - t0) for out in outputs: print(out.shape) print('==============================') flops, params = profile(model, inputs=(x, ), verbose=False) print('==============================') print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2)) print('Params : {:.2f} M'.format(params / 1e6))