import torch import torch.nn as nn try: from .rtcdet_basic import Conv, ELANBlock, DSBlock except: from rtcdet_basic import Conv, ELANBlock, DSBlock model_urls = { 'elannet_pico': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_pico.pth", 'elannet_nano': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_nano.pth", 'elannet_tiny': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_tiny.pth", 'elannet_small': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_small.pth", 'elannet_medium': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_medium.pth", 'elannet_large': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_large.pth", 'elannet_huge': "https://github.com/yjh0410/image_classification_pytorch/releases/download/weight/elannet_huge.pth", } # ---------------------------- Backbones ---------------------------- # ELANNet-P5 class ELANNet(nn.Module): def __init__(self, width=1.0, depth=1.0, act_type='silu', norm_type='BN', depthwise=False): super(ELANNet, self).__init__() # ------------------ Basic parameters ------------------ self.width = width self.depth = depth self.expand_ratios = [0.5, 0.5, 0.5, 0.25] self.feat_dims = [round(64*width), round(128*width), round(256*width), round(512*width), round(1024*width), round(1024*width)] # ------------------ Network parameters ------------------ ## P1/2 self.layer_1 = nn.Sequential( Conv(3, self.feat_dims[0], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type), Conv(self.feat_dims[0], self.feat_dims[0], k=3, p=1, 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), ELANBlock(self.feat_dims[1], self.feat_dims[2], self.expand_ratios[0], self.depth, act_type, norm_type, depthwise) ) ## P3/8 self.layer_3 = nn.Sequential( DSBlock(self.feat_dims[2], self.feat_dims[2], act_type, norm_type, depthwise), ELANBlock(self.feat_dims[2], self.feat_dims[3], self.expand_ratios[1], self.depth, act_type, norm_type, depthwise) ) ## P4/16 self.layer_4 = nn.Sequential( DSBlock(self.feat_dims[3], self.feat_dims[3], act_type, norm_type, depthwise), ELANBlock(self.feat_dims[3], self.feat_dims[4], self.expand_ratios[2], self.depth, act_type, norm_type, depthwise) ) ## P5/32 self.layer_5 = nn.Sequential( DSBlock(self.feat_dims[4], self.feat_dims[4], act_type, norm_type, depthwise), ELANBlock(self.feat_dims[4], self.feat_dims[5], self.expand_ratios[3], self.depth, act_type, norm_type, 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(k) model.load_state_dict(checkpoint_state_dict) else: print('No pretrained for {}'.format(model_name)) return model ## build ELAN-Net def build_backbone(cfg, pretrained=False): # model backbone = ELANNet( width=cfg['width'], depth=cfg['depth'], act_type=cfg['bk_act'], norm_type=cfg['bk_norm'], depthwise=cfg['bk_depthwise'] ) # check whether to load imagenet pretrained weight if pretrained: if cfg['width'] == 0.25 and cfg['depth'] == 0.34 and cfg['bk_depthwise']: backbone = load_weight(backbone, model_name='elannet_pico') elif cfg['width'] == 0.25 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='elannet_nano') elif cfg['width'] == 0.375 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='elannet_tiny') elif cfg['width'] == 0.5 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='elannet_small') elif cfg['width'] == 0.75 and cfg['depth'] == 0.67: backbone = load_weight(backbone, model_name='elannet_medium') elif cfg['width'] == 1.0 and cfg['depth'] == 1.0: backbone = load_weight(backbone, model_name='elannet_large') elif cfg['width'] == 1.25 and cfg['depth'] == 1.34: backbone = load_weight(backbone, model_name='elannet_huge') feat_dims = backbone.feat_dims[-3:] return backbone, feat_dims if __name__ == '__main__': import time from thop import profile cfg = { 'pretrained': True, 'bk_act': 'silu', 'bk_norm': 'BN', 'bk_depthwise': False, 'width': 1.0, 'depth': 1.0, } model, feats = build_backbone(cfg) 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))