import torch import torch.nn as nn try: from .rtmdet_v2_basic import Conv, MCBlock, DSBlock except: from rtmdet_v2_basic import Conv, MCBlock, DSBlock model_urls = { 'mcnet_p': None, 'mcnet_n': None, 'mcnet_t': None, 'mcnet_s': None, 'mcnet_m': None, 'mcnet_l': None, 'mcnet_x': None, } # ---------------------------- Backbones ---------------------------- class MixedConvNet(nn.Module): def __init__(self, width=1.0, depth=1.0, num_heads=4, act_type='silu', norm_type='BN', depthwise=False): super(MixedConvNet, self).__init__() # ------------------ Basic parameters ------------------ self.feat_dims_base = [64, 128, 256, 512, 1024] self.nblocks_base = [3, 6, 9, 3] self.feat_dims = [round(dim * width) for dim in self.feat_dims_base] self.nblocks = [round(nblock * depth) for nblock in self.nblocks_base] self.num_heads = num_heads self.act_type = act_type self.norm_type = norm_type self.depthwise = depthwise # ------------------ Network parameters ------------------ ## P1/2 self.layer_1 = nn.Sequential( Conv(3, self.feat_dims[0], k=3, p=1, s=2, act_type=self.act_type, norm_type=self.norm_type), Conv(self.feat_dims[0], self.feat_dims[0], k=3, p=1, act_type=self.act_type, norm_type=self.norm_type, depthwise=self.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=self.act_type, norm_type=self.norm_type), MCBlock(self.feat_dims[1], self.feat_dims[1], self.nblocks[0], self.num_heads, True, self.act_type, self.norm_type, self.depthwise) ) ## P3/8 self.layer_3 = nn.Sequential( DSBlock(self.feat_dims[1], self.feat_dims[2], self.num_heads, self.act_type, self.norm_type, self.depthwise), MCBlock(self.feat_dims[2], self.feat_dims[2], self.nblocks[1], self.num_heads, True, self.act_type, self.norm_type, self.depthwise) ) ## P4/16 self.layer_4 = nn.Sequential( DSBlock(self.feat_dims[2], self.feat_dims[3], self.num_heads, self.act_type, self.norm_type, self.depthwise), MCBlock(self.feat_dims[3], self.feat_dims[3], self.nblocks[2], self.num_heads, True, self.act_type, self.norm_type, self.depthwise) ) ## P5/32 self.layer_5 = nn.Sequential( DSBlock(self.feat_dims[3], self.feat_dims[4], self.num_heads, self.act_type, self.norm_type, self.depthwise), MCBlock(self.feat_dims[4], self.feat_dims[4], self.nblocks[3], self.num_heads, True, self.act_type, self.norm_type, self.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 MCNet def build_backbone(cfg, pretrained=False): # model backbone = MixedConvNet(cfg['width'], cfg['depth'], cfg['bk_num_heads'], cfg['bk_act'], cfg['bk_norm'], 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='mcnet_p') elif cfg['width'] == 0.25 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='mcnet_n') elif cfg['width'] == 0.375 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='mcnet_t') elif cfg['width'] == 0.5 and cfg['depth'] == 0.34: backbone = load_weight(backbone, model_name='mcnet_s') elif cfg['width'] == 0.75 and cfg['depth'] == 0.67: backbone = load_weight(backbone, model_name='mcnet_m') elif cfg['width'] == 1.0 and cfg['depth'] == 1.0: backbone = load_weight(backbone, model_name='mcnet_l') elif cfg['width'] == 1.25 and cfg['depth'] == 1.34: backbone = load_weight(backbone, model_name='mcnet_x') feat_dims = backbone.feat_dims[-3:] return backbone, feat_dims if __name__ == '__main__': import time from thop import profile cfg = { ## Backbone 'backbone': 'mcnet', 'pretrained': True, 'bk_act': 'silu', 'bk_norm': 'BN', 'bk_depthwise': False, 'bk_num_heads': 4, 'width': 0.25, 'depth': 0.34, 'stride': [8, 16, 32], # P3, P4, P5 'max_stride': 32, } 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))