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| #!/usr/bin/env python3 | |
| # -*- coding:utf-8 -*- | |
| import math | |
| import torch.nn as nn | |
| from yolov6.layers.common import * | |
| from yolov6.utils.torch_utils import initialize_weights | |
| from yolov6.models.efficientrep import EfficientRep | |
| from yolov6.models.reppan import RepPANNeck | |
| from yolov6.models.effidehead import Detect, build_effidehead_layer | |
| class Model(nn.Module): | |
| '''YOLOv6 model with backbone, neck and head. | |
| The default parts are EfficientRep Backbone, Rep-PAN and | |
| Efficient Decoupled Head. | |
| ''' | |
| def __init__(self, config, channels=3, num_classes=None, anchors=None): # model, input channels, number of classes | |
| super().__init__() | |
| # Build network | |
| num_layers = config.model.head.num_layers | |
| self.backbone, self.neck, self.detect = build_network(config, channels, num_classes, anchors, num_layers) | |
| # Init Detect head | |
| begin_indices = config.model.head.begin_indices | |
| out_indices_head = config.model.head.out_indices | |
| self.stride = self.detect.stride | |
| self.detect.i = begin_indices | |
| self.detect.f = out_indices_head | |
| self.detect.initialize_biases() | |
| # Init weights | |
| initialize_weights(self) | |
| def forward(self, x): | |
| x = self.backbone(x) | |
| x = self.neck(x) | |
| x = self.detect(x) | |
| return x | |
| def _apply(self, fn): | |
| self = super()._apply(fn) | |
| self.detect.stride = fn(self.detect.stride) | |
| self.detect.grid = list(map(fn, self.detect.grid)) | |
| return self | |
| def make_divisible(x, divisor): | |
| # Upward revision the value x to make it evenly divisible by the divisor. | |
| return math.ceil(x / divisor) * divisor | |
| def build_network(config, channels, num_classes, anchors, num_layers): | |
| depth_mul = config.model.depth_multiple | |
| width_mul = config.model.width_multiple | |
| num_repeat_backbone = config.model.backbone.num_repeats | |
| channels_list_backbone = config.model.backbone.out_channels | |
| num_repeat_neck = config.model.neck.num_repeats | |
| channels_list_neck = config.model.neck.out_channels | |
| num_anchors = config.model.head.anchors | |
| num_repeat = [(max(round(i * depth_mul), 1) if i > 1 else i) for i in (num_repeat_backbone + num_repeat_neck)] | |
| channels_list = [make_divisible(i * width_mul, 8) for i in (channels_list_backbone + channels_list_neck)] | |
| backbone = EfficientRep( | |
| in_channels=channels, | |
| channels_list=channels_list, | |
| num_repeats=num_repeat | |
| ) | |
| neck = RepPANNeck( | |
| channels_list=channels_list, | |
| num_repeats=num_repeat | |
| ) | |
| head_layers = build_effidehead_layer(channels_list, num_anchors, num_classes) | |
| head = Detect(num_classes, anchors, num_layers, head_layers=head_layers) | |
| return backbone, neck, head | |
| def build_model(cfg, num_classes, device): | |
| model = Model(cfg, channels=3, num_classes=num_classes, anchors=cfg.model.head.anchors).to(device) | |
| return model | |