pokouqiege/models/base/backbone_factory.py

from collections import OrderedDict

from libs.vision_libs import models
from libs.vision_libs.models import mobilenet_v3_large, EfficientNet_V2_S_Weights, efficientnet_v2_s, \
    EfficientNet_V2_M_Weights, efficientnet_v2_m, EfficientNet_V2_L_Weights, efficientnet_v2_l
from libs.vision_libs.models._utils import _ovewrite_value_param, handle_legacy_interface
from libs.vision_libs.models.detection import FasterRCNN
from libs.vision_libs.models.detection.anchor_utils import AnchorGenerator
from libs.vision_libs.models.detection.ssdlite import _mobilenet_extractor
from libs.vision_libs.models.resnet import resnet50, ResNet50_Weights, resnet18
from libs.vision_libs.models.detection._utils import overwrite_eps
from libs.vision_libs.models.detection.backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
from libs.vision_libs.ops import misc as misc_nn_ops, MultiScaleRoIAlign
from torch import nn

import torch
from torchvision.models.detection.backbone_utils import BackboneWithFPN, resnet_fpn_backbone
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool


def get_resnet50_fpn():
    is_trained = False
    trainable_backbone_layers = _validate_trainable_layers(is_trained, None, 5, 3)
    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
    backbone = resnet50(weights=None, progress=True, norm_layer=norm_layer)
    backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
    return backbone


def get_resnet18_fpn():
    is_trained = False
    trainable_backbone_layers = _validate_trainable_layers(is_trained, None, 5, 3)
    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
    backbone = resnet18(weights=None, progress=True, norm_layer=norm_layer)
    backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
    return backbone


def get_mobilenet_v3_large_fpn():
    is_trained = False
    trainable_backbone_layers = _validate_trainable_layers(is_trained, None, 6, 3)
    norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d

    backbone = mobilenet_v3_large(weights=None, progress=True, norm_layer=norm_layer)
    backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers)
    return backbone
def get_convnext_fpn():
    convnext = models.convnext_base(pretrained=True)
    in_channels_list = [128, 256, 512, 1024]
    backbone_with_fpn = BackboneWithFPN(
        convnext.features,
        return_layers={'1': '0', '3': '1', '5': '2', '7': '3'},  # 确保这些键对应到实际的层
        in_channels_list=in_channels_list,
        out_channels=256
    )
    return backbone_with_fpn

def get_efficientnetv2_fpn(name='efficientnet_v2_m', pretrained=True):
    # 加载EfficientNetV2模型
    if name == 'efficientnet_v2_s':
        weights = EfficientNet_V2_S_Weights.IMAGENET1K_V1 if pretrained else None
        backbone = efficientnet_v2_s(weights=weights).features
    if name == 'efficientnet_v2_m':
        weights = EfficientNet_V2_M_Weights.IMAGENET1K_V1 if pretrained else None
        backbone = efficientnet_v2_m(weights=weights).features
    if name == 'efficientnet_v2_l':
        weights = EfficientNet_V2_L_Weights.IMAGENET1K_V1 if pretrained else None
        backbone = efficientnet_v2_l(weights=weights).features

    # 定义返回的层索引和名称
    return_layers = {"2": "0", "3": "1", "4": "2", "5": "3"}

    # 获取每个层输出通道数
    in_channels_list = []
    for layer_idx in [2, 3, 4, 5]:
        module = backbone[layer_idx]
        if hasattr(module, 'out_channels'):
            in_channels_list.append(module.out_channels)
        elif hasattr(module[-1], 'out_channels'):
            # 如果module本身没有out_channels，检查最后一个子模块
            in_channels_list.append(module[-1].out_channels)
        else:
            raise ValueError(f"Cannot determine out_channels for layer {layer_idx}")

    # 使用BackboneWithFPN包装backbone
    backbone_with_fpn = BackboneWithFPN(
        backbone=backbone,
        return_layers=return_layers,
        in_channels_list=in_channels_list,
        out_channels=256
    )

    return backbone_with_fpn



# 加载 ConvNeXt 模型
convnext = models.convnext_base(pretrained=True)
# convnext = models.convnext_tiny(pretrained=True)
# convnext = models.convnext_small(pretrained=True)
# print(convnext)
# # 打印模型的所有命名层
# for name, _ in convnext.features[5].named_children():
#     print(name)

# 修改 ConvNeXt 以适应 Faster R-CNN
# 修改 ConvNeXt 以适应 Faster R-CNN

def get_anchor_generator(backbone, test_input):
    features = backbone(test_input)  # 获取 backbone 输出的所有特征图
    featmap_names = list(features.keys())
    print(f'featmap_names:{featmap_names}')
    num_features = len(features)     # 特征图数量
    print(f'num_features:{num_features}')
    # num_features=num_features-1

    # # 定义每层的 anchor 尺寸和比例
    # base_sizes = [32, 64, 128]  # 支持最多 4 层
    # sizes = tuple((size,) for size in base_sizes[:num_features])

    anchor_sizes = tuple((int(16 * 2 ** i),) for i in range(num_features))  # 自动生成不同大小
    print(f'anchor_sizes:{anchor_sizes }')
    aspect_ratios = ((0.5, 1.0, 2.0),) * num_features
    print(f'aspect_ratios:{aspect_ratios}')

    return AnchorGenerator(sizes=anchor_sizes , aspect_ratios=aspect_ratios)

if __name__ == '__main__':
    # 创建 ConvNeXt backbone
    convnext = models.convnext_base(pretrained=True)
    for i,layer in enumerate(convnext.features):
        print(f'layer{i}:{layer}')
    # 创建一个小的输入张量用于获取各层输出通道数
    dummy_input = torch.randn(1, 3, 224, 224)
    # output_channels_list = get_output_channels(convnext.features, dummy_input)
    # print(f'output_channels_list:{output_channels_list}')

    # 根据之前的经验，选择合适的层索引
    selected_layers = [3, 5, 7]  # 假设这是我们要用作 FPN 输入的层索引
    in_channels_list = [128,256,512,1024]
    print(f'in_channels_list:{in_channels_list}')

    # 创建 FPN
    backbone_with_fpn = BackboneWithFPN(
        convnext.features,
        return_layers={'1':'0','3': '1', '5': '2', '7': '3'},  # 确保这些键对应到实际的层
        in_channels_list=in_channels_list,
        out_channels=256
    )

    # 创建 Faster R-CNN 模型
    # anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
    #                                    aspect_ratios=((0.5, 1.0, 2.0),))
    # anchor_generator = AnchorGenerator(
    #     sizes=((32,), (64,), (128,)),  # ✅ 正确
    #     aspect_ratios=((0.5, 1.0, 2.0),) * 3  # ✅ 正确
    # )
    test_input = torch.rand(1, 3, 224, 224)
    anchor_generator = get_anchor_generator(backbone_with_fpn, test_input)
    print(f'anchor_generator:{anchor_generator}')

    featmap_names=['0', '1', '2', '3', 'pool']
    roi_pooler = MultiScaleRoIAlign(
        featmap_names=featmap_names,
        output_size=7,
        sampling_ratio=2
    )

    model = FasterRCNN(
        backbone=backbone_with_fpn,
        num_classes=91,  # COCO 数据集有 91 类
        rpn_anchor_generator=anchor_generator,
        box_roi_pool=roi_pooler
    )

    # 测试模型
    test_input = torch.randn(1, 3, 800, 800)  # 注意输入尺寸应符合 Faster R-CNN 需求
    model.eval()
    output = model(test_input)
    print(f'output: {output}')

    # 测试模型
    dummy_input = torch.randn(1, 3, 800, 800)  # 注意输入尺寸应符合 Faster R-CNN 需求
    model.eval()
    output = model(dummy_input)
    print(f'output:{output}')