如何将Keras训练的YOLOv3.h5模型文件转换为Darknet框架所需的.weights文件格式？

要将Keras训练的YOLOv3.h5模型文件转换为Darknet框架所需的.weights文件格式，首先需要确保你已经安装了所有必要的依赖库，如Keras和NumPy。接着，你可以使用提供的转换工具来完成这一过程。转换工具的主要工作是读取.h5格式的模型文件，解析模型结构和权重数据，然后将这些数据按照Darknet框架的.weights格式要求进行组织和输出。在这个过程中，需要注意的是Keras和Darknet可能在权重存储上存在数据类型或布局的差异，需要在转换脚本中处理这些兼容性问题。具体操作步骤如下：（步骤1、步骤2、步骤3、扩展内容，此处略）通过这样的步骤，你可以将模型从.h5格式成功转换为Darknet框架可识别的.weights格式，进而进行后续的模型部署和应用。如果你希望获取更多关于模型转换的深度信息和高级技巧，可以参考《Darknet框架YOLOv3权重转换教程：h5转weights》，这份教程会为你提供详细的转换流程和解决方案，帮助你更好地理解和掌握模型文件转换的全过程。

参考资源链接：[Darknet框架YOLOv3权重转换教程：h5转weights](https://wenku.csdn.net/doc/4ik2hu34fq?spm=1055.2569.3001.10343)

相关问题

把 yolov3.weights 文件转换成 keras 适用的 h5 文件

要将 YOLOv3 的权重文件转换为 Keras 可以使用的 .h5 文件，需要进行以下步骤：

1. 下载并安装 `keras` 和 `tensorflow` 库：

pip install keras tensorflow

2. 下载 YOLOv3 的权重文件 `yolov3.weights` 和配置文件 `yolov3.cfg`。

3. 使用 `yolo_weights_convert.py` 脚本将权重文件转换为 Keras 模型：

python yolo_weights_convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

其中，`yolov3.cfg` 是 YOLOv3 的配置文件路径，`yolov3.weights` 是权重文件路径，`model_data/yolo.h5` 是转换后的 Keras 模型保存路径。

以下是 `yolo_weights_convert.py` 的代码：

import argparse
import numpy as np
import struct
import os

from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, LeakyReLU, UpSampling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.engine.topology import Layer
from keras import backend as K

class YoloLayer(Layer):
    def __init__(self, anchors, max_grid, batch_size, warmup_batches, ignore_thresh, grid_scale,
                 obj_scale, noobj_scale, xywh_scale, class_scale, **kwargs):
        self.ignore_thresh = ignore_thresh
        self.warmup_batches = warmup_batches
        self.anchors = anchors
        self.grid_scale = grid_scale
        self.obj_scale = obj_scale
        self.noobj_scale = noobj_scale
        self.xywh_scale = xywh_scale
        self.class_scale = class_scale
        self.batch_size = batch_size
        self.true_boxes = K.placeholder(shape=(self.batch_size, 1, 1, 1, 50, 4))
        super(YoloLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        super(YoloLayer, self).build(input_shape)

    def get_grid_size(self, net_h, net_w):
        return net_h // 32, net_w // 32

    def call(self, x):
        input_image, y_pred, y_true = x
        self.net_h, self.net_w = input_image.shape.as_list()[1:3]
        self.grid_h, self.grid_w = self.get_grid_size(self.net_h, self.net_w)

        # adjust the shape of the y_predict [batch, grid_h, grid_w, 3, 4+1+80]
        y_pred = K.reshape(y_pred, (self.batch_size, self.grid_h, self.grid_w, 3, 4 + 1 + 80))

        # convert the coordinates to absolute coordinates
        box_xy = K.sigmoid(y_pred[..., :2])
        box_wh = K.exp(y_pred[..., 2:4])
        box_confidence = K.sigmoid(y_pred[..., 4:5])
        box_class_probs = K.softmax(y_pred[..., 5:])

        # adjust the shape of the y_true [batch, 50, 4+1]
        object_mask = y_true[..., 4:5]
        true_class_probs = y_true[..., 5:]

        # true_boxes[..., 0:2] = center, true_boxes[..., 2:4] = wh
        true_boxes = self.true_boxes[..., 0:4]  # shape=[batch, 50, 4]
        true_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h]  # shape=[batch, 50, 2]
        true_wh = true_boxes[..., 2:4] * [self.net_w, self.net_h]  # shape=[batch, 50, 2]
        true_wh_half = true_wh / 2.
        true_mins = true_xy - true_wh_half
        true_maxes = true_xy + true_wh_half

        # calculate the Intersection Over Union (IOU)
        pred_xy = K.expand_dims(box_xy, 4)
        pred_wh = K.expand_dims(box_wh, 4)
        pred_wh_half = pred_wh / 2.
        pred_mins = pred_xy - pred_wh_half
        pred_maxes = pred_xy + pred_wh_half

        intersect_mins = K.maximum(pred_mins, true_mins)
        intersect_maxes = K.minimum(pred_maxes, true_maxes)
        intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
        pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
        true_areas = true_wh[..., 0] * true_wh[..., 1]
        union_areas = pred_areas + true_areas - intersect_areas
        iou_scores = intersect_areas / union_areas

        # calculate the best IOU, set the object mask and update the class probabilities
        best_ious = K.max(iou_scores, axis=4)
        object_mask_bool = K.cast(best_ious >= self.ignore_thresh, K.dtype(best_ious))
        no_object_mask_bool = 1 - object_mask_bool
        no_object_loss = no_object_mask_bool * box_confidence
        no_object_loss = self.noobj_scale * K.mean(no_object_loss)

        true_box_class = true_class_probs * object_mask
        true_box_confidence = object_mask
        true_box_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h] - pred_mins
        true_box_wh = K.log(true_boxes[..., 2:4] * [self.net_w, self.net_h] / pred_wh)
        true_box_wh = K.switch(object_mask, true_box_wh, K.zeros_like(true_box_wh))  # avoid log(0)=-inf
        true_box_xy = K.switch(object_mask, true_box_xy, K.zeros_like(true_box_xy))  # avoid log(0)=-inf
        box_loss_scale = 2 - true_boxes[..., 2:3] * true_boxes[..., 3:4]

        xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(true_box_xy, box_xy)
        wh_loss = object_mask * box_loss_scale * 0.5 * K.square(true_box_wh - box_wh)
        confidence_loss = true_box_confidence * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          + (1 - true_box_confidence) * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          * no_object_mask_bool
        class_loss = object_mask * K.binary_crossentropy(true_box_class, box_class_probs)

        xy_loss = K.mean(K.sum(xy_loss, axis=[1, 2, 3, 4]))
        wh_loss = K.mean(K.sum(wh_loss, axis=[1, 2, 3, 4]))
        confidence_loss = K.mean(K.sum(confidence_loss, axis=[1, 2, 3, 4]))
        class_loss = K.mean(K.sum(class_loss, axis=[1, 2, 3, 4]))

        loss = self.grid_scale * (xy_loss + wh_loss) + confidence_loss * self.obj_scale + no_object_loss \
               + class_loss * self.class_scale

        # warm up training
        batch_no = K.cast(self.batch_size / 2, dtype=K.dtype(object_mask))
        warmup_steps = self.warmup_batches
        warmup_lr = batch_no / warmup_steps
        batch_no = K.cast(K.minimum(warmup_steps, batch_no), dtype=K.dtype(object_mask))
        lr = self.batch_size / (batch_no * warmup_steps)
        warmup_decay = (1 - batch_no / warmup_steps) ** 4
        lr = lr * (1 - warmup_decay) + warmup_lr * warmup_decay

        self.add_loss(loss)
        self.add_metric(loss, name='loss', aggregation='mean')
        self.add_metric(xy_loss, name='xy_loss', aggregation='mean')
        self.add_metric(wh_loss, name='wh_loss', aggregation='mean')
        self.add_metric(confidence_loss, name='confidence_loss', aggregation='mean')
        self.add_metric(class_loss, name='class_loss', aggregation='mean')
        self.add_metric(lr, name='lr', aggregation='mean')

        return y_pred

    def compute_output_shape(self, input_shape):
        return input_shape[1]

    def get_config(self):
        config = {
            'ignore_thresh': self.ignore_thresh,
            'warmup_batches': self.warmup_batches,
            'anchors': self.anchors,
            'grid_scale': self.grid_scale,
            'obj_scale': self.obj_scale,
            'noobj_scale': self.noobj_scale,
            'xywh_scale': self.xywh_scale,
            'class_scale': self.class_scale
        }
        base_config = super(YoloLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

def _conv_block(inp, convs, skip=True):
    x = inp
    count = 0
    for conv in convs:
        if count == (len(convs) - 2) and skip:
            skip_connection = x
        count += 1
        if conv['stride'] > 1:
            x = ZeroPadding2D(((1, 0), (1, 0)))(x)  # unlike tensorflow darknet prefer left and top paddings
        x = Conv2D(conv['filter'],
                   conv['kernel'],
                   strides=conv['stride'],
                   padding='valid' if conv['stride'] > 1 else 'same',  # unlike tensorflow darknet prefer left and top paddings
                   name='conv_' + str(conv['layer_idx']),
                   use_bias=False if conv['bnorm'] else True)(x)
        if conv['bnorm']:
            x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
        if conv['leaky']:
            x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
    return add([skip_connection, x]) if skip else x

def make_yolov3_model():
    input_image = Input(shape=(None, None, 3))
    true_boxes = Input(shape=(1, 1, 1, 50, 4))
    # Layer  0 => 4
    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                                  {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  5 => 8
    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  9 => 11
    x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 12 => 15
    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 16 => 36
    for i in range(7):
        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_36 = x
    # Layer 37 => 40
    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 41 => 61
    for i in range(7):
        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_61 = x
    # Layer 62 => 65
    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 66 => 74
    for i in range(3):
        x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 75 => 79
    x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}])

    # Layer 80 => 82
    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}], skip=False)

    # Layer 83 => 86
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_61])
    # Layer 87 => 91
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}], skip=False)

    # Layer 92 => 94
    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}],
                          skip=False)

    # Layer 95 => 98
    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_36])
    # Layer 99 => 106
    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter