h5文件转换成weights文件

将H5文件转换为Weights文件是深度学习模型迁移和部署的一种常见需求。H5文件通常包含了深度学习模型的权重和架构。而Weights文件仅包含模型的权重部分。

实现H5文件转换为Weights文件有多种方法。常见的方法是使用深度学习框架中的相关函数或外部工具。以下是一种常见的实现步骤：

1. 导入所需的库和工具，如TensorFlow或Keras。
2. 加载H5文件，可以使用相关函数（如`load_model`）加载整个模型，或使用相关函数（如`load_weights`）仅加载权重。
3. 利用相关函数获取模型的权重，如`get_weights`。
4. 将权重保存为Weights文件，可以使用文件操作函数（如`np.save`）将权重保存为二进制文件，或使用相关函数（如`save_weights`）将权重保存为特定格式的文件。

需要注意的是，不同的深度学习框架可能会有不同的实现方法和函数名称。因此，在具体实现时，应查阅相关框架的文档，了解适用于该框架的具体方法和函数。

总之，将H5文件转换为Weights文件可以通过加载H5文件、获取权重和保存为新的文件来实现。这样就可以将深度学习模型的权重部分提取出来，方便进行模型的迁移和部署。

相关问题

pt文件怎样转换成weights文件

如果您有一个PyTorch的模型文件，通常是`.pt`或`.pth`文件，您可以使用以下代码将其转换为`.h5`或`.weights`文件：

import torch
import numpy as np
import h5py

# 加载PyTorch模型
model = torch.load('model.pt')

# 将PyTorch模型中的参数提取出来并转换为numpy数组
weights = {}
for key in model.keys():
    weights[key] = model[key].numpy()

# 保存为h5文件
with h5py.File('model.h5', 'w') as f:
    for key in weights.keys():
        f.create_dataset(key, data=weights[key])

# 或者保存为weights文件
np.savez('model.weights', **weights)

这样，您就可以将PyTorch模型转换为Keras或TensorFlow模型所需的`.h5`或`.weights`文件。

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