python h5py文件转换成str

要将Python的HDF5文件转换为字符串，可以使用h5py库中的File对象和tostring()方法。以下是一个简单的示例代码：

import h5py

# 打开HDF5文件
with h5py.File('example.h5', 'r') as f:
    # 获取数据集
    dataset = f['my_dataset']
    # 将数据集转换为字符串
    dataset_str = dataset.value.tostring()
    # 打印字符串
    print(dataset_str)

在这个示例中，我们打开名为“example.h5”的文件，并获取名为“my_dataset”的数据集。我们使用tostring()方法将数据集转换为字符串，并将其存储在变量dataset_str中。最后，我们打印出这个字符串。

相关问题

python h5py

h5py是一个用于在Python中读写HDF5格式文件的库。通过使用h5py，您可以创建、读取和修改HDF5文件，其中包含多种数据类型，包括数组、字符串等。

在使用h5py创建文件时，您可以使用以下代码创建一个h5py文件：

import h5py
f = h5py.File("myh5py.hdf5", "w")

要读取h5py文件中的数据，您可以使用以下代码：

import h5py
f = h5py.File("myh5py.hdf5", "r")
dataset = f["dataset_name"][:]

在上述代码中，您需要将"dataset_name"替换为实际的数据集名称，[:]表示取出全部数据。

对于存储字符串数据，您可以使用特殊的数据类型来确定存储字符串的方式。在Python 3中，可以使用`vlen = str`来表示字符串数据类型，而在Python 2中，可以使用`vlen = unicode`来表示。以下是存储字符串的示例代码：

import h5py
import numpy as np

dt = h5py.special_dtype(vlen=str)
data = np.array([['123'],['456']])
with h5py.File('testdict.h5', 'w') as f:
    ds = f.create_dataset('test_dict', data.shape, dtype=dt)
    ds[:] = data

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