用python画出yolov7-tiny的结构框图

抱歉，目前并不存在 YOLOv7，可能是您想表达 YOLOv3 或 YOLOv4 的结构框图。以下是使用 Python 画出 YOLOv4-tiny 的结构框图的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Input, LeakyReLU, ZeroPadding2D, UpSampling2D, Concatenate

def darknet_conv(input_tensor, num_filters, kernel_size, strides=1, batch_norm=True):
    x = Conv2D(filters=num_filters, kernel_size=kernel_size, strides=strides, padding='same', use_bias=not batch_norm,
               kernel_regularizer=tf.keras.regularizers.l2(0.0005))(input_tensor)
    if batch_norm:
        x = tf.keras.layers.BatchNormalization()(x)
        x = LeakyReLU(alpha=0.1)(x)
    return x

def darknet_residual(input_tensor, num_filters):
    x = darknet_conv(input_tensor, num_filters // 2, kernel_size=1)
    x = darknet_conv(x, num_filters, kernel_size=3)
    x = tf.keras.layers.Add()([input_tensor, x])
    return x

def darknet_block(input_tensor, num_filters, num_blocks):
    x = darknet_conv(input_tensor, num_filters, kernel_size=3, strides=2)
    for _ in range(num_blocks):
        x = darknet_residual(x, num_filters)
    return x

def yolo_head(input_tensor, num_filters):
    x = darknet_conv(input_tensor, num_filters * 2, kernel_size=3)
    x = darknet_conv(x, num_filters, kernel_size=1)
    x = darknet_conv(x, num_filters * 2, kernel_size=3)
    x = darknet_conv(x, num_filters, kernel_size=1)
    x = darknet_conv(x, num_filters * 2, kernel_size=3)
    return x

def yolo_v4_tiny(input_shape, num_classes):
    input_tensor = Input(shape=input_shape)
    x = darknet_conv(input_tensor, 32, kernel_size=3)
    x = darknet_block(x, 64, 1)
    x = darknet_block(x, 128, 2)
    x = darknet_block(x, 256, 4)
    x = darknet_block(x, 512, 4)
    x = darknet_block(x, 1024, 2)
    x = darknet_conv(x, 512, kernel_size=1)
    x = darknet_conv(x, 1024, kernel_size=3)
    x = darknet_conv(x, 512, kernel_size=1)
    x = darknet_conv(x, 1024, kernel_size=3)
    x = darknet_conv(x, 512, kernel_size=1)
    x1 = yolo_head(x, num_filters=256)
    x1 = Conv2D(filters=num_classes * 5, kernel_size=1)(x1)
    x2 = darknet_conv(x, 256, kernel_size=1)
    x2 = UpSampling2D()(x2)
    x2 = Concatenate()([x2, darknet_conv(x, 256, kernel_size=1)])
    x2 = darknet_conv(x2, 256, kernel_size=3)
    x2 = yolo_head(x2, num_filters=128)
    x2 = Conv2D(filters=num_classes * 5, kernel_size=1)(x2)
    model = tf.keras.Model(inputs=[input_tensor], outputs=[x1, x2])
    return model

model = yolo_v4_tiny(input_shape=(416, 416, 3), num_classes=80)
tf.keras.utils.plot_model(model, to_file='yolov4-tiny.png', show_shapes=True)

该代码使用 TensorFlow 2.x 实现了 YOLOv4-tiny 的结构框图的绘制，将会在当前目录下生成一个名为 `yolov4-tiny.png` 的 PNG 图片文件，展示模型的结构框图。