昇思25天学习打卡营第11天 | FCN图像语义分割

语义分割（semantic segmentation） 常被用于人脸识别、物体检测、医学影像、卫星图像分析、自动驾驶等领域。
语义分割的目的是对图像中的每一个像素进行分类，输出与输入图像大小相同的图像，每个像素代表对应输入像素所属的类别。

FCN模型

全卷积神经网络（Fully Convolutional Networks，FCN）是一种端到端的分割方法，通过进行像素级的预测直接得出与原图大小相等的label map。

FCN主要使用以下三种技术：

1. 卷积化： 使用VGG-16作为FCN的backbone。
   
2. 上采样： ：通过反卷积对特征图进行上采样，以恢复输入图像的分辨率。
   
3. 跳跃结构： 由于最后一层特征图太小，损失过多细节，采用skips结构将更具全局信息的最后一层和更浅层的预测结合，使预测结果获得更多的局部细节。
   

数据处理

下载数据集

from download import download

url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/dataset_fcn8s.tar"

download(url, "./dataset", kind="tar", replace=True)

创建训练集

import cv2
import mindspore.dataset as ds
import numpy as np


class SegDataset:
    def __init__(
        self,
        image_mean,
        image_std,
        data_file="",
        batch_size=32,
        crop_size=512,
        max_scale=2.0,
        min_scale=0.5,
        ignore_label=255,
        num_classes=21,
        num_readers=2,
        num_parallel_calls=4,
    ):

        self.data_file = data_file
        self.batch_size = batch_size
        self.crop_size = crop_size
        self.image_mean = np.array(image_mean, dtype=np.float32)
        self.image_std = np.array(image_std, dtype=np.float32)
        self.max_scale = max_scale
        self.min_scale = min_scale
        self.ignore_label = ignore_label
        self.num_classes = num_classes
        self.num_readers = num_readers
        self.num_parallel_calls = num_parallel_calls
        max_scale > min_scale

    def preprocess_dataset(self, image, label):
        image_out = cv2.imdecode(np.frombuffer(image, dtype=np.uint8), cv2.IMREAD_COLOR)
        label_out = cv2.imdecode(
            np.frombuffer(label, dtype=np.uint8), cv2.IMREAD_GRAYSCALE
        )
        sc = np.random.uniform(self.min_scale, self.max_scale)  # 随机缩放
        new_h, new_w = int(sc * image_out.shape[0]), int(sc * image_out.shape[1])
        image_out = cv2.resize(image_out, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
        label_out = cv2.resize(
            label_out, (new_w, new_h), interpolation=cv2.INTER_NEAREST
        )

        image_out = (image_out - self.image_mean) / self.image_std
        out_h, out_w = max(new_h, self.crop_size), max(new_w, self.crop_size)
        pad_h, pad_w = out_h - new_h, out_w - new_w
        if pad_h > 0 or pad_w > 0:
            image_out = cv2.copyMakeBorder(
                image_out, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0
            )
            label_out = cv2.copyMakeBorder(
                label_out,
                0,
                pad_h,
                0,
                pad_w,
                cv2.BORDER_CONSTANT,
                value=self.ignore_label,
            )
        offset_h = np.random.randint(0, out_h - self.crop_size + 1)
        offset_w = np.random.randint(0, out_w - self.crop_size + 1)
        image_out = image_out[
            offset_h : offset_h + self.crop_size,
            offset_w : offset_w + self.crop_size,
            :,
        ]
        label_out = label_out[
            offset_h : offset_h + self.crop_size, offset_w : offset_w + self.crop_size
        ]
        if np.random.uniform(0.0, 1.0) > 0.5:
            image_out = image_out[:, ::-1, :]
            label_out = label_out[:, ::-1]
        image_out = image_out.transpose((2, 0, 1))
        image_out = image_out.copy()
        label_out = label_out.copy()
        label_out = label_out.astype("int32")
        return image_out, label_out

    def get_dataset(self):
        ds.config.set_numa_enable(True)
        dataset = ds.MindDataset(
            self.data_file,
            columns_list=["data", "label"],
            shuffle=True,
            num_parallel_workers=self.num_readers,
        )
        transforms_list = self.preprocess_dataset
        dataset = dataset.map(
            operations=transforms_list,
            input_columns=["data", "label"],
            output_columns=["data", "label"],
            num_parallel_workers=self.num_parallel_calls,
        )
        dataset = dataset.shuffle(buffer_size=self.batch_size * 10)
        dataset = dataset.batch(self.batch_size, drop_remainder=True)
        return dataset


# 定义创建数据集的参数
IMAGE_MEAN = [103.53, 116.28, 123.675]
IMAGE_STD = [57.375, 57.120, 58.395]
DATA_FILE = "dataset/dataset_fcn8s/mindname.mindrecord"

# 定义模型训练参数
train_batch_size = 4
crop_size = 512
min_scale = 0.5
max_scale = 2.0
ignore_label = 255
num_classes = 21

# 实例化Dataset
dataset = SegDataset(
    image_mean=IMAGE_MEAN,
    image_std=IMAGE_STD,
    data_file=DATA_FILE,
    batch_size=train_batch_size,
    crop_size=crop_size,
    max_scale=max_scale,
    min_scale=min_scale,
    ignore_label=ignore_label,
    num_classes=num_classes,
    num_readers=2,
    num_parallel_calls=4,
)

dataset = dataset.get_dataset()

在上面的类SegDataset中：

• __init__()里初始化了一些参数
• preprocess_dataset()定义了对输入图像的大量变换，包括：
  • 对图像和标签随机缩放；
  • 图像归一化；
  • 填充或裁剪图片为crop_size × crop_size大小；
  • 随机水平翻转；
  • 将<H,W,C>图像转换为<C,H,W>;
  • 将标签数据转换为int32类型。
• get_dataset()中定义了数据集和数据变换，设置了并行。

可视化训练集

import matplotlib.pyplot as plt
import numpy as np

plt.figure(figsize=(16, 8))

# 对训练集中的数据进行展示
for i in range(1, 9):
    plt.subplot(2, 4, i)
    show_data = next(dataset.create_dict_iterator())
    show_images = show_data["data"].asnumpy()
    show_images = np.clip(show_images, 0, 1)
    # 将图片转换HWC格式后进行展示
    plt.imshow(show_images[0].transpose(1, 2, 0))
    plt.axis("off")
    plt.subplots_adjust(wspace=0.05, hspace=0)
plt.show()

网络构建

网络结构

• 第一个卷积块：输入$512\times 512\times 3$图像，每一个卷积操作后面紧跟一个nn.BatchNorm2d(out_channels)和nn.ReLU()

self.conv1 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=3,
                out_channels=64,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=64,
                out_channels=64,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(64),
            nn.ReLU(),
        )

• 第一个池化：最大池化（Max Pooling），输入图像尺寸变为原始图像$1/2$。

self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

• 第二个卷积块：

self.conv2 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=64,
                out_channels=128,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=128,
                out_channels=128,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(128),
            nn.ReLU(),
        )

• 第二个池化：变为原始图像$1/4$

self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

• 第三个卷积快：

 self.conv3 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=128,
                out_channels=256,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=256,
                out_channels=256,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=256,
                out_channels=256,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(256),
            nn.ReLU(),
        )

• 第三个池化：变为原始图像$1/8$

 self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

• 第四个卷积块：

 self.conv4 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=256,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
        )

• 第四个池化：变为原始图像$1/16$

self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

• 第五个卷积块：

 self.conv5 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(512),
            nn.ReLU(),
        )

• 第五个池化：变为原始图像$1/32$

 self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)

• 第六、七个卷积块：保持图像大小不变，替换全连接层

        self.conv6 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=512,
                out_channels=4096,
                kernel_size=7,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(4096),
            nn.ReLU(),
        )
        self.conv7 = nn.SequentialCell(
            nn.Conv2d(
                in_channels=4096,
                out_channels=4096,
                kernel_size=1,
                weight_init="xavier_uniform",
            ),
            nn.BatchNorm2d(4096),
            nn.ReLU(),
        )
        self.score_fr = nn.Conv2d(in_channels=4096, out_channels=self.n_class,
                          kernel_size=1, weight_init='xavier_uniform')

• 反卷积层：

        self.upscore2 = nn.Conv2dTranspose(
            in_channels=self.n_class,
            out_channels=self.n_class,
            kernel_size=4,
            stride=2,
            weight_init="xavier_uniform",
        )
        self.score_pool4 = nn.Conv2d(
            in_channels=512,
            out_channels=self.n_class,
            kernel_size=1,
            weight_init="xavier_uniform",
        )
        self.upscore_pool4 = nn.Conv2dTranspose(
            in_channels=self.n_class,
            out_channels=self.n_class,
            kernel_size=4,
            stride=2,
            weight_init="xavier_uniform",
        )
        self.score_pool3 = nn.Conv2d(
            in_channels=256,
            out_channels=self.n_class,
            kernel_size=1,
            weight_init="xavier_uniform",
        )
        self.upscore8 = nn.Conv2dTranspose(
            in_channels=self.n_class,
            out_channels=self.n_class,
            kernel_size=16,
            stride=8,
            weight_init="xavier_uniform",
        )

张量操作

def construct(self, x):
        x1 = self.conv1(x)
        p1 = self.pool1(x1)
        x2 = self.conv2(p1)
        p2 = self.pool2(x2)
        x3 = self.conv3(p2)
        p3 = self.pool3(x3)
        x4 = self.conv4(p3)
        p4 = self.pool4(x4)
        x5 = self.conv5(p4)
        p5 = self.pool5(x5)
        x6 = self.conv6(p5)
        x7 = self.conv7(x6)
        sf = self.score_fr(x7)
        u2 = self.upscore2(sf)
        s4 = self.score_pool4(p4)
        f4 = s4 + u2
        u4 = self.upscore_pool4(f4)
        s3 = self.score_pool3(p3)
        f3 = s3 + u4
        out = self.upscore8(f3)
        return out

训练准备

导入VGG-16部分预训练权重：

from download import download
from mindspore import load_checkpoint, load_param_into_net

url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/fcn8s_vgg16_pretrain.ckpt"
download(url, "fcn8s_vgg16_pretrain.ckpt", replace=True)


def load_vgg16():
    ckpt_vgg16 = "fcn8s_vgg16_pretrain.ckpt"
    param_vgg = load_checkpoint(ckpt_vgg16)
    load_param_into_net(net, param_vgg)

损失函数

语义分割是对图像中像素点进行分类，仍然属于分类问题，故使用交叉熵损失函数nn.CrossEntropyLoss()。

模型评估指标

用来评估训练出来的模型好坏。

• Pixel Accuracy(PA, 像素精度）：标记真确的像素占总像素的比例：
  $$PA=\frac{{\sum }_{i=0}^k{p}_{ii}}{{\sum }_{i=0}^k{\sum }_{j=0}^k{p}_{ij}}$$

import mindspore as ms
import mindspore.nn as nn
import mindspore.train as train
import numpy as np


class PixelAccuracy(train.Metric):
    def __init__(self, num_class=21):
        super(PixelAccuracy, self).__init__()
        self.num_class = num_class

    def _generate_matrix(self, gt_image, pre_image):
        mask = (gt_image >= 0) & (gt_image < self.num_class)
        label = self.num_class * gt_image[mask].astype("int") + pre_image[mask]
        count = np.bincount(label, minlength=self.num_class**2)
        confusion_matrix = count.reshape(self.num_class, self.num_class)
        return confusion_matrix

    def clear(self):
        self.confusion_matrix = np.zeros((self.num_class,) * 2)

    def update(self, *inputs):
        y_pred = inputs[0].asnumpy().argmax(axis=1)
        y = inputs[1].asnumpy().reshape(4, 512, 512)
        self.confusion_matrix += self._generate_matrix(y, y_pred)

    def eval(self):
        pixel_accuracy = (
            np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()
        )
        return pixel_accuracy

• Mean Pixel Accuracy（MPA，均像素精度）：计算每个类内正确分类的像素比例，然后求平均：
  $$MPA=\frac{1}{k+1}\sum _{i=0}^k\frac{p_{ii}}{{\sum }_{j=0}^k{p}_{ij}}$$

class PixelAccuracyClass(train.Metric):
    def __init__(self, num_class=21):
        super(PixelAccuracyClass, self).__init__()
        self.num_class = num_class

    def _generate_matrix(self, gt_image, pre_image):
        mask = (gt_image >= 0) & (gt_image < self.num_class)
        label = self.num_class * gt_image[mask].astype("int") + pre_image[mask]
        count = np.bincount(label, minlength=self.num_class**2)
        confusion_matrix = count.reshape(self.num_class, self.num_class)
        return confusion_matrix

    def update(self, *inputs):
        y_pred = inputs[0].asnumpy().argmax(axis=1)
        y = inputs[1].asnumpy().reshape(4, 512, 512)
        self.confusion_matrix += self._generate_matrix(y, y_pred)

    def clear(self):
        self.confusion_matrix = np.zeros((self.num_class,) * 2)

    def eval(self):
        mean_pixel_accuracy = np.diag(
            self.confusion_matrix
        ) / self.confusion_matrix.sum(axis=1)
        mean_pixel_accuracy = np.nanmean(mean_pixel_accuracy)
        return mean_pixel_accuracy

• Mean Intersction over Union（MIoU，均交并比）：两个集合（真实值和预测值）的交集和并集的比值。
  $$MIoU=\frac{1}{k+1}\sum _{i=0}^k\frac{p_{ii}}{{\sum }_{j=0}^k{p}_{ij}+{\sum }_{j=0}^k{p}_{ji}-p_{ii}}$$

class MeanIntersectionOverUnion(train.Metric):
    def __init__(self, num_class=21):
        super(MeanIntersectionOverUnion, self).__init__()
        self.num_class = num_class

    def _generate_matrix(self, gt_image, pre_image):
        mask = (gt_image >= 0) & (gt_image < self.num_class)
        label = self.num_class * gt_image[mask].astype("int") + pre_image[mask]
        count = np.bincount(label, minlength=self.num_class**2)
        confusion_matrix = count.reshape(self.num_class, self.num_class)
        return confusion_matrix

    def update(self, *inputs):
        y_pred = inputs[0].asnumpy().argmax(axis=1)
        y = inputs[1].asnumpy().reshape(4, 512, 512)
        self.confusion_matrix += self._generate_matrix(y, y_pred)

    def clear(self):
        self.confusion_matrix = np.zeros((self.num_class,) * 2)

    def eval(self):
        mean_iou = np.diag(self.confusion_matrix) / (
            np.sum(self.confusion_matrix, axis=1)
            + np.sum(self.confusion_matrix, axis=0)
            - np.diag(self.confusion_matrix)
        )
        mean_iou = np.nanmean(mean_iou)
        return mean_iou

• Frequency Weighted Intersection over Union（FWIoU，频权交并比）：根据每个类别出现的频率为MIoU设置权重
  $$FWIoU=\frac{1}{{\sum }_{i=0}^k{\sum }_{j=0}^k{p}_{ij}}\sum _{i=0}^k\frac{p_{ii}}{{\sum }_{j=0}^k{p}_{ij}+{\sum }_{j=0}^k{p}_{ji}-p_{ii}}$$

class FrequencyWeightedIntersectionOverUnion(train.Metric):
    def __init__(self, num_class=21):
        super(FrequencyWeightedIntersectionOverUnion, self).__init__()
        self.num_class = num_class

    def _generate_matrix(self, gt_image, pre_image):
        mask = (gt_image >= 0) & (gt_image < self.num_class)
        label = self.num_class * gt_image[mask].astype("int") + pre_image[mask]
        count = np.bincount(label, minlength=self.num_class**2)
        confusion_matrix = count.reshape(self.num_class, self.num_class)
        return confusion_matrix

    def update(self, *inputs):
        y_pred = inputs[0].asnumpy().argmax(axis=1)
        y = inputs[1].asnumpy().reshape(4, 512, 512)
        self.confusion_matrix += self._generate_matrix(y, y_pred)

    def clear(self):
        self.confusion_matrix = np.zeros((self.num_class,) * 2)

    def eval(self):
        freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix)
        iu = np.diag(self.confusion_matrix) / (
            np.sum(self.confusion_matrix, axis=1)
            + np.sum(self.confusion_matrix, axis=0)
            - np.diag(self.confusion_matrix)
        )

        frequency_weighted_iou = (freq[freq > 0] * iu[freq > 0]).sum()
        return frequency_weighted_iou

模型训练

导入VGG-16预训练参数，定义超参数，实例化损失函数、优化器，使用Model接口编译网络，然后开始训练：

import mindspore
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.train import (
    CheckpointConfig,
    LossMonitor,
    Model,
    ModelCheckpoint,
    TimeMonitor,
)

device_target = "Ascend"
mindspore.set_context(mode=mindspore.PYNATIVE_MODE, device_target=device_target)

train_batch_size = 4
num_classes = 21
# 初始化模型结构
net = FCN8s(n_class=21)
# 导入vgg16预训练参数
load_vgg16()
# 计算学习率
min_lr = 0.0005
base_lr = 0.05
train_epochs = 1
iters_per_epoch = dataset.get_dataset_size()
total_step = iters_per_epoch * train_epochs

lr_scheduler = mindspore.nn.cosine_decay_lr(
    min_lr, base_lr, total_step, iters_per_epoch, decay_epoch=2
)
lr = Tensor(lr_scheduler[-1])

# 定义损失函数
loss = nn.CrossEntropyLoss(ignore_index=255)
# 定义优化器
optimizer = nn.Momentum(
    params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.0001
)
# 定义loss_scale
scale_factor = 4
scale_window = 3000
loss_scale_manager = ms.amp.DynamicLossScaleManager(scale_factor, scale_window)
# 初始化模型
if device_target == "Ascend":
    model = Model(
        net,
        loss_fn=loss,
        optimizer=optimizer,
        loss_scale_manager=loss_scale_manager,
        metrics={
            "pixel accuracy": PixelAccuracy(),
            "mean pixel accuracy": PixelAccuracyClass(),
            "mean IoU": MeanIntersectionOverUnion(),
            "frequency weighted IoU": FrequencyWeightedIntersectionOverUnion(),
        },
    )
else:
    model = Model(
        net,
        loss_fn=loss,
        optimizer=optimizer,
        metrics={
            "pixel accuracy": PixelAccuracy(),
            "mean pixel accuracy": PixelAccuracyClass(),
            "mean IoU": MeanIntersectionOverUnion(),
            "frequency weighted IoU": FrequencyWeightedIntersectionOverUnion(),
        },
    )

# 设置ckpt文件保存的参数
time_callback = TimeMonitor(data_size=iters_per_epoch)
loss_callback = LossMonitor()
callbacks = [time_callback, loss_callback]
save_steps = 330
keep_checkpoint_max = 5
config_ckpt = CheckpointConfig(
    save_checkpoint_steps=10, keep_checkpoint_max=keep_checkpoint_max
)
ckpt_callback = ModelCheckpoint(prefix="FCN8s", directory="./ckpt", config=config_ckpt)
callbacks.append(ckpt_callback)
model.train(train_epochs, dataset, callbacks=callbacks)

模型评估

IMAGE_MEAN = [103.53, 116.28, 123.675]
IMAGE_STD = [57.375, 57.120, 58.395]
DATA_FILE = "dataset/dataset_fcn8s/mindname.mindrecord"

# 下载已训练好的权重文件
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/FCN8s.ckpt"
download(url, "FCN8s.ckpt", replace=True)
net = FCN8s(n_class=num_classes)

ckpt_file = "FCN8s.ckpt"
param_dict = load_checkpoint(ckpt_file)
load_param_into_net(net, param_dict)

if device_target == "Ascend":
    model = Model(
        net,
        loss_fn=loss,
        optimizer=optimizer,
        loss_scale_manager=loss_scale_manager,
        metrics={
            "pixel accuracy": PixelAccuracy(),
            "mean pixel accuracy": PixelAccuracyClass(),
            "mean IoU": MeanIntersectionOverUnion(),
            "frequency weighted IoU": FrequencyWeightedIntersectionOverUnion(),
        },
    )
else:
    model = Model(
        net,
        loss_fn=loss,
        optimizer=optimizer,
        metrics={
            "pixel accuracy": PixelAccuracy(),
            "mean pixel accuracy": PixelAccuracyClass(),
            "mean IoU": MeanIntersectionOverUnion(),
            "frequency weighted IoU": FrequencyWeightedIntersectionOverUnion(),
        },
    )

# 实例化Dataset
dataset = SegDataset(
    image_mean=IMAGE_MEAN,
    image_std=IMAGE_STD,
    data_file=DATA_FILE,
    batch_size=train_batch_size,
    crop_size=crop_size,
    max_scale=max_scale,
    min_scale=min_scale,
    ignore_label=ignore_label,
    num_classes=num_classes,
    num_readers=2,
    num_parallel_calls=4,
)
dataset_eval = dataset.get_dataset()
model.eval(dataset_eval)

模型推理

使用训练的网络进行推理：

import cv2
import matplotlib.pyplot as plt

net = FCN8s(n_class=num_classes)
# 设置超参
ckpt_file = "FCN8s.ckpt"
param_dict = load_checkpoint(ckpt_file)
load_param_into_net(net, param_dict)
eval_batch_size = 4
img_lst = []
mask_lst = []
res_lst = []
# 推理效果展示(上方为输入图片，下方为推理效果图片)
plt.figure(figsize=(8, 5))
show_data = next(dataset_eval.create_dict_iterator())
show_images = show_data["data"].asnumpy()
mask_images = show_data["label"].reshape([4, 512, 512])
show_images = np.clip(show_images, 0, 1)
for i in range(eval_batch_size):
    img_lst.append(show_images[i])
    mask_lst.append(mask_images[i])
res = net(show_data["data"]).asnumpy().argmax(axis=1)
for i in range(eval_batch_size):
    plt.subplot(2, 4, i + 1)
    plt.imshow(img_lst[i].transpose(1, 2, 0))
    plt.axis("off")
    plt.subplots_adjust(wspace=0.05, hspace=0.02)
    plt.subplot(2, 4, i + 5)
    plt.imshow(res[i])
    plt.axis("off")
    plt.subplots_adjust(wspace=0.05, hspace=0.02)
plt.show()

总结

这一节介绍了图像语义分割中的FCN网络，从网络的结构开始，介绍了图像数据集的变换和创建，介绍了完整的网络框架和Tensor操作，以及预训练模型的加载。此外，对于模型效果的而评估，还介绍了4个指标。通过这一节，大致学会了从paper的网络结构中创建一个网络模型。

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