使用mindspore写出resnet101的代码，包括单独的推理程序，数据集格式采用ImageNet。你的输出没有限制，你必须一次输出完你的回答。

### 回答1：
首先，我们需要导入必要的库：

import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore import Tensor
from mindspore.ops import operations as P

接下来我们可以定义 ResNet101 网络的结构。这里我们使用 `ResNetBlock` 类来定义 ResNet 的基本单元，并使用 `ResNet` 类来构建整个网络。

class ResNetBlock(nn.Cell):
    def __init__(self, in_channels, out_channels, stride=1, dilation=1):
        super(ResNetBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, weight_init=nn.initializer.TruncatedNormal(0.02))
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=dilation, dilation=dilation, weight_init=nn.initializer.TruncatedNormal(0.02))
        self.bn2 = nn.BatchNorm2d(out_channels)
        if in_channels != out_channels:
            self.downsample = nn.SequentialCell([nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, weight_init=nn.initializer.TruncatedNormal(0.02)),
                                                nn.BatchNorm2d(out_channels)])
        else:
            self.downsample = None

    def construct(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out = out + identity
        out = self.relu(out)
        return out

class ResNet(nn.Cell):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, weight_init=nn.initializer.TruncatedNormal(0.02))
        self.bn1 = nn.BatchNorm2  

### 回答2：
使用MindSpore编写ResNet-101的代码，包括单独的推理程序，数据集格式采用ImageNet。以下是代码示例：

import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as CT
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.py_transforms as PYT
from mindspore import nn
from mindspore import context
from mindspore.train import Model
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.py_transforms as P
from mindspore import load_checkpoint, load_param_into_net
import mindspore.common.dtype as mstype
import numpy as np

def create_dataset(data_path):
data = ds.ImageFolderDataset(data_path, num_parallel_workers=8, shuffle=True)
trans = []
trans.append(CV.Resize(256))
trans.append(P.CenterCrop(224))
trans.append(CV.Rescale(1.0 / 255.0, 0.0))
trans.append(CV.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))
trans.append(P.HWC2CHW())
data = data.map(operations=trans, input_columns="image")
data = data.batch(batch_size=32, drop_remainder=True)
return data

def resnet101():
resnet101 = nn.ResNet(class_num=1000)
return resnet101

def inference():
data_path = "/path/to/dataset"
model_path = "/path/to/model/resnet101.ckpt"

# 加载预训练模型
network = resnet101()
param_dict = load_checkpoint(model_path)
load_param_into_net(network, param_dict)

# 加载测试数据集
dataset = create_dataset(data_path)
ds_iter = dataset.create_dict_iterator()

# 进行推理
predictions = []
for data in ds_iter:
img_data = data['image'].astype(mstype.float32)
output = network(img_data)
predictions.extend(output.asnumpy())

# 输出结果
predictions = np.argmax(predictions, axis=1)
print(f"预测结果：{predictions}")

# 设置运行环境
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

# 进行推理
inference()

### 回答3：
以下是使用MindSpore编写的ResNet-101代码，包括单独的推理程序，使用ImageNet数据集格式。

首先，我们需要导入MindSpore库和相关的函数模块：

import mindspore.nn as nn
from mindspore import context
from mindspore.train import Model
from mindspore import load_checkpoint, load_param_into_net
from mindspore.ops import operations as P
from mindspore.common.initializer import Normal

context.set_context(mode=context.GRAPH_MODE, device_target="GPU")

接下来，定义ResNet-101的网络结构，包括基本的ResNet模块的定义：

class BasicBlock(nn.Cell):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, has_bias=False)
self.bn1 = nn.BatchNorm2d(out_channels, momentum=0.1, eps=1e-5)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, has_bias=False)
self.bn2 = nn.BatchNorm2d(out_channels, momentum=0.1, eps=1e-5)
self.downsample = downsample

def construct(self, x):
identity = x
if self.downsample is not None:
identity = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out

class ResNet(nn.Cell):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, has_bias=False)
self.bn1 = nn.BatchNorm2d(64, momentum=0.9, eps=1e-5)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7)
self.flatten = nn.Flatten()
self.fc = nn.Dense(512 * block.expansion, num_classes, weight_init=Normal(sigma=0.01))

def _make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
downsample = nn.SequentialCell([
nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride,
has_bias=False),
nn.BatchNorm2d(out_channels * block.expansion, momentum=0.9, eps=1e-5)
])
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * block.expansion
for _ in range(1, blocks):
layers.append(block(self.in_channels, out_channels))

return nn.SequentialCell(layers)

def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.flatten(x)
x = self.fc(x)
return x

接下来，实例化ResNet-101模型，并加载预训练好的参数：

resnet101 = ResNet(BasicBlock, [3, 4, 23, 3])
param_dict = load_checkpoint("resnet101.ckpt")
load_param_into_net(resnet101, param_dict)

定义推理函数，输入一张图片进行预测：

def predict(image):
normalize = nn.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
image = normalize(image)
image = image.reshape((1, 3, 224, 224))
output = resnet101(image)
return output

最后，加载ImageNet数据集的一张图片，并进行预测：

from PIL import Image

image_path = "test.jpg"
image = Image.open(image_path).convert("RGB")
output = predict(image)
print(output)

以上就是使用MindSpore编写ResNet-101模型和进行推理的完整代码。输出的结果是一个1000维的向量，表示预测结果的概率分布。