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
时间: 2023-09-03 11:27:28 浏览: 155
基于resnet101的cifar10数据集图像分类Pytorch实现代码+文档说明
### 回答1:
首先,您需要安装MindSpore和相关的依赖项。接下来,您可以在MindSpore中使用ResNet101架构定义模型并训练它。
以下是MindSpore中实现ResNet101的示例代码:
```python
import mindspore.nn as nn
import mindspore.common.dtype as mstype
# ResNet101网络结构
class ResNet101(nn.Cell):
def __init__(self, num_classes=1000):
super(ResNet101, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = nn.SequentialCell([
Bottleneck(64, 64, 256, 1, False),
Bottleneck(256, 64, 256, 1, False),
Bottleneck(256, 64, 256, 1, False)
])
self.layer2 = nn.SequentialCell([
Bottleneck(256, 128, 512, 2, False),
Bottleneck(512, 128, 512, 1, False),
Bottleneck(512, 128, 512, 1, False),
Bottleneck(512, 128, 512, 1, False),
Bottleneck(512, 128, 512, 1, False),
Bottleneck(512, 128, 512, 1, False)
])
self.layer3 = nn.SequentialCell([
Bottleneck(512, 256, 1024, 2, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False),
Bottleneck(1024, 256, 1024, 1, False)
])
self.layer4 = nn.SequentialCell([
Bottleneck(1024, 512, 2048, 2, False),
Bottleneck(2048, 512, 2048, 1, False),
Bottleneck(2048, 512, 2048, 1, False)
])
self.avgpool = nn.AvgPool2d(7, 1)
self.fc = nn.Dense(2048, num_classes)
def construct(self, x):
### 回答2:
要使用MindSpore编写ResNet-101的代码,可以按照以下步骤进行。
首先,导入所需要的库和模块:
```python
import mindspore.nn as nn
import mindspore.ops.operations as P
import mindspore.dataset as ds
from mindspore.dataset.transforms import c_transforms as C
from mindspore.dataset.vision import Inter
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore import dtype as mstype
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
from mindspore.parallel._utils import _get_device_num
```
然后,定义ResNet-101的网络结构:
```python
class ResidualBlock(nn.Cell):
def __init__(self, in_channel, out_channel, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=(3, 3),
stride=stride, pad_mode='pad', padding=0)
self.bn1 = nn.BatchNorm2d(num_features=out_channel)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=(3, 3),
stride=1, pad_mode='pad', padding=0)
self.bn2 = nn.BatchNorm2d(num_features=out_channel)
self.relu2 = nn.ReLU()
if stride != 1 or in_channel != out_channel:
self.shortcut = nn.SequentialCell([
nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=(1, 1), stride=stride, has_bias=False),
nn.BatchNorm2d(num_features=out_channel)
])
else:
self.shortcut = nn.SequentialCell()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
identity = self.shortcut(identity)
out += identity
out = self.relu2(out)
return out
class ResNet101(nn.Cell):
def __init__(self, num_classes=1000):
super(ResNet101, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(7, 7), stride=2, pad_mode='pad', padding=0)
self.bn1 = nn.BatchNorm2d(num_features=64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
self.layer1 = self._make_layer(64, 256, 3, stride=1)
self.layer2 = self._make_layer(256, 512, 4, stride=2)
self.layer3 = self._make_layer(512, 1024, 23, stride=2)
self.layer4 = self._make_layer(1024, 2048, 3, stride=2)
self.avgpool = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.fc = nn.Dense(in_channels=2048, out_channels=num_classes)
def _make_layer(self, in_channel, out_channel, blocks, stride):
layers = []
layers.append(ResidualBlock(in_channel, out_channel, stride))
for _ in range(1, blocks):
layers.append(ResidualBlock(out_channel, out_channel))
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,定义推理程序:
```python
def test_net(model, dataset, repeat_num=1):
acc = nn.Accuracy()
model.eval()
for _ in range(repeat_num):
for i, (inputs, labels) in enumerate(dataset.create_dict_iterator()):
labels = labels.asnumpy()
output = model(inputs)
output = output.asnumpy()
acc.update(output, labels)
return acc.eval()
if __name__ == "__main__":
# 设置设备环境
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# 加载训练数据集
data_home = "/path/to/imagenet"
imagenet_dataset = ds.ImageNetDataset(data_home, usage="eval", shuffle=False)
# 数据增强和预处理
imagenet_dataset = imagenet_dataset.map(input_columns="label", operations=C.TypeCast(mstype.int32))
resize_op = C.Resize((224, 224), interpolation=Inter.LINEAR)
normalize_op = C.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
imagenet_dataset = imagenet_dataset.map(input_columns="image",
operations=resize_op)
imagenet_dataset = imagenet_dataset.map(input_columns="image",
operations=normalize_op)
imagenet_dataset = imagenet_dataset.batch(32, drop_remainder=True)
imagenet_dataset = imagenet_dataset.repeat(1)
# 加载ResNet-101模型并进行推理
net = ResNet101()
# 加载预训练的权重
param_dict = load_checkpoint("/path/to/resnet101.ckpt")
load_param_into_net(net, param_dict)
# 计算准确率
acc = test_net(net, imagenet_dataset)
print("Accuracy: ", acc)
```
最后,替换`/path/to/imagenet`为ImageNet数据集的路径,`/path/to/resnet101.ckpt`为ResNet-101预训练权重的路径,并执行以上代码即可得到ResNet-101模型在ImageNet数据集上的推理结果。
### 回答3:
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore import Tensor
class Bottleneck(nn.Cell):
def __init__(self, in_channel, out_channel, stride=1, downsample=False):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=1, has_bias=False)
self.bn1 = nn.BatchNorm2d(out_channel)
self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=stride, pad_mode='same', has_bias=False)
self.bn2 = nn.BatchNorm2d(out_channel)
self.conv3 = nn.Conv2d(out_channel, out_channel * 4, kernel_size=1, has_bias=False)
self.bn3 = nn.BatchNorm2d(out_channel * 4)
self.relu = nn.ReLU()
self.downsample = downsample
self.downsample_layer = nn.SequentialCell()
if downsample:
self.downsample_layer = nn.SequentialCell(
[nn.Conv2d(in_channel, out_channel * 4, kernel_size=1, stride=stride, has_bias=False),
nn.BatchNorm2d(out_channel * 4)]
)
self.add = P.TensorAdd()
self.mul = P.Mul()
def construct(self, x):
identity = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
if self.downsample:
identity = self.downsample_layer(x)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResNet101(nn.Cell):
def __init__(self, num_classes=1000):
super(ResNet101, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode='pad', has_bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
# Define the four stages of ResNet
self.stage1 = self._make_layer(64, 3, stride=1)
self.stage2 = self._make_layer(128, 4, stride=2)
self.stage3 = self._make_layer(256, 23, stride=2)
self.stage4 = self._make_layer(512, 3, stride=2)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.dense = nn.Dense(2048, num_classes)
def _make_layer(self, out_channels, num_blocks, stride):
layers = []
stride_arr = [stride] + [1] * (num_blocks - 1)
for stride in stride_arr:
layers.append(Bottleneck(out_channels//4, out_channels, stride, downsample=True))
return nn.SequentialCell(layers)
def construct(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.maxpool(out)
out = self.stage1(out)
out = self.stage2(out)
out = self.stage3(out)
out = self.stage4(out)
out = self.mean(out, (2, 3))
out = self.flatten(out)
out = self.dense(out)
return out
# Inference Code
import mindspore.dataset as ds
from mindspore.dataset.vision import image_transform
import numpy as np
def resnet101_inference():
# Load ImageNet dataset
test_data_dir = '/path/to/ImageNet_dataset'
batch_size = 1
num_classes = 1000
eval_dataset = ds.ImageFolderDataset(test_data_dir, num_parallel_workers=8, shuffle=False)
eval_dataset = eval_dataset.map(operations=image_transforms.transforms.ToTensor())
eval_dataset = eval_dataset.batch(batch_size, drop_remainder=True)
eval_dataset = eval_dataset.repeat(1)
# Load pre-trained ResNet101 model
model_path = '/path/to/resnet101.ckpt'
net = ResNet101(num_classes)
param_dict = mindspore.load_checkpoint(model_path)
mindspore.load_param_into_net(net, param_dict)
# Run inference
for data in eval_dataset.create_dict_iterator():
image = data['image']
label = data['label']
output = net(image)
output = np.argmax(output.asnumpy(), axis=1)
print(f"Image: {image}, Predicted Label: {output}, True Label: {label}")
resnet101_inference()
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