PyTorch框架——基于WebUI：Gradio深度学习ShuffleNetv2神经网络蔬菜图像识别分类系统

第一步：准备数据

蔬菜数据集，英文为Vegetable。

train 目录下有15000 张图片。

共十五种植物的幼苗图片集，分别为classes = ['Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal', 'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber', 'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato' ]

具体信息如下：

第二步：搭建模型

ShuffleNet_V2是由旷视科技的Ma, Ningning等人在《ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design【ECCV-2018】》【论文地址】一文中提出的改进模型，论文中提出了高效网络架构设计的两大原则：第一，使用直接指标(如速度)而非间接指标(如FLOPs)；第二，提出了四条与跨平台的设计指南，并在该指南指导下设计了ShuffleNet_V2

ShuffleNetV2的一些关键特点：

‌优化的分组卷积‌：ShuffleNetV2使用了一种称为“channel split”的技术，将输入通道分成两半，分别进行不同的处理，然后合并结果以获得更好的性能‌1。
‌自适应分组卷积‌：ShuffleNetV2根据输入数据动态调整分组数量，以实现更高的效率‌1。
‌多尺度特征融合‌：引入了多尺度特征融合模块，以更好地捕捉不同尺度的特征‌1。
‌通道剪枝‌：应用通道剪枝策略来进一步减少计算复杂度，同时保持准确性‌1。
‌内存访问成本最小化‌：ShuffleNetV2试图最小化内存访问成本（MAC），通过精细调整组的数量和结构，找到了计算效率和模型性能之间的最佳平衡点‌2。
‌直接面向实际运行速度的优化‌：在设计过程中，除了理论上的计算量（FLOPs）外，还直接考虑了模型在实际硬件上的运行速度，包括CPU和GPU的特定性能特征‌2。
‌均衡通道宽度‌：保持每层网络的通道数相对均衡可以减少内存访问的开销，并且对模型性能影响不大‌2。

第三步：训练代码

1）损失函数为：交叉熵损失函数

2）ShuffleNet_V2代码：

from functools import partial
from typing import Any, Callable, List, Optionalimport torch
import torch.nn as nn
from torch import Tensorfrom ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_named_param, handle_legacy_interface__all__ = ["ShuffleNetV2","ShuffleNet_V2_X0_5_Weights","ShuffleNet_V2_X1_0_Weights","ShuffleNet_V2_X1_5_Weights","ShuffleNet_V2_X2_0_Weights","shufflenet_v2_x0_5","shufflenet_v2_x1_0","shufflenet_v2_x1_5","shufflenet_v2_x2_0",
]def channel_shuffle(x: Tensor, groups: int) -> Tensor:batchsize, num_channels, height, width = x.size()channels_per_group = num_channels // groups# reshapex = x.view(batchsize, groups, channels_per_group, height, width)x = torch.transpose(x, 1, 2).contiguous()# flattenx = x.view(batchsize, num_channels, height, width)return xclass InvertedResidual(nn.Module):def __init__(self, inp: int, oup: int, stride: int) -> None:super().__init__()if not (1 <= stride <= 3):raise ValueError("illegal stride value")self.stride = stridebranch_features = oup // 2if (self.stride == 1) and (inp != branch_features << 1):raise ValueError(f"Invalid combination of stride {stride}, inp {inp} and oup {oup} values. If stride == 1 then inp should be equal to oup // 2 << 1.")if self.stride > 1:self.branch1 = nn.Sequential(self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),nn.BatchNorm2d(inp),nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),nn.BatchNorm2d(branch_features),nn.ReLU(inplace=True),)else:self.branch1 = nn.Sequential()self.branch2 = nn.Sequential(nn.Conv2d(inp if (self.stride > 1) else branch_features,branch_features,kernel_size=1,stride=1,padding=0,bias=False,),nn.BatchNorm2d(branch_features),nn.ReLU(inplace=True),self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),nn.BatchNorm2d(branch_features),nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),nn.BatchNorm2d(branch_features),nn.ReLU(inplace=True),)@staticmethoddef depthwise_conv(i: int, o: int, kernel_size: int, stride: int = 1, padding: int = 0, bias: bool = False) -> nn.Conv2d:return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)def forward(self, x: Tensor) -> Tensor:if self.stride == 1:x1, x2 = x.chunk(2, dim=1)out = torch.cat((x1, self.branch2(x2)), dim=1)else:out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)out = channel_shuffle(out, 2)return outclass ShuffleNetV2(nn.Module):def __init__(self,stages_repeats: List[int],stages_out_channels: List[int],num_classes: int = 1000,inverted_residual: Callable[..., nn.Module] = InvertedResidual,) -> None:super().__init__()_log_api_usage_once(self)if len(stages_repeats) != 3:raise ValueError("expected stages_repeats as list of 3 positive ints")if len(stages_out_channels) != 5:raise ValueError("expected stages_out_channels as list of 5 positive ints")self._stage_out_channels = stages_out_channelsinput_channels = 3output_channels = self._stage_out_channels[0]self.conv1 = nn.Sequential(nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),nn.BatchNorm2d(output_channels),nn.ReLU(inplace=True),)input_channels = output_channelsself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# Static annotations for mypyself.stage2: nn.Sequentialself.stage3: nn.Sequentialself.stage4: nn.Sequentialstage_names = [f"stage{i}" for i in [2, 3, 4]]for name, repeats, output_channels in zip(stage_names, stages_repeats, self._stage_out_channels[1:]):seq = [inverted_residual(input_channels, output_channels, 2)]for i in range(repeats - 1):seq.append(inverted_residual(output_channels, output_channels, 1))setattr(self, name, nn.Sequential(*seq))input_channels = output_channelsoutput_channels = self._stage_out_channels[-1]self.conv5 = nn.Sequential(nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),nn.BatchNorm2d(output_channels),nn.ReLU(inplace=True),)self.fc = nn.Linear(output_channels, num_classes)def _forward_impl(self, x: Tensor) -> Tensor:# See note [TorchScript super()]x = self.conv1(x)x = self.maxpool(x)x = self.stage2(x)x = self.stage3(x)x = self.stage4(x)x = self.conv5(x)x = x.mean([2, 3])  # globalpoolx = self.fc(x)return xdef forward(self, x: Tensor) -> Tensor:return self._forward_impl(x)def _shufflenetv2(weights: Optional[WeightsEnum],progress: bool,*args: Any,**kwargs: Any,
) -> ShuffleNetV2:if weights is not None:_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))model = ShuffleNetV2(*args, **kwargs)if weights is not None:model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))return model_COMMON_META = {"min_size": (1, 1),"categories": _IMAGENET_CATEGORIES,"recipe": "https://github.com/ericsun99/Shufflenet-v2-Pytorch",
}class ShuffleNet_V2_X0_5_Weights(WeightsEnum):IMAGENET1K_V1 = Weights(# Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorchurl="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth",transforms=partial(ImageClassification, crop_size=224),meta={**_COMMON_META,"num_params": 1366792,"_metrics": {"ImageNet-1K": {"acc@1": 60.552,"acc@5": 81.746,}},"_ops": 0.04,"_file_size": 5.282,"_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""",},)DEFAULT = IMAGENET1K_V1class ShuffleNet_V2_X1_0_Weights(WeightsEnum):IMAGENET1K_V1 = Weights(# Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorchurl="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth",transforms=partial(ImageClassification, crop_size=224),meta={**_COMMON_META,"num_params": 2278604,"_metrics": {"ImageNet-1K": {"acc@1": 69.362,"acc@5": 88.316,}},"_ops": 0.145,"_file_size": 8.791,"_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""",},)DEFAULT = IMAGENET1K_V1class ShuffleNet_V2_X1_5_Weights(WeightsEnum):IMAGENET1K_V1 = Weights(url="https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth",transforms=partial(ImageClassification, crop_size=224, resize_size=232),meta={**_COMMON_META,"recipe": "https://github.com/pytorch/vision/pull/5906","num_params": 3503624,"_metrics": {"ImageNet-1K": {"acc@1": 72.996,"acc@5": 91.086,}},"_ops": 0.296,"_file_size": 13.557,"_docs": """These weights were trained from scratch by using TorchVision's `new training recipe<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.""",},)DEFAULT = IMAGENET1K_V1class ShuffleNet_V2_X2_0_Weights(WeightsEnum):IMAGENET1K_V1 = Weights(url="https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth",transforms=partial(ImageClassification, crop_size=224, resize_size=232),meta={**_COMMON_META,"recipe": "https://github.com/pytorch/vision/pull/5906","num_params": 7393996,"_metrics": {"ImageNet-1K": {"acc@1": 76.230,"acc@5": 93.006,}},"_ops": 0.583,"_file_size": 28.433,"_docs": """These weights were trained from scratch by using TorchVision's `new training recipe<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.""",},)DEFAULT = IMAGENET1K_V1@register_model()
@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1))
def shufflenet_v2_x0_5(*, weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:"""Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design<https://arxiv.org/abs/1807.11164>`__.Args:weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): Thepretrained weights to use. See:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below formore details, and possible values. By default, no pre-trainedweights are used.progress (bool, optional): If True, displays a progress bar of thedownload to stderr. Default is True.**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``base class. Please refer to the `source code<https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_for more details about this class... autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights:members:"""weights = ShuffleNet_V2_X0_5_Weights.verify(weights)return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)@register_model()
@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1))
def shufflenet_v2_x1_0(*, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:"""Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design<https://arxiv.org/abs/1807.11164>`__.Args:weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): Thepretrained weights to use. See:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights` below formore details, and possible values. By default, no pre-trainedweights are used.progress (bool, optional): If True, displays a progress bar of thedownload to stderr. Default is True.**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``base class. Please refer to the `source code<https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_for more details about this class... autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights:members:"""weights = ShuffleNet_V2_X1_0_Weights.verify(weights)return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)@register_model()
@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1))
def shufflenet_v2_x1_5(*, weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:"""Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design<https://arxiv.org/abs/1807.11164>`__.Args:weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): Thepretrained weights to use. See:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights` below formore details, and possible values. By default, no pre-trainedweights are used.progress (bool, optional): If True, displays a progress bar of thedownload to stderr. Default is True.**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``base class. Please refer to the `source code<https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_for more details about this class... autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights:members:"""weights = ShuffleNet_V2_X1_5_Weights.verify(weights)return _shufflenetv2(weights, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)@register_model()
@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1))
def shufflenet_v2_x2_0(*, weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:"""Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design<https://arxiv.org/abs/1807.11164>`__.Args:weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): Thepretrained weights to use. See:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights` below formore details, and possible values. By default, no pre-trainedweights are used.progress (bool, optional): If True, displays a progress bar of thedownload to stderr. Default is True.**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``base class. Please refer to the `source code<https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_for more details about this class... autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights:members:"""weights = ShuffleNet_V2_X2_0_Weights.verify(weights)return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)