!pip install mindspore

!pip install numpy

!pip install matplotlib

!pip install opencv-python

!pip install pillow

!pip install matplotlib -i https://repo.huaweicloud.com/repository/pypi/simple/

!wget <https://dtse-mirrors.obs.cn-north-4.myhuaweicloud.com/case/0036/skin-cancer-detection.tar>

!mkdir pifudata_Maker

!tar -xvf skin-cancer-detection.tar -C pifudata_Maker/

import osdiseases = {0: "basal_cell_carcinoma",1: "melanoma",2: "nevus",3: "pigmented_benign_keratosis",}target_directory = "pifudata_Maker/images"for folder in ["train", "val"]:folder_path = os.path.join(target_directory, folder)for number, disease in diseases.items():old_folder_path = os.path.join(folder_path, disease)if not os.path.exists(old_folder_path):print(f"Folder not found: {old_folder_path}")continuenew_folder_name = f"{number}"new_folder_path = os.path.join(folder_path, new_folder_name)os.rename(old_folder_path, new_folder_path)print(f"Renamed {old_folder_path} to {new_folder_path}")

!mkdir pretrainmodel

!wget -P pretrainmodel <https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/resnet50_224_new.ckpt>

sudo apt-get install wget

import osimport matplotlib.pyplot as pltdiseases = {0: "basal_cell_carcinoma",1: "melanoma",2: "nevus",3: "pigmented_benign_keratosis",}data_path_train = "pifudata_Maker/images/train"data_path_val = "pifudata_Maker/images/val"file_counts_train = {}file_counts_val = {}for folder_name in os.listdir(data_path_train):folder_path = os.path.join(data_path_train, folder_name)if os.path.isdir(folder_path):count = len(os.listdir(folder_path))file_counts_train[diseases.get(folder_name, folder_name)] = countfor folder_name in os.listdir(data_path_val):folder_path = os.path.join(data_path_val, folder_name)if os.path.isdir(folder_path):count = len(os.listdir(folder_path))file_counts_val[diseases.get(folder_name, folder_name)] = counttrain_categories = list(file_counts_train.keys())plt.figure(figsize=(12, 8))train_bars = plt.bar(train_categories, file_counts_train.values(), width=0.4, color='blue', label='Train')val_bars = plt.bar([train_categories.index(cat) + 0.4 for cat in train_categories], [file_counts_val.get(cat, 0) for cat in train_categories], width=0.4, color='green', label='Validation')plt.legend()plt.xlabel('labels')plt.ylabel('Number of Images')plt.title('Training and Validation Datasets')train_list = [diseases[int(i)] for i in train_categories]plt.xticks([train_categories.index(cat) + 0.2 for cat in train_categories], train_list, rotation=30, fontsize='small')for i, (train_bar, val_bar) in enumerate(zip(train_bars, val_bars)):train_height = train_bar.get_height()val_height = val_bar.get_height() if val_bar else 0plt.text(train_bar.get_x() + train_bar.get_width() / 2, train_height, '{}'.format(train_height), ha='center', va='bottom')plt.text(val_bar.get_x() + val_bar.get_width() / 2, val_height, '{}'.format(val_height), ha='center', va='bottom') if val_bar else Noneplt.show()

# 超参数定义batch_size = 30 # 批量大小image_size = 224 # 训练图像空间大小num_epochs = 5 # 训练周期数lr = 0.001 # 学习率momentum = 0.6 # 动量workers = 4 # 并行线程个数import mindsporemindspore.set_context(device_target="CPU")

import mindspore as msimport mindspore.dataset as dsimport mindspore.dataset.vision as vision# 数据集目录路径data_path_train = "pifudata_Maker/images/train"data_path_val = "pifudata_Maker/images/val"# 创建训练数据集def create_dataset_canidae(dataset_path, usage):"""数据加载"""data_set = ds.ImageFolderDataset(dataset_path,num_parallel_workers=workers,shuffle=True,)# 数据增强操作mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]std = [0.229 * 255, 0.224 * 255, 0.225 * 255]scale = 32if usage == "train":# Define map operations for training datasettrans = [vision.RandomCropDecodeResize(size=image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),vision.RandomHorizontalFlip(prob=0.5),vision.Normalize(mean=mean, std=std),vision.HWC2CHW()]else:# Define map operations for inference datasettrans = [vision.Decode(),vision.Resize(image_size + scale),vision.CenterCrop(image_size),vision.Normalize(mean=mean, std=std),vision.HWC2CHW()]# 数据映射操作data_set = data_set.map(operations=trans,input_columns='image',num_parallel_workers=workers)# 批量操作data_set = data_set.batch(batch_size)return data_setdataset_train = create_dataset_canidae(data_path_train, "train")step_size_train = dataset_train.get_dataset_size()dataset_val = create_dataset_canidae(data_path_val, "val")step_size_val = dataset_val.get_dataset_size()print(step_size_train)print(step_size_val)data = next(dataset_val.create_dict_iterator())images = data["image"]labels = data["label"]# print(data["image"][0])print("Tensor of image", images.shape)print("Labels:", labels)

import matplotlib.pyplot as pltimport numpy as np# class_name对应label，按文件夹字符串从小到大的顺序标记labelclass_name = {0: "basal_cell_carcinoma",1: "melanoma",2: "nevus",3: "pigmented_benign_keratosis",}# print(images[0])plt.figure(figsize=(5, 5))for i in range(4):# 获取图像及其对应的label# print(images[i])data_image = images[i].asnumpy()data_label = labels[i]# 处理图像供展示使用data_image = np.transpose(data_image, (1, 2, 0))mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])data_image = std * data_image + meandata_image = np.clip(data_image, 0, 1)# 显示图像plt.subplot(2, 2, i+1)plt.imshow(data_image)plt.title(class_name[int(labels[i].asnumpy())])plt.axis("off")plt.show()

from typing import Type, Union, List, Optionalfrom mindspore import nn, trainfrom mindspore.common.initializer import Normalweight_init = Normal(mean=0, sigma=0.02)gamma_init = Normal(mean=1, sigma=0.02)class ResidualBlockBase(nn.Cell):expansion: int = 1 # 最后一个卷积核数量与第一个卷积核数量相等def __init__(self, in_channel: int, out_channel: int,stride: int = 1, norm: Optional[nn.Cell] = None,down_sample: Optional[nn.Cell] = None) -> None:super(ResidualBlockBase, self).__init__()if not norm:self.norm = nn.BatchNorm2d(out_channel)else:self.norm = normself.conv1 = nn.Conv2d(in_channel, out_channel,kernel_size=3, stride=stride,weight_init=weight_init)self.conv2 = nn.Conv2d(in_channel, out_channel,kernel_size=3, weight_init=weight_init)self.relu = nn.ReLU()self.down_sample = down_sampledef construct(self, x):"""ResidualBlockBase construct."""identity = x # shortcuts分支out = self.conv1(x) # 主分支第一层：3*3卷积层out = self.norm(out)out = self.relu(out)out = self.conv2(out) # 主分支第二层：3*3卷积层out = self.norm(out)if self.down_sample is not None:identity = self.down_sample(x)out += identity # 输出为主分支与shortcuts之和out = self.relu(out)return outclass ResidualBlock(nn.Cell):expansion = 4 # 最后一个卷积核的数量是第一个卷积核数量的4倍def __init__(self, in_channel: int, out_channel: int,stride: int = 1, down_sample: Optional[nn.Cell] = None) -> None:super(ResidualBlock, self).__init__()self.conv1 = nn.Conv2d(in_channel, out_channel,kernel_size=1, weight_init=weight_init)self.norm1 = nn.BatchNorm2d(out_channel)self.conv2 = nn.Conv2d(out_channel, out_channel,kernel_size=3, stride=stride,weight_init=weight_init)self.norm2 = nn.BatchNorm2d(out_channel)self.conv3 = nn.Conv2d(out_channel, out_channel * self.expansion,kernel_size=1, weight_init=weight_init)self.norm3 = nn.BatchNorm2d(out_channel * self.expansion)self.relu = nn.ReLU()self.down_sample = down_sampledef construct(self, x):identity = x # shortscuts分支out = self.conv1(x) # 主分支第一层：1*1卷积层out = self.norm1(out)out = self.relu(out)out = self.conv2(out) # 主分支第二层：3*3卷积层out = self.norm2(out)out = self.relu(out)out = self.conv3(out) # 主分支第三层：1*1卷积层out = self.norm3(out)if self.down_sample is not None:identity = self.down_sample(x)out += identity # 输出为主分支与shortcuts之和out = self.relu(out)return outdef make_layer(last_out_channel, block: Type[Union[ResidualBlockBase, ResidualBlock]],channel: int, block_nums: int, stride: int = 1):down_sample = None # shortcuts分支if stride != 1 or last_out_channel != channel * block.expansion:down_sample = nn.SequentialCell([nn.Conv2d(last_out_channel, channel * block.expansion,kernel_size=1, stride=stride, weight_init=weight_init),nn.BatchNorm2d(channel * block.expansion, gamma_init=gamma_init)])layers = []layers.append(block(last_out_channel, channel, stride=stride, down_sample=down_sample))in_channel = channel * block.expansion# 堆叠残差网络for _ in range(1, block_nums):layers.append(block(in_channel, channel))return nn.SequentialCell(layers)from mindspore import load_checkpoint, load_param_into_netclass ResNet(nn.Cell):def __init__(self, block: Type[Union[ResidualBlockBase, ResidualBlock]],layer_nums: List[int], num_classes: int, input_channel: int) -> None:super(ResNet, self).__init__()self.relu = nn.ReLU()# 第一个卷积层，输入channel为3（彩色图像），输出channel为64self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, weight_init=weight_init)self.norm = nn.BatchNorm2d(64)# 最大池化层，缩小图片的尺寸self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')# 各个残差网络结构块定义，self.layer1 = make_layer(64, block, 64, layer_nums[0])self.layer2 = make_layer(64 * block.expansion, block, 128, layer_nums[1], stride=2)self.layer3 = make_layer(128 * block.expansion, block, 256, layer_nums[2], stride=2)self.layer4 = make_layer(256 * block.expansion, block, 512, layer_nums[3], stride=2)# 平均池化层self.avg_pool = nn.AvgPool2d()# flattern层self.flatten = nn.Flatten()# 全连接层self.fc = nn.Dense(in_channels=input_channel, out_channels=num_classes)def construct(self, x):x = self.conv1(x)x = self.norm(x)x = self.relu(x)x = self.max_pool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avg_pool(x)x = self.flatten(x)x = self.fc(x)return xdef download_pretrained_model(url, save_path):# 创建目录os.makedirs(os.path.dirname(save_path), exist_ok=True)# 下载文件urllib.request.urlretrieve(url, save_path)print(f"预训练模型已下载到: {save_path}")def _resnet(model_url: str, block: Type[Union[ResidualBlockBase, ResidualBlock]],layers: List[int], num_classes: int, pretrained: bool, pretrianed_ckpt: str,input_channel: int):model = ResNet(block, layers, num_classes, input_channel)if pretrained:# 加载预训练模型# download(url=model_url, path=pretrianed_ckpt, replace=True)param_dict = load_checkpoint(pretrianed_ckpt)load_param_into_net(model, param_dict)return modeldef resnet50(num_classes: int = 1000, pretrained: bool = False):"ResNet50模型"resnet50_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/resnet50_224_new.ckpt"resnet50_ckpt = "./pretrainmodel/resnet50_224_new.ckpt"return _resnet(resnet50_url, ResidualBlock, [3, 4, 6, 3], num_classes,pretrained, resnet50_ckpt, 2048)

!mkdir pretrainmodel
!wget -P pretrainmodel <https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/resnet50_224_new.ckpt>

# 代码中首先导入了必要的MindSpore模块和函数，并设置了运行环境。import mindsporefrom mindspore import nn, trainfrom mindspore.nn import Loss, Accuracy!pip install downloadimport mindspore as msfrom download import downloadnetwork = resnet50(pretrained=True)# 通过替换ResNet50的原始全连接层和平均池化层来适配新的任务# 全连接层输入层的大小in_channels = network.fc.in_channels# 输出通道数大小为皮肤肿瘤分类数4head = nn.Dense(in_channels, 4)# 重置全连接层network.fc = head# 平均池化层kernel size为7avg_pool = nn.AvgPool2d(kernel_size=7)# 重置平均池化层network.avg_pool = avg_poolimport mindspore as msimport mindspore# 定义优化器和损失函数opt = nn.Momentum(params=network.trainable_params(), learning_rate=lr, momentum=momentum)loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')# 实例化模型model = train.Model(network, loss_fn, opt, metrics={"Accuracy": Accuracy()})def forward_fn(inputs, targets):logits = network(inputs)loss = loss_fn(logits, targets)return lossgrad_fn = mindspore.ops.value_and_grad(forward_fn, None, opt.parameters)def train_step(inputs, targets):loss, grads = grad_fn(inputs, targets)opt(grads)return loss# 创建迭代器data_loader_train = dataset_train.create_tuple_iterator(num_epochs=num_epochs)# 最佳模型保存路径best_ckpt_dir = "./BestCheckpoint"best_ckpt_path = "./BestCheckpoint/resnet50-best.ckpt"import osimport time# 开始循环训练print("Start Training Loop ...")best_acc = 0# 训练循环中，数据通过迭代器被加载，模型在每个epoch中更新权重，并计算训练损失。# 在每个epoch结束时，模型在验证集上评估准确率，并保存具有最高准确率的模型检查点。for epoch in range(num_epochs):losses = []network.set_train()epoch_start = time.time()# 为每轮训练读入数据for i, (images, labels) in enumerate(data_loader_train):labels = labels.astype(ms.int32)loss = train_step(images, labels)losses.append(loss)# 每个epoch结束后，验证准确率acc = model.eval(dataset_val)['Accuracy']epoch_end = time.time()epoch_seconds = (epoch_end - epoch_start) * 1000step_seconds = epoch_seconds/step_size_trainprint("-" * 20)print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (epoch+1, num_epochs, sum(losses)/len(losses), acc))print("epoch time: %5.3f ms, per step time: %5.3f ms" % (epoch_seconds, step_seconds))if acc > best_acc:best_acc = accif not os.path.exists(best_ckpt_dir):os.mkdir(best_ckpt_dir)ms.save_checkpoint(network, best_ckpt_path)print("=" * 80)print(f"End of validation the best Accuracy is: {best_acc: 5.3f}, "f"save the best ckpt file in {best_ckpt_path}", flush=True)

import matplotlib.pyplot as pltimport mindspore as msdef visualize_model(best_ckpt_path, val_ds):net = resnet50()# 全连接层输入层的大小in_channels = net.fc.in_channels# 输出通道数大小为分类数4head = nn.Dense(in_channels, 4)# 重置全连接层net.fc = head# 平均池化层kernel size为7avg_pool = nn.AvgPool2d(kernel_size=7)# 重置平均池化层net.avg_pool = avg_pool# 加载模型参数param_dict = ms.load_checkpoint(best_ckpt_path)ms.load_param_into_net(net, param_dict)model = train.Model(net)# 加载验证集的数据进行验证data = next(val_ds.create_dict_iterator())images = data["image"].asnumpy()labels = data["label"].asnumpy()#print(labels)class_name = {0: "basal_cell_carcinoma",1: "melanoma",2: "nevus",3: "pigmented_benign_keratosis"}# 预测图像类别data_pre=ms.Tensor(data["image"])output = model.predict(data_pre)# print(output)pred = np.argmax(output.asnumpy(), axis=1)# 显示图像及图像的预测值plt.figure(figsize=(10, 10))for i in range(16):plt.subplot(4, 4, i + 1)# 若预测正确，显示为蓝色；若预测错误，显示为红色color = 'blue' if pred[i] == labels[i] else 'red'plt.title('predict:{}'.format(class_name[pred[i]]), color=color,fontsize=7)picture_show = np.transpose(images[i], (1, 2, 0))mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])picture_show = std * picture_show + meanpicture_show = np.clip(picture_show, 0, 1)plt.imshow(picture_show)plt.axis('off')plt.show()visualize_model('BestCheckpoint/resnet50-best.ckpt', dataset_val)

import numpy as npimport matplotlib.pyplot as pltimport mindspore as msfrom mindspore import nnfrom mindspore.train.serialization import load_checkpoint, load_param_into_netimport mindspore.numpy as mnpdef preprocess_image(image_path):# 使用PIL加载图像from PIL import Imageimage = Image.open(image_path)# 转换图像为RGB模式image = image.convert('RGB')# 调整图像大小以匹配模型输入image = image.resize((224, 224)) # 假设模型输入大小为224x224# 将图像转换为numpy数组image_array = np.array(image)# 归一化图像数组image_array = image_array / 255.0image_array = np.transpose(image_array, (2, 0, 1))# 扩展维度以匹配模型输入，例如 (1, 3, 224, 224)image_array = np.expand_dims(image_array, axis=0)return image_array.astype(np.float32) # 确保数据类型为float32def visualize_prediction(image_path, best_ckpt_path):net = resnet50()# 全连接层输入层的大小in_channels = net.fc.in_channels# 输出通道数大小为分类数4head = nn.Dense(in_channels, 4)# 重置全连接层net.fc = head# 平均池化层kernel size为7avg_pool = nn.AvgPool2d(kernel_size=7)# 重置平均池化层net.avg_pool = avg_pool# 加载模型参数param_dict = ms.load_checkpoint(best_ckpt_path)ms.load_param_into_net(net, param_dict)model = train.Model(net)# 预处理图像image = preprocess_image(image_path)data_pre = ms.Tensor(image)print(data_pre.shape)print(type(data_pre))class_name = {0: "basal_cell_carcinoma",1: "melanoma",2: "nevus",3: "pigmented_benign_keratosis"}output = model.predict(data_pre)print(output)pred = np.argmax(output.asnumpy(), axis=1)print(pred)plt.figure(figsize=(5, 5))plt.subplot(1, 1, 1)plt.title('predict:{}'.format(class_name[pred[0]]))picture_show = image[0] # 已经是归一化后的图像picture_show = np.transpose(picture_show, (1, 2, 0)) # 转换为(224, 224, 3)plt.imshow(picture_show)plt.axis('off')plt.show()# 调用函数，传入图片路径和模型检查点路径visualize_prediction('pifudata_Maker/images/val/2/ISIC_0000005.jpg','BestCheckpoint/resnet50-best.ckpt')