单独用模型实现预测（先运行multiclass产生模型后使用，该文件绝对路径待修改）

predict.py

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+import paddle
+import numpy as np
+from PIL import Image
+
+'''
+参数配置
+'''
+train_parameters = {
+    "input_size": [3, 224, 224],                              # 输入图片的shape
+    "class_dim": 5,                                          # 分类数
+    "label_dict": {'0': 'baihe', '1': 'dangshen', '2': 'gouqi', '3': 'huaihua', '4': 'jinyinhua'},    # 标签字典
+}
+
+# 预测图片预处理
+def load_image(img_path):
+    img = Image.open(img_path)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img = img.resize((224, 224), Image.BILINEAR)
+    img = np.array(img).astype('float32')
+    img = img.transpose((2, 0, 1)) / 255  # HWC to CHW 及归一化
+    return img
+
+
+class ConvPool(paddle.nn.Layer):
+    """ 卷积+池化 """
+
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 pool_stride,
+                 groups,
+                 conv_stride=1,
+                 conv_padding=1,
+                 ):
+        super(ConvPool, self).__init__()
+
+        for i in range(groups):
+            self.add_sublayer(  # 添加子层实例
+                'bb_%d' % i,
+                paddle.nn.Conv2D(  # layer
+                    in_channels=num_channels,  # 通道数
+                    out_channels=num_filters,  # 卷积核个数
+                    kernel_size=filter_size,  # 卷积核大小
+                    stride=conv_stride,  # 步长
+                    padding=conv_padding,  # padding
+                )
+            )
+            self.add_sublayer(
+                'relu%d' % i,
+                paddle.nn.ReLU()
+            )
+            num_channels = num_filters
+
+        self.add_sublayer(
+            'Maxpool',
+            paddle.nn.MaxPool2D(
+                kernel_size=pool_size,  # 池化核大小
+                stride=pool_stride  # 池化步长
+            )
+        )
+
+    def forward(self, inputs):
+        x = inputs
+        for prefix, sub_layer in self.named_children():
+            # print(prefix,sub_layer)
+            x = sub_layer(x)
+        return x
+
+
+class VGGNet(paddle.nn.Layer):
+
+    def __init__(self):
+        super(VGGNet, self).__init__()
+        self.convpool01 = ConvPool(
+            3, 64, 3, 2, 2, 2)  # 3:通道数，64：卷积核个数，3:卷积核大小，2:池化核大小，2:池化步长，2:连续卷积个数
+        self.convpool02 = ConvPool(
+            64, 128, 3, 2, 2, 2)
+        self.convpool03 = ConvPool(
+            128, 256, 3, 2, 2, 3)
+        self.convpool04 = ConvPool(
+            256, 512, 3, 2, 2, 3)
+        self.convpool05 = ConvPool(
+            512, 512, 3, 2, 2, 3)
+        self.pool_5_shape = 512 * 7 * 7
+        self.fc01 = paddle.nn.Linear(self.pool_5_shape, 4096)
+        self.fc02 = paddle.nn.Linear(4096, 4096)
+        self.fc03 = paddle.nn.Linear(4096, train_parameters['class_dim'])
+
+    def forward(self, inputs, label=None):
+        # print('input_shape:', inputs.shape) #[8, 3, 224, 224]
+        """前向计算"""
+        out = self.convpool01(inputs)
+        # print('convpool01_shape:', out.shape)           #[8, 64, 112, 112]
+        out = self.convpool02(out)
+        # print('convpool02_shape:', out.shape)           #[8, 128, 56, 56]
+        out = self.convpool03(out)
+        # print('convpool03_shape:', out.shape)           #[8, 256, 28, 28]
+        out = self.convpool04(out)
+        # print('convpool04_shape:', out.shape)           #[8, 512, 14, 14]
+        out = self.convpool05(out)
+        # print('convpool05_shape:', out.shape)           #[8, 512, 7, 7]
+
+        out = paddle.reshape(out, shape=[-1, 512 * 7 * 7])
+        out = self.fc01(out)
+        out = self.fc02(out)
+        out = self.fc03(out)
+
+        if label is not None:
+            acc = paddle.metric.accuracy(input=out, label=label)
+            return out, acc
+        else:
+            return out
+
+
+label_dic = train_parameters['label_dict']
+
+# 加载模型
+model__state_dict = paddle.load('D:/aistudio/work/checkpoints/save_dir_final.pdparams')
+model_predict = VGGNet()
+model_predict.set_state_dict(model__state_dict)
+
+infer_img_path = "D:/aistudio/data/Chinese Medicine/baihe/b (1).jpg"
+print(infer_img_path)
+
+infer_img = load_image(infer_img_path)
+infer_img = infer_img[np.newaxis, :, :, :]  # reshape(-1,3,224,224)
+infer_img = paddle.to_tensor(infer_img)
+result = model_predict(infer_img)
+lab = np.argmax(result.numpy())
+print("样本: {},被预测为:{}".format(infer_img_path, label_dic[str(lab)]))