|
|
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)]))
|
