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@ -7,6 +7,7 @@ import os
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# Create your views here.
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def index(request):
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return render(request,'index.html',{})
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@ -31,16 +32,151 @@ def upload(request):
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# 多次写入
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for i in rev_file.chunks():
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f.write(i)
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# 写完之后要关闭
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f.close()
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print('上传成功')
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str = predict_image(file_path)
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print(str)
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# 返回
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return render(request, 'results.html', {})
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return render(request, 'results.html', {"data":str})
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except Exception as e:
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print('上传失败')
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print(e)
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return render(request, 'upload.html', {})
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else:
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print('其他请求')
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return render(request, 'upload.html', {})
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return render(request, 'upload.html', {})
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import paddle
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import numpy as np
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from PIL import Image
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'''
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参数配置
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'''
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train_parameters = {
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"input_size": [3, 224, 224], # 输入图片的shape
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"class_dim": 5, # 分类数
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"label_dict": {'0': '百合', '1': '党参', '2': '枸杞', '3': '槐花', '4': '金银花'}, # 标签字典
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}
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class ConvPool(paddle.nn.Layer):
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""" 卷积+池化 """
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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pool_size,
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pool_stride,
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groups,
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conv_stride=1,
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conv_padding=1,
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):
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super(ConvPool, self).__init__()
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for i in range(groups):
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self.add_sublayer( # 添加子层实例
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'bb_%d' % i,
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paddle.nn.Conv2D( # layer
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in_channels=num_channels, # 通道数
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out_channels=num_filters, # 卷积核个数
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kernel_size=filter_size, # 卷积核大小
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stride=conv_stride, # 步长
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padding=conv_padding, # padding
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)
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)
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self.add_sublayer(
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'relu%d' % i,
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paddle.nn.ReLU()
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)
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num_channels = num_filters
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self.add_sublayer(
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'Maxpool',
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paddle.nn.MaxPool2D(
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kernel_size=pool_size, # 池化核大小
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stride=pool_stride # 池化步长
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)
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)
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def forward(self, inputs):
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x = inputs
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for prefix, sub_layer in self.named_children():
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# print(prefix,sub_layer)
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x = sub_layer(x)
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return x
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class VGGNet(paddle.nn.Layer):
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def __init__(self):
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super(VGGNet, self).__init__()
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self.convpool01 = ConvPool(
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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
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self.convpool02 = ConvPool(
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64, 128, 3, 2, 2, 2)
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self.convpool03 = ConvPool(
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128, 256, 3, 2, 2, 3)
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self.convpool04 = ConvPool(
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256, 512, 3, 2, 2, 3)
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self.convpool05 = ConvPool(
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512, 512, 3, 2, 2, 3)
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self.pool_5_shape = 512 * 7 * 7
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self.fc01 = paddle.nn.Linear(self.pool_5_shape, 4096)
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self.fc02 = paddle.nn.Linear(4096, 4096)
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self.fc03 = paddle.nn.Linear(4096, train_parameters['class_dim'])
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def forward(self, inputs, label=None):
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# print('input_shape:', inputs.shape) #[8, 3, 224, 224]
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"""前向计算"""
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out = self.convpool01(inputs)
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# print('convpool01_shape:', out.shape) #[8, 64, 112, 112]
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out = self.convpool02(out)
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# print('convpool02_shape:', out.shape) #[8, 128, 56, 56]
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out = self.convpool03(out)
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# print('convpool03_shape:', out.shape) #[8, 256, 28, 28]
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out = self.convpool04(out)
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# print('convpool04_shape:', out.shape) #[8, 512, 14, 14]
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out = self.convpool05(out)
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# print('convpool05_shape:', out.shape) #[8, 512, 7, 7]
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out = paddle.reshape(out, shape=[-1, 512 * 7 * 7])
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out = self.fc01(out)
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out = self.fc02(out)
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out = self.fc03(out)
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if label is not None:
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acc = paddle.metric.accuracy(input=out, label=label)
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return out, acc
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else:
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return out
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# 预测图片预处理
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def load_image(img_path):
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img = Image.open(img_path)
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img = img.resize((224, 224), Image.BILINEAR)
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img = np.array(img).astype('float32')
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img = img.transpose((2, 0, 1)) / 255 # HWC to CHW 及归一化
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return img
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def predict_image(file_path):
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label_dic = train_parameters['label_dict']
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# 加载模型
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model__state_dict = paddle.load('D:/aistudio/work/checkpoints/save_dir_final.pdparams')
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model_predict = VGGNet()
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model_predict.set_state_dict(model__state_dict)
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infer_img = load_image(file_path)
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infer_img = infer_img[np.newaxis, :, :, :] # reshape(-1,3,224,224)
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infer_img = paddle.to_tensor(infer_img)
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result = model_predict(infer_img)
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lab = np.argmax(result.numpy())
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print("样本: {},被预测为:{}".format(file_path, label_dic[str(lab)]))
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return label_dic[str(lab)]
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