DeepLearning（基於caffe）實戰項目（9）--Python測試訓練好的model

        之前曾用Matlab測試訓練好的model（詳細見：http://blog.csdn.net/sihailongwang/article/details/72700482），現在打算用Python測試訓練好的model，這裏用imagenet爲例。

step1：準備階段

        1.這裏我們需要將編譯好的“pycaffe文件”複製到你的Python程序所在的文件夾下

        2.將待測圖片也複製到該文件夾下

        3.新建一個TXT文件（名爲synset_words.txt）（爲了將輸出的值映射到標籤時用）

        4.根據train_val.prototxt，改寫所需要的deploy.prototxt

name: "CaffeNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  inner_product_param {
    num_output: 1000
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc8"
  top: "prob"
}

step2：均值文件轉換（.binaryproto轉.py）：

# encoding: utf-8
#!/usr/bin/python

import sys
caffe_root = '../PythonCode/'  #該文件要從路徑{caffe_root}/examples下運行，否則要調整這一行。
sys.path.insert(0, caffe_root + 'pycaffe')

import caffe
import numpy as np

MEAN_PROTO_PATH = 'mean.binaryproto'               # 待轉換的binaryproto圖像均值文件路徑
MEAN_NPY_PATH = 'mean.npy'                         # 轉換後的npy圖像均值文件路徑

blob = caffe.proto.caffe_pb2.BlobProto()           # 創建protobuf blob
data = open(MEAN_PROTO_PATH, 'rb' ).read()         # 讀入mean.binaryproto文件內容
blob.ParseFromString(data)                         # 解析文件內容到blob

array = np.array(caffe.io.blobproto_to_array(blob))# 將blob中的均值轉換成numpy格式
mean_npy = array[0]                                # 一個array中可以有多組均值存在，故需要通過下標選擇其中一組均值
np.save(MEAN_NPY_PATH ,mean_npy)

step3：核心程序：

# encoding: utf-8
#!/usr/bin/python

import numpy as np
import matplotlib.pyplot as plt
import sys
caffe_root = '../python/'  
sys.path.insert(0, caffe_root + 'pycaffe')   #指定到caffe所在的路徑下
import caffe

import os
if os.path.isfile(caffe_root + 'bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):
    print 'It is OK !'
else:
    print 'Please download this model'

caffe.set_mode_cpu()
model_def = caffe_root + 'bvlc_reference_caffenet/deploy.prototxt'
model_weights = caffe_root + 'bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
net = caffe.Net(model_def,      # 定義模型結構
                model_weights,  # 包含了模型的訓練權值
                caffe.TEST)     # 使用測試模式

#加載均值文件（注意是npy格式的）
mu = np.load(caffe_root + 'caffe/imagenet/ilsvrc_2012_mean.npy')
mu = mu.mean(1).mean(1)  
print 'mean-subtracted values:', zip('BGR', mu)

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))  
transformer.set_mean('data', mu)            
transformer.set_raw_scale('data', 255)      
transformer.set_channel_swap('data', (2,1,0))  

#設置輸入圖像大小
net.blobs['data'].reshape(50,         # batch 大小
                           3,         # 3-channel (BGR) images
                         227, 227)    # 圖像大小爲:227x227

image = caffe.io.load_image(caffe_root + '../examples/images/cat.jpg')
transformed_image = transformer.preprocess('data', image)
plt.imshow(image)
plt.show()

# 將圖像數據放入到net中
net.blobs['data'].data[...] = transformed_image

#進行分類測試
output = net.forward()
output_prob = output['prob'][0]  
print 'predicted class is:', output_prob.argmax()

#映射到標籤中
labels_file = caffe_root + '../python/synset_words.txt'
if not os.path.exists(labels_file):
    print'This file is not be found'
labels = np.loadtxt(labels_file, str, delimiter='\t')
print 'output label:', labels[output_prob.argmax()]