Caffe2 - (四) 基于 squeezenet 分类的模型测试

Caffe2 模型加载与测试

Model Zoo

这里以 squeezenet 模型为例，对图片中的 object 分类.

下载训练好的模型：

python -m caffe2.python.models.download -i squeezenet

模型加载：

读取 protobuf 文件：

with open("init_net.pb") as f:
init_net = f.read()
with open("predict_net.pb") as f:
predict_net = f.read()

采用 workspace.Predictor 函数从 protobufs 加载 blobs：

p = workspace.Predictor(init_net, predict_net)

运行 net 并得到结果：

results = p.run([img])

results 是多维数组的形式，存储概率值.

每一行是识别 object 属于某一类的概率.

完整代码

# -------------------------------
# Configuration
# -------------------------------

CAFFE2_ROOT = "~/caffe2"
CAFFE_MODELS = "~/caffe2/caffe2/python/models" # 均值文件保存到与 model 同一路径

from caffe2.proto import caffe2_pb2
import numpy as np
import skimage.io
import skimage.transform
from matplotlib import pyplot
import os
from caffe2.python import core, workspace
import urllib2
print("Required modules imported.")

IMAGE_LOCATION =  "https://cdn.pixabay.com/photo/2015/02/10/21/28/flower-631765_1280.jpg"
MODEL = 'squeezenet', 'init_net.pb', 'predict_net.pb', 'ilsvrc_2012_mean.npy', 227

# codes - these help decypher the output and source from a list from AlexNet's object codes to provide an result like "tabby cat" or "lemon" depending on what's in the picture you submit to the neural network.
# The list of output codes for the AlexNet models (also squeezenet)
codes =  "https://gist.githubusercontent.com/aaronmarkham/cd3a6b6ac071eca6f7b4a6e40e6038aa/raw/9edb4038a37da6b5a44c3b5bc52e448ff09bfe5b/alexnet_codes"
print "Config set!"

# -------------------------------
# Pre-processing image
# -------------------------------
def crop_center(img,cropx,cropy):
y,x,c = img.shape
startx = x//2-(cropx//2)
starty = y//2-(cropy//2)
return img[starty:starty+cropy,startx:startx+cropx]

def rescale(img, input_height, input_width):
print("Original image shape:" + str(img.shape) + " and remember it should be in H, W, C!")
print("Model's input shape is %dx%d") % (input_height, input_width)
aspect = img.shape[1]/float(img.shape[0])
print("Orginal aspect ratio: " + str(aspect))
if(aspect>1):
# landscape orientation - wide image
res = int(aspect * input_height)
imgScaled = skimage.transform.resize(img, (input_width, res))
if(aspect<1):
# portrait orientation - tall image
res = int(input_width/aspect)
imgScaled = skimage.transform.resize(img, (res, input_height))
if(aspect == 1):
imgScaled = skimage.transform.resize(img, (input_width, input_height))
pyplot.figure()
pyplot.imshow(imgScaled)
pyplot.axis('on')
pyplot.title('Rescaled image')
print("New image shape:" + str(imgScaled.shape) + " in HWC")
return imgScaled
print "Functions set."

# set paths and variables from model choice and prep image
CAFFE2_ROOT = os.path.expanduser(CAFFE2_ROOT)
CAFFE_MODELS = os.path.expanduser(CAFFE_MODELS)

# mean can be 128 or custom based on the model
# gives better results to remove the colors found in all of the training images
MEAN_FILE = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[3])
if not os.path.exists(MEAN_FILE):
mean = 128
else:
mean = np.load(MEAN_FILE).mean(1).mean(1)
mean = mean[:, np.newaxis, np.newaxis]
print "mean was set to: ", mean

INPUT_IMAGE_SIZE = MODEL[4]

# make sure all of the files are around...
if not os.path.exists(CAFFE2_ROOT):
print("Houston, you may have a problem.")
INIT_NET = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[1])
print 'INIT_NET = ', INIT_NET
PREDICT_NET = os.path.join(CAFFE_MODELS, MODEL[0], MODEL[2])
print 'PREDICT_NET = ', PREDICT_NET
if not os.path.exists(INIT_NET):
print(INIT_NET + " not found!")
else:
print "Found ", INIT_NET, "...Now looking for", PREDICT_NET
if not os.path.exists(PREDICT_NET):
print "Caffe model file, " + PREDICT_NET + " was not found!"
else:
print "All needed files found! Loading the model in the next block."

# 图片读取与转换
img = skimage.img_as_float(skimage.io.imread(IMAGE_LOCATION)).astype(np.float32)
img = rescale(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
img = crop_center(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
print "After crop: " , img.shape
pyplot.figure()
pyplot.imshow(img)
pyplot.axis('on')
pyplot.title('Cropped')

# switch to CHW
img = img.swapaxes(1, 2).swapaxes(0, 1)
pyplot.figure()
for i in range(3):
pyplot.subplot(1, 3, i+1)
pyplot.imshow(img[i])
pyplot.axis('off')
pyplot.title('RGB channel %d' % (i+1))

# switch to BGR
img = img[(2, 1, 0), :, :]

# remove mean for better results
# 减均值
img = img * 255 - mean

# add batch size
img = img[np.newaxis, :, :, :].astype(np.float32)
print "NCHW: ", img.shape

# -------------------------------
# 网络初始化
# -------------------------------
with open(INIT_NET) as f:
init_net = f.read()
with open(PREDICT_NET) as f:
predict_net = f.read()

p = workspace.Predictor(init_net, predict_net)

# -------------------------------
# 网络预测
# -------------------------------
# run the net and return prediction
results = p.run([img]) #

# turn it into something we can play with and examine which is in a multi-dimensional array
results = np.asarray(results)
print "results shape: ", results.shape
# results shape:  (1, 1, 1000, 1, 1)

# -------------------------------
# 得到最终结果
# -------------------------------
results = np.delete(results, 1)
index = 0
highest = 0
arr = np.empty((0,2), dtype=object)
arr[:,0] = int(10)
arr[:,1:] = float(10)
for i, r in enumerate(results):
# imagenet index begins with 1!
i=i+1
arr = np.append(arr, np.array([[i,r]]), axis=0)
if (r > highest):
highest = r
index = i

print index, " :: ", highest

# lookup the code and return the result
# top 3 results
# sorted(arr, key=lambda x: x[1], reverse=True)[:3]

# now we can grab the code list
response = urllib2.urlopen(codes)

# and lookup our result from the list
for line in response:
code, result = line.partition(":")[::2]
if (code.strip() == str(index)):
print result.strip()[1:-2]

# output
# 985  ::  0.979059
# daisy

Reference

[1] - Loading Pre-Trained Models