mxnet如何查看中间结果

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mxnet如何查看权重，中间输出结果

参考：https://blog.csdn.net/u010414386/article/details/55668880

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import mxnet as mx
import cv2

ctx = mx.cpu()

sym, arg_params, aux_params = mx.model.load_checkpoint('model/light_classify', 299)
args = sym.get_internals().list_outputs() #获得所有中间输出
print("args:", args)
internals = sym.get_internals()
conv1 = internals['conv_1_conv2d_output']
conv1_bn = internals['conv_1_batchnorm_output']
group = mx.symbol.Group([sym, conv1, conv1_bn])

mod = mx.mod.Module(symbol=group, context=ctx, label_names=None)
mod.bind(for_training=False, data_shapes=[('data', (1,3,16,16))],
         label_shapes=mod._label_shapes)
mod.set_params(arg_params, aux_params, allow_missing=True)
with open('synset.txt', 'r') as f:
    labels = [l.rstrip() for l in f]

import matplotlib.pyplot as plt
import numpy as np
# define a simple data batch
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])

def get_image(url, show=False):
    # download and show the image. Remove query string from the file name.
    # fname = mx.test_utils.download(url, fname=url.split('/')[-1].split('?')[0])
    img = mx.image.imread(url)
    if img is None:
        return None
    if show:
        plt.imshow(img.asnumpy())
        plt.axis('off')
    # convert into format (batch, RGB, width, height)
    img = mx.image.imresize(img, 16, 16) # resize
    img = img.transpose((2, 0, 1)) # Channel first
    # print("+++++++++++", img.asnumpy().__repr__())
    img = img.expand_dims(axis=0) # batchify
    img = img.astype('float32') # for gpu context
    return img

def predict():
    # path = 'pic/B1000_1.jpg'
    # path = 'pic/black/190.121.11.119_20180101_080207_00124_1.jpg' # black
    path = 'pic/green/aa2600000005_1.jpg' # green
    # path = 'pic/yellow/wuhe_zhangheng_S_34_9-00_9-30-033_0.jpg'
    # path = "pic/red/aa1700000017_0.jpg"
    path = '/data0/hhq/data/light/test/yellow/190.201.11.101_20180101_075246_00231_1_0.jpg'

    img = get_image(path, show=False)
    # compute the predict probabilities
    mod.forward(Batch([img]))
    prob = mod.get_outputs()[0].asnumpy()
    conv1_out = mod.get_outputs()[1]
    conv1_bn_out = mod.get_outputs()[2]
    # print the top-5
    prob = np.squeeze(prob)
    a = np.argsort(prob)[::-1]
    for i in a[0:5]:
        print('probability=%f, class=%s' %(prob[i], labels[i]))

predict()

caffe查看中间结果

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caffe_root='/data0/hhq/project/caffe-master/'
import sys
sys.path.insert(0, caffe_root+'python')
import caffe
import numpy as np
import struct
import matplotlib.pyplot as plt
import cv2

# caffe.set_mode_gpu()
# caffe.set_device(1)

deploy='model_caffe/light_classify.prototxt'
caffe_model= 'model_caffe/light_classify.caffemodel'

# image_path ='pic/red/aa1700000017_0.jpg' # red

# image_path = 'pic/yellow/wuhe_zhangheng_S_34_9-00_9-30-033_0.jpg' # yellow

# image_path ='pic/black/190.121.11.119_20180101_080207_00134_0.jpg' # black

image_path = 'pic/green/aa2600000006_0.jpg'

net = caffe.Net(deploy, caffe_model, caffe.TEST)

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2, 0, 1))
transformer.set_raw_scale('data', 225)
# transformer.set_channel_swap('data', (2, 1, 0))  # should not exchange
net.blobs['data'].reshape(1, 3, 16, 16)

im = caffe.io.load_image(image_path)

image_pro = transformer.preprocess('data', im)

net.blobs['data'].data[...] = image_pro

out = net.forward()

fc = net.blobs['fc'].data[0].flatten()
conv1 = net.blobs['conv_1_conv2d'].data[0]
conv1_bn = net.blobs['conv_1_batchnorm'].data[0]
prob = net.blobs['fc'].data[0].flatten()
print(fc)
print(prob)
order = prob.argsort()[3]

print('the class is:', order)