CODEWITHSUNDEEP
pythondeep-learningcaffe
Paco Muñoz Martinez
Paco Muñoz Martinez

Reputation: 79

Caffe execution

I am starting to use caffe for deep learning. I have the .caffemodel file with my trained weights and a particular neural network. I am using python interface.

I've seen that I can load my network and my weights by doing this:

solver=caffe.get_solver('prototxtfile.prototxt')
solver.net.copy_from('weights.caffemodel')

But I do not want to fine-tuned my application. I just want to use those weights. I want to execute the network and for each image from the Imagenet data set I want to obtain the result of the classification (not the accuracy of an entire batch). How can I do that?

Thank you very much.

Upvotes: 2

Views: 562

Answers (2)

rafaspadilha
rafaspadilha

Reputation: 629

This is the code I use when I need to forward an image through my network:

import caffe

caffe.set_mode_cpu()             #If you are using CPU
#caffe.set_mode_gpu()            #or if you are using GPU

model_def = "path/to/deploy.prototxt"         #architecture
model_weights = "path/to/weights.caffemodel"  #weights

net = caffe.Net(model_def,      # defines the structure of the model
                model_weights,
                caffe.TEST)     # use test mode (e.g., don't perform dropout)


#Let's forward a single image (let's say inputImg)
#'data' is the name of my input blob
net.blobs["data"].data[0] = inputImg
out = net.forward()

# to get the final softmax probability
# in my case, 'prob' is the name of our last blob
# a softmax layer that will output the score/probability for our problem
outputScore = net.blobs["prob"].data[0]   #[0] here because we forwarded a single image

In this example, the inputImg dimensions must match the dimensions of the images used during training, as well as all preprocessing done.

Upvotes: 2

Bastian
Bastian

Reputation: 1593

Try to understand the attached lines of python code and adjust them to your needs. It's not my code but I wrote a similar piece to test my models.
The source is: https://www.cc.gatech.edu/~zk15/deep_learning/classify_test.py

If you don't want to fine-tune a pre-trained model, it's obvious that you don't need a solver. The solver is what optimizes the model. If you want to predict the class probability for an image, you actually just have to do a forward pass. Keep in mind that your deploy.prototxt must have a proper last layer which uses either a softmax or sigmoid function (depending on the architecture). You can't use the loss function from the train_val.prototxt for this.

import numpy as np
import matplotlib.pyplot as plt

# Make sure that caffe is on the python path:
caffe_root = '../'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

# Set the right path to your model definition file, pretrained model weights,
# and the image you would like to classify.
MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'
PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
IMAGE_FILE = 'images/cat.jpg'

caffe.set_mode_cpu()
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),
                       channel_swap=(2,1,0),
                       raw_scale=255,
                       image_dims=(256, 256))
input_image = caffe.io.load_image(IMAGE_FILE)
plt.imshow(input_image)

prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically
print 'prediction shape:', prediction[0].shape
plt.plot(prediction[0])
print 'predicted class:', prediction[0].argmax()
plt.show()

Upvotes: 2

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