Comment prédire en pycaffe?

Question

J'ai un model qui a été formé sur CIFAR-10, mais je ne me rends pas compte comment je peux faire une prédiction dans la pycaffe.

J'ai reçu une image de lmdb mais je ne sais pas comment la charger dans un réseau et obtenir une classe prédite.

Mon code:

net = caffe.Net('acc81/model.prototxt', 
       'acc81/cifar10_full_iter_70000.caffemodel.h5', 
       caffe.TEST) 
lmdb_env = lmdb.open('cifar10_test_lmdb/') 
lmdb_txn = lmdb_env.begin() 
lmdb_cursor = lmdb_txn.cursor() 
for key, value in lmdb_cursor: 
    datum = caffe.proto.caffe_pb2.Datum() 
    datum.ParseFromString(value) 
    image = caffe.io.datum_to_array(datum) 
    image = image.astype(np.uint8) 

    # What's next with the image variable? 
    # If i try: 
    # out = net.forward_all(data=np.asarray([image])) 
    # I get Exception: Input blob arguments do not match net inputs. 

    print("Image class is " + label) 

Answer 1

Utilisez ce script python

# Run the script with anaconda-python 
# $ /home/<path to anaconda directory>/anaconda/bin/python LmdbClassification.py 
import sys 
import numpy as np 
import lmdb 
import caffe 
from collections import defaultdict 
caffe.set_mode_gpu() 

# Modify the paths given below 
deploy_prototxt_file_path = '/home/<username>/caffe/examples/cifar10/cifar10_deploy.prototxt' # Network definition file 
caffe_model_file_path = '/home/<username>/caffe/examples/cifar10/cifar10_iter_5000.caffemodel' # Trained Caffe model file 
test_lmdb_path = '/home/<username>/caffe/examples/cifar10/cifar10_test_lmdb/' # Test LMDB database path 
mean_file_binaryproto = '/home/<username>/caffe/examples/cifar10/mean.binaryproto' # Mean image file 

# Extract mean from the mean image file 
mean_blobproto_new = caffe.proto.caffe_pb2.BlobProto() 
f = open(mean_file_binaryproto, 'rb') 
mean_blobproto_new.ParseFromString(f.read()) 
mean_image = caffe.io.blobproto_to_array(mean_blobproto_new) 
f.close() 

# CNN reconstruction and loading the trained weights 
net = caffe.Net(deploy_prototxt_file_path, caffe_model_file_path, caffe.TEST) 

count = 0 
correct = 0 
matrix = defaultdict(int) # (real,pred) -> int 
labels_set = set() 

lmdb_env = lmdb.open(test_lmdb_path) 
lmdb_txn = lmdb_env.begin() 
lmdb_cursor = lmdb_txn.cursor() 

for key, value in lmdb_cursor: 
    datum = caffe.proto.caffe_pb2.Datum() 
     datum.ParseFromString(value) 
     label = int(datum.label) 
     image = caffe.io.datum_to_array(datum) 
     image = image.astype(np.uint8) 
    out = net.forward_all(data=np.asarray([image]) - mean_image) 
    plabel = int(out['prob'][0].argmax(axis=0)) 
    count += 1 
    iscorrect = label == plabel 
    correct += (1 if iscorrect else 0) 
    matrix[(label, plabel)] += 1 
    labels_set.update([label, plabel]) 

    if not iscorrect: 
      print("\rError: key = %s, expected %i but predicted %i" % (key, label, plabel)) 
     sys.stdout.write("\rAccuracy: %.1f%%" % (100.*correct/count)) 
     sys.stdout.flush() 

print("\n" + str(correct) + " out of " + str(count) + " were classified correctly") 
print "" 
print "Confusion matrix:" 
print "(r , p) | count" 
for l in labels_set: 
    for pl in labels_set: 
     print "(%i , %i) | %i" % (l, pl, matrix[(l,pl)])