def test_solver_debug(self):
solver = bct.CaffeSolver(debug=True)
self.assertEqual(solver.sp['test_iter'], '1')
solver = bct.CaffeSolver(debug=True, onlytrain=True)
self.assertFalse('test_iter' in solver.sp.keys())
def test_solver_copy(self):
solver = bct.CaffeSolver()
solver.write(osp.join(self.workdir, 'solver.prototxt'))
solver2 = bct.CaffeSolver(empty=True)
solver2.add_from_file(osp.join(self.workdir, 'solver.prototxt'))
for k1, k2 in zip(sorted(solver.sp.keys()), sorted(solver2.sp.keys())):
self.assertEqual(k1, k2)
self.assertEqual(solver.sp[k1], solver2.sp[k2])
# try again with the onlytrain mode
solver = bct.CaffeSolver(onlytrain=True)
solver.write(osp.join(self.workdir, 'solver.prototxt'))
solver2 = bct.CaffeSolver(empty=True)
solver2.add_from_file(osp.join(self.workdir, 'solver.prototxt'))
for k1, k2 in zip(sorted(solver.sp.keys()), sorted(solver2.sp.keys())):
self.assertEqual(k1, k2)
self.assertEqual(solver.sp[k1], solver2.sp[k2])
def test_solver_onlytrain(self):
solver = bct.CaffeSolver(onlytrain=True)
for k in solver.sp.keys():
self.assertEqual(k.find('test'), -1)
def test_solver_empty(self):
solver = bct.CaffeSolver(empty=True)
self.assertEqual(len(solver.sp.keys()), 0)
def test_workdir_setup(self):
solver = bct.CaffeSolver(debug=True)
solver.write(osp.join(self.workdir, 'solver.prototxt'))
n = caffe.NetSpec()
n.data, n.label = L.ImageData(transform_param=dict(crop_size=224,
mean_value=128),
source='../static/imlist.txt',
batch_size=50,
ntop=2)
net = vgg_core(n, learn=True)
net.score = L.InnerProduct(net.fc7,
num_output=2,
param=[
dict(lr_mult=5, decay_mult=1),
dict(lr_mult=10, decay_mult=0)
])
net.loss = L.SoftmaxWithLoss(net.score, n.label)
with open(osp.join(self.workdir, 'trainnet.prototxt'), 'w') as w:
w.write(str(net.to_proto()))
with open(osp.join(self.workdir, 'testnet.prototxt'), 'w') as w:
w.write(str(net.to_proto()))
caffefile = '/runs/templates/VGG_ILSVRC_16_layers_initial.caffemodel'
if osp.isfile(caffefile):
shutil.copyfile(caffefile,
osp.join(self.workdir, 'initial.caffemodel'))
bct.run(self.workdir, nbr_iters=3)
self.assertTrue(osp.isfile(osp.join(self.workdir, 'train.log')))
self.assertTrue(
osp.isfile(osp.join(self.workdir, 'snapshot_iter_3.caffemodel')))
caffemodel, iter_ = bct.find_latest_caffemodel(self.workdir)
self.assertEqual(iter_, 3)
net = bct.load_model(self.workdir,
caffemodel,
gpuid=0,
net_prototxt='testnet.prototxt',
phase=caffe.TEST)
estlist, scorelist = bct.classify_from_datalayer(net,
n_testinstances=3,
batch_size=50,
scorelayer='score')
self.assertEqual(len(scorelist), 3)
self.assertEqual(len(estlist), 3)
self.assertEqual(len(scorelist[0]), 2)
img = np.asarray(Image.open('../static/bbc.jpg'))[:224, :224, :]
imglist = []
for itt in range(6):
imglist.append(img)
estlist, scorelist = bct.classify_from_imlist(imglist, net,
bct.Transformer(), 4)
self.assertEqual(len(scorelist), 6)
self.assertEqual(len(estlist), 6)
self.assertEqual(len(scorelist[0]), 2)