def test_find_latest_caffemodel(self):
for iter_ in [1, 25, 121]:
os.system('touch ./workdir/snapshot_iter_{}.caffemodel'.format(iter_))
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[0], 'snapshot_iter_121.caffemodel')
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[1], 121)
self.assertEqual(bct.find_latest_caffemodel(self.workdir, prefix = 'not-snapshot')[0], None)
self.assertEqual(bct.find_latest_caffemodel(self.workdir, suffix = 'not-caffemodel')[0], None)
self.assertEqual(bct.find_latest_caffemodel(self.workdir, suffix = 'not-caffemodel')[1], 0)
def test_find_latest_caffemodel(self):
for iter_ in [1, 25, 121]:
os.system(
'touch ./workdir/snapshot_iter_{}.caffemodel'.format(iter_))
self.assertEqual(
bct.find_latest_caffemodel(self.workdir)[0],
'snapshot_iter_121.caffemodel')
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[1], 121)
self.assertEqual(
bct.find_latest_caffemodel(self.workdir, prefix='not-snapshot')[0],
None)
self.assertEqual(
bct.find_latest_caffemodel(self.workdir,
suffix='not-caffemodel')[0], None)
self.assertEqual(
bct.find_latest_caffemodel(self.workdir,
suffix='not-caffemodel')[1], 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)
def test_find_latest_caffemodel_edge(self):
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[0], None)
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[1], 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)
def test_find_latest_caffemodel_edge(self):
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[0], None)
self.assertEqual(bct.find_latest_caffemodel(self.workdir)[1], 0)
