def cycle_runs(workdir, cycle_size = 1000, ncycles = 10, gpuid = 0, batch_size_test = 50, n_testinstances = 50, testnet_prototxt = 'testnet.prototxt', snapshot_prefix = 'snapshot', scorelayer = 'score'):
"""
cycle_runs is a wrapper around run and classify_from_datalayer. After training the net for cycle_sizes iterations, it will run through the TEST net and store the results to disk.
"""
for cycle in range(ncycles):
run(workdir = workdir, nbr_iters = cycle_size, gpuid = gpuid)
caffemodel = find_latest_caffemodel(workdir, snapshot_prefix = snapshot_prefix)
net = load_model(workdir, caffemodel, gpuid = gpuid, net_prototxt = testnet_prototxt, phase = caffe.TEST)
scorelist = classify_from_datalayer(net, n_testinstances = n_testinstances, batch_size = batch_size_test, scorelayer = scorelayer)
bmt.psave(scorelist, osp.join(workdir, 'predictions_using_' + caffemodel + '.p'))
del net
def write_split(basedir, workdir, region, split, labelset, scale_method,
scale_factor):
imlist, imdict = load_data(basedir, split, labelset, region)
im_mean = bct.calculate_image_mean(imlist[::10])
with open(osp.join(workdir, '{}list.txt'.format(split)), 'w') as fp:
for im in imlist:
fp.write(im + '\n')
with open(osp.join(workdir, '{}dict.json'.format(split)), 'w') as outfile:
json.dump(imdict, outfile)
pyparams = dict(batch_size=45,
imlistfile='{}list.txt'.format(split),
imdictfile='{}dict.json'.format(split),
imgs_per_batch=3,
crop_size=224,
scaling_method=scale_method,
scaling_factor=scale_factor,
rand_offset=1,
random_seed=100,
im_mean=list(im_mean),
dalevel='cheap')
psave(pyparams, osp.join(workdir, split + 'pyparams.pkl'))
#net = caffe.NetSpec()
#net['data'], net['label'] = L.Python(module = 'beijbom_caffe_data_layers', layer = 'RandomPointDataLayer', ntop=2, param_str=str(pyparams))
#net = vgg_core(net)
#net.score = L.InnerProduct(net.fc7, num_output=len(labelset), param=[dict(lr_mult=5, decay_mult=1), dict(lr_mult=10, decay_mult=0)])
#net.loss = L.SoftmaxWithLoss(net.score, net.label)
with open(osp.join(workdir, '{}net.prototxt'.format(split)), 'w') as w:
n = bct.vgg(pyparams, 'RandomPointDataLayer', len(labelset))
#w.write(str(net.to_proto()))
w.write(str(n.to_proto()))
def cycle_runs(
workdir,
cycle_size=1000,
ncycles=10,
gpuid=0,
batch_size_test=50,
n_testinstances=50,
testnet_prototxt="testnet.prototxt",
snapshot_prefix="snapshot",
scorelayer="score",
):
"""
cycle_runs is a wrapper around run and classify_from_datalayer. After training the net for cycle_sizes iterations, it will run through the TEST net and store the results to disk.
"""
for cycle in range(ncycles):
run(workdir=workdir, nbr_iters=cycle_size, gpuid=gpuid)
caffemodel = find_latest_caffemodel(workdir, snapshot_prefix=snapshot_prefix)
net = load_model(workdir, caffemodel, gpuid=gpuid, net_prototxt=testnet_prototxt, phase=caffe.TEST)
scorelist = classify_from_datalayer(
net, n_testinstances=n_testinstances, batch_size=batch_size_test, scorelayer=scorelayer
)
bmt.psave(scorelist, osp.join(workdir, "predictions_using_" + caffemodel + ".p"))
del net
def test_save_load(self):
a = {'field': 22, 'another_field': 'value'}
bmt.psave(a, osp.join(self.workdir, 'test.p'))
b = bmt.pload(osp.join(self.workdir, 'test.p'))
self.assertEqual(a, b)
def test_save_load(self):
a = {'field':22, 'another_field':'value'}
bmt.psave(a, osp.join(self.workdir, 'test.p'))
b = bmt.pload(osp.join(self.workdir, 'test.p'))
self.assertEqual(a, b)
