def _build_optimizer(optim_method, optim_param):
if optim_method == 'AdaGrad':
optim = optimizer.AdagradOptimizer(**optim_param)
elif optim_method == 'SgdOptimizer':
optim = optimizer.SgdOptimizer(**optim_param)
elif optim_method == 'Adam':
optim = optimizer.AdamOptimizer(**optim_param)
else:
raise Exception('Did you foget to implement {}?'.format(optim_method))
return optim
def test_multiple_optimizers(self):
from caffe2.python import brew, core, optimizer
from caffe2.python.model_helper import ModelHelper
model = ModelHelper(name="test")
fc1 = brew.fc(model, 'data', 'fc1', 100, 50)
fc2 = brew.fc(model, fc1, 'fc2', 50, 25)
pred = brew.fc(model, fc2, 'fc3', 25, 10)
(softmax, loss) = model.SoftmaxWithLoss(
[pred, 'label'],
['softmax', 'loss'],
)
model.AddGradientOperators([loss])
param_to_device = optimizer._get_param_to_device(model)
def infer_blob_device(blob_name):
return optimizer.get_param_device(blob_name,
"{}_grad".format(blob_name),
param_to_device)
sgd_1 = optimizer.SgdOptimizer(base_learning_rate=0.1)
sgd_2 = optimizer.SgdOptimizer(base_learning_rate=0.2)
adagrad = optimizer.AdagradOptimizer()
# Check same optimizer share the same learning rate.
with core.DeviceScope(infer_blob_device("fc1_w")):
sgd_1(model.net, model.param_init_net, "fc1_w", "fc1_w_grad")
with core.DeviceScope(infer_blob_device("fc1_b")):
sgd_1(model.net, model.param_init_net, "fc1_b", "fc1_b_grad")
fc1_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'WeightedSum' and op.input[0] == 'fc1_w' or \
op.input[0] == 'fc1_b':
fc1_lr_blobs.append(op.input[3])
self.assertEqual(fc1_lr_blobs[0], fc1_lr_blobs[1])
# Check different instance of the same optimizer has a different lr.
with core.DeviceScope(infer_blob_device("fc2_w")):
sgd_2(model.net, model.param_init_net, "fc2_w", "fc2_w_grad")
with core.DeviceScope(infer_blob_device("fc2_b")):
sgd_2(model.net, model.param_init_net, "fc2_b", "fc2_b_grad")
fc2_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'WeightedSum' and op.input[0] == 'fc2_w' or \
op.input[0] == 'fc2_b':
self.assertTrue(op.input[3] not in fc1_lr_blobs)
fc2_lr_blobs.append(op.input[3])
self.assertEqual(fc2_lr_blobs[0], fc2_lr_blobs[1])
# Check different optimizer type case
with core.DeviceScope(infer_blob_device("fc3_w")):
adagrad(model.net, model.param_init_net, "fc3_w", "fc3_w_grad")
with core.DeviceScope(infer_blob_device("fc3_b")):
adagrad(model.net, model.param_init_net, "fc3_b", "fc3_b_grad")
fc3_lr_blobs = []
for op in model.net.Proto().op:
if op.type == 'Adagrad' and op.input[0] == 'fc3_w' or \
op.input[0] == 'fc3_b':
self.assertTrue(op.input[3] not in fc2_lr_blobs)
self.assertTrue(op.input[3] not in fc1_lr_blobs)
fc3_lr_blobs.append(op.input[3])
self.assertEqual(fc3_lr_blobs[0], fc3_lr_blobs[1])
