def test_lr_injection(self):
model, perfect_model, data, label = self._createDense()
opt = self.build_optimizer(model,
max_gradient_norm=1,
allow_lr_injection=True)
workspace.FeedBlob('data', data[0])
workspace.FeedBlob('label', label[0])
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net, True)
# Test LR injection initialized properly
self.assertIsNotNone(opt._lr_multiplier)
self.assertEqual(optimizer.get_lr_injection(), 1)
# Test that we're able to modify the value of the lr_injection
optimizer.set_lr_injection(0)
self.assertEqual(optimizer.get_lr_injection(), 0)
# Test that setting the lr_injector properly propogates to the
# lr_multiplier. Here, we have both lr_injector and norm_ratio that
# affect the lr_multiplier
workspace.RunNet(model.net.Proto().name)
self.assertEqual(workspace.FetchBlob('lr_multiplier'), 0)
def test_lr_injection(self):
model, perfect_model, data, label = self._createDense()
opt = self.build_optimizer(
model, max_gradient_norm=1, allow_lr_injection=True
)
workspace.FeedBlob('data', data[0])
workspace.FeedBlob('label', label[0])
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net, True)
# Test LR injection initialized properly
self.assertIsNotNone(opt._lr_multiplier)
self.assertEqual(optimizer.get_lr_injection(), 1)
# Test that we're able to modify the value of the lr_injection
optimizer.set_lr_injection(0)
self.assertEqual(optimizer.get_lr_injection(), 0)
# Test that setting the lr_injector properly propogates to the
# lr_multiplier. Here, we have both lr_injector and norm_ratio that
# affect the lr_multiplier
workspace.RunNet(model.net.Proto().name)
self.assertEqual(workspace.FetchBlob('lr_multiplier'), 0)