def test_optimizer_feed(self, resnet18):
"""Feed grouped parameters to optimizer, see what happens"""
lrs = [0.01, 0.001]
momentums = [0.02, 0.002]
weight_decays = [0.03, 0.003]
param_groups = helper.group_parameters(
resnet18, [['conv1.*weight'], ['downsample.*.weight']], lrs,
momentums, weight_decays)
optimizer = optim.SGD(param_groups)
optimizer.step()
def test_raises(self):
with pytest.raises(TypeError,
match="must be list of list of patterns"):
helper.group_parameters(None, [['downsample.1.weight'], 'conv1'])
with pytest.raises(TypeError, match="must match"):
helper.group_parameters(None, [['downsample.1.weight'], ['conv1']],
lrs=[0.1])
with pytest.raises(TypeError, match="must match"):
helper.group_parameters(None, [['downsample.1.weight'], ['conv1']],
momentums=[0.1])
with pytest.raises(TypeError, match="must match"):
helper.group_parameters(None, [['downsample.1.weight'], ['conv1']],
weight_decays=[0.1])
def test_lr_momentum_decay(self, resnet18):
lrs = [0.01, 0.001]
momentums = [0.02, 0.002]
weight_decays = [0.03, 0.003]
param_groups = helper.group_parameters(
resnet18, [['conv1.*weight'], ['downsample.*.weight']], lrs,
momentums, weight_decays)
assert param_groups[0]['lr'] == lrs[0]
assert param_groups[1]['lr'] == lrs[1]
assert param_groups[0]['momentum'] == momentums[0]
assert param_groups[1]['momentum'] == momentums[1]
assert param_groups[0]['weight_decay'] == weight_decays[0]
assert param_groups[1]['weight_decay'] == weight_decays[1]
def test_single_key(self, resnet18):
param_groups = helper.group_parameters(resnet18,
[['downsample.1.weight']])
assert len(list(param_groups[0]['params'])) == 3