def test_get_lr_parameter_with_group():
net = LeNet5()
conv_lr = 0.1
default_lr = 0.3
conv_params = list(
filter(lambda x: 'conv' in x.name, net.trainable_params()))
no_conv_params = list(
filter(lambda x: 'conv' not in x.name, net.trainable_params()))
group_params = [{
'params': conv_params,
'lr': conv_lr
}, {
'params': no_conv_params,
'lr': default_lr
}]
opt = SGD(group_params)
assert opt.is_group_lr is True
for param in opt.parameters:
lr = opt.get_lr_parameter(param)
if 'conv' in param.name:
cur_name = 'learning_rate_group_' + '0'
else:
cur_name = 'learning_rate_group_' + '1'
assert lr.name == cur_name
lr_list = opt.get_lr_parameter(conv_params)
for lr, param in zip(lr_list, conv_params):
assert lr.name == 'learning_rate_group_' + '0'
def test_get_lr_parameter_with_no_group():
net = LeNet5()
conv_weight_decay = 0.8
conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
group_params = [{'params': conv_params, 'weight_decay': conv_weight_decay},
{'params': no_conv_params}]
opt = SGD(group_params)
assert opt.is_group_lr is False
for param in opt.parameters:
lr = opt.get_lr_parameter(param)
assert lr.name == opt.learning_rate.name
params_error = [1, 2, 3]
with pytest.raises(TypeError):
opt.get_lr_parameter(params_error)
def test_get_lr_parameter_with_order_group():
net = LeNet5()
conv_lr = 0.1
conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
group_params = [{'params': conv_params, 'lr': conv_lr},
{'order_params': net.trainable_params()}]
opt = SGD(group_params)
assert opt.is_group_lr is True
for param in opt.parameters:
lr = opt.get_lr_parameter(param)
assert lr.name == 'lr_' + param.name
def test_order_params_2():
net = LeNet5()
conv_weight_decay = 0.01
fc1_lr = (0.5, 0.4, 0.3)
default_lr = 0.1
default_wd = 0.0
conv_params = list(
filter(lambda x: 'conv' in x.name, net.trainable_params()))
fc1_params = list(filter(lambda x: 'fc1' in x.name,
net.trainable_params()))
group_params = [{
'params': fc1_params,
'lr': fc1_lr
}, {
'params': conv_params,
'weight_decay': conv_weight_decay
}, {
'order_params': fc1_params + conv_params
}]
opt = SGD(group_params, learning_rate=default_lr, weight_decay=default_wd)
assert opt.is_group is True
assert opt.is_group_lr is True
assert opt.is_group_params_ordered is True
all_lr = opt.get_lr_parameter(fc1_params + conv_params)
for weight_decay, decay_flags, lr, param, order_param in zip(
opt.weight_decay, opt.decay_flags, all_lr, opt.parameters,
fc1_params + conv_params):
if 'conv' in param.name:
assert np.all(lr.data.asnumpy() == Tensor(
np.array([default_lr] * 3), mstype.float32).asnumpy())
assert weight_decay == conv_weight_decay
assert decay_flags is True
elif 'fc1' in param.name:
assert np.all(
lr.data.asnumpy() == Tensor(fc1_lr, mstype.float32).asnumpy())
assert weight_decay == default_wd
assert decay_flags is False
else:
assert np.all(lr.data.asnumpy() == Tensor(
np.array([default_lr] * 3), mstype.float32).asnumpy())
assert weight_decay == default_wd
assert decay_flags is False
assert param.name == order_param.name
if 'conv' in param.name:
assert lr.name == 'learning_rate'
elif 'fc1' in param.name:
assert lr.name == 'learning_rate_group_' + '0'
