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layer_parameter_sharing_test.py
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layer_parameter_sharing_test.py
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from caffe2.python import core, scope
from caffe2.python.modeling.parameter_sharing import (
ParameterSharing,
)
from caffe2.python.optimizer import AdagradOptimizer, AdamOptimizer
from caffe2.python.layer_test_util import LayersTestCase
class ParameterSharingTest(LayersTestCase):
def test_layer_parameter_name(self):
output_dims = 2
with scope.NameScope('global_scope'):
fc1_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w, 'global_scope/fc/w')
self.assertEqual(fc1_output(), 'global_scope/fc/output')
with scope.NameScope('nested_scope'):
fc2_output = self.model.FC(
fc1_output,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/nested_scope/fc/w')
self.assertEqual(fc2_output(),
'global_scope/nested_scope/fc/output')
fc3_output = self.model.FC(
fc1_output,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/nested_scope/fc_auto_0/w')
self.assertEqual(fc3_output(),
'global_scope/nested_scope/fc_auto_0/output')
def test_layer_shared_parameter_name_different_namescopes(self):
output_dims = 2
with scope.NameScope('global_scope'):
with ParameterSharing({'scope_1': 'scope_0'}):
with scope.NameScope('scope_0'):
fc1_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/scope_0/fc/w')
self.assertEqual(fc1_output(),
'global_scope/scope_0/fc/output')
with scope.NameScope('scope_1'):
fc2_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/scope_0/fc/w')
self.assertEqual(fc2_output(),
'global_scope/scope_1/fc/output')
def test_layer_shared_parameter_name_within_same_namescope(self):
output_dims = 2
with scope.NameScope('global_scope'):
with ParameterSharing({'fc_auto_0': 'fc'}):
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/fc/w')
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/fc/w')
def test_layer_shared_parameter_name_within_same_namescope_customized_name(self):
output_dims = 2
with scope.NameScope('global_scope'):
with ParameterSharing({'new_fc': 'shared_fc'}):
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
name='shared_fc'
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/shared_fc/w')
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
name='new_fc'
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/shared_fc/w')
def test_layer_shared_parameter_name_different_shapes(self):
output_dims = 2
with scope.NameScope('global_scope'):
with ParameterSharing({'fc_auto_0': 'fc'}):
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
self.assertEqual(self.model.layers[-1].w,
'global_scope/fc/w')
with self.assertRaisesRegex(ValueError, 'Got inconsistent shapes .*'):
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims + 1
)
def test_layer_duplicated_parameter_init(self):
output_dims = 2
with scope.NameScope('global_scope'):
with ParameterSharing({'new_fc': 'shared_fc'}):
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
name='shared_fc'
)
self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
name='new_fc'
)
train_init_net = core.Net('train_init_net')
train_net = core.Net('train_net')
for layer in self.model.layers:
layer.add_operators(train_net, train_init_net)
op_outputs = []
for op in train_init_net._net.op:
op_outputs.extend(op.output)
# only fill these parameter blobs once
self.assertEqual(
sorted(op_outputs),
['global_scope/shared_fc/b', 'global_scope/shared_fc/w']
)
def test_layer_shared_parameter_optim_validator(self):
"""
This test is to cover the _validate_param_optim function in
layer_model_helper class.
"""
output_dims = 2
adagrad_optim = AdagradOptimizer(
alpha=0.004,
epsilon=0.02,
)
self.model.default_optimizer = adagrad_optim
# the following covers the branch -- optim is None
with scope.NameScope('global_scope_0'):
with ParameterSharing({'scope_1': 'scope_0'}):
with scope.NameScope('scope_0'):
fc1_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=self.model.NoOptim,
)
with scope.NameScope('scope_1'), self.assertRaises(Exception):
fc2_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims
)
# the following covers the branch -- optim is NoOptim
with scope.NameScope('global_scope_1'):
with ParameterSharing({'scope_1': 'scope_0'}):
with scope.NameScope('scope_0'):
fc1_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=None,
)
with scope.NameScope('scope_1'), self.assertRaises(Exception):
fc2_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=self.model.NoOptim,
)
# the following covers the branch -- optim is an instance of Optimizer
adagrad_optim_2 = AdagradOptimizer(
alpha=0.005,
epsilon=0.02,
)
adam_optim = AdamOptimizer()
self.model.default_optimizer = adagrad_optim_2
with scope.NameScope('global_scope_2'):
with ParameterSharing({'scope_1': 'scope_0', 'scope_2': 'scope_0'}):
with scope.NameScope('scope_0'):
fc1_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=None, # it will use adagrad_optim_2
)
with scope.NameScope('scope_1'), self.assertRaises(Exception):
fc2_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=adagrad_optim,
)
with scope.NameScope('scope_2'), self.assertRaises(Exception):
fc2_output = self.model.FC(
self.model.input_feature_schema.float_features,
output_dims,
weight_optim=adam_optim,
)