def test_add_layer_top(self):
# Drop output layer so that layers can be added to top
self.mh.drop_layer_top()
layer1 = layer_factory.FullyConnected(name='fc1', num_hidden=5)
assert 'fc1' not in list(self.mh.layer_type_dict.keys())
outputs_pre = self.mh.symbol.get_internals().list_outputs()
self.mh.add_layer_top([layer1])
outputs_post = self.mh.symbol.get_internals().list_outputs()
assert 'fc1' in list(self.mh.layer_type_dict.keys())
assert outputs_post == outputs_pre + [
'fc1_weight', 'fc1_bias', 'fc1_output'
]
def test_add_layer_bottom_list(self):
layer1 = layer_factory.Convolution(name='conv1_1',
kernel=(3, 3),
num_filter=10)
layer2 = layer_factory.FullyConnected(name='fc1', num_hidden=10)
for layer_name in ['fc1', 'conv1_1']:
assert layer_name not in list(self.mh.layer_type_dict.keys())
outputs_pre = self.mh.symbol.get_internals().list_outputs()
self.mh.add_layer_bottom([layer1, layer2])
outputs_post = self.mh.symbol.get_internals().list_outputs()
for layer_name in ['fc1', 'conv1_1']:
assert layer_name in list(self.mh.layer_type_dict.keys())
assert outputs_post == [
self.data_name, 'conv1_1_weight', 'conv1_1_bias', 'conv1_1_output',
'fc1_weight', 'fc1_bias', 'fc1_output'
] + outputs_pre[1:]
def test_add_layer_top_2(self):
self.mh.drop_layer_top()
layer1 = layer_factory.FullyConnected(name='fc1', num_hidden=5)
layer2 = layer_factory.Convolution(name='conv1_1',
kernel=(3, 3),
num_filter=10)
for layer_name in ['fc1', 'conv1_1']:
assert layer_name not in list(self.mh.layer_type_dict.keys())
outputs_pre = self.mh.symbol.get_internals().list_outputs()
self.mh.add_layer_top([layer1])
self.mh.add_layer_top([layer2])
outputs_post = self.mh.symbol.get_internals().list_outputs()
for layer_name in ['fc1', 'conv1_1']:
assert layer_name in list(self.mh.layer_type_dict.keys())
assert outputs_post == outputs_pre + [
'fc1_weight', 'fc1_bias', 'fc1_output', 'conv1_1_weight',
'conv1_1_bias', 'conv1_1_output'
]
def test_create_layer(self):
layer = layer_factory.FullyConnected(name='fc1',
num_hidden=10,
no_bias=False)
sym = layer.create_layer(self.data)
assert sorted(sym.attr_dict().keys()) == sorted(
['fc1', 'fc1_weight', 'fc1_bias'])
assert sym.attr_dict()['fc1'] == {
'num_hidden': '10',
'no_bias': 'False'
}
assert sym.attr_dict()['fc1_weight'] == {
'num_hidden': '10',
'no_bias': 'False'
}
assert sym.attr_dict()['fc1_bias'] == {
'num_hidden': '10',
'no_bias': 'False'
}
assert sym.get_internals().list_outputs() == [
'data', 'fc1_weight', 'fc1_bias', 'fc1_output'
]
def test_add_layer_top_model_error(self):
# Assert model error raised when a layer is added above an output layer
layer1 = layer_factory.FullyConnected(name='fc1', num_hidden=5)
with self.assertRaises(model_handler.exceptions.ModelError):
self.mh.add_layer_top([layer1])