def test_update_sym(self):
mod = mx.module.Module.load('tests/data/testnetv1',
0,
label_names=['softmaxoutput1_label'])
mh = model_handler.ModelHandler(mod, mx.context.cpu, 1)
symbol = mh.symbol.get_internals()[self.act1_id]
self.mh.aux_params = {1: 2, 2: 3, 4: 5, 7: 3, 6: 8, 3: 5, 12: 5}
mh.update_sym(symbol)
assert mh.layer_type_dict == OrderedDict([('conv1', 'Convolution'),
('act1', 'Activation')])
assert mh.symbol.tojson() == symbol.tojson()
self._compare_symbols(mh.symbol, symbol)
assert sorted(mh.arg_params.keys()) == sorted(
['conv1_weight', 'conv1_bias'])
assert mh.aux_params == {}
mod = mx.module.Module.load('tests/data/testnetv1',
0,
label_names=['softmaxoutput1_label'])
mh = model_handler.ModelHandler(mod, mx.context.cpu, 1)
symbol = mh.symbol.get_internals()[self.conv2_id]
mh.update_sym(symbol)
assert mh.layer_type_dict == OrderedDict([('conv1', 'Convolution'),
('act1', 'Activation'),
('conv2', 'Convolution')])
assert mh.symbol.tojson() == symbol.tojson()
self._compare_symbols(mh.symbol, symbol)
assert sorted(mh.arg_params.keys()) == sorted(
['conv1_weight', 'conv2_bias', 'conv1_bias', 'conv2_weight'])
assert mh.aux_params == {}
def setUp(self):
np.random.seed(1)
self.data_name = 'data'
mod = mx.module.Module.load('tests/data/testnetv1',
0,
label_names=['softmaxoutput1_label'],
data_names=(self.data_name, ))
self.mh = model_handler.ModelHandler(mod, mx.context.cpu, 1,
self.data_name)
self.imglist = [[0, 'accordion/image_0001.jpg'],
[0, 'accordion/image_0002.jpg'],
[1, 'ant/image_0001.jpg'], [1, 'ant/image_0002.jpg'],
[2, 'anchor/image_0001.jpg'],
[2, 'anchor/image_0002.jpg']]
self.image_iter = mx.image.ImageIter(
2, (3, 224, 224),
imglist=self.imglist,
path_root='tests/data/test_images',
label_name='softmaxoutput1_label',
data_name=self.data_name)
self.symbol_dict = json.loads(self.mh.symbol.tojson())
self.nodes = self.symbol_dict['nodes']
self.arg_nodes = self.symbol_dict['arg_nodes']
self.heads = self.symbol_dict['heads']
self.act1_id = 4
self.conv2_id = 7
def test_constructor_no_weight(self):
# Assert module that is unbinded can be used to init a ModelHandler object and can add/drop layers
sym, _, _ = mx.model.load_checkpoint('tests/data/testnetv1', 0)
mod = mx.module.Module(sym, label_names=['softmaxoutput1_label'])
mh = model_handler.ModelHandler(mod, mx.context.cpu, 1)
assert np.array_equal(mh.arg_params, {})
assert np.array_equal(mh.aux_params, {})
mh.drop_layer_top()
mh.drop_layer_bottom()
def test_constructor_binded_module(self):
# Assert module that is binded can be used to init a ModelHandler object and can add/drop layers
mod = mx.module.Module.load('tests/data/testnetv1',
0,
label_names=['softmaxoutput1_label'])
mod.bind(data_shapes=[('data', self.image_iter.provide_data[0][1])],
label_shapes=self.image_iter.provide_label)
assert mod.binded and mod.params_initialized
mh = model_handler.ModelHandler(mod, mx.context.cpu, 1)
mh.drop_layer_top()
mh.drop_layer_bottom()
def test_resnet(self):
"""Assert that ModelHandler can drop layers from resnet without errors"""
RepurposerTestUtils.download_resnet()
mod = mx.mod.Module.load('resnet-101', 0)
mh = model_handler.ModelHandler(mod)
old_layer_names = mh.layer_names
mh.drop_layer_top()
mh.drop_layer_bottom()
assert sorted(
list(set(old_layer_names).difference(set(
mh.layer_names)))) == sorted(['bn_data', 'softmax'])
def _build_split_net():
"""Instantiate MH for a model that diverges into two and then joins back into one."""
data = mx.sym.var('data')
data = mx.sym.flatten(data=data, name='flatten0')
fc1 = mx.sym.FullyConnected(data, num_hidden=5, name='a_1')
fc2 = mx.sym.FullyConnected(fc1, num_hidden=5, name='a_2')
fc3 = mx.sym.FullyConnected(fc2, num_hidden=5, name='a_3')
fc2 = mx.sym.FullyConnected(data, num_hidden=5, name='b_1')
fc2b = mx.sym.FullyConnected(fc2, num_hidden=5, name='b_2')
plus = fc3.__add__(fc2b)
softmax = mx.sym.SoftmaxOutput(plus, name='softmax')
mod = mx.mod.Module(softmax)
mh = model_handler.ModelHandler(mod)
plus_layer_name = mh.layer_names[6]
return mh, plus_layer_name
def test_drop_layer_top_two_outputs(self):
# Build a symbol with two softmax output layers
data = mx.sym.Variable('data')
fc1 = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=128)
act1 = mx.sym.Activation(data=fc1, name='relu1', act_type="relu")
fc2 = mx.sym.FullyConnected(data=act1, name='fc2', num_hidden=64)
act2 = mx.sym.Activation(data=fc2, name='relu2', act_type="relu")
fc3 = mx.sym.FullyConnected(data=act2, name='fc3', num_hidden=10)
fc4 = mx.sym.FullyConnected(data=fc3, name='fc4_1', num_hidden=10)
sm1 = mx.sym.SoftmaxOutput(data=fc4, name='softmax1')
fc5 = mx.sym.FullyConnected(data=fc3, name='fc4_2', num_hidden=10)
sm2 = mx.sym.SoftmaxOutput(data=fc5, name='softmax2')
softmax = mx.sym.Group([sm1, sm2])
mod = mx.mod.Module(softmax,
label_names=['softmax1_label', 'softmax2_label'])
mh = model_handler.ModelHandler(mod)
with self.assertRaises(exceptions.ModelError):
mh.drop_layer_top()
