def test_networkpass_on_generate_function(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
@callback.on_generate_function_by_name('Convolution')
def change_convolution_param(f):
print('{}'.format(f.proto.convolution_param.pad.dim[:]))
f.proto.convolution_param.pad.dim[:] = [1, 1]
return f
@callback.on_function_pass_by_type('Affine')
def change_affine_param(f, variables, param_scope):
param_name = f.inputs[1].proto.name
input_shape = f.inputs[0].proto.shape.dim[:]
w_shape = f.inputs[1].proto.shape.dim[:]
rng = np.random.RandomState(388)
with nn.parameter_scope('', param_scope):
W = nn.Variable.from_numpy_array(
rng.randn(np.prod(input_shape[1:]), w_shape[1]))
nn.parameter.set_parameter(param_name, W)
W.need_grad = True
with get_saved_test_model(module) as nnp_file:
ref_nnp = legacy_nnp_graph.NnpLoader(nnp_file)
nnp = nnp_graph.NnpLoader(nnp_file)
for ref_v, v in zip(nnp_check(ref_nnp, 'left', callback),
nnp_check(nnp, 'right', callback)):
verify_equivalence(ref_v, v)
def test_networkpass_use_up_to(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
callback.use_up_to('Tanh_out_1')
callback.use_up_to('Convolution_out_3')
with get_saved_test_model(module) as nnp_file:
ref_nnp = legacy_nnp_graph.NnpLoader(nnp_file)
nnp = nnp_graph.NnpLoader(nnp_file)
for ref_v, v in zip(nnp_check(ref_nnp, 'left', callback),
nnp_check(nnp, 'right', callback)):
verify_equivalence(ref_v, v)
def test_networkpass_remove_and_rewire(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
callback.remove_and_rewire('affine1_1')
callback.remove_and_rewire('c1-c1')
with get_saved_test_model(module) as nnp_file:
ref_nnp = legacy_nnp_graph.NnpLoader(nnp_file)
nnp = nnp_graph.NnpLoader(nnp_file)
for ref_v, v in zip(nnp_check(ref_nnp, 'left', callback),
nnp_check(nnp, 'right', callback)):
verify_equivalence(ref_v, v)
def test_networkpass_fix_parameter(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
callback.fix_parameters()
with get_saved_test_model(module) as nnp_file:
nnp = nnp_graph.NnpLoader(nnp_file)
assert_parameter_scope_empty()
for network_name in sorted(nnp.get_network_names()):
network = nnp.get_network(
network_name, batch_size=32, callback=callback)
assert_parameter_scope_empty()
def test_networkpass_set_variable(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
ref_callback = legacy_nnp_graph.NnpNetworkPass(verbose)
for inp_name, inp_shape in inputs:
inp_shape = (1, *inp_shape[1:]) # change shape
callback.set_variable(inp_name, nn.Variable(inp_shape))
ref_callback.set_variable(inp_name, nn.Variable(inp_shape))
with get_saved_test_model(module) as nnp_file:
ref_nnp = legacy_nnp_graph.NnpLoader(nnp_file)
nnp = nnp_graph.NnpLoader(nnp_file)
for ref_v, v in zip(nnp_check(ref_nnp, 'left', ref_callback),
nnp_check(nnp, 'right', callback)):
verify_equivalence(ref_v, v)
def test_nnp_load_parameter_scope(module):
'''This test tests whether equivalency between new or old implementation of nnp_graph
'''
_, inputs = module
verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)
@callback.on_generate_function_by_name('Convolution')
def change_convolution_param(f):
print('{}'.format(f.proto.convolution_param.pad.dim[:]))
f.proto.convolution_param.pad.dim[:] = [1, 1]
return f
@callback.on_function_pass_by_type('Affine')
def change_affine_param(f, variables, param_scope):
param_name = f.inputs[1].proto.name
input_shape = f.inputs[0].proto.shape.dim[:]
w_shape = f.inputs[1].proto.shape.dim[:]
rng = np.random.RandomState(388)
with nn.parameter_scope('', param_scope):
W = nn.Variable.from_numpy_array(
rng.randn(np.prod(input_shape[1:]), w_shape[1]))
W.need_grad = True
nn.parameter.set_parameter(param_name, W)
ref_params = {}
with get_saved_test_model(module) as nnp_file:
nnp = legacy_nnp_graph.NnpLoader(nnp_file)
for network_name in sorted(nnp.get_network_names()):
network = nnp.get_network(network_name,
batch_size=32,
callback=callback)
ref_params[network_name] = nn.get_parameters().copy()
nn.clear_parameters()
params = {}
nnp = nnp_graph.NnpLoader(nnp_file)
assert_parameter_scope_empty()
for network_name in sorted(nnp.get_network_names()):
network = nnp.get_network(network_name,
batch_size=32,
callback=callback)
params[network_name] = nn.get_parameters()
assert_parameters_equal(ref_params, params)
def modifier(x):
callback = nnp_graph.NnpNetworkPass(1)
callback.remove_and_rewire('Affine')
callback.set_variable('Affine_in', x)
return callback