def test_fill_non_constant_input_with_specific_mapping_several_image_matched(self):
inputs = {'data': (1, 3, 10, 10)}
input_feeder = InputFeeder([{'name': 'input', 'type': 'INPUT', 'value': '.'}], {'input': InputInfo_test(shape=(1, 3, 10, 10))}, lambda name: inputs[name])
result = input_feeder.fill_non_constant_inputs([DataRepresentation([np.zeros((10, 10, 3)), np.ones((10, 10, 3))], identifier=['0', '1'])])[0]
expected_data = np.zeros((1, 3, 10, 10))
assert 'input' in result
assert np.array_equal(result['input'], expected_data)
def test_fill_non_constant_inputs_with_specific_mapping_not_all_image_in_batch_matched_raise_config_error(
self):
input_feeder = InputFeeder([{
'name': 'input1',
'type': 'INPUT',
'value': '0'
}, {
'name': 'input2',
'type': 'INPUT',
'value': '1'
}], {
'input1': (1, 3, 10, 10),
'input2': (1, 3, 10, 10)
}, {})
with pytest.raises(ConfigError):
input_feeder.fill_non_constant_inputs([
DataRepresentation(
[np.zeros((10, 10, 3)),
np.ones((10, 10, 3))],
identifier=['0', '1']),
DataRepresentation(
[np.zeros((10, 10, 3)),
np.ones((10, 10, 3))],
identifier=['0', '2'])
])
def test_fill_non_constant_input_with_specific_mapping_not_all_image_in_batch_matched_raise_config_error(self):
input_feeder = InputFeeder([{'name': 'input', 'type': 'INPUT', 'value': '0+'}], {'input': InputInfo_test(shape=(1, 3, 10, 10))}, {})
with pytest.raises(ConfigError):
input_feeder.fill_non_constant_inputs([
DataRepresentation(np.zeros((10, 10, 3)), identifier='0'),
DataRepresentation(np.zeros((10, 10, 3)), identifier='1')
])
def test_fill_non_constant_input_with_one_input_without_specific_mapping_batch_1(self):
inputs = {'data': (1, 3, 10, 10)}
input_feeder = InputFeeder([], {'input': InputInfo_test(shape=(1, 3, 10, 10))}, lambda name: inputs[name])
result = input_feeder.fill_non_constant_inputs([DataRepresentation(np.zeros((10, 10, 3)), identifier='0')])[0]
expected_data = np.zeros((1, 3, 10, 10))
assert 'input' in result
assert np.array_equal(result['input'], expected_data)
def test_fill_non_constant_inputs_with_specific_mapping_batch_2(self):
input_feeder = InputFeeder(
[{
'name': 'input1',
'type': 'INPUT',
'value': '0'
}, {
'name': 'input2',
'type': 'INPUT',
'value': '1'
}], {
'input1': InputInfo_test(shape=(1, 3, 10, 10)),
'input2': InputInfo_test(shape=(1, 3, 10, 10))
}, {})
result = input_feeder.fill_non_constant_inputs([
DataRepresentation([np.zeros(
(10, 10, 3)), np.ones((10, 10, 3))],
identifier=['0', '1']),
DataRepresentation([np.zeros(
(10, 10, 3)), np.ones((10, 10, 3))],
identifier=['0', '1'])
])[0]
expected_data = [np.zeros((2, 3, 10, 10)), np.ones((2, 3, 10, 10))]
assert 'input1' in result
assert np.array_equal(result['input1'], expected_data[0])
assert 'input2' in result
assert np.array_equal(result['input2'], expected_data[1])
def test_fill_non_constant_inputs_without_specific_mapping_batch_1(self):
input_feeder = InputFeeder([], { 'input1': InputInfo_test(shape=(1, 3, 10, 10)), 'input2': InputInfo_test(shape=(1, 3, 10, 10))}, {})
result = input_feeder.fill_non_constant_inputs([DataRepresentation(np.zeros((10, 10, 3)), identifier='0')])[0]
expected_data = np.zeros((1, 3, 10, 10))
assert 'input1' in result
assert np.array_equal(result['input1'], expected_data)
assert 'input2' in result
assert np.array_equal(result['input2'], expected_data)
def test_fill_non_constant_input_with_specific_mapping_batch_2(self):
input_feeder = InputFeeder([{'name': 'input', 'type': 'INPUT', 'value': '.'}], {'input': InputInfo_test(shape=(1, 3, 10, 10))}, {})
result = input_feeder.fill_non_constant_inputs([
DataRepresentation(np.zeros((10, 10, 3)), identifier='0'),
DataRepresentation(np.zeros((10, 10, 3)), identifier='1')
])[0]
expected_data = np.zeros((2, 3, 10, 10))
assert 'input' in result
assert np.array_equal(result['input'], expected_data)
def test_fill_non_const_input_with_multi_infer_data_batch_1(self):
input_feeder = InputFeeder({}, {'input': (1, 3, 10, 10)}, {})
result = input_feeder.fill_non_constant_inputs([
DataRepresentation([np.zeros((10, 10, 3)), np.ones((10, 10, 3))], {'multi_infer': True}, identifier='0')
])
expected = [{'input': np.zeros((1, 3, 10, 10))}, {'input': np.ones((1, 3, 10, 10))}]
assert len(result) == len(expected)
assert np.array_equal(result[0]['input'], expected[0]['input'])
assert np.array_equal(result[1]['input'], expected[1]['input'])
def test_set_input_precision_for_non_constant_input(self):
input_feeder = InputFeeder([{
'name': 'input_u8',
'type': 'INPUT',
'precision': 'U8'
}], {'input_u8': (1, 3, 10, 10)}, {})
result = input_feeder.fill_non_constant_inputs([
DataRepresentation(np.zeros((10, 10, 3)), identifier='0'),
])
expected = [{'input_u8': np.zeros((1, 3, 10, 10), dtype=np.uint8)}]
assert len(result) == len(expected)
assert np.array_equal(result[0]['input_u8'], expected[0]['input_u8'])
assert result[0]['input_u8'].dtype == expected[0]['input_u8'].dtype
def test_create_input_feeder_with_several_precision_info_in_wrong_format_raises_config_error(self):
with pytest.raises(ConfigError):
inputs = {'data': (1, 3, 10, 10), 'const_data': (1, 4)}
InputFeeder(
[{'name': 'const_data', 'type': 'CONST_INPUT', 'value': [[1, 2, 3, 4]], 'precision': 'FP32'}],
inputs, lambda name: inputs[name], input_precisions_list=['U8', 'FP16']
)
def test_create_input_feeder_with_only_image_info_in_network_inputs_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'info',
'type': 'IMAGE_INFO'
}], {'info': (1, 3)}, {})
def test_set_input_precision_for_image_info_input(self):
input_feeder = InputFeeder(
[{'name': 'im_info', 'type': 'IMAGE_INFO', 'precision': 'U8'}],
{'input': (1, 3, 10, 10), 'im_info': (1, 3)}, lambda name: [1, 3])
result = input_feeder.fill_non_constant_inputs([
DataRepresentation(
np.zeros((10, 10, 3)),
identifier='0'
),
])
expected = [{'input': np.zeros((1, 3, 10, 10)), 'im_info': np.array([[10, 10, 1]], dtype=np.uint8)}]
assert len(result) == len(expected)
assert np.array_equal(result[0]['input'], expected[0]['input'])
assert result[0]['input'].dtype == expected[0]['input'].dtype
assert np.array_equal(result[0]['im_info'], expected[0]['im_info'])
assert result[0]['im_info'].dtype == expected[0]['im_info'].dtype
def test_create_input_feeder_with_config_inputs_and_empty_network_inputs_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'const_data',
'type': 'CONST_INPUT',
'value': '[1, 1, 1, 1]'
}], {}, {})
def test_set_invalid_input_precision_for_non_constant_input_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'input',
'type': 'INPUT',
'precision': 'U2'
}], {'input': (1, 3, 10, 10)}, {})
def test_create_input_feeder_with_config_inputs_not_in_network_inputs_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'data2',
'type': 'INPUT',
'value': '.'
}], {'data': (1, 3, 10, 10)}, {})
def test_create_input_feeder_not_all_non_constant_inputs_in_config_raise_config_error(self):
with pytest.raises(ConfigError):
inputs = {'0': (1, 3, 10, 10), '1': (1, 3, 10, 10)}
InputFeeder(
[{'name': '0', 'type': 'INPUT', 'value': '.'}],
inputs, lambda name: inputs[name]
)
def test_create_input_feeder_config_inputs_contain_only_const_inputs_with_not_list_value(self):
inputs = {'data': (1, 3, 10, 10), 'const_data': (1, 4)}
input_feeder = InputFeeder(
[{'name': 'const_data', 'type': 'CONST_INPUT', 'value': 0}],
inputs, lambda name: inputs[name]
)
assert input_feeder.const_inputs['const_data'] == np.array(0)
assert not input_feeder.inputs_mapping
assert input_feeder.non_constant_inputs == ['data']
def test_create_input_feeder_config_inputs_fully_match_to_network_inputs(
self):
input_feeder = InputFeeder([{
'name': 'data',
'type': 'INPUT',
'value': '.'
}], {'data': (1, 3, 10, 10)}, {})
assert not input_feeder.const_inputs
assert input_feeder.inputs_mapping == {'data': re.compile('.')}
assert input_feeder.non_constant_inputs == ['data']
def test_set_invalid_input_precision_for_image_info_input_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'im_info',
'type': 'IMAGE_INFO',
'precision': 'U2'
}], {
'input': (1, 3, 10, 10),
'im_info': (1, 3)
}, {})
def test_set_input_precision_for_constant_input(self):
input_feeder = InputFeeder([{
'name': 'input_u8',
'type': 'CONST_INPUT',
'value': [1, 2, 3],
'precision': 'U8'
}], {
'input': (1, 3, 10, 10),
'input_u8': (3, )
}, {})
assert input_feeder.const_inputs['input_u8'].dtype == np.uint8
def test_create_input_feeder_without_config_inputs_and_wint_input_precision(self):
inputs = {'data': (1, 3, 10, 10)}
input_feeder = InputFeeder(
[],
inputs, lambda name: inputs[name], input_precisions_list=['U8']
)
assert 'data' in input_feeder.precision_mapping
assert input_feeder.precision_mapping['data'] == np.uint8
assert len(input_feeder.inputs_config) == 1
assert input_feeder.inputs_config[0]['name'] == 'data' and input_feeder.inputs_config[0]['precision'] == 'U8'
def test_set_invalid_input_precision_for_constant_input_raise_config_error(
self):
with pytest.raises(ConfigError):
InputFeeder([{
'name': 'input_u8',
'type': 'CONST_INPUT',
'value': [1, 2, 3],
'precision': 'U2'
}], {
'input': (1, 3, 10, 10),
'input_u8': (3, )
}, {})
def test_create_input_feeder_with_precision_info_for_specific_layer(self):
inputs = {'data': (1, 3, 10, 10), 'const_data': (1, 4)}
input_feeder = InputFeeder(
[{'name': 'const_data', 'type': 'CONST_INPUT', 'value': [[1, 2, 3, 4]], 'precision': 'FP32'}],
inputs, lambda name: inputs[name], input_precisions_list=['data:U8']
)
assert 'const_data' in input_feeder.precision_mapping
assert input_feeder.precision_mapping['const_data'] == np.float32
assert input_feeder.const_inputs['const_data'].dtype == np.float32
assert len(input_feeder.inputs_config) == 2
assert input_feeder.inputs_config[0]['name'] == 'const_data' and input_feeder.inputs_config[0]['precision'] == 'FP32'
assert input_feeder.inputs_config[1]['name'] == 'data' and input_feeder.inputs_config[1]['precision'] == 'U8'
def test_create_input_feeder_without_inputs_raise_config_error(self):
with pytest.raises(ConfigError):
InputFeeder([], {}, {})
def test_create_input_feeder_without_config_inputs(self):
input_feeder = InputFeeder([], {'data': (1, 3, 10, 10)}, {})
assert not input_feeder.const_inputs
assert not input_feeder.inputs_mapping
assert input_feeder.non_constant_inputs == ['data']
