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_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_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_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_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_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_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_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_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_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