def test_super_resolution_example():
"""Test the super resolution example in the example/onnx folder"""
sys.path.insert(0, os.path.join(CURR_PATH, '../../../example/onnx/'))
import super_resolution
sym, arg_params, aux_params = super_resolution.import_onnx()
assert sym is not None
assert arg_params is not None
inputs = sym.list_inputs()
assert len(inputs) == 9
for i, input_param in enumerate([
'param_7', 'param_5', 'param_3', 'param_1', 'input_0', 'param_0',
'param_2', 'param_4', 'param_6'
]):
assert inputs[i] == input_param
assert len(sym.list_outputs()) == 1
assert sym.list_outputs()[0] == 'reshape5_output'
attrs_keys = sym.attr_dict().keys()
assert len(attrs_keys) == 19
for i, key_item in enumerate([
'reshape4', 'param_5', 'param_4', 'param_7', 'param_6', 'param_1',
'param_0', 'param_3', 'param_2', 'reshape2', 'reshape3',
'reshape0', 'reshape1', 'convolution2', 'convolution3',
'convolution0', 'convolution1', 'reshape5', 'transpose0'
]):
assert key_item in attrs_keys
param_keys = arg_params.keys()
assert len(param_keys) == 8
for i, param_item in enumerate([
'param_5', 'param_4', 'param_7', 'param_6', 'param_1', 'param_0',
'param_3', 'param_2'
]):
assert param_item in param_keys
logging.info("Asserted the result of the onnx model conversion")
output_img_dim = 672
input_image, img_cb, img_cr = super_resolution.get_test_image()
result_img = super_resolution.perform_inference(sym, arg_params,
aux_params, input_image,
img_cb, img_cr)
assert hashlib.md5(
result_img.tobytes()).hexdigest() == '0d98393a49b1d9942106a2ed89d1e854'
assert result_img.size == (output_img_dim, output_img_dim)
def test_super_resolution_example():
"""Test the super resolution example in the example/onnx folder"""
sys.path.insert(0, os.path.join(CURR_PATH, '../../../../example/onnx/'))
import super_resolution
sym, arg_params, aux_params = super_resolution.import_onnx()
logging.info("Asserted the result of the onnx model conversion")
output_img_dim = 672
input_image, img_cb, img_cr = super_resolution.get_test_image()
result_img = super_resolution.perform_inference(sym, arg_params, aux_params,
input_image, img_cb, img_cr)
assert hashlib.md5(result_img.tobytes()).hexdigest() == '0d98393a49b1d9942106a2ed89d1e854'
assert result_img.size == (output_img_dim, output_img_dim)
def test_super_resolution_example():
"""Test the super resolution example in the example/onnx folder"""
sys.path.insert(0, os.path.join(CURR_PATH, '../../../example/onnx/'))
import super_resolution
sym, arg_params, aux_params = super_resolution.import_onnx()
assert sym is not None
assert arg_params is not None
inputs = sym.list_inputs()
assert len(inputs) == 9
for i, input_param in enumerate(['9', '7', '5', '3', '1', '2', '4', '6', '8']):
assert inputs[i] == input_param
assert len(sym.list_outputs()) == 1
assert sym.list_outputs()[0] == 'reshape5_output'
attrs_keys = sym.attr_dict().keys()
assert len(attrs_keys) == 23
for i, key_item in enumerate(['reshape4', 'convolution2', 'convolution0',
'transpose0', '6', 'reshape0', 'reshape2',
'reshape3', '3', 'reshape1', '5', '4', '7',
'convolution1', '9', '2', 'convolution3',
'reshape5', '8', 'pad1', 'pad0', 'pad3',
'pad2']):
assert key_item in attrs_keys
param_keys = arg_params.keys()
assert len(param_keys) == 8
for i, param_item in enumerate(['3', '2', '5', '4', '7', '6', '9', '8']):
assert param_item in param_keys
logging.info("Asserted the result of the onnx model conversion")
output_img_dim = 672
input_image, img_cb, img_cr = super_resolution.get_test_image()
result_img = super_resolution.perform_inference(sym, arg_params, aux_params,
input_image, img_cb, img_cr)
assert hashlib.md5(result_img.tobytes()).hexdigest() == '0d98393a49b1d9942106a2ed89d1e854'
assert result_img.size == (output_img_dim, output_img_dim)