def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[
1] != weights['conv2d_weight'].shape[1] * attrs[0]['groups']:
return False
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1]:
return False
if inputs['input_data'].shape[1] != attrs[0]['groups']:
return False
if attrs[0]['dilations'][0] != 1 or attrs[0]['dilations'][1] != 1:
return False
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000:
return False
return True
def generate_trt_nodes_num(attrs, dynamic_shape):
inputs = program_config.inputs
if not dynamic_shape:
return 0, 3
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000:
return 0, 3
return 1, 2
def generate_trt_nodes_num(attrs, dynamic_shape):
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7000:
if dynamic_shape == True:
return 0, 3
else:
return 1, 2
else:
return 0, 3
def test_apis(self):
print('trt compile version:', get_trt_compile_version())
print('trt runtime version:', get_trt_runtime_version())
program, params = get_sample_model()
config = self.get_config(program, params)
predictor = create_predictor(config)
in_names = predictor.get_input_names()
in_handle = predictor.get_input_handle(in_names[0])
in_data = np.ones((1, 6, 32, 32)).astype(np.float32)
in_handle.copy_from_cpu(in_data)
predictor.run()
def add_skip_trt_case(self):
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:
def teller(program_config, predictor_config):
if not predictor_config.tensorrt_dynamic_shape_enabled():
return True
return False
self.add_skip_case(
teller, SkipReasons.TRT_NOT_IMPLEMENTED,
"Need to repair the case: the output of GPU and tensorrt has diff in trt6, the prelu static plugin has bug."
)
def generate_trt_nodes_num(attrs, dynamic_shape):
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7130:
if attrs[0]['axis'] == 1:
return 1, 2
else:
return 0, 3
else:
if dynamic_shape:
return 0, 3
if attrs[0]['axis'] == 1:
return 1, 2
else:
return 0, 3
def generate_trt_nodes_num(attrs, dynamic_shape):
inputs = program_config.inputs
if dynamic_shape:
for i in range(len(attrs[0]["starts"])):
if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0:
return 0, 3
if not dynamic_shape:
for x in attrs[0]["axes"]:
if x == 0:
return 0, 3
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000:
return 0, 3
return 1, 2
def generate_trt_nodes_num(attrs, dynamic_shape):
valid_version = (7, 0, 0)
compile_version = paddle_infer.get_trt_compile_version()
runtime_version = paddle_infer.get_trt_runtime_version()
self.assertTrue(compile_version == runtime_version)
# Dimension one only runs on Paddle OP
if self.dims == 1:
return 0, 3
if compile_version >= valid_version:
return 1, 2
else:
if attrs[0]['approximate'] == True:
return 0, 3
else:
return 1, 2
def is_paddings_valid(self, program_config: ProgramConfig) -> bool:
exclusive = program_config.ops[0].attrs['exclusive']
paddings = program_config.ops[0].attrs['paddings']
ksize = program_config.ops[0].attrs['ksize']
pooling_type = program_config.ops[0].attrs['pooling_type']
global_pooling = program_config.ops[0].attrs['global_pooling']
if global_pooling == False:
if pooling_type == 'avg':
for index in range(len(ksize)):
if ksize[index] <= paddings[index]:
return False
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:
if program_config.ops[0].attrs['pooling_type'] == 'avg':
return False
return True
def add_skip_trt_case(self):
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:
def teller1(program_config, predictor_config):
if len(self.dynamic_shape.min_input_shape) != 0:
inputs = program_config.inputs
if len(inputs['input_data'].shape) == 1 or len(
inputs['index_data'].shape) == 1:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_SUPPORT,
"Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1. under trt7.0 "
)
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[
1] != weights['conv2d_weight'].shape[1] * attrs[0]['groups']:
return False
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:
if attrs[0]['padding_algorithm'] == 'SAME' and (
attrs[0]['strides'][0] > 1 or attrs[0]['strides'][1] > 1):
return False
return True
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
## dim should be in (-rank, rank), and not NONE
rank = len(inputs['input_data'].shape)
for x in attrs[0]["dim"]:
if x >= rank or x <= -rank:
return False
if len(attrs[0]["dim"]) == 0:
return False
ver = paddle_infer.get_trt_compile_version()
if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:
if attrs[0]['out_dtype'] == 2:
return False
return True