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[Fix] Fix unittest of ncnn. (#309) 

* fix test_pytorch_functions

* fix test_mmocr_models
pull/1/head
hanrui1sensetime 2021-12-21 18:03:50 +08:00 committed by GitHub
parent c1ed41c465
commit 33bde90744
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 19 additions and 18 deletions
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8
tests/test_codebase/test_mmocr/test_mmocr_models.py
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@ -165,7 +165,7 @@ def test_bidirectionallstm(backend: Backend):
wrapped_model=wrapped_model, wrapped_model=wrapped_model,
model_inputs=rewrite_inputs, model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg, deploy_cfg=deploy_cfg,
run_with_backend=False) run_with_backend=True)
if is_backend_output: if is_backend_output:
model_output = model_outputs.cpu().numpy() model_output = model_outputs.cpu().numpy()
rewrite_output = rewrite_outputs[0].cpu().numpy() rewrite_output = rewrite_outputs[0].cpu().numpy()
@ -200,7 +200,8 @@ def test_simple_test_of_single_stage_text_detector(backend: Backend):
rewrite_outputs, is_backend_output = get_rewrite_outputs( rewrite_outputs, is_backend_output = get_rewrite_outputs(
wrapped_model=wrapped_model, wrapped_model=wrapped_model,
model_inputs=rewrite_inputs, model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg) deploy_cfg=deploy_cfg,
run_with_backend=True)
if is_backend_output: if is_backend_output:
rewrite_outputs = rewrite_outputs[0] rewrite_outputs = rewrite_outputs[0]
@ -254,7 +255,8 @@ def test_crnndecoder(backend: Backend, rnn_flag: bool):
wrapped_model=wrapped_model, wrapped_model=wrapped_model,
model_inputs=rewrite_inputs, model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg, deploy_cfg=deploy_cfg,
run_with_backend=False) run_with_backend=True)
rewrite_outputs = [rewrite_outputs[-1]]
if is_backend_output: if is_backend_output:
for model_output, rewrite_output in zip(model_outputs, for model_output, rewrite_output in zip(model_outputs,
rewrite_outputs): rewrite_outputs):

29
tests/test_pytorch/test_pytorch_functions.py
View File

@ -42,7 +42,7 @@ def test_get_attribute():
def model_func(tensor): def model_func(tensor):
x = tensor.size() x = tensor.size()
assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor) assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor)
return x[0] * tensor return torch.tensor(x)
input = torch.zeros([1, 2, 3, 4]) input = torch.zeros([1, 2, 3, 4])
wrapped_func = WrapFunction(model_func) wrapped_func = WrapFunction(model_func)
@ -50,7 +50,7 @@ def test_get_attribute():
wrapped_func, wrapped_func,
model_inputs={'tensor': input}, model_inputs={'tensor': input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert rewrite_outputs is not None, 'Got unexpected rewrite ' assert rewrite_outputs is not None, 'Got unexpected rewrite '
'outputs: {}'.format(rewrite_outputs) 'outputs: {}'.format(rewrite_outputs)
@ -71,9 +71,9 @@ def test_group_norm_ncnn():
wrapped_func, wrapped_func,
model_inputs={'input': input}, model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert np.allclose(model_output, rewrite_output, rtol=1e-03, atol=1e-05) assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.NCNN) @backend_checker(Backend.NCNN)
@ -89,9 +89,9 @@ def test_interpolate_static():
wrapped_func, wrapped_func,
model_inputs={'input': input}, model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert np.allclose(model_output, rewrite_output, rtol=1e-03, atol=1e-05) assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.NCNN) @backend_checker(Backend.NCNN)
@ -109,9 +109,9 @@ def test_linear_ncnn():
wrapped_func, wrapped_func,
model_inputs={'input': input}, model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert np.allclose(model_output, rewrite_output, rtol=1e-03, atol=1e-05) assert np.allclose(model_output, rewrite_output[0], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.TENSORRT) @backend_checker(Backend.TENSORRT)
@ -127,10 +127,10 @@ def test_repeat_static():
deploy_cfg = get_trt_config(['output'], [1]) deploy_cfg = get_trt_config(['output'], [1])
rewrite_output, is_backend_ouptut = get_rewrite_outputs( rewrite_output, is_backend_output = get_rewrite_outputs(
wrapped_func, model_inputs={'input': input}, deploy_cfg=deploy_cfg) wrapped_func, model_inputs={'input': input}, deploy_cfg=deploy_cfg)
if is_backend_ouptut: if is_backend_output:
rewrite_output = rewrite_output[0].detach().cpu() rewrite_output = rewrite_output[0].detach().cpu()
assert np.allclose( assert np.allclose(
@ -145,7 +145,7 @@ def test_size_of_tensor_static():
def model_func(input): def model_func(input):
x = torch.Tensor.size(input) x = torch.Tensor.size(input)
assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor) assert isinstance(x[0], int) and not isinstance(x[0], torch.Tensor)
return x[0] * input return torch.tensor(x)
input = torch.zeros([1, 2, 3, 4]) input = torch.zeros([1, 2, 3, 4])
wrapped_func = WrapFunction(model_func) wrapped_func = WrapFunction(model_func)
@ -153,7 +153,7 @@ def test_size_of_tensor_static():
wrapped_func, wrapped_func,
model_inputs={'input': input}, model_inputs={'input': input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert rewrite_outputs is not None, 'Got unexpected rewrite ' assert rewrite_outputs is not None, 'Got unexpected rewrite '
'outputs: {}'.format(rewrite_outputs) 'outputs: {}'.format(rewrite_outputs)
@ -181,9 +181,8 @@ class TestTopk:
wrapped_func, wrapped_func,
model_inputs={'input': TestTopk.input}, model_inputs={'input': TestTopk.input},
deploy_cfg=deploy_cfg_ncnn, deploy_cfg=deploy_cfg_ncnn,
run_with_backend=False) run_with_backend=True)
assert np.allclose(model_output, output[0], rtol=1e-03, atol=1e-05)
assert np.allclose(model_output, output[1], rtol=1e-03, atol=1e-05)
@backend_checker(Backend.TENSORRT) @backend_checker(Backend.TENSORRT)
@pytest.mark.parametrize('k', [1, 3, 4]) @pytest.mark.parametrize('k', [1, 3, 4])