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[Fix] fix unittest and suppress warning (#1552) 

* fix unittest and some warning

* fix read string

* snake
pull/1583/head
q.yao 2022-12-22 19:57:36 +08:00 committed by GitHub
parent 0e656067a6
commit e0ed95ebc4
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GPG Key ID: 4AEE18F83AFDEB23
13 changed files with 104 additions and 169 deletions
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9
csrc/mmdeploy/backend_ops/tensorrt/roi_align/trt_roi_align.cpp
View File

@ -201,14 +201,15 @@ nvinfer1::IPluginV2 *TRTRoIAlignCreator::createPlugin(
if (field_name.compare("mode") == 0) {
int data_size = fc->fields[i].length;
ASSERT(data_size > 0);
const char *data_start = static_cast<const char *>(fc->fields[i].data);
std::string poolModeStr(data_start, data_size);
if (poolModeStr == "avg") {
std::string pool_mode(data_start);
if (pool_mode == "avg") {
poolMode = 1;
} else if (poolModeStr == "max") {
} else if (pool_mode == "max") {
poolMode = 0;
} else {
std::cout << "Unknown pool mode \"" << poolModeStr << "\"." << std::endl;
std::cout << "Unknown pool mode \"" << pool_mode << "\"." << std::endl;
}
ASSERT(poolMode >= 0);
}

13
mmdeploy/backend/tensorrt/utils.py
View File

@ -13,15 +13,17 @@ from mmdeploy.utils import get_root_logger
from .init_plugins import load_tensorrt_plugin
def save(engine: trt.ICudaEngine, path: str) -> None:
def save(engine: Any, path: str) -> None:
"""Serialize TensorRT engine to disk.
Args:
engine (tensorrt.ICudaEngine): TensorRT engine to be serialized.
engine (Any): TensorRT engine to be serialized.
path (str): The absolute disk path to write the engine.
"""
with open(path, mode='wb') as f:
f.write(bytearray(engine.serialize()))
if isinstance(engine, trt.ICudaEngine):
engine = engine.serialize()
f.write(bytearray(engine))
def load(path: str, allocator: Optional[Any] = None) -> trt.ICudaEngine:
@ -226,7 +228,10 @@ def from_onnx(onnx_model: Union[str, onnx.ModelProto],
builder.int8_calibrator = config.int8_calibrator
# create engine
engine = builder.build_engine(network, config)
if hasattr(builder, 'build_serialized_network'):
engine = builder.build_serialized_network(network, config)
else:
engine = builder.build_engine(network, config)
assert engine is not None, 'Failed to create TensorRT engine'

2
mmdeploy/codebase/mmdet/deploy/object_detection_model.py
View File

@ -601,7 +601,7 @@ class NCNNEnd2EndModel(End2EndModel):
scores = out[:, :, 1:2]
boxes = out[:, :, 2:6] * scales
dets = torch.cat([boxes, scores], dim=2)
return dets, torch.tensor(labels, dtype=torch.int32)
return dets, labels.to(torch.int32)
@__BACKEND_MODEL.register_module('sdk')

15
mmdeploy/codebase/mmdet/models/backbones.py
View File

@ -46,7 +46,7 @@ def focus__forward__ncnn(self, x):
x = x.reshape(batch_size, c * h, 1, w)
_b, _c, _h, _w = x.shape
g = _c // 2
g = torch.div(_c, 2, rounding_mode='floor')
# fuse to ncnn's shufflechannel
x = x.view(_b, g, 2, _h, _w)
x = torch.transpose(x, 1, 2).contiguous()
@ -55,13 +55,14 @@ def focus__forward__ncnn(self, x):
x = x.reshape(_b, c * h * w, 1, 1)
_b, _c, _h, _w = x.shape
g = _c // 2
g = torch.div(_c, 2, rounding_mode='floor')
# fuse to ncnn's shufflechannel
x = x.view(_b, g, 2, _h, _w)
x = torch.transpose(x, 1, 2).contiguous()
x = x.view(_b, -1, _h, _w)
x = x.reshape(_b, c * 4, h // 2, w // 2)
x = x.reshape(_b, c * 4, torch.div(h, 2, rounding_mode='floor'),
torch.div(w, 2, rounding_mode='floor'))
return self.conv(x)
@ -198,8 +199,12 @@ def shift_window_msa__forward__default(self, query, hw_shape):
[query,
query.new_zeros(B, C, self.window_size, query.shape[-1])],
dim=-2)
slice_h = (H + self.window_size - 1) // self.window_size * self.window_size
slice_w = (W + self.window_size - 1) // self.window_size * self.window_size
slice_h = torch.div(
(H + self.window_size - 1), self.window_size,
rounding_mode='floor') * self.window_size
slice_w = torch.div(
(W + self.window_size - 1), self.window_size,
rounding_mode='floor') * self.window_size
query = query[:, :, :slice_h, :slice_w]
query = query.permute(0, 2, 3, 1).contiguous()
H_pad, W_pad = query.shape[1], query.shape[2]

23
mmdeploy/core/rewriters/rewriter_utils.py
View File

@ -328,6 +328,29 @@ class RewriterRegistry:
return decorator
def remove_record(self, object: Any, filter_cb: Optional[Callable] = None):
"""Remove record.
Args:
object (Any): The object to remove.
filter_cb (Callable): Check if the object need to be remove.
Defaults to None.
"""
key_to_pop = []
for key, records in self._rewrite_records.items():
for rec in records:
if rec['_object'] == object:
if filter_cb is not None:
if filter_cb(rec):
continue
key_to_pop.append((key, rec))
for key, rec in key_to_pop:
records = self._rewrite_records[key]
records.remove(rec)
if len(records) == 0:
self._rewrite_records.pop(key)
class ContextCaller:
"""A callable object used in RewriteContext.

8
mmdeploy/mmcv/ops/nms.py
View File

@ -511,7 +511,7 @@ def multiclass_nms(boxes: Tensor,
@FUNCTION_REWRITER.register_rewriter(
func_name='mmdeploy.mmcv.ops.nms.bbox_nms._multiclass_nms',
func_name='mmdeploy.mmcv.ops.nms._multiclass_nms',
backend=Backend.COREML.value)
def multiclass_nms__coreml(boxes: Tensor,
scores: Tensor,
@ -574,8 +574,7 @@ def multiclass_nms__coreml(boxes: Tensor,
@FUNCTION_REWRITER.register_rewriter(
func_name='mmdeploy.mmcv.ops.nms.bbox_nms._multiclass_nms',
ir=IR.TORCHSCRIPT)
func_name='mmdeploy.mmcv.ops.nms._multiclass_nms', ir=IR.TORCHSCRIPT)
def multiclass_nms__torchscript(boxes: Tensor,
scores: Tensor,
max_output_boxes_per_class: int = 1000,
@ -676,8 +675,7 @@ class AscendBatchNMSOp(torch.autograd.Function):
@FUNCTION_REWRITER.register_rewriter(
func_name='mmdeploy.mmcv.ops.nms.bbox_nms._multiclass_nms',
backend='ascend')
func_name='mmdeploy.mmcv.ops.nms._multiclass_nms', backend='ascend')
def multiclass_nms__ascend(boxes: Tensor,
scores: Tensor,
max_output_boxes_per_class: int = 1000,

8
mmdeploy/utils/test.py
View File

@ -14,6 +14,11 @@ from mmengine import Config
from mmengine.model import BaseModel
from torch import nn
try:
from torch.testing import assert_close as torch_assert_close
except Exception:
from torch.testing import assert_allclose as torch_assert_close
import mmdeploy.codebase # noqa: F401,F403
from mmdeploy.core import RewriterContext, patch_model
from mmdeploy.utils import (IR, Backend, get_backend, get_dynamic_axes,
@ -293,8 +298,7 @@ def assert_allclose(expected: List[Union[torch.Tensor, np.ndarray]],
if isinstance(actual[i], (list, np.ndarray)):
actual[i] = torch.tensor(actual[i])
try:
torch.testing.assert_allclose(
actual[i], expected[i], rtol=1e-03, atol=1e-05)
torch_assert_close(actual[i], expected[i], rtol=1e-03, atol=1e-05)
except AssertionError as error:
if tolerate_small_mismatch:
assert '(0.00%)' in str(error), str(error)

113
tests/test_codebase/test_mmdet/test_mmdet_models.py
View File

@ -9,6 +9,12 @@ import mmengine
import numpy as np
import pytest
import torch
try:
from torch.testing import assert_close as torch_assert_close
except Exception:
from torch.testing import assert_allclose as torch_assert_close
from mmengine import Config
from mmengine.config import ConfigDict
@ -237,7 +243,7 @@ def test__anchorgenerator__single_level_grid_priors():
# test forward
with RewriterContext({}, backend_type):
wrap_output = wrapped_func(x)
torch.testing.assert_allclose(output, wrap_output)
torch_assert_close(output, wrap_output)
onnx_prefix = tempfile.NamedTemporaryFile().name
@ -341,23 +347,6 @@ def get_ssd_head_model():
return model
def get_fcos_head_model():
"""FCOS Head Config."""
test_cfg = Config(
dict(
deploy_nms_pre=0,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))
from mmdet.models.dense_heads import FCOSHead
model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg)
model.requires_grad_(False)
return model
def get_focus_backbone_model():
"""Backbone Focus Config."""
from mmdet.models.backbones.csp_darknet import Focus
@ -412,10 +401,8 @@ def get_reppoints_head_model():
def get_detrhead_model():
"""DETR head Config."""
from mmdet.models import build_head
from mmdet.utils import register_all_modules
register_all_modules()
model = build_head(
from mmdet.registry import MODELS
model = MODELS.build(
dict(
type='DETRHead',
num_classes=4,
@ -431,8 +418,7 @@ def get_detrhead_model():
dict(
type='MultiheadAttention',
embed_dims=4,
num_heads=1,
dropout=0.1)
num_heads=1)
],
ffn_cfgs=dict(
type='FFN',
@ -442,8 +428,6 @@ def get_detrhead_model():
ffn_drop=0.,
act_cfg=dict(type='ReLU', inplace=True),
),
feedforward_channels=32,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DetrTransformerDecoder',
@ -454,8 +438,7 @@ def get_detrhead_model():
attn_cfgs=dict(
type='MultiheadAttention',
embed_dims=4,
num_heads=1,
dropout=0.1),
num_heads=1),
ffn_cfgs=dict(
type='FFN',
embed_dims=4,
@ -465,7 +448,6 @@ def get_detrhead_model():
act_cfg=dict(type='ReLU', inplace=True),
),
feedforward_channels=32,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn',
'norm', 'ffn', 'norm')),
)),
@ -536,7 +518,7 @@ def test_focus_forward(backend_type):
for model_output, rewrite_output in zip(model_outputs[0], rewrite_outputs):
model_output = model_output.squeeze()
rewrite_output = rewrite_output.squeeze()
torch.testing.assert_allclose(
torch_assert_close(
model_output, rewrite_output, rtol=1e-03, atol=1e-05)
@ -578,77 +560,6 @@ def test_l2norm_forward(backend_type):
model_output[0], rewrite_output, rtol=1e-03, atol=1e-05)
def test_predict_by_feat_of_fcos_head_ncnn():
backend_type = Backend.NCNN
check_backend(backend_type)
fcos_head = get_fcos_head_model()
fcos_head.cpu().eval()
s = 128
batch_img_metas = [{
'scale_factor': np.ones(4),
'pad_shape': (s, s, 3),
'img_shape': (s, s, 3)
}]
output_names = ['detection_output']
deploy_cfg = Config(
dict(
backend_config=dict(type=backend_type.value),
onnx_config=dict(output_names=output_names, input_shape=None),
codebase_config=dict(
type='mmdet',
task='ObjectDetection',
model_type='ncnn_end2end',
post_processing=dict(
score_threshold=0.05,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
keep_top_k=100,
background_label_id=-1,
))))
# the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
# (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
# the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
# (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
seed_everything(1234)
cls_score = [
torch.rand(1, fcos_head.num_classes, pow(2, i), pow(2, i))
for i in range(5, 0, -1)
]
seed_everything(5678)
bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)]
seed_everything(9101)
centernesses = [
torch.rand(1, 1, pow(2, i), pow(2, i)) for i in range(5, 0, -1)
]
# to get outputs of onnx model after rewrite
batch_img_metas[0]['img_shape'] = torch.Tensor([s, s])
wrapped_model = WrapModel(
fcos_head,
'predict_by_feat',
batch_img_metas=batch_img_metas,
with_nms=True)
rewrite_inputs = {
'cls_scores': cls_score,
'bbox_preds': bboxes,
'centernesses': centernesses
}
rewrite_outputs, is_backend_output = get_rewrite_outputs(
wrapped_model=wrapped_model,
model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg)
# output should be of shape [1, N, 6]
if is_backend_output:
assert rewrite_outputs[0].shape[-1] == 6
else:
assert rewrite_outputs.shape[-1] == 6
@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME, Backend.NCNN])
def test_predict_by_feat_of_rpn_head(backend_type: Backend):
check_backend(backend_type)

4
tests/test_codebase/test_mmdet3d/test_voxel_detection_model.py
View File

@ -57,6 +57,10 @@ class TestVoxelDetectionModel:
deploy_cfg=deploy_cfg,
model_cfg=model_cfg)
@classmethod
def teardown_class(cls):
cls.wrapper.recover()
@pytest.mark.skipif(
reason='Only support GPU test',
condition=not torch.cuda.is_available())

25
tests/test_codebase/test_mmpose/test_mmpose_models.py
View File

@ -1,5 +1,4 @@
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
@ -93,30 +92,6 @@ def get_cross_resolution_weighting_model():
return model
@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
def test_cross_resolution_weighting_forward(backend_type: Backend):
check_backend(backend_type, True)
model = get_cross_resolution_weighting_model()
model.cpu().eval()
imgs = torch.rand(1, 16, 16, 16)
deploy_cfg = generate_mmpose_deploy_config(backend_type.value)
rewrite_inputs = {'x': imgs}
model_outputs = model.forward(imgs)
wrapped_model = WrapModel(model, 'forward')
rewrite_outputs, is_backend_output = get_rewrite_outputs(
wrapped_model=wrapped_model,
model_inputs=rewrite_inputs,
deploy_cfg=deploy_cfg)
if isinstance(rewrite_outputs, dict):
rewrite_outputs = rewrite_outputs['output']
for model_output, rewrite_output in zip(model_outputs, rewrite_outputs):
model_output = model_output.cpu().numpy()
if isinstance(rewrite_output, torch.Tensor):
rewrite_output = rewrite_output.detach().cpu().numpy()
assert np.allclose(
model_output, rewrite_output, rtol=1e-03, atol=1e-05)
@pytest.mark.parametrize('backend_type', [Backend.ONNXRUNTIME])
def test_estimator_forward(backend_type: Backend):
check_backend(backend_type, True)

27
tests/test_core/test_function_rewriter.py
View File

@ -1,6 +1,11 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
try:
from torch.testing import assert_close as torch_assert_close
except Exception:
from torch.testing import assert_allclose as torch_assert_close
from mmdeploy.core import FUNCTION_REWRITER, RewriterContext
from mmdeploy.core.rewriters.function_rewriter import FunctionRewriter
from mmdeploy.core.rewriters.rewriter_utils import collect_env
@ -26,19 +31,19 @@ def test_function_rewriter():
with RewriterContext(cfg, backend='tensorrt'):
result = torch.add(x, y)
# replace add with sub
torch.testing.assert_allclose(result, x - y)
torch_assert_close(result, x - y)
result = torch.mul(x, y)
# replace add with sub
torch.testing.assert_allclose(result, x - y)
torch_assert_close(result, x - y)
result = torch.add(x, y)
# recovery origin function
torch.testing.assert_allclose(result, x + y)
torch_assert_close(result, x + y)
with RewriterContext(cfg):
result = torch.add(x, y)
# replace should not happen with wrong backend
torch.testing.assert_allclose(result, x + y)
torch_assert_close(result, x + y)
# test different config
@FUNCTION_REWRITER.register_rewriter(
@ -49,16 +54,16 @@ def test_function_rewriter():
with RewriterContext(cfg, backend='tensorrt'):
result = x.add(y)
# replace add with multi
torch.testing.assert_allclose(result, x * y)
torch_assert_close(result, x * y)
result = x.add(y)
# recovery origin function
torch.testing.assert_allclose(result, x + y)
torch_assert_close(result, x + y)
with RewriterContext(cfg):
result = x.add(y)
# replace add with multi
torch.testing.assert_allclose(result, x * y)
torch_assert_close(result, x * y)
# test origin_func
@FUNCTION_REWRITER.register_rewriter(
@ -70,11 +75,15 @@ def test_function_rewriter():
with RewriterContext(cfg):
result = torch.add(x, y)
# replace with origin + 1
torch.testing.assert_allclose(result, x + y + 1)
torch_assert_close(result, x + y + 1)
# remove torch.add
del FUNCTION_REWRITER._origin_functions[-1]
torch.testing.assert_allclose(torch.add(x, y), x + y)
torch_assert_close(torch.add(x, y), x + y)
FUNCTION_REWRITER._registry.remove_record(sub_func)
FUNCTION_REWRITER._registry.remove_record(mul_func_class)
FUNCTION_REWRITER._registry.remove_record(origin_add_func)
def test_rewrite_empty_function():

8
tests/test_core/test_module_rewriter.py
View File

@ -1,6 +1,10 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
try:
from torch.testing import assert_close as torch_assert_close
except Exception:
from torch.testing import assert_allclose as torch_assert_close
from mmdeploy.core import MODULE_REWRITER, patch_model
@ -29,7 +33,7 @@ def test_module_rewriter():
rewritten_model = patch_model(model, cfg=cfg, backend='tensorrt')
rewritten_bottle_nect = rewritten_model.layer1[0]
rewritten_result = rewritten_bottle_nect(x)
torch.testing.assert_allclose(rewritten_result, result * 2)
torch_assert_close(rewritten_result, result * 2)
# wrong backend should not be rewritten
model = resnet50().eval()
@ -38,7 +42,7 @@ def test_module_rewriter():
rewritten_model = patch_model(model, cfg=cfg)
rewritten_bottle_nect = rewritten_model.layer1[0]
rewritten_result = rewritten_bottle_nect(x)
torch.testing.assert_allclose(rewritten_result, result)
torch_assert_close(rewritten_result, result)
def test_pass_redundant_args_to_model():

18
tests/test_ops/test_ops.py
View File

@ -769,17 +769,13 @@ def test_gather(backend,
assert importlib.util.find_spec('onnxruntime') is not None, 'onnxruntime \
not installed.'
import numpy as np
import onnxruntime
session = onnxruntime.InferenceSession(gather_model.SerializeToString())
model_outputs = session.run(
output_names,
dict(
zip(input_names, [
np.array(data, dtype=np.float32),
np.array(indice[0], dtype=np.int64)
])))
model_outputs = [model_output for model_output in model_outputs]
from mmdeploy.backend.onnxruntime import ORTWrapper
ort_model = ORTWrapper(
gather_model.SerializeToString(),
device='cpu',
output_names=output_names)
model_outputs = ort_model(dict(zip(input_names, [data, indice[0]])))
model_outputs = ort_model.output_to_list(model_outputs)
ncnn_outputs = ncnn_model(
dict(zip(input_names, [data.float(), indice.float()])))