[Fix] Reanme ppl to pplnn for all cases (open-mmlab#273)

AllentDan · RunningLeon · web-flow · commit 6816bb304771 · 2021-12-14T15:04:39.000+08:00
* add speed benchmark for pplnn and replace all ppl in docs with pplnn

* replace ppl with pplnn for all codes

* docs zh

* fix crnn config path

* update docstring

* rename

* update ppl to pplnn in readme_zh

Co-authored-by: maningsheng &lt;mnsheng@yeah.net&gt;
diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -36,7 +36,7 @@ MMDeploy 是一个开源深度学习模型部署工具箱，它是 [OpenMMLab](h
 
 - [x] ONNX Runtime
 - [x] TensorRT
-- [x] PPL
+- [x] PPLNN
 - [x] ncnn
 
 ## 安装
diff --git a/configs/_base_/backends/ppl.py b/configs/_base_/backends/ppl.py
diff --git a/configs/_base_/backends/pplnn.py b/configs/_base_/backends/pplnn.py
@@ -0,0 +1 @@
+backend_config = dict(type='pplnn')
diff --git a/configs/mmcls/classification_pplnn_dynamic-224x224.py b/configs/mmcls/classification_pplnn_dynamic-224x224.py
@@ -1,4 +1,4 @@
-_base_ = ['./classification_dynamic.py', '../_base_/backends/ppl.py']
+_base_ = ['./classification_dynamic.py', '../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=None)
 
diff --git a/configs/mmcls/classification_tensorrt-fp16_static-224x224.py b/configs/mmcls/classification_tensorrt-fp16_static-224x224.py
diff --git a/configs/mmdet/detection/detection_pplnn_dynamic-800x1344.py b/configs/mmdet/detection/detection_pplnn_dynamic-800x1344.py
@@ -1,4 +1,4 @@
-_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/ppl.py']
+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=None)
 
diff --git a/configs/mmdet/instance-seg/instance-seg_pplnn_dynamic-800x1344.py b/configs/mmdet/instance-seg/instance-seg_pplnn_dynamic-800x1344.py
@@ -1,5 +1,5 @@
 _base_ = [
-    '../_base_/base_instance-seg_dynamic.py', '../../_base_/backends/ppl.py'
+    '../_base_/base_instance-seg_dynamic.py', '../../_base_/backends/pplnn.py'
 ]
 
 onnx_config = dict(input_shape=None)
diff --git a/configs/mmedit/super-resolution/super-resolution_pplnn_dynamic-32x32.py b/configs/mmedit/super-resolution/super-resolution_pplnn_dynamic-32x32.py
@@ -1,4 +1,4 @@
-_base_ = ['./super-resolution_dynamic.py', '../../_base_/backends/ppl.py']
+_base_ = ['./super-resolution_dynamic.py', '../../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=None)
 
diff --git a/configs/mmocr/text-detection/text-detection_pplnn_dynamic-640x640.py b/configs/mmocr/text-detection/text-detection_pplnn_dynamic-640x640.py
@@ -1,4 +1,4 @@
-_base_ = ['./text-detection_dynamic.py', '../../_base_/backends/ppl.py']
+_base_ = ['./text-detection_dynamic.py', '../../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=None)
 
diff --git a/configs/mmseg/segmentation_pplnn_dynamic-1024x2048.py b/configs/mmseg/segmentation_pplnn_dynamic-1024x2048.py
@@ -1,4 +1,4 @@
-_base_ = ['./segmentation_static.py', '../_base_/backends/ppl.py']
+_base_ = ['./segmentation_dynamic.py', '../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=[2048, 1024])
 
diff --git a/configs/mmseg/segmentation_pplnn_dynamic-512x1024.py b/configs/mmseg/segmentation_pplnn_dynamic-512x1024.py
@@ -1,4 +1,4 @@
-_base_ = ['./segmentation_dynamic.py', '../_base_/backends/ppl.py']
+_base_ = ['./segmentation_dynamic.py', '../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=None)
 
diff --git a/configs/mmseg/segmentation_pplnn_static-1024x2048.py b/configs/mmseg/segmentation_pplnn_static-1024x2048.py
@@ -1,4 +1,4 @@
-_base_ = ['./segmentation_dynamic.py', '../_base_/backends/ppl.py']
+_base_ = ['./segmentation_static.py', '../_base_/backends/pplnn.py']
 
 onnx_config = dict(input_shape=[2048, 1024])
 
diff --git a/docs/api.rst b/docs/api.rst
@@ -18,7 +18,7 @@ apis/ncnn
 .. automodule:: mmdeploy.apis.ncnn
     :members:
 
-apis/ppl
+apis/pplnn
 --------
-.. automodule:: mmdeploy.apis.ppl
+.. automodule:: mmdeploy.apis.pplnn
     :members:
diff --git a/docs/backends/pplnn.md b/docs/backends/pplnn.md
@@ -13,7 +13,7 @@ This tutorial is based on Linux systems like Ubuntu-18.04.
 Example:
 ```bash
 python tools/deploy.py \
-    configs/mmdet/detection/detection_ppl_dynamic-800x1344.py \
+    configs/mmdet/detection/detection_pplnn_dynamic-800x1344.py \
     /mmdetection_dir/mmdetection/configs/retinanet/retinanet_r50_fpn_1x_coco.py \
     /tmp/snapshots/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth \
     tests/data/tiger.jpeg \
diff --git a/docs/tutorials/how_to_convert_model.md b/docs/tutorials/how_to_convert_model.md
@@ -115,7 +115,7 @@ The table below lists the models that are guaranteed to be exportable to other b
 | SRCNN              | MMEditing        |      Y      |    Y     |  N   |   Y   |    N     | $MMSEG_DIR/configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py                     |
 | ESRGAN             | MMEditing        |      Y      |    Y     |  N   |   Y   |    N     | $MMSEG_DIR/configs/restorers/esrgan/esrgan_psnr_x4c64b23g32_g1_1000k_div2k.py         |
 | DBNet              | MMOCR            |      Y      |    Y     |  Y   |   Y   |    Y     | $MMOCR_DIR/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py                    |
-| CRNN               | MMOCR            |      Y      |    Y     |  Y   |   N   |    N     | $MMOCR_DIR/configs/textrecog/tps/crnn_academic_dataset.py                             |
+| CRNN               | MMOCR            |      Y      |    Y     |  Y   |   N   |    N     | $MMOCR_DIR/configs/textrecog/crnn/crnn_academic_dataset.py                             |
 
 
 ### Reminders
diff --git a/docs_zh_CN/api.rst b/docs_zh_CN/api.rst
@@ -18,7 +18,7 @@ apis/ncnn
 .. automodule:: mmdeploy.apis.ncnn
     :members:
 
-apis/ppl
+apis/pplnn
 --------
-.. automodule:: mmdeploy.apis.ppl
+.. automodule:: mmdeploy.apis.pplnn
     :members:
diff --git a/docs_zh_CN/backends/pplnn.md b/docs_zh_CN/backends/pplnn.md
diff --git a/mmdeploy/apis/ppl/__init__.py b/mmdeploy/apis/ppl/__init__.py
diff --git a/mmdeploy/apis/pplnn/__init__.py b/mmdeploy/apis/pplnn/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdeploy.backend.pplnn import is_available
+
+__all__ = ['is_available']
+
+if is_available():
+    from mmdeploy.backend.pplnn.onnx2pplnn import onnx2pplnn
+
+    __all__ += ['onnx2pplnn']
diff --git a/mmdeploy/backend/__init__.py b/mmdeploy/backend/__init__.py
@@ -2,7 +2,7 @@
 from mmdeploy.backend.ncnn import is_available as ncnn_available
 from mmdeploy.backend.onnxruntime import is_available as ort_available
 from mmdeploy.backend.openvino import is_available as openvino_available
-from mmdeploy.backend.ppl import is_available as ppl_available
+from mmdeploy.backend.pplnn import is_available as pplnn_available
 from mmdeploy.backend.tensorrt import is_available as trt_available
 
 __all__ = []
@@ -15,9 +15,9 @@
 if trt_available():
     from .tensorrt import TRTWrapper  # noqa: F401,F403
     __all__.append('TRTWrapper')
-if ppl_available():
-    from .ppl import PPLWrapper  # noqa: F401,F403
-    __all__.append('PPLWrapper')
+if pplnn_available():
+    from .pplnn import PPLNNWrapper  # noqa: F401,F403
+    __all__.append('PPLNNWrapper')
 if openvino_available():
     from .openvino import OpenVINOWrapper  # noqa: F401,F403
     __all__.append('OpenVINOWrapper')
diff --git a/mmdeploy/backend/ppl/__init__.py b/mmdeploy/backend/ppl/__init__.py
diff --git a/mmdeploy/backend/pplnn/__init__.py b/mmdeploy/backend/pplnn/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import importlib
+
+
+def is_available():
+    """Check whether pplnn is installed.
+
+    Returns:
+        bool: True if pplnn package is installed.
+    """
+    return importlib.util.find_spec('pyppl') is not None
+
+
+if is_available():
+    from .wrapper import PPLNNWrapper, register_engines
+    __all__ = ['register_engines', 'PPLNNWrapper']
diff --git a/mmdeploy/backend/pplnn/onnx2pplnn.py b/mmdeploy/backend/pplnn/onnx2pplnn.py
@@ -7,30 +7,30 @@
 from .wrapper import register_engines
 
 
-def onnx2ppl(algo_file: str,
-             onnx_model: str,
-             device: str = 'cuda:0',
-             input_shapes: Optional[Sequence[Sequence[int]]] = None,
-             **kwargs):
-    """Convert ONNX to PPL.
-
-    PPL is capable of optimizing onnx model. The optimized algorithm is saved
+def onnx2pplnn(algo_file: str,
+               onnx_model: str,
+               device: str = 'cuda:0',
+               input_shapes: Optional[Sequence[Sequence[int]]] = None,
+               **kwargs):
+    """Convert ONNX to PPLNN.
+
+    PPLNN is capable of optimizing onnx model. The optimized algorithm is saved
     into `algo_file` in json format. Note that `input_shapes` actually require
     multiple shapes of inputs in its original design. But in the pipeline of
     our codebase, we only pass one input shape which can be modified by users'
     own preferences.
 
     Args:
-        algo_file (str): File path to save PPL optimization algorithm.
+        algo_file (str): File path to save PPLNN optimization algorithm.
         onnx_model (str): Input onnx model.
         device (str): A string specifying cuda device, defaults to 'cuda:0'.
-        input_shapes (Sequence[Sequence[int]] | None): shapes for PPL
+        input_shapes (Sequence[Sequence[int]] | None): shapes for PPLNN
             optimization, default to None.
 
     Examples:
-        >>> from mmdeploy.apis.ppl import onnx2ppl
+        >>> from mmdeploy.apis.pplnn import onnx2pplnn
         >>>
-        >>> onnx2ppl(algo_file = 'example.json', onnx_model = 'example.onnx')
+        >>> onnx2pplnn(algo_file = 'example.json', onnx_model = 'example.onnx')
     """
     if device == 'cpu':
         device_id = -1
@@ -39,7 +39,8 @@ def onnx2ppl(algo_file: str,
         '`cpu` or `cuda`'
         device_id = parse_cuda_device_id(device)
     if input_shapes is None:
-        input_shapes = [[1, 3, 224, 224]]  # PPL default shape for optimization
+        input_shapes = [[1, 3, 224,
+                         224]]  # PPLNN default shape for optimization
 
     engines = register_engines(
         device_id,
diff --git a/mmdeploy/backend/pplnn/wrapper.py b/mmdeploy/backend/pplnn/wrapper.py
@@ -20,20 +20,21 @@ def register_engines(device_id: int,
                      input_shapes: Sequence[Sequence[int]] = None,
                      export_algo_file: str = None,
                      import_algo_file: str = None) -> List[pplnn.Engine]:
-    """Register engines for ppl runtime.
+    """Register engines for pplnn runtime.
 
     Args:
         device_id (int): Specifying device index. `-1` for cpu.
         disable_avx512 (bool): Whether to disable avx512 for x86.
             Defaults to `False`.
         quick_select (bool): Whether to use default algorithms.
             Defaults to `False`.
-        input_shapes (Sequence[Sequence[int]]): shapes for PPL optimization.
-        export_algo_file (str): File path for exporting PPL optimization file.
-        import_algo_file (str): File path for loading PPL optimization file.
+        input_shapes (Sequence[Sequence[int]]): shapes for PPLNN optimization.
+        export_algo_file (str): File path for exporting PPLNN optimization
+            file.
+        import_algo_file (str): File path for loading PPLNN optimization file.
 
     Returns:
-        list[pplnn.Engine]: A list of registered ppl engines.
+        list[pplnn.Engine]: A list of registered pplnn engines.
     """
     engines = []
     if device_id == -1:
@@ -101,21 +102,21 @@ def register_engines(device_id: int,
     return engines
 
 
-@BACKEND_WRAPPER.register_module(Backend.PPL.value)
-class PPLWrapper(BaseWrapper):
-    """PPL wrapper for inference.
+@BACKEND_WRAPPER.register_module(Backend.PPLNN.value)
+class PPLNNWrapper(BaseWrapper):
+    """PPLNN wrapper for inference.
 
     Args:
         onnx_file (str): Path of input ONNX model file.
-        algo_file (str): Path of PPL algorithm file.
+        algo_file (str): Path of PPLNN algorithm file.
         device_id (int): Device id to put model.
 
     Examples:
-        >>> from mmdeploy.backend.ppl import PPLWrapper
+        >>> from mmdeploy.backend.pplnn import PPLNNWrapper
         >>> import torch
         >>>
         >>> onnx_file = 'model.onnx'
-        >>> model = PPLWrapper(onnx_file, 'end2end.json', 0)
+        >>> model = PPLNNWrapper(onnx_file, 'end2end.json', 0)
         >>> inputs = dict(input=torch.randn(1, 3, 224, 224))
         >>> outputs = model(inputs)
         >>> print(outputs)
@@ -129,7 +130,7 @@ def __init__(self,
                  **kwargs):
 
         # enable quick select by default to speed up pipeline
-        # TODO: open it to users after ppl supports saving serialized models
+        # TODO: open it to users after pplnn supports saving serialized models
 
         # TODO: assert device is gpu
         device_id = parse_device_id(device)
@@ -174,7 +175,7 @@ def forward(self, inputs: Dict[str,
         for name, input_tensor in inputs.items():
             input_tensor = input_tensor.contiguous()
             self.inputs[name].ConvertFromHost(input_tensor.cpu().numpy())
-        self.__ppl_execute()
+        self.__pplnn_execute()
         outputs = {}
         for i in range(self.runtime.GetOutputCount()):
             out_tensor = self.runtime.GetOutputTensor(i).ConvertToHost()
@@ -183,8 +184,8 @@ def forward(self, inputs: Dict[str,
         return outputs
 
     @TimeCounter.count_time()
-    def __ppl_execute(self):
-        """Run inference with PPL."""
+    def __pplnn_execute(self):
+        """Run inference with PPLNN."""
         status = self.runtime.Run()
         assert status == pplcommon.RC_SUCCESS, 'Run() failed: ' + \
             pplcommon.GetRetCodeStr(status)
diff --git a/mmdeploy/codebase/base/backend_model.py b/mmdeploy/codebase/base/backend_model.py
@@ -40,9 +40,9 @@ def _build_wrapper(backend: Backend,
             from mmdeploy.backend.tensorrt import TRTWrapper
             return TRTWrapper(
                 engine=backend_files[0], output_names=output_names)
-        elif backend == Backend.PPL:
-            from mmdeploy.backend.ppl import PPLWrapper
-            return PPLWrapper(
+        elif backend == Backend.PPLNN:
+            from mmdeploy.backend.pplnn import PPLNNWrapper
+            return PPLNNWrapper(
                 onnx_file=backend_files[0],
                 algo_file=backend_files[1],
                 device=device,
diff --git a/mmdeploy/utils/constants.py b/mmdeploy/utils/constants.py
@@ -39,7 +39,7 @@ class Backend(AdvancedEnum):
     PYTORCH = 'pytorch'
     TENSORRT = 'tensorrt'
     ONNXRUNTIME = 'onnxruntime'
-    PPL = 'ppl'
+    PPLNN = 'pplnn'
     NCNN = 'ncnn'
     OPENVINO = 'openvino'
     DEFAULT = 'default'
diff --git a/mmdeploy/utils/test.py b/mmdeploy/utils/test.py
@@ -34,8 +34,8 @@ def backend_checker(backend: Backend, require_plugin: bool = False):
         from mmdeploy.apis.tensorrt import is_available
         if require_plugin:
             from mmdeploy.apis.tensorrt import is_plugin_available
-    elif backend == Backend.PPL:
-        from mmdeploy.apis.ppl import is_available
+    elif backend == Backend.PPLNN:
+        from mmdeploy.apis.pplnn import is_available
     elif backend == Backend.NCNN:
         from mmdeploy.apis.ncnn import is_available
         if require_plugin:
@@ -82,8 +82,8 @@ def check_backend(backend: Backend, require_plugin: bool = False):
         from mmdeploy.apis.tensorrt import is_available
         if require_plugin:
             from mmdeploy.apis.tensorrt import is_plugin_available
-    elif backend == Backend.PPL:
-        from mmdeploy.apis.ppl import is_available
+    elif backend == Backend.PPLNN:
+        from mmdeploy.apis.pplnn import is_available
     elif backend == Backend.NCNN:
         from mmdeploy.apis.ncnn import is_available
         if require_plugin:
diff --git a/tests/test_backend/test_wrapper.py b/tests/test_backend/test_wrapper.py
diff --git a/tools/check_env.py b/tools/check_env.py
diff --git a/tools/deploy.py b/tools/deploy.py

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-_base_ = ['./classification_dynamic.py', '../_base_/backends/ppl.py']`
	`1`	`+_base_ = ['./classification_dynamic.py', '../_base_/backends/pplnn.py']`
`2`	`2`
`3`	`3`	`onnx_config = dict(input_shape=None)`
`4`	`4`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/ppl.py']`
	`1`	`+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/pplnn.py']`
`2`	`2`
`3`	`3`	`onnx_config = dict(input_shape=None)`
`4`	`4`
Original file line number	Diff line number	Diff line change
`@@ -1,5 +1,5 @@`
`1`	`1`	`_base_ = [`
`2`		`- '../_base_/base_instance-seg_dynamic.py', '../../_base_/backends/ppl.py'`
	`2`	`+ '../_base_/base_instance-seg_dynamic.py', '../../_base_/backends/pplnn.py'`
`3`	`3`	`]`
`4`	`4`
`5`	`5`	`onnx_config = dict(input_shape=None)`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-_base_ = ['./super-resolution_dynamic.py', '../../_base_/backends/ppl.py']`
	`1`	`+_base_ = ['./super-resolution_dynamic.py', '../../_base_/backends/pplnn.py']`
`2`	`2`
`3`	`3`	`onnx_config = dict(input_shape=None)`
`4`	`4`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-_base_ = ['./text-detection_dynamic.py', '../../_base_/backends/ppl.py']`
	`1`	`+_base_ = ['./text-detection_dynamic.py', '../../_base_/backends/pplnn.py']`
`2`	`2`
`3`	`3`	`onnx_config = dict(input_shape=None)`
`4`	`4`
Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-_base_ = ['./segmentation_static.py', '../_base_/backends/ppl.py']`
	`1`	`+_base_ = ['./segmentation_dynamic.py', '../_base_/backends/pplnn.py']`
`2`	`2`
`3`	`3`	`onnx_config = dict(input_shape=[2048, 1024])`
`4`	`4`