[Enhancement] Update function name and docstring in mmdeploy.pytorch (#191)

* Update function name and docstring in mmdeploy.python

* remove  in docstring

mmdeploy/pytorch/functions/__init__.py

@@ -1,13 +1,13 @@
-from .getattribute import getattribute_static
+from .getattribute import tensor__getattribute__ncnn
-from .group_norm import group_norm_ncnn
+from .group_norm import group_norm__ncnn
-from .interpolate import interpolate_static
+from .interpolate import interpolate__ncnn
-from .linear import linear_ncnn
+from .linear import linear__ncnn
-from .repeat import repeat_static
+from .repeat import tensor__repeat__tensorrt
-from .size import size_of_tensor_static
+from .size import tensor__size__ncnn
-from .topk import topk_dynamic, topk_static
+from .topk import topk__dynamic, topk__tensorrt
 
 __all__ = [
-    'getattribute_static', 'group_norm_ncnn', 'interpolate_static',
+    'tensor__getattribute__ncnn', 'group_norm__ncnn', 'interpolate__ncnn',
-    'linear_ncnn', 'repeat_static', 'size_of_tensor_static', 'topk_static',
+    'linear__ncnn', 'tensor__repeat__tensorrt', 'tensor__size__ncnn',
-    'topk_dynamic'
+    'topk__dynamic', 'topk__tensorrt'
 ]

mmdeploy/pytorch/functions/getattribute.py

@@ -5,8 +5,12 @@ from mmdeploy.core import FUNCTION_REWRITER
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.Tensor.__getattribute__', backend='ncnn')
-def getattribute_static(ctx, self, name):
+def tensor__getattribute__ncnn(ctx, self: torch.Tensor, name: str):
-    """Rewrite `__getattribute__` for NCNN backend."""
+    """Rewrite `__getattribute__` of `torch.Tensor` for NCNN backend.
+
+    Shape node is not supported by ncnn. This function transform dynamic shape
+    to constant shape.
+    """
 
     ret = ctx.origin_func(self, name)
     if name == 'shape':

mmdeploy/pytorch/functions/group_norm.py

@@ -7,7 +7,7 @@ from mmdeploy.core import FUNCTION_REWRITER
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.nn.functional.group_norm', backend='ncnn')
-def group_norm_ncnn(
+def group_norm__ncnn(
     ctx,
     input: torch.Tensor,
     num_groups: int,
@@ -15,7 +15,11 @@ def group_norm_ncnn(
     bias: Union[torch.Tensor, torch.NoneType] = None,
     eps: float = 1e-05,
 ) -> torch.Tensor:
-    """Rewrite `group_norm` for NCNN backend."""
+    """Rewrite `group_norm` for NCNN backend.
+
+    InstanceNorm in ncnn require input with shape [C, H, W]. So we have to
+    reshape the input tensor before it.
+    """
     input_shape = input.shape
     batch_size = input_shape[0]
     # We cannot use input.reshape(batch_size, num_groups, -1, 1)

mmdeploy/pytorch/functions/interpolate.py

@@ -1,16 +1,26 @@
+from typing import Optional, Tuple, Union
+
+import torch
+
 from mmdeploy.core import FUNCTION_REWRITER
 
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.nn.functional.interpolate', backend='ncnn')
-def interpolate_static(ctx,
+def interpolate__ncnn(ctx,
-                       input,
+                      input: torch.Tensor,
-                       size=None,
+                      size: Optional[Union[int, Tuple[int], Tuple[int, int],
-                       scale_factor=None,
+                                           Tuple[int, int, int]]] = None,
-                       mode='nearest',
+                      scale_factor: Optional[Union[float,
-                       align_corners=None,
+                                                   Tuple[float]]] = None,
-                       recompute_scale_factor=None):
+                      mode: str = 'nearest',
-    """Rewrite `interpolate` for NCNN backend."""
+                      align_corners: Optional[bool] = None,
+                      recompute_scale_factor: Optional[bool] = None):
+    """Rewrite `interpolate` for NCNN backend.
+
+    NCNN require `size` should be constant in ONNX Node. We use `scale_factor`
+    instead of `size` to avoid dynamic size.
+    """
 
     input_size = input.shape
     if scale_factor is None:

mmdeploy/pytorch/functions/linear.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Optional, Union
 
 import torch
 
@@ -7,13 +7,18 @@ from mmdeploy.core import FUNCTION_REWRITER
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.nn.functional.linear', backend='ncnn')
-def linear_ncnn(
+def linear__ncnn(
     ctx,
     input: torch.Tensor,
     weight: torch.Tensor,
-    bias: Union[torch.Tensor, torch.NoneType] = None,
+    bias: Optional[Union[torch.Tensor, torch.NoneType]] = None,
 ):
-    """Rewrite `linear` for NCNN backend."""
+    """Rewrite `linear` for NCNN backend.
+
+    The broadcast rules are different between ncnn and PyTorch. This function
+    add extra reshape and transpose to support linear operation of different
+    input shape.
+    """
 
     origin_func = ctx.origin_func
 

mmdeploy/pytorch/functions/repeat.py

@@ -1,10 +1,19 @@
+from typing import Sequence, Union
+
+import torch
+
 from mmdeploy.core import FUNCTION_REWRITER
 
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.Tensor.repeat', backend='tensorrt')
-def repeat_static(ctx, input, *size):
+def tensor__repeat__tensorrt(ctx, input: torch.Tensor,
-    """Rewrite `repeat` for NCNN backend."""
+                             *size: Union[torch.Size, Sequence[int]]):
+    """Rewrite `repeat` for TensorRT backend.
+
+    Some layers in TensorRT can not be applied on batch axis. add extra axis
+    before operation and remove it afterward.
+    """
 
     origin_func = ctx.origin_func
     if input.dim() == 1 and len(size) == 1:

mmdeploy/pytorch/functions/size.py

@@ -5,8 +5,12 @@ from mmdeploy.core import FUNCTION_REWRITER
 
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.Tensor.size', backend='ncnn')
-def size_of_tensor_static(ctx, self, *args):
+def tensor__size__ncnn(ctx, self, *args):
-    """Rewrite `size` for NCNN backend."""
+    """Rewrite `size` for NCNN backend.
+
+    ONNX Shape node is not supported in ncnn. This function return integal
+    instead of Torch.Size to avoid ONNX Shape node.
+    """
 
     ret = ctx.origin_func(self, *args)
     if isinstance(ret, torch.Tensor):

mmdeploy/pytorch/functions/topk.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import torch
 
 from mmdeploy.core import FUNCTION_REWRITER
@@ -6,8 +8,16 @@ from mmdeploy.core import FUNCTION_REWRITER
 @FUNCTION_REWRITER.register_rewriter(func_name='torch.topk', backend='default')
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.Tensor.topk', backend='default')
-def topk_dynamic(ctx, input, k, dim=None, largest=True, sorted=True):
+def topk__dynamic(ctx,
-    """Rewrite `topk` for default backend."""
+                  input: torch.Tensor,
+                  k: int,
+                  dim: Optional[int] = None,
+                  largest: bool = True,
+                  sorted: bool = True):
+    """Rewrite `topk` for default backend.
+
+    Cast k to tensor and makesure k is smaller than input.shape[dim].
+    """
 
     if dim is None:
         dim = int(input.ndim - 1)
@@ -25,8 +35,17 @@ def topk_dynamic(ctx, input, k, dim=None, largest=True, sorted=True):
     func_name='torch.topk', backend='tensorrt')
 @FUNCTION_REWRITER.register_rewriter(
     func_name='torch.Tensor.topk', backend='tensorrt')
-def topk_static(ctx, input, k, dim=None, largest=True, sorted=True):
+def topk__tensorrt(ctx,
-    """Rewrite `topk` for TensorRT backend."""
+                   input: torch.Tensor,
+                   k: int,
+                   dim: Optional[int] = None,
+                   largest: bool = True,
+                   sorted: bool = True):
+    """Rewrite `topk` for TensorRT backend.
+
+    TensorRT does not support topk with dynamic k. This function cast k to
+    constant integer.
+    """
 
     if dim is None:
         dim = int(input.ndim - 1)

mmdeploy/pytorch/ops/__init__.py

@@ -1,11 +1,12 @@
-from .adaptive_avg_pool import (adaptive_avg_pool1d_op, adaptive_avg_pool2d_op,
+from .adaptive_avg_pool import (adaptive_avg_pool1d__default,
-                                adaptive_avg_pool3d_op)
+                                adaptive_avg_pool2d__default,
-from .grid_sampler import grid_sampler_default
+                                adaptive_avg_pool3d__default)
-from .instance_norm import instance_norm_trt
+from .grid_sampler import grid_sampler__default
-from .squeeze import squeeze_default
+from .instance_norm import instance_norm__tensorrt
+from .squeeze import squeeze__default
 
 __all__ = [
-    'adaptive_avg_pool1d_op', 'adaptive_avg_pool2d_op',
+    'adaptive_avg_pool1d__default', 'adaptive_avg_pool2d__default',
-    'adaptive_avg_pool3d_op', 'grid_sampler_default', 'instance_norm_trt',
+    'adaptive_avg_pool3d__default', 'grid_sampler__default',
-    'squeeze_default'
+    'instance_norm__tensorrt', 'squeeze__default'
 ]

mmdeploy/pytorch/ops/adaptive_avg_pool.py

@@ -1,3 +1,4 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 # Modified from:
 # https://github.com/pytorch/pytorch/blob/9ade03959392e5a90b74261012de1d806cab2253/torch/onnx/symbolic_opset9.py
 
@@ -53,18 +54,27 @@ adaptive_avg_pool3d = _adaptive_pool('adaptive_avg_pool3d', 'AveragePool',
 
 
 @SYMBOLIC_REWRITER.register_symbolic('adaptive_avg_pool1d', is_pytorch=True)
-def adaptive_avg_pool1d_op(ctx, *args):
+def adaptive_avg_pool1d__default(ctx, *args):
-    """Register default symbolic function for `adaptive_avg_pool1d`."""
+    """Register default symbolic function for `adaptive_avg_pool1d`.
+
+    Align symbolic of adaptive_pool between different torch version.
+    """
     return adaptive_avg_pool1d(*args)
 
 
 @SYMBOLIC_REWRITER.register_symbolic('adaptive_avg_pool2d', is_pytorch=True)
-def adaptive_avg_pool2d_op(ctx, *args):
+def adaptive_avg_pool2d__default(ctx, *args):
-    """Register default symbolic function for `adaptive_avg_pool2d`."""
+    """Register default symbolic function for `adaptive_avg_pool2d`.
+
+    Align symbolic of adaptive_pool between different torch version.
+    """
     return adaptive_avg_pool2d(*args)
 
 
 @SYMBOLIC_REWRITER.register_symbolic('adaptive_avg_pool3d', is_pytorch=True)
-def adaptive_avg_pool3d_op(ctx, *args):
+def adaptive_avg_pool3d__default(ctx, *args):
-    """Register default symbolic function for `adaptive_avg_pool3d`."""
+    """Register default symbolic function for `adaptive_avg_pool3d`.
+
+    Align symbolic of adaptive_pool between different torch version.
+    """
     return adaptive_avg_pool3d(*args)

mmdeploy/pytorch/ops/grid_sampler.py

@@ -10,7 +10,12 @@ def grid_sampler(g,
                  interpolation_mode,
                  padding_mode,
                  align_corners=False):
-    """Symbolic function for `grid_sampler`."""
+    """Symbolic function for `grid_sampler`.
+
+    PyTorch does not support export grid_sampler to ONNX by default. We add the
+    support here. `grid_sampler` will be exported as ONNX node
+    'mmcv::grid_sampler'
+    """
     return g.op(
         'mmcv::grid_sampler',
         input,
@@ -21,6 +26,9 @@ def grid_sampler(g,
 
 
 @SYMBOLIC_REWRITER.register_symbolic('grid_sampler', is_pytorch=True)
-def grid_sampler_default(ctx, *args):
+def grid_sampler__default(ctx, *args):
-    """Register default symbolic function for `grid_sampler`."""
+    """Register default symbolic function for `grid_sampler`.
+
+    Add support to grid_sample to ONNX.
+    """
     return grid_sampler(*args)

mmdeploy/pytorch/ops/instance_norm.py

@@ -1,3 +1,4 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 # Modified from:
 # https://github.com/pytorch/pytorch/blob/9ade03959392e5a90b74261012de1d806cab2253/torch/onnx/symbolic_opset9.py
 
@@ -63,7 +64,7 @@ def instance_norm(g, input, num_groups, weight, bias, eps, cudnn_enabled):
 
 @SYMBOLIC_REWRITER.register_symbolic(
     'group_norm', backend='tensorrt', is_pytorch=True)
-def instance_norm_trt(ctx, *args):
+def instance_norm__tensorrt(ctx, *args):
     """Register symbolic function for TensorRT backend.
 
     Notes:

mmdeploy/pytorch/ops/squeeze.py

@@ -4,8 +4,12 @@ from mmdeploy.core import SYMBOLIC_REWRITER
 
 
 @SYMBOLIC_REWRITER.register_symbolic('squeeze', is_pytorch=True)
-def squeeze_default(ctx, g, self, dim=None):
+def squeeze__default(ctx, g, self, dim=None):
-    """Register default symbolic function for `squeeze`."""
+    """Register default symbolic function for `squeeze`.
+
+    squeeze might be exported with IF node in ONNX, which is not supported in
+    lots of backend.
+    """
     if dim is None:
         dims = []
         for i, size in enumerate(self.type().sizes()):