[Feature] Add mmocr ncnn support (open-mmlab#53)

* first

* fix0

* fix1

* dirty work

* wip

* add allocator

* finally done!

* lint

* fix lint

* better gather

* better onnx2ncnn

* fix expand

* [Fix] NCNN TensorSlice op bugs (open-mmlab#42)

* fix custom ops support, fix multiple mark bug, add name mapping

* check if the value_info need to be added

* remove unnecessary print

* add nms implement

* two stage split wip

* add two stage split

* add split retinanet visualize

* add two stage split (wip)

* finish two stage split

* fix lint

* move parse string to mmdeploy.utils

* add calib data generator

* create calib dataset

* finish end2end int8

* add split two stage tensorrt visualize

* fix tensorslice bugs

* fix lint

* fix clang-format

* remove comments

* int param

* fix lint

Co-authored-by: grimoire <[email protected]>

* add two stage ncnn support

* remove unused ops

* git unused config

* remove no_grad, should add in refactor

* add ncnn wrapper

* fix lint

* size return tuple

* Resolve grammar error

* Fix lint

* Trim Trailing Whitespace

* fix trim

* update wrapper

* remove logs

* remove

* csrc optimize

* add ncnn dbnet support

* finish crnn support

* add comment

Co-authored-by: hanrui1sensetime <[email protected]>

backend_ops/ncnn/onnx2ncnn/onnx2ncnn.cpp

@@ -1917,6 +1917,7 @@ static void fuse_lstm_gru_rnn(onnx::GraphProto* mutable_graph,
     onnx::NodeProto* node = mutable_graph->mutable_node(i);
 
     // LSTM(bi) <= LSTM(bi) - Transpose - Reshape - Transpose
+    // or LSTM(bi) <= LSTM(bi) - Transpose Constant - Reshape - Transpose
     if (node->op_type() == "LSTM" || node->op_type() == "GRU" ||
         node->op_type() == "RNN") {
       if (node_reference[node->output(0)] != 1) continue;
@@ -1926,6 +1927,13 @@ static void fuse_lstm_gru_rnn(onnx::GraphProto* mutable_graph,
       onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
       onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
 
+      // skip if second ops is constant
+      if (node3->op_type() == "Constant") {
+        if (i + 3 >= node_count) continue;
+        node3 = mutable_graph->mutable_node(i + 3);
+        i += 1;
+      }
+
       if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
         continue;
 

configs/mmocr/base_static.py

@@ -0,0 +1,6 @@
+_base_ = ['../_base_/torch2onnx.py']
+codebase = 'mmocr'
+
+# 'TextDetection' or 'TextRecognition'
+task = 'TextDetection'
+pytorch2onnx = dict(input_names=['input'], output_names=['output'])

configs/mmocr/ncnn.py

@@ -0,0 +1 @@
+_base_ = ['./base_static.py', '../_base_/backends/ncnn.py']

mmdeploy/mmocr/apis/inference.py

@@ -240,6 +240,48 @@ def forward_of_backend(self,
         return trt_pred
 
 
+class NCNNDetector(DeployBaseTextDetector):
+    """The class for evaluating NCNN file of text detection."""
+
+    def __init__(self,
+                 model_file: Iterable[str],
+                 cfg: Union[mmcv.Config, mmcv.ConfigDict],
+                 device_id: int,
+                 show_score: bool = False):
+        super(NCNNDetector, self).__init__(cfg, device_id, show_score)
+        from mmdeploy.apis.ncnn import NCNNWrapper
+        self.model = NCNNWrapper(
+            model_file[0], model_file[1], output_names=['output'])
+
+    def forward_of_backend(self,
+                           img: torch.Tensor,
+                           img_metas: Iterable,
+                           rescale: bool = False):
+        pred = self.model({'input': img})['output']
+        return pred
+
+
+class NCNNRecognizer(DeployBaseRecognizer):
+    """The class for evaluating NCNN file of recognition."""
+
+    def __init__(self,
+                 model_file: Iterable[str],
+                 cfg: Union[mmcv.Config, mmcv.ConfigDict],
+                 device_id: int,
+                 show_score: bool = False):
+        super(NCNNRecognizer, self).__init__(cfg, device_id, show_score)
+        from mmdeploy.apis.ncnn import NCNNWrapper
+        self.model = NCNNWrapper(
+            model_file[0], model_file[1], output_names=['output'])
+
+    def forward_of_backend(self,
+                           img: torch.Tensor,
+                           img_metas: Iterable,
+                           rescale: bool = False):
+        pred = self.model({'input': img})['output']
+        return pred
+
+
 def get_classes_from_config(model_cfg: Union[str, mmcv.Config], **kwargs):
     # load cfg if necessary
     model_cfg = load_config(model_cfg)[0]
@@ -268,9 +310,15 @@ def get_classes_from_config(model_cfg: Union[str, mmcv.Config], **kwargs):
     Task.TEXT_RECOGNITION: TensorRTRecognizer
 }
 
+TASK_NCNN_MAP = {
+    Task.TEXT_DETECTION: NCNNDetector,
+    Task.TEXT_RECOGNITION: NCNNRecognizer
+}
+
 BACKEND_TASK_MAP = {
     Backend.ONNXRUNTIME: TASK_ONNXRUNTIME_MAP,
-    Backend.TENSORRT: TASK_TENSORRT_MAP
+    Backend.TENSORRT: TASK_TENSORRT_MAP,
+    Backend.NCNN: TASK_NCNN_MAP
 }
 
 

mmdeploy/mmocr/models/textrecog/__init__.py

@@ -1,4 +1,6 @@
+from .layers import *  # noqa: F401, F403
 from .recognizer.base import forward_of_base_recognizer
+from .recognizer.decoders import *  # noqa: F401, F403
 from .recognizer.encode_decode_recognizer import \
     simple_test_of_encode_decode_recognizer
 

mmdeploy/mmocr/models/textrecog/layers/__init__.py

@@ -0,0 +1,3 @@
+from .lstm_layer import forward_of_bidirectionallstm
+
+__all__ = ['forward_of_bidirectionallstm']

mmdeploy/mmocr/models/textrecog/layers/lstm_layer.py

@@ -0,0 +1,15 @@
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmocr.models.textrecog.layers.lstm_layer'
+    '.BidirectionalLSTM.forward',
+    backend='ncnn')
+def forward_of_bidirectionallstm(ctx, self, input):
+    self.rnn.batch_first = True
+    recurrent, _ = self.rnn(input)
+    self.rnn.batch_first = False
+
+    output = self.embedding(recurrent)
+
+    return output

mmdeploy/mmocr/models/textrecog/recognizer/decoders/__init__.py

@@ -0,0 +1,3 @@
+from .crnn_decoder import forward_train_of_crnndecoder
+
+__all__ = ['forward_train_of_crnndecoder']

mmdeploy/mmocr/models/textrecog/recognizer/decoders/crnn_decoder.py

@@ -0,0 +1,19 @@
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmocr.models.textrecog.decoders.CRNNDecoder.forward_train',
+    backend='ncnn')
+def forward_train_of_crnndecoder(ctx, self, feat, out_enc, targets_dict,
+                                 img_metas):
+    assert feat.size(2) == 1, 'feature height must be 1'
+    if self.rnn_flag:
+        x = feat.squeeze(2)  # [N, C, W]
+        x = x.permute(0, 2, 1)  # [N, W, C]
+        outputs = self.decoder(x)
+    else:
+        x = self.decoder(feat)
+        x = x.permute(0, 3, 1, 2).contiguous()
+        n, w, c, h = x.size()
+        outputs = x.view(n, w, c * h)
+    return outputs

mmdeploy/pytorch/functions/__init__.py

@@ -1,10 +1,11 @@
 from .getattribute import getattribute_static
 from .interpolate import interpolate_static
+from .linear import linear_ncnn
 from .repeat import repeat_static
 from .size import size_of_tensor_static
 from .topk import topk_dynamic, topk_static
 
 __all__ = [
-    'getattribute_static', 'interpolate_static', 'repeat_static',
-    'size_of_tensor_static', 'topk_static', 'topk_dynamic'
+    'getattribute_static', 'interpolate_static', 'linear_ncnn',
+    'repeat_static', 'size_of_tensor_static', 'topk_static', 'topk_dynamic'
 ]

mmdeploy/pytorch/functions/linear.py

@@ -0,0 +1,42 @@
+from typing import Union
+
+import torch
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='torch.nn.functional.linear', backend='ncnn')
+def linear_ncnn(
+    ctx,
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    bias: Union[torch.Tensor, torch.NoneType] = None,
+):
+    origin_func = ctx.origin_func
+
+    dim = input.dim()
+
+    if dim == 2:
+        return origin_func(input, weight, bias)
+    else:
+        out = origin_func(input, weight)
+
+        # permute
+        out = out.transpose(1, dim - 1)
+
+        # ncnn only support [c, h, w] and [c, 1, 1] broadcast
+        out_shape = out.shape
+        batch_size = out_shape[0]
+        broad_cast_size = out_shape[1]
+        out = out.reshape([batch_size, broad_cast_size, -1, 1])
+
+        # add bias
+        bias = bias.view([1, -1, 1, 1])
+        out = out + bias
+
+        # permute back
+        out = out.reshape(out_shape)
+        out = out.transpose(1, dim - 1)
+
+        return out

mmdeploy/pytorch/ops/__init__.py

@@ -2,8 +2,10 @@
                                 adaptive_avg_pool3d_op)
 from .grid_sampler import grid_sampler_default
 from .instance_norm import instance_norm_trt
+from .squeeze import squeeze_default
 
 __all__ = [
     'adaptive_avg_pool1d_op', 'adaptive_avg_pool2d_op',
-    'adaptive_avg_pool3d_op', 'grid_sampler_default', 'instance_norm_trt'
+    'adaptive_avg_pool3d_op', 'grid_sampler_default', 'instance_norm_trt',
+    'squeeze_default'
 ]

mmdeploy/pytorch/ops/squeeze.py

@@ -0,0 +1,15 @@
+import torch.onnx.symbolic_helper as sym_help
+
+from mmdeploy.core import SYMBOLIC_REGISTER
+
+
+@SYMBOLIC_REGISTER.register_symbolic('squeeze', is_pytorch=True)
+def squeeze_default(ctx, g, self, dim=None):
+    if dim is None:
+        dims = []
+        for i, size in enumerate(self.type().sizes()):
+            if size == 1:
+                dims.append(i)
+    else:
+        dims = [sym_help._get_const(dim, 'i', 'dim')]
+    return g.op('Squeeze', self, axes_i=dims)