fix ascend (#1667)

docs/en/01-how-to-build/cmake_option.md

@@ -94,7 +94,7 @@
   </tr>
   <tr>
     <td>MMDEPLOY_TARGET_BACKENDS</td>
-    <td>{"trt", "ort", "pplnn", "ncnn", "openvino", "torchscript", "snpe", "tvm"}</td>
+    <td>{"trt", "ort", "pplnn", "ncnn", "openvino", "torchscript", "snpe", "tvm", "acl"}</td>
     <td>N/A</td>
     <td>Enabling inference engine. <b>By default, no target inference engine is set, since it highly depends on the use case.</b> When more than one engine are specified, it has to be set with a semicolon separated list of inference backend names, e.g. <pre><code>-DMMDEPLOY_TARGET_BACKENDS="trt;ort;pplnn;ncnn;openvino"</code></pre>
     After specifying the inference engine, it's package path has to be passed to cmake as follows, <br>

docs/zh_cn/01-how-to-build/cmake_option.md

@@ -97,7 +97,7 @@
 
 <tr>
     <td>MMDEPLOY_TARGET_BACKENDS</td>
-    <td>{"trt", "ort", "pplnn", "ncnn", "openvino", "torchscript", "snpe", "coreml", "tvm"}</td>
+    <td>{"trt", "ort", "pplnn", "ncnn", "openvino", "torchscript", "snpe", "coreml", "tvm", "acl"}</td>
     <td>N/A</td>
     <td> <b>默认情况下，SDK不设置任何后端</b>, 因为它与应用场景高度相关。 当选择多个后端时， 中间使用分号隔开。比如，<pre><code>-DMMDEPLOY_TARGET_BACKENDS="trt;ort;pplnn;ncnn;openvino"</code></pre>
     构建时，几乎每个后端，都需设置一些路径变量，用来查找依赖包。<br>

mmdeploy/apis/visualize.py

@@ -75,19 +75,21 @@ def visualize_model(model_cfg: Union[str, mmcv.Config],
         # check headless
         import tkinter
         tkinter.Tk()
-        if isinstance(img, str) or not isinstance(img, Sequence):
-            img = [img]
-        for single_img in img:
-            task_processor.visualize(
-                image=single_img,
-                model=model,
-                result=result,
-                output_file=output_file,
-                window_name=backend.value,
-                show_result=show_result)
     except Exception as e:
         from mmdeploy.utils import get_root_logger
         logger = get_root_logger()
         logger.warn(
             f'render and display result skipped for headless device, exception {e}'  # noqa: E501
         )
+        show_result = False
+
+    if isinstance(img, str) or not isinstance(img, Sequence):
+        img = [img]
+    for single_img in img:
+        task_processor.visualize(
+            image=single_img,
+            model=model,
+            result=result,
+            output_file=output_file,
+            window_name=backend.value,
+            show_result=show_result)

mmdeploy/backend/ascend/backend_manager.py

@@ -85,7 +85,8 @@ class AscendManager(BaseBackendManager):
 
         om_files = []
         for model_id, onnx_path in enumerate(ir_files):
-            om_path = osp.splitext(onnx_path)[0] + '.om'
+            om_name = osp.splitext(osp.split(onnx_path)[1])[0] + '.om'
+            om_path = osp.join(work_dir, om_name)
             from_onnx(onnx_path, work_dir, model_inputs[model_id])
             om_files.append(om_path)
         backend_files = om_files

mmdeploy/backend/ascend/wrapper.py

@@ -393,6 +393,7 @@ class AscendWrapper(BaseWrapper):
             self.__ascend_execute()
 
             for binding in self._model_desc.outputs:
+                tensor = outputs[binding.name]
                 self._copy_buffer_to_tensor(
                     self._output.buffers[binding.index], tensor)
 

mmdeploy/codebase/mmdet/core/post_processing/bbox_nms.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from typing import Any
+
 import torch
 from packaging import version
 from torch import Tensor
@@ -405,9 +407,14 @@ def multiclass_nms__torchscript(ctx,
 class AscendBatchNMSOp(torch.autograd.Function):
 
     @staticmethod
-    def forward(ctx, bboxes: torch.Tensor, scores: torch.Tensor,
+    def forward(ctx,
-                score_threshold: float, iou_threshold: float,
+                bboxes: torch.Tensor,
-                max_size_per_class: int, max_total_size: int):
+                scores: torch.Tensor,
+                score_threshold: float,
+                iou_threshold: float,
+                max_size_per_class: int,
+                max_total_size: int,
+                dummy_output: Any = None):
         """Dummy nms forward
         Args:
         boxes (torch.Tensor): boxes in shape (batch, N, C, 4).
@@ -416,6 +423,7 @@ class AscendBatchNMSOp(torch.autograd.Function):
         iou_threshold (float): the iou threshold.
         max_size_per_class (int): max size per class.
         max_total_size (int): max total size.
+        dummy_output (Any): output for this op
 
         Returns:
             (torch.Tensor): boxes,(1, N, 4)
@@ -425,16 +433,29 @@ class AscendBatchNMSOp(torch.autograd.Function):
         """
 
         # Python implementation for onnx export
-        nmsed_boxes = bboxes[:, :max_total_size, 0, :]
+        if dummy_output is not None:
-        nmsed_scores = scores[:, :max_total_size, 0]
+            dets, labels = dummy_output
-        nmsed_classes = torch.arange(max_total_size, dtype=torch.long)
+            nmsed_boxes = dets[..., :4]
-        nmsed_num = torch.Tensor([max_total_size])
+            nmsed_scores = dets[..., 4]
+            nmsed_classes = labels
+            nmsed_num = labels.new_tensor([max_total_size])
+        else:
+            nmsed_boxes = bboxes[:, :max_total_size, 0, :]
+            nmsed_scores = scores[:, :max_total_size, 0]
+            nmsed_classes = torch.arange(max_total_size, dtype=torch.long)
+            nmsed_num = torch.Tensor([max_total_size])
 
         return nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_num
 
     @staticmethod
-    def symbolic(g, bboxes, scores, score_thr, iou_thr, max_size_p_class,
+    def symbolic(g,
-                 max_t_size):
+                 bboxes,
+                 scores,
+                 score_thr,
+                 iou_thr,
+                 max_size_p_class,
+                 max_t_size,
+                 dummy_output=None):
         nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_num = g.op(
             'mmdeploy::BatchMultiClassNMS',
             bboxes,
@@ -480,11 +501,21 @@ def multiclass_nms__ascend(ctx,
         tuple[Tensor, Tensor]: (dets, labels), `dets` of shape [N, num_det, 5]
             and `labels` of shape [N, num_det].
     """
+    origin_output = ctx.origin_func(
+        boxes,
+        scores,
+        max_output_boxes_per_class=max_output_boxes_per_class,
+        iou_threshold=iou_threshold,
+        score_threshold=score_threshold,
+        pre_top_k=pre_top_k,
+        keep_top_k=keep_top_k)
+
     boxes = boxes if boxes.dim() == 4 else boxes.unsqueeze(2)
     keep_top_k = max_output_boxes_per_class if keep_top_k < 0 else min(
         max_output_boxes_per_class, keep_top_k)
     nmsed_boxes, nmsed_scores, nmsed_classes, _ = AscendBatchNMSOp.apply(
-        boxes, scores, score_threshold, iou_threshold, keep_top_k, keep_top_k)
+        boxes, scores, score_threshold, iou_threshold, keep_top_k, keep_top_k,
+        origin_output)
 
     dets = torch.cat([nmsed_boxes, nmsed_scores.unsqueeze(2)], dim=-1)
     return dets, nmsed_classes

mmdeploy/codebase/mmdet/deploy/utils.py

@@ -250,6 +250,9 @@ def __gather_topk__trt(ctx,
 @FUNCTION_REWRITER.register_rewriter(
     'mmdeploy.codebase.mmdet.deploy.utils.__gather_topk',
     backend=Backend.COREML.value)
+@FUNCTION_REWRITER.register_rewriter(
+    'mmdeploy.codebase.mmdet.deploy.utils.__gather_topk',
+    backend=Backend.ASCEND.value)
 def __gather_topk__nonbatch(ctx,
                             *inputs: Sequence[torch.Tensor],
                             inds: torch.Tensor,

mmdeploy/codebase/mmdet/models/roi_heads/single_level_roi_extractor.py

@@ -110,6 +110,7 @@ class AscendRoiExtractor(Function):
     @staticmethod
     def symbolic(g, *args):
         """Symbolic function for creating onnx op."""
+        args = args[:-1]
         aligned = args[-1]
         featmap_strides = [1 / stride for stride in args[-2]]
         finest_scale = args[-3]
@@ -137,20 +138,26 @@ class AscendRoiExtractor(Function):
     @staticmethod
     def forward(ctx, *args):
         """Run forward."""
-        # aligned = args[-1]
+        dummy_output = args[-1]
-        featmap_strides = args[-2]
+        args = args[:-1]
-        # finest_scale = args[-3]
-        # roi_scale_factor = args[-4]
-        # sampling_ratio = args[-5]
-        output_size = args[-7]
-        inputs = args[:len(featmap_strides)]
-        rois = args[len(featmap_strides)]
 
-        num_proposals = rois.shape[0]
+        if dummy_output is not None:
-        channel = inputs[0].shape[1]
+            return dummy_output
+        else:
+            # aligned = args[-1]
+            featmap_strides = args[-2]
+            # finest_scale = args[-3]
+            # roi_scale_factor = args[-4]
+            # sampling_ratio = args[-5]
+            output_size = args[-7]
+            inputs = args[:len(featmap_strides)]
+            rois = args[len(featmap_strides)]
 
-        return rois.new_zeros(
+            num_proposals = rois.shape[0]
-            (num_proposals, channel, output_size[1], output_size[0]))
+            channel = inputs[0].shape[1]
+
+            return rois.new_zeros(
+                (num_proposals, channel, output_size[1], output_size[0]))
 
 
 @FUNCTION_REWRITER.register_rewriter(
@@ -183,9 +190,12 @@ def single_roi_extractor__forward__ascend(ctx,
         roi_scale_factor = 1.0
 
     featmap_strides = [float(s) for s in featmap_strides]
+
+    origin_output = ctx.origin_func(self, feats, rois, roi_scale_factor)
     return AscendRoiExtractor.apply(*feats, rois, out_size, pool_mode,
                                     sampling_ratio, roi_scale_factor,
-                                    finest_scale, featmap_strides, aligned)
+                                    finest_scale, featmap_strides, aligned,
+                                    origin_output)
 
 
 @FUNCTION_REWRITER.register_rewriter(