remove imports (#1207)

* remove imports

* update doc

* detailed docstring

* rephrase

docs/en/04-supported-codebases/mmocr.md

@@ -21,6 +21,8 @@ Please refer to [install.md](https://mmocr.readthedocs.io/en/latest/install.html
 
 Note that ncnn, pplnn, and OpenVINO only support the configs of DBNet18 for DBNet.
 
+For CRNN models with TensorRT-int8 backend, we recommend TensorRT 7.2.3.4 and CUDA 10.2.
+
 For the PANet with the [checkpoint](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth) pretrained on ICDAR dataset, if you want to convert the model to TensorRT with 16 bits float point, please try the following script.
 
 ```python

docs/zh_cn/04-supported-codebases/mmocr.md

@@ -21,6 +21,8 @@ mmocr 是一个基于 PyTorch 和 mmdetection 的开源工具箱，用于文本
 
 请注意，ncnn、pplnn 和 OpenVINO 仅支持 DBNet 的 DBNet18 配置。
 
+CRNN 模型的 TensorRT int8量化只在 TensorRT 7.2.3.4 和 CUDA10.2下测试可用。
+
 对于在 ICDAR 数据集上预训 [checkpoint](https://download.openmmlab.com/mmocr/textdet/panet/panet_r18_fpem_ffm_sbn_600e_icdar2015_20210219-42dbe46a.pth) 的 PANet，如果要将模型转为具有 fp16 TensorRT，请尝试以下脚本。
 
 ```python

mmdeploy/apis/onnx/partition.py

@@ -8,7 +8,8 @@ import onnx.utils
 from mmdeploy.apis.core import PIPELINE_MANAGER
 from mmdeploy.core.optimizers import (attribute_to_dict, create_extractor,
                                       get_new_name, parse_extractor_io_string,
-                                      remove_identity, rename_value)
+                                      remove_identity, remove_imports,
+                                      rename_value)
 from mmdeploy.utils import get_root_logger
 
 
@@ -198,6 +199,9 @@ def extract_partition(model: Union[str, onnx.ModelProto],
                 dim.dim_value = 0
                 dim.dim_param = f'dim_{idx}'
 
+    # remove mmdeploy domain if useless
+    remove_imports(extracted_model)
+
     # save extract_model if save_file is given
     if save_file is not None:
         onnx.save(extracted_model, save_file)

mmdeploy/codebase/mmdet/deploy/object_detection.py

@@ -42,8 +42,7 @@ def process_model_config(model_cfg: mmcv.Config,
         transforms = cfg.data.test.pipeline[1]['transforms']
         for trans in transforms:
             trans_type = trans['type']
-            if trans_type == 'Resize' and len(
+            if trans_type == 'Resize' and len(input_shape) != 1:
-                    input_shape) != 1 and input_shape[0] != input_shape[1]:
                 trans['keep_ratio'] = False
             elif trans_type == 'Pad':
                 if 'size_divisor' in trans:

mmdeploy/core/optimizers/__init__.py

@@ -2,10 +2,10 @@
 from .extractor import create_extractor, parse_extractor_io_string
 from .function_marker import mark, reset_mark_function_count
 from .optimize import (attribute_to_dict, get_new_name, remove_identity,
-                       rename_value)
+                       remove_imports, rename_value)
 
 __all__ = [
     'mark', 'reset_mark_function_count', 'create_extractor',
     'parse_extractor_io_string', 'remove_identity', 'attribute_to_dict',
-    'rename_value', 'get_new_name'
+    'rename_value', 'get_new_name', 'remove_imports'
 ]

mmdeploy/core/optimizers/optimize.py

@@ -206,3 +206,24 @@ def remove_identity(model: onnx.ModelProto):
         pass
 
     remove_nodes(model, is_identity)
+
+
+def remove_imports(model: onnx.ModelProto):
+    """Remove useless imports from an ONNX model.
+
+    The domain like `mmdeploy` might influence model conversion for
+    some backends.
+
+    Args:
+        model (onnx.ModelProto): Input onnx model.
+    """
+    logger = get_root_logger()
+    dst_domain = ['']
+    for node in model.graph.node:
+        if hasattr(node, 'module') and (node.module not in dst_domain):
+            dst_domain.append(node.module)
+    src_domains = [oi.domain for oi in model.opset_import]
+    for i, src_domain in enumerate(src_domains):
+        if src_domain not in dst_domain:
+            logger.info(f'remove opset_import {src_domain}')
+            model.opset_import.pop(i)