fix shufflenetv2 with trt (#645)

* fix shufflenetv2 and pspnet

* fix ci

* remove print

mmdeploy/apis/calibration.py

@@ -6,7 +6,8 @@
 from mmcv.parallel import MMDataParallel
 
 from mmdeploy.core import patch_model
-from mmdeploy.utils import cfg_apply_marks, load_config
+from mmdeploy.utils import (IR, cfg_apply_marks, get_backend, get_ir_config,
+                            load_config)
 from .core import PIPELINE_MANAGER, no_mp
 from .utils import create_calib_input_data as create_calib_input_data_impl
 
@@ -61,7 +62,10 @@ def create_calib_input_data(calib_file: str,
         dataset = task_processor.build_dataset(dataset_cfg, dataset_type)
 
         # patch model
-        patched_model = patch_model(model, cfg=deploy_cfg)
+        backend = get_backend(deploy_cfg)
+        ir = IR.get(get_ir_config(deploy_cfg)['type'])
+        patched_model = patch_model(
+            model, cfg=deploy_cfg, backend=backend, ir=ir)
 
         dataloader = task_processor.build_dataloader(
             dataset, 1, 1, dist=False, shuffle=False)

mmdeploy/apis/onnx/export.py

@@ -7,7 +7,7 @@
 
 from mmdeploy.apis.core import PIPELINE_MANAGER
 from mmdeploy.core import RewriterContext, patch_model
-from mmdeploy.utils import Backend, get_root_logger
+from mmdeploy.utils import IR, Backend, get_ir_config, get_root_logger
 from .optimizer import *  # noqa
 from .passes import optimize_onnx
 
@@ -91,20 +91,21 @@ def _add_or_update(cfg: dict, key: str, val: Any):
         verbose=verbose,
         keep_initializers_as_inputs=keep_initializers_as_inputs)
     _add_or_update(deploy_cfg, 'ir_config', ir_config)
-
+    ir = IR.get(get_ir_config(deploy_cfg)['type'])
     if isinstance(backend, Backend):
         backend = backend.value
     backend_config = dict(type=backend)
     _add_or_update(deploy_cfg, 'backend_config', backend_config)
 
     context_info['cfg'] = deploy_cfg
+    context_info['ir'] = ir
     if 'backend' not in context_info:
         context_info['backend'] = backend
     if 'opset' not in context_info:
         context_info['opset'] = opset_version
 
     # patch model
-    patched_model = patch_model(model, cfg=deploy_cfg, backend=backend)
+    patched_model = patch_model(model, cfg=deploy_cfg, backend=backend, ir=ir)
 
     if 'onnx_custom_passes' not in context_info:
         onnx_custom_passes = optimize_onnx if optimize else None

mmdeploy/apis/torch_jit/trace.py

@@ -6,7 +6,7 @@
 from packaging.version import parse as version_parse
 
 from mmdeploy.core import RewriterContext, patch_model
-from mmdeploy.utils import IR, Backend, get_root_logger
+from mmdeploy.utils import IR, Backend, get_ir_config, get_root_logger
 from ..core import PIPELINE_MANAGER
 
 
@@ -87,7 +87,8 @@ def _add_or_update(cfg: dict, key: str, val: Any):
 
     # patch model
     if isinstance(func, torch.nn.Module):
-        func = patch_model(func, cfg=deploy_cfg, backend=backend)
+        ir = IR.get(get_ir_config(deploy_cfg)['type'])
+        func = patch_model(func, cfg=deploy_cfg, backend=backend, ir=ir)
 
     with RewriterContext(**context_info), torch.no_grad():
         # for exporting models with weight that depends on inputs

mmdeploy/codebase/mmcls/models/backbones/__init__.py

@@ -1,8 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .shufflenet_v2 import shufflenetv2_backbone__forward__ncnn
+from .shufflenet_v2 import shufflenetv2_backbone__forward__default
 from .vision_transformer import visiontransformer__forward__ncnn
 
 __all__ = [
-    'shufflenetv2_backbone__forward__ncnn',
+    'shufflenetv2_backbone__forward__default',
     'visiontransformer__forward__ncnn',
 ]

mmdeploy/codebase/mmcls/models/backbones/shufflenet_v2.py

@@ -2,23 +2,16 @@
 import torch
 
 from mmdeploy.core import FUNCTION_REWRITER
-from mmdeploy.utils import Backend
 
 
-# torch.chunk will export dynamic shape slice, which will lead integer input
-# on ncnn backend. So the model needs to rewrite.
 @FUNCTION_REWRITER.register_rewriter(
-    func_name='mmcls.models.backbones.shufflenet_v2.InvertedResidual.forward',
-    backend=Backend.NCNN.value)
-@FUNCTION_REWRITER.register_rewriter(
-    func_name='mmcls.models.backbones.shufflenet_v2.InvertedResidual.forward',
-    backend=Backend.TORCHSCRIPT.value)
-def shufflenetv2_backbone__forward__ncnn(ctx, self, x):
-    """Rewrite `forward` of InvertedResidual used in shufflenet_v2 for ncnn
-    backend.
+    func_name='mmcls.models.backbones.shufflenet_v2.InvertedResidual.forward')
+def shufflenetv2_backbone__forward__default(ctx, self, x):
+    """Rewrite `forward` of InvertedResidual used in shufflenet_v2.
 
     The chunk in original InvertedResidual.forward will convert to dynamic
-    `Slice` operator in ONNX, which will raise error in ncnn.
+    `Slice` operator in ONNX, which will raise error in ncnn, torchscript
+     and tensorrt. Here we replace `chunk` with `split`.
 
     Args:
         ctx (ContextCaller): The context with additional information.

mmdeploy/utils/config_utils.py

@@ -14,13 +14,13 @@ def load_config(*args) -> List[mmcv.Config]:
         args (str | Sequence[str]): The path to the config file(s).
 
     Returns:
-        List[mmcv.Config]: The content of config.
+        List[mmcv.Config | dict]: The content of config.
     """
 
     def _load_config(cfg):
         if isinstance(cfg, str):
             cfg = mmcv.Config.fromfile(cfg)
-        if not isinstance(cfg, (mmcv.Config, mmcv.ConfigDict)):
+        if not isinstance(cfg, (mmcv.Config, mmcv.ConfigDict, dict)):
             raise TypeError('deploy_cfg must be a filename or Config object, '
                             f'but got {type(cfg)}')
         return cfg