fix int8

docs/FAQ.md

@@ -6,9 +6,9 @@
   - [Model Inference](#model-inference)
     - [**Q: Inference take a long time on a single image.**](#q-inference-take-a-long-time-on-a-single-image)
     - [**Q: Memory leak when inference.**](#q-memory-leak-when-inference)
-    - [**Q: error: parameter check failed at: engine.cpp::setBindingDimensions::1046, condition: profileMinDims.d[i] <= dimensions.d[i]**](#q-error-parameter-check-failed-at-enginecppsetbindingdimensions1046-condition-profilemindimsdi--dimensionsdi)
+    - [**Q: error: parameter check failed at: engine.cpp::setBindingDimensions::1046, condition: profileMinDims.d\[i\] \<= dimensions.d\[i\]**](#q-error-parameter-check-failed-at-enginecppsetbindingdimensions1046-condition-profilemindimsdi--dimensionsdi)
     - [**Q: FP16 model is slower than FP32 model**](#q-fp16-model-is-slower-than-fp32-model)
-    - [**Q: error: [TensorRT] INTERNAL ERROR: Assertion failed: cublasStatus == CUBLAS_STATUS_SUCCESS**](#q-error-tensorrt-internal-error-assertion-failed-cublasstatus--cublas_status_success)
+    - [**Q: error: \[TensorRT\] INTERNAL ERROR: Assertion failed: cublasStatus == CUBLAS\_STATUS\_SUCCESS**](#q-error-tensorrt-internal-error-assertion-failed-cublasstatus--cublas_status_success)
 
 This page provides some frequently asked questions and their solutions.
 
@@ -33,13 +33,13 @@ This is a bug of on old version TensorRT, read [this](https://forums.developer.n
 The input tensor shape is out of the range. Please enlarge the `opt_shape_param` when converting the model.
 
 ```python
-    opt_shape_param=[
-        [
-            [1,3,224,224],    # min tensor shape
-            [1,3,800,1312],  # shape used to do int8 calib
-            [1,3,1344,1344], # max tensor shape
-        ]
-    ]
+shape_ranges=dict(
+    x=dict(
+        min=[1,3,320,320],
+        opt=[1,3,800,1344],
+        max=[1,3,1344,1344],
+    )
+)
 ```
 
 ### **Q: FP16 model is slower than FP32 model**

docs/getting_started.md

@@ -46,27 +46,25 @@ trt_model = mmdet2trt(  ...,
 
 ## int8 support
 
-**int8 mode** needs more configs.
-
 - set `input8_mode=True`.
-- provide calibrate dataset, the `__getitem__()` method of dataset should return a list of tensor with shape (C,H,W), the shape **must** be the same as `opt_shape_param[0][1][1:]` (optimize shape). The tensor should do the same preprocess as the model. There is a default dataset, you can also set your custom one.
+- provide calibrate dataset, the `__getitem__()` method of dataset should return a list of tensor with shape (C,H,W), the shape **must** be the same as `shape_range['x']['opt'][1:]` (optimize shape). The tensor should do the same preprocess as the model. There is a default dataset, you can also set your custom one.
 - set the calibrate algorithm, support `entropy` and `minmax`.
 
 ```python
 from mmdet2trt import mmdet2trt, Int8CalibDataset
-cfg_path="..."  # mmdetection config path
-model_path="..." # mmdetection checkpoint path
-image_path_list = [...] # lists of image pathes
-opt_shape_param=[
-    [
-        [...],
-        [...],
-        [...],
-    ]
-]
-calib_dataset = Int8CalibDataset(image_path_list, cfg_path, opt_shape_param)
+cfg_path="..."  # MMDetection config path
+model_path="..." # MMDetection checkpoint path
+image_path_list = [...] # lists of image paths
+shape_ranges=dict(
+    x=dict(
+        min=[...],
+        opt=[...],
+        max=[...],
+    )
+)
+calib_dataset = Int8CalibDataset(image_path_list, cfg_path, shape_ranges)
 trt_model = mmdet2trt(cfg_path, model_path,
-                    opt_shape_param=opt_shape_param,
+                    shape_ranges=shape_ranges,
                     int8_mode=True,
                     int8_calib_dataset=calib_dataset,
                     int8_calib_alg="entropy")

mmdet2trt/mmdet2trt.py

@@ -2,15 +2,14 @@
 import time
 from typing import Any, Dict
 
+import mmengine
 import tensorrt as trt
 import torch
 from mmdet2trt.models.builder import build_wrapper
 from mmdet2trt.models.detectors import TwoStageDetectorWraper
 from mmdet.apis import init_detector
 from torch2trt_dynamic import BuildEngineConfig, module2trt
 
-import mmcv
-
 logger = logging.getLogger('mmdet2trt')
 
 
@@ -20,25 +19,26 @@ class Int8CalibDataset():
     feed to int8_calib_dataset
     """
 
-    def __init__(self, image_paths, config, opt_shape_param):
+    def __init__(self, image_paths, config, shape_ranges):
         r"""
         datas used to calibrate int8 model
         feed to int8_calib_dataset
         Args:
             image_paths (list[str]): image paths to calib
             config (str|dict): config of mmdetection model
-            opt_shape_param: same as mmdet2trt
+            shape_ranges: same as mmdet2trt
         """
-        from mmdet.apis.inference import LoadImage
-        from mmdet.datasets.pipelines import Compose
+        from mmcv.transforms import Compose
+        from mmengine.registry import init_default_scope
         if isinstance(config, str):
-            config = mmcv.Config.fromfile(config)
+            config = mmengine.Config.fromfile(config)
 
+        init_default_scope(config.get('default_scope', 'mmdet'))
         self.cfg = config
         self.image_paths = image_paths
-        self.opt_shape = opt_shape_param[0][1]
+        self.opt_shape = shape_ranges['x']['opt']
 
-        test_pipeline = [LoadImage()] + config.data.test.pipeline[1:]
+        test_pipeline = config.val_dataloader.dataset.pipeline
         self.test_pipeline = Compose(test_pipeline)
 
     def __len__(self):
@@ -47,14 +47,14 @@ def __len__(self):
     def __getitem__(self, index):
         image_path = self.image_paths[index]
 
-        data = dict(img=image_path)
+        data = dict(img=image_path, img_path=image_path)
         data = self.test_pipeline(data)
 
-        tensor = data['img'][0].unsqueeze(0)
+        tensor = data['inputs'].unsqueeze(0)
         tensor = torch.nn.functional.interpolate(
             tensor, self.opt_shape[-2:]).squeeze(0)
 
-        return [tensor]
+        return dict(x=tensor.cuda())
 
 
 def _get_shape_ranges(config):