[Fix] Fix dataloader for ncnn quantization (#2018)

* fix mmpose create_input when feed ndarray

* fix dataloader for ncnn-ppq

mmdeploy/codebase/mmpose/deploy/pose_detection.py

@@ -197,15 +197,15 @@ class PoseDetection(BaseTask):
         from mmcv.transforms import Compose
         from mmpose.registry import TRANSFORMS
         cfg = self.model_cfg
-        if isinstance(imgs, str):
-            imgs = [mmcv.imread(imgs)]
-        elif isinstance(imgs, (list, tuple)):
-            img_data = []
-            for img in imgs:
-                if isinstance(img, str):
-                    img_data.append(mmcv.imread(img))
-                else:
-                    img_data.append(img)
+        if isinstance(imgs, (list, tuple)):
+            if not isinstance(imgs[0], (np.ndarray, str)):
+                raise AssertionError('imgs must be strings or numpy arrays')
+        elif isinstance(imgs, (np.ndarray, str)):
+            imgs = [imgs]
+        else:
+            raise AssertionError('imgs must be strings or numpy arrays')
+        if isinstance(imgs, (list, tuple)) and isinstance(imgs[0], str):
+            img_data = [mmcv.imread(img) for img in imgs]
             imgs = img_data
         person_results = []
         bboxes = []

tools/onnx2ncnn_quant_table.py

@@ -4,6 +4,7 @@ import logging
 from copy import deepcopy
 
 from mmengine import Config
+from torch.utils.data import DataLoader
 
 from mmdeploy.apis.utils import build_task_processor
 from mmdeploy.utils import get_root_logger, load_config
@@ -31,9 +32,11 @@ def get_table(onnx_path: str,
         from quant_image_dataset import QuantizationImageDataset
         dataset = QuantizationImageDataset(
             path=image_dir, deploy_cfg=deploy_cfg, model_cfg=model_cfg)
-        calib_dataloader['dataset'] = dataset
-        dataloader = task_processor.build_dataloader(calib_dataloader)
-        # dataloader = DataLoader(dataset, batch_size=1)
+
+        def collate(data_batch):
+            return data_batch[0]
+
+        dataloader = DataLoader(dataset, batch_size=1, collate_fn=collate)
     else:
         dataset = task_processor.build_dataset(calib_dataloader['dataset'])
         calib_dataloader['dataset'] = dataset
@@ -44,16 +47,10 @@ def get_table(onnx_path: str,
     # get an available input shape randomly
     for _, input_data in enumerate(dataloader):
         input_data = data_preprocessor(input_data)
-        input_tensor = input_data[0]
-        if isinstance(input_tensor, list):
-            input_shape = input_tensor[0].shape
-            collate_fn = lambda x: data_preprocessor(x[0])[0].to(  # noqa: E731
-                device)
-        else:
-            input_shape = input_tensor.shape
-            collate_fn = lambda x: data_preprocessor(x)[0].to(  # noqa: E731
-                device)
-        break
+        input_tensor = input_data['inputs']
+        input_shape = input_tensor.shape
+        collate_fn = lambda x: data_preprocessor(x)['inputs'].to(  # noqa: E731
+            device)
 
     from ppq import QuantizationSettingFactory, TargetPlatform
     from ppq.api import export_ppq_graph, quantize_onnx_model