adapt_videopredictor_to_eas (#268)

easycv/models/video_recognition/ClipBertTwoStream.py

@@ -28,8 +28,10 @@ class ClipBertTwoStream(BaseModel):
         self.test_cfg = test_cfg
         self.multi_class = multi_class
 
-        self.vison_pretrained = get_checkpoint(vison_pretrained)
-        self.text_pretrained = get_checkpoint(text_pretrained)
+        self.vison_pretrained = get_checkpoint(
+            vison_pretrained) if vison_pretrained else vison_pretrained
+        self.text_pretrained = get_checkpoint(
+            text_pretrained) if text_pretrained else text_pretrained
         loss_cls = dict(type='CrossEntropyLoss') if not multi_class else dict(
             type='AsymmetricLoss')
         self.loss_cls = builder.build_loss(loss_cls)

easycv/models/video_recognition/recognizer3d.py

@@ -30,7 +30,7 @@ class Recognizer3D(BaseModel):
         self.train_cfg = train_cfg
         self.test_cfg = test_cfg
         self.pretrained = get_checkpoint(
-            pretrained) if pretrained != None else pretrained
+            pretrained) if pretrained else pretrained
         self.activate_fn = nn.Softmax(dim=1)
 
         # aux_info is the list of tensor names beyond 'imgs' and 'label' which

easycv/predictors/video_classifier.py

@@ -8,7 +8,10 @@ from PIL import Image, ImageFile
 from easycv.datasets.registry import PIPELINES
 from easycv.file import io
 from easycv.framework.errors import ValueError
+from easycv.models.builder import build_model
 from easycv.utils.misc import deprecated
+from easycv.utils.mmlab_utils import (dynamic_adapt_for_mmlab,
+                                      remove_adapt_for_mmlab)
 from easycv.utils.registry import build_from_cfg
 from .base import Predictor, PredictorV2
 from .builder import PREDICTORS
@@ -100,11 +103,16 @@ class VideoClassificationPredictor(PredictorV2):
         """
         if self.pipelines is not None:
             pipelines = self.pipelines
-        else:
+        elif 'test_pipeline' in self.cfg:
             pipelines = self.cfg.get('test_pipeline', [])
+        else:
+            pipelines = self.cfg.get('val_pipeline', [])
         for idx, pipeline in enumerate(pipelines):
             if pipeline['type'] == 'Collect' and 'label' in pipeline['keys']:
                 pipeline['keys'].remove('label')
+            if pipeline['type'] == 'VideoToTensor' and 'label' in pipeline[
+                    'keys']:
+                pipeline['keys'].remove('label')
             pipelines[idx] = pipeline
 
         pipelines = [build_from_cfg(p, PIPELINES) for p in pipelines]
@@ -113,6 +121,19 @@ class VideoClassificationPredictor(PredictorV2):
         processor = Compose(pipelines)
         return processor
 
+    def _build_model(self):
+        # Use mmdet model
+        dynamic_adapt_for_mmlab(self.cfg)
+        if 'vison_pretrained' in self.cfg.model:
+            self.cfg.model.vison_pretrained = None
+        if 'text_pretrained' in self.cfg.model:
+            self.cfg.model.text_pretrained = None
+
+        model = build_model(self.cfg.model)
+        # remove adapt for mmdet to avoid conflict using mmdet models
+        remove_adapt_for_mmlab(self.cfg)
+        return model
+
     def postprocess(self, inputs, *args, **kwargs):
         """Return top-k results."""
         output_prob = inputs['prob'].data.cpu()