Make clip always output dict in open_clip traininig

src/open_clip/factory.py

@@ -116,6 +116,7 @@ def create_model(
         pretrained_image: bool = False,
         pretrained_hf: bool = True,
         cache_dir: Optional[str] = None,
+        output_dict: Optional[bool] = None,
 ):
     has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
     if has_hf_hub_prefix:
@@ -215,6 +216,10 @@ def create_model(
         model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
         model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
 
+        # to always output dict even if it is clip
+        if output_dict and hasattr(model, "output_dict"):
+            model.output_dict = True
+
         if jit:
             model = torch.jit.script(model)
 
@@ -259,6 +264,7 @@ def create_model_and_transforms(
         image_std: Optional[Tuple[float, ...]] = None,
         aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
         cache_dir: Optional[str] = None,
+        output_dict: Optional[bool] = None,
 ):
     model = create_model(
         model_name,
@@ -273,6 +279,7 @@ def create_model_and_transforms(
         pretrained_image=pretrained_image,
         pretrained_hf=pretrained_hf,
         cache_dir=cache_dir,
+        output_dict=output_dict,
     )
 
     image_mean = image_mean or getattr(model.visual, 'image_mean', None)

src/training/main.py

@@ -221,6 +221,7 @@ def main(args):
         image_mean=args.image_mean,
         image_std=args.image_std,
         aug_cfg=args.aug_cfg,
+        output_dict=True,
     )
     random_seed(args.seed, args.rank)
 

src/training/train.py

@@ -38,9 +38,6 @@ def update(self, val, n=1):
         self.count += n
         self.avg = self.sum / self.count
 
-def is_clip(model):
-    return type(model) in [CLIP, CustomTextCLIP]
-
 def postprocess_clip_output(model_out):
     return {
         "image_features": model_out[0],
@@ -98,10 +95,6 @@ def train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, args
         if args.accum_freq == 1:
             with autocast():
                 model_out = model(images, texts)
-                # for clip if it does not output_dict
-                module = model.module if type(model) == DistributedDataParallel else model
-                if is_clip(module) and not module.output_dict:
-                    model_out = postprocess_clip_output(model_out)
                 logit_scale = model_out["logit_scale"]
                 losses = loss(**model_out, output_dict=True)
 
@@ -114,10 +107,6 @@ def train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, args
             with torch.no_grad():
                 with autocast():
                     model_out = model(images, texts)
-                    # for clip if it does not output_dict
-                    module = model.module if type(model) == DistributedDataParallel else model
-                    if is_clip(module) and not module.output_dict:
-                        model_out = postprocess_clip_output(model_out)
                     model_out.pop("logit_scale")
                     for key, val in model_out.items():
                         if key in accum_features:
@@ -142,10 +131,6 @@ def train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, args
                 texts = accum_texts[j]
                 with autocast():
                     model_out = model(images, texts, output_dict=True)
-                    # for clip if it does not output_dict
-                    module = model.module if type(model) == DistributedDataParallel else model
-                    if is_clip(module) and not model.output_dict:
-                        model_out = postprocess_clip_output(model_out)
                     logit_scale = model_out.pop("logit_scale")
                     for key, val in accum_features:
                         accumulated = accum_features[key]
@@ -267,10 +252,6 @@ def evaluate(model, data, epoch, args, tb_writer=None):
 
                 with autocast():
                     model_out = model(images, texts, output_dict=True)
-                    # for clip if it does not output_dict
-                    module = model.module if type(model) == DistributedDataParallel else model
-                    if is_clip(module) and not module.output_dict:
-                        model_out = postprocess_clip_output(model_out)
                     image_features = model_out["image_features"]
                     text_features = model_out["text_features"]
                     logit_scale = model_out["logit_scale"]

tests/test_training_simple.py

@@ -8,6 +8,12 @@
 
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
+if hasattr(torch._C, '_jit_set_profiling_executor'):
+    # legacy executor is too slow to compile large models for unit tests
+    # no need for the fusion performance here
+    torch._C._jit_set_profiling_executor(True)
+    torch._C._jit_set_profiling_mode(False)
+
 @pytest.mark.skipif(sys.platform.startswith('darwin'), reason="macos pickle bug with locals")
 def test_training():
     main([
@@ -77,3 +83,21 @@ def test_training_unfreezing_vit():
     '--lock-image',
     '--lock-image-unlocked-groups', '5'
     ])
+
+
+@pytest.mark.skipif(sys.platform.startswith('darwin'), reason="macos pickle bug with locals")
+def test_training_clip_with_jit():
+    main([
+    '--save-frequency', '1',
+    '--zeroshot-frequency', '1',
+    '--dataset-type', "synthetic",
+    '--train-num-samples', '16',
+    '--warmup', '1',
+    '--batch-size', '4',
+    '--lr', '1e-3',
+    '--wd', '0.1',
+    '--epochs', '1',
+    '--workers', '2',
+    '--model', 'ViT-B-32',
+    '--torchscript'
+    ])