Fixing tests for Perceiver (#14739)

* Adding some slow test to check for perceiver at least from a high level.

* Re-enabling fast tests for Perceiver ImageClassification.

* Perceiver might try to run without Tokenizer (Fast doesn't exist) and
with FeatureExtractor some text only pipelines.

* Oops.

* Adding a comment for `update_config_with_model_class`.

* Remove `model_architecture` to get `tiny_config`.

* Finalize rebase.

* Smarter way to handle undefined FastTokenizer.

* Remove old code.

* Addressing some nits.

* Don't instantiate `None`.

src/transformers/models/perceiver/modeling_perceiver.py

@@ -1268,6 +1268,7 @@ def forward(
         output_hidden_states=None,
         labels=None,
         return_dict=None,
+        pixel_values=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1296,6 +1297,10 @@ def forward(
             >>> predicted_class_idx = logits.argmax(-1).item()
             >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
         """
+        if inputs is not None and pixel_values is not None:
+            raise ValueError("You cannot use both `inputs` and `pixel_values`")
+        elif inputs is None and pixel_values is not None:
+            inputs = pixel_values
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.perceiver(
@@ -1399,6 +1404,7 @@ def forward(
         output_hidden_states=None,
         labels=None,
         return_dict=None,
+        pixel_values=None,
     ):
         r"""
         labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1427,6 +1433,10 @@ def forward(
             >>> predicted_class_idx = logits.argmax(-1).item()
             >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
         """
+        if inputs is not None and pixel_values is not None:
+            raise ValueError("You cannot use both `inputs` and `pixel_values`")
+        elif inputs is None and pixel_values is not None:
+            inputs = pixel_values
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.perceiver(

src/transformers/pipelines/__init__.py

@@ -528,8 +528,8 @@ def pipeline(
     load_tokenizer = type(model_config) in TOKENIZER_MAPPING or model_config.tokenizer_class is not None
     load_feature_extractor = type(model_config) in FEATURE_EXTRACTOR_MAPPING or feature_extractor is not None
 
-    if task in {"audio-classification"}:
-        # Audio classification will never require a tokenizer.
+    if task in {"audio-classification", "image-classification"}:
+        # These will never require a tokenizer.
         # the model on the other hand might have a tokenizer, but
         # the files could be missing from the hub, instead of failing
         # on such repos, we just force to not load it.

tests/test_pipelines_common.py

@@ -77,12 +77,15 @@ def get_tiny_config_from_class(configuration_class):
     model_tester = model_tester_class(parent=None)
 
     if hasattr(model_tester, "get_pipeline_config"):
-        return model_tester.get_pipeline_config()
+        config = model_tester.get_pipeline_config()
     elif hasattr(model_tester, "get_config"):
-        return model_tester.get_config()
+        config = model_tester.get_config()
     else:
+        config = None
         logger.warning(f"Model tester {model_tester_class.__name__} has no `get_config()`.")
 
+    return config
+
 
 @lru_cache(maxsize=100)
 def get_tiny_tokenizer_from_checkpoint(checkpoint):
@@ -100,11 +103,17 @@ def get_tiny_tokenizer_from_checkpoint(checkpoint):
     return tokenizer
 
 
-def get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config):
+def get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config, feature_extractor_class):
     try:
         feature_extractor = AutoFeatureExtractor.from_pretrained(checkpoint)
     except Exception:
-        feature_extractor = None
+        try:
+            if feature_extractor_class is not None:
+                feature_extractor = feature_extractor_class()
+            else:
+                feature_extractor = None
+        except Exception:
+            feature_extractor = None
     if hasattr(tiny_config, "image_size") and feature_extractor:
         feature_extractor = feature_extractor.__class__(size=tiny_config.image_size, crop_size=tiny_config.image_size)
 
@@ -168,7 +177,9 @@ def test(self):
                         self.skipTest(f"Ignoring {ModelClass}, cannot create a simple tokenizer")
                 else:
                     tokenizer = None
-                feature_extractor = get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config)
+                feature_extractor = get_tiny_feature_extractor_from_checkpoint(
+                    checkpoint, tiny_config, feature_extractor_class
+                )
 
                 if tokenizer is None and feature_extractor is None:
                     self.skipTest(
@@ -218,6 +229,13 @@ def data(n):
                         if not tokenizer_classes:
                             # We need to test even if there are no tokenizers.
                             tokenizer_classes = [None]
+                        else:
+                            # Remove the non defined tokenizers
+                            # ByT5 and Perceiver are bytes-level and don't define
+                            # FastTokenizer, we can just ignore those.
+                            tokenizer_classes = [
+                                tokenizer_class for tokenizer_class in tokenizer_classes if tokenizer_class is not None
+                            ]
 
                         for tokenizer_class in tokenizer_classes:
                             if tokenizer_class is not None:

tests/test_pipelines_image_classification.py

@@ -14,12 +14,7 @@
 
 import unittest
 
-from transformers import (
-    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
-    PerceiverConfig,
-    PreTrainedTokenizer,
-    is_vision_available,
-)
+from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, PreTrainedTokenizer, is_vision_available
 from transformers.pipelines import ImageClassificationPipeline, pipeline
 from transformers.testing_utils import (
     is_pipeline_test,
@@ -28,6 +23,7 @@
     require_tf,
     require_torch,
     require_vision,
+    slow,
 )
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
@@ -50,12 +46,7 @@ class ImageClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
     model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 
     def get_test_pipeline(self, model, tokenizer, feature_extractor):
-        if isinstance(model.config, PerceiverConfig):
-            self.skipTest(
-                "Perceiver model tester is defined with a language one, which has no feature_extractor, so the automated test cannot work here"
-            )
-
-        image_classifier = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)
+        image_classifier = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor, top_k=2)
         examples = [
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
@@ -167,3 +158,48 @@ def test_custom_tokenizer(self):
         image_classifier = pipeline("image-classification", model="lysandre/tiny-vit-random", tokenizer=tokenizer)
 
         self.assertIs(image_classifier.tokenizer, tokenizer)
+
+    @slow
+    @require_torch
+    def test_perceiver(self):
+        # Perceiver is not tested by `run_pipeline_test` properly.
+        # That is because the type of feature_extractor and model preprocessor need to be kept
+        # in sync, which is not the case in the current design
+        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.4385, "label": "tabby, tabby cat"},
+                {"score": 0.321, "label": "tiger cat"},
+                {"score": 0.0502, "label": "Egyptian cat"},
+                {"score": 0.0137, "label": "crib, cot"},
+                {"score": 0.007, "label": "radiator"},
+            ],
+        )
+
+        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.5658, "label": "tabby, tabby cat"},
+                {"score": 0.1309, "label": "tiger cat"},
+                {"score": 0.0722, "label": "Egyptian cat"},
+                {"score": 0.0707, "label": "remote control, remote"},
+                {"score": 0.0082, "label": "computer keyboard, keypad"},
+            ],
+        )
+
+        image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
+        outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.3022, "label": "tabby, tabby cat"},
+                {"score": 0.2362, "label": "Egyptian cat"},
+                {"score": 0.1856, "label": "tiger cat"},
+                {"score": 0.0324, "label": "remote control, remote"},
+                {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
+            ],
+        )