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Rename ImageGPT (#14526)

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* Rename

* Add MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING
This commit is contained in:
NielsRogge
2021-11-29 10:19:11 +01:00
committed by GitHub
parent 4ee0b755bd
commit 25156eb296
9 changed files with 36 additions and 19 deletions
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2
tests/test_modeling_common.py
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@@ -61,6 +61,7 @@ if is_torch_available():
from transformers import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
@@ -150,6 +151,7 @@ class ModelTesterMixin:
elif model_class in [
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
*get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
*get_values(MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING),
*get_values(MODEL_FOR_MASKED_LM_MAPPING),
*get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
]:

14
tests/test_modeling_imagegpt.py
View File

@@ -34,7 +34,7 @@ if is_torch_available():
from transformers import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalLM,
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
)
@@ -207,14 +207,14 @@ class ImageGPTModelTester:
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
model = ImageGPTForCausalLM(config)
model = ImageGPTForCausalImageModeling(config)
model.to(torch_device)
model.eval()
labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
result = model(pixel_values, token_type_ids=token_type_ids, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
# ImageGPTForCausalLM doens't have tied input- and output embeddings
# ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
def create_and_check_imagegpt_for_image_classification(
@@ -255,9 +255,9 @@ class ImageGPTModelTester:
class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(ImageGPTForCausalLM, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
(ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
)
all_generative_model_classes = (ImageGPTForCausalLM,) if is_torch_available() else ()
all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
test_missing_keys = False
input_name = "pixel_values"
@@ -273,7 +273,7 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCa
return inputs_dict
# we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalLM doesn't have tied input- and output embeddings
# we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
def _check_scores(self, batch_size, scores, length, config):
expected_shape = (batch_size, config.vocab_size - 1)
self.assertIsInstance(scores, tuple)
@@ -519,7 +519,7 @@ class ImageGPTModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_causal_lm_head(self):
model = ImageGPTForCausalLM.from_pretrained("openai/imagegpt-small").to(torch_device)
model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
feature_extractor = self.default_feature_extractor
image = prepare_img()