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Unverified Commit 374a2f69 authored by NielsRogge's avatar NielsRogge Committed by GitHub
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Clean up CLIP tests (#17380) 


Co-authored-by: default avatarNiels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
parent d9809298
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Showing with 14 additions and 109 deletions
tests/models/clip/test_modeling_clip.py
View file @ 374a2f69
......@@ -100,6 +100,10 @@ class CLIPVisionModelTester:
self.initializer_range = initializer_range
self.scope = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
......@@ -160,8 +164,8 @@ class CLIPVisionModelTest(ModelTesterMixin, unittest.TestCase):
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="CLIP does not use inputs_embeds")
def test_inputs_embeds(self):
# CLIP does not use inputs_embeds
pass
def test_model_common_attributes(self):
......@@ -189,114 +193,17 @@ class CLIPVisionModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 1
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# CLIP has a different seq_length
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_length = num_patches + 1
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
# skip this test as CLIPVisionModel has no base class and is
# not available in MODEL_MAPPING
@unittest.skip(reason="CLIPVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
# skip this test as CLIPVisionModel has no base class and is
# not available in MODEL_MAPPING
@unittest.skip(reason="CLIPVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
......@@ -416,17 +323,15 @@ class CLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="CLIP does not use inputs_embeds")
def test_inputs_embeds(self):
# CLIP does not use inputs_embeds
pass
# skip this test as CLIPTextModel has no base class and is
# not available in MODEL_MAPPING
@unittest.skip(reason="CLIPTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
# skip this test as CLIPTextModel has no base class and is
# not available in MODEL_MAPPING
@unittest.skip(reason="CLIPTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
......@@ -495,19 +400,19 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
# hidden_states are tested in individual model tests
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
# input_embeds are tested in individual model tests
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
def test_inputs_embeds(self):
pass
# tested in individual model tests
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
# CLIPModel does not have input/output embeddings
@unittest.skip(reason="CLIPModel does not have input/output embeddings")
def test_model_common_attributes(self):
pass
......
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