def test_find_labels(self):
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
self.assertEqual(find_labels(BertForSequenceClassification),
["labels"])
self.assertEqual(find_labels(BertForPreTraining),
["labels", "next_sentence_label"])
self.assertEqual(find_labels(BertForQuestionAnswering),
["start_positions", "end_positions"])
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
self.assertEqual(find_labels(TFBertForSequenceClassification),
["labels"])
self.assertEqual(find_labels(TFBertForPreTraining),
["labels", "next_sentence_label"])
self.assertEqual(find_labels(TFBertForQuestionAnswering),
["start_positions", "end_positions"])
if is_flax_available():
# Flax models don't have labels
from transformers import (
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
)
self.assertEqual(find_labels(FlaxBertForSequenceClassification),
[])
self.assertEqual(find_labels(FlaxBertForPreTraining), [])
self.assertEqual(find_labels(FlaxBertForQuestionAnswering), [])
class FlaxBeitModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = ((FlaxBeitModel, FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling)
if is_flax_available() else ())
def setUp(self) -> None:
self.model_tester = FlaxBeitModelTester(self)
self.config_tester = ConfigTester(self,
config_class=BeitConfig,
has_text_modality=False,
hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
# We need to override this test because in Beit, the seq_len equals the number of patches + 1
# we compute that here
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
config.return_dict = True
num_patches = (config.image_size // config.patch_size)**2
seq_length = num_patches + 1
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
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)
outputs = model(
**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions),
self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads, seq_length,
seq_length
],
)
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)
outputs = model(
**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self.assertEqual(len(attentions),
self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads, seq_length,
seq_length
],
)
# We neeed to override this test because Beit's forward signature is different than text models.
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# We neeed to override this test because Beit expects pixel_values instead of input_ids
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(
inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(pixel_values, **kwargs):
return model(pixel_values=pixel_values, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(
**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(
**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# We need to override this test because in Beit, the seq_len equals the number of patches + 1
# we compute that here
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
num_patches = (config.image_size // config.patch_size)**2
seq_length = num_patches + 1 # we add 1 for the [CLS] token
outputs = model(
**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
self.assertEqual(len(hidden_states),
self.model_tester.num_hidden_layers + 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_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(
*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained(
"microsoft/beit-base-patch16-224")
outputs = model(np.ones((1, 3, 224, 224)))
self.assertIsNotNone(outputs)
from ..vit.test_modeling_vit import ViTModelTester
if is_torch_available():
import torch
from transformers import (
BertModel,
CLIPVisionModel,
DeiTModel,
RobertaModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderModel,
ViTModel,
)
if is_flax_available():
from transformers import FlaxVisionTextDualEncoderModel
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
# Inspired by
# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
# From PyTorch internals
class FlaxBeitModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = ((FlaxBeitModel, FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling)
if is_flax_available() else ())
def setUp(self) -> None:
self.model_tester = FlaxBeitModelTester(self)
self.config_tester = ConfigTester(self,
config_class=BeitConfig,
has_text_modality=False,
hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
# We need to override this test because Beit's forward signature is different than text models.
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# We need to override this test because Beit expects pixel_values instead of input_ids
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common(
)
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(
inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(pixel_values, **kwargs):
return model(pixel_values=pixel_values, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(
**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(
**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(
*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained(
"microsoft/beit-base-patch16-224")
outputs = model(np.ones((1, 3, 224, 224)))
self.assertIsNotNone(outputs)