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# Copyright 2021 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
import unittest | |
import numpy as np | |
from transformers import ViTConfig, is_flax_available | |
from transformers.testing_utils import require_flax, slow | |
from ...test_configuration_common import ConfigTester | |
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor | |
if is_flax_available(): | |
import jax | |
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel | |
class FlaxViTModelTester(unittest.TestCase): | |
def __init__( | |
self, | |
parent, | |
batch_size=13, | |
image_size=30, | |
patch_size=2, | |
num_channels=3, | |
is_training=True, | |
use_labels=True, | |
hidden_size=32, | |
num_hidden_layers=5, | |
num_attention_heads=4, | |
intermediate_size=37, | |
hidden_act="gelu", | |
hidden_dropout_prob=0.1, | |
attention_probs_dropout_prob=0.1, | |
type_sequence_label_size=10, | |
initializer_range=0.02, | |
): | |
self.parent = parent | |
self.batch_size = batch_size | |
self.image_size = image_size | |
self.patch_size = patch_size | |
self.num_channels = num_channels | |
self.is_training = is_training | |
self.use_labels = use_labels | |
self.hidden_size = hidden_size | |
self.num_hidden_layers = num_hidden_layers | |
self.num_attention_heads = num_attention_heads | |
self.intermediate_size = intermediate_size | |
self.hidden_act = hidden_act | |
self.hidden_dropout_prob = hidden_dropout_prob | |
self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
self.type_sequence_label_size = type_sequence_label_size | |
self.initializer_range = initializer_range | |
# 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 = ViTConfig( | |
image_size=self.image_size, | |
patch_size=self.patch_size, | |
num_channels=self.num_channels, | |
hidden_size=self.hidden_size, | |
num_hidden_layers=self.num_hidden_layers, | |
num_attention_heads=self.num_attention_heads, | |
intermediate_size=self.intermediate_size, | |
hidden_act=self.hidden_act, | |
hidden_dropout_prob=self.hidden_dropout_prob, | |
attention_probs_dropout_prob=self.attention_probs_dropout_prob, | |
is_decoder=False, | |
initializer_range=self.initializer_range, | |
) | |
return config, pixel_values | |
def create_and_check_model(self, config, pixel_values): | |
model = FlaxViTModel(config=config) | |
result = model(pixel_values) | |
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) | |
image_size = (self.image_size, self.image_size) | |
patch_size = (self.patch_size, self.patch_size) | |
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) | |
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) | |
def create_and_check_for_image_classification(self, config, pixel_values): | |
config.num_labels = self.type_sequence_label_size | |
model = FlaxViTForImageClassification(config=config) | |
result = model(pixel_values) | |
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) | |
# test greyscale images | |
config.num_channels = 1 | |
model = FlaxViTForImageClassification(config) | |
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) | |
result = model(pixel_values) | |
def prepare_config_and_inputs_for_common(self): | |
config_and_inputs = self.prepare_config_and_inputs() | |
( | |
config, | |
pixel_values, | |
) = config_and_inputs | |
inputs_dict = {"pixel_values": pixel_values} | |
return config, inputs_dict | |
class FlaxViTModelTest(FlaxModelTesterMixin, unittest.TestCase): | |
all_model_classes = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else () | |
def setUp(self) -> None: | |
self.model_tester = FlaxViTModelTester(self) | |
self.config_tester = ConfigTester(self, config_class=ViTConfig, has_text_modality=False, hidden_size=37) | |
def test_config(self): | |
self.config_tester.run_common_tests() | |
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_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) | |
# We need to override this test because ViT'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 ViT 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) | |
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_from_pretrained(self): | |
for model_class_name in self.all_model_classes: | |
model = model_class_name.from_pretrained("google/vit-base-patch16-224") | |
outputs = model(np.ones((1, 3, 224, 224))) | |
self.assertIsNotNone(outputs) | |