238 lines
8.7 KiB
Python
238 lines
8.7 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 The HuggingFace Inc. 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
|
|
|
|
from transformers import RegNetConfig, is_flax_available
|
|
from transformers.testing_utils import require_flax, slow
|
|
from transformers.utils import cached_property, is_vision_available
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
|
|
|
|
|
|
if is_flax_available():
|
|
import jax
|
|
import jax.numpy as jnp
|
|
|
|
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
from transformers import AutoImageProcessor
|
|
|
|
|
|
class FlaxRegNetModelTester(unittest.TestCase):
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=3,
|
|
image_size=32,
|
|
num_channels=3,
|
|
embeddings_size=10,
|
|
hidden_sizes=[10, 20, 30, 40],
|
|
depths=[1, 1, 2, 1],
|
|
is_training=True,
|
|
use_labels=True,
|
|
hidden_act="relu",
|
|
num_labels=3,
|
|
scope=None,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.image_size = image_size
|
|
self.num_channels = num_channels
|
|
self.embeddings_size = embeddings_size
|
|
self.hidden_sizes = hidden_sizes
|
|
self.depths = depths
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.hidden_act = hidden_act
|
|
self.num_labels = num_labels
|
|
self.scope = scope
|
|
self.num_stages = len(hidden_sizes)
|
|
|
|
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()
|
|
|
|
return config, pixel_values
|
|
|
|
def get_config(self):
|
|
return RegNetConfig(
|
|
num_channels=self.num_channels,
|
|
embeddings_size=self.embeddings_size,
|
|
hidden_sizes=self.hidden_sizes,
|
|
depths=self.depths,
|
|
hidden_act=self.hidden_act,
|
|
num_labels=self.num_labels,
|
|
image_size=self.image_size,
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values):
|
|
model = FlaxRegNetModel(config=config)
|
|
result = model(pixel_values)
|
|
|
|
# Output shape (b, c, h, w)
|
|
self.parent.assertEqual(
|
|
result.last_hidden_state.shape,
|
|
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
|
|
)
|
|
|
|
def create_and_check_for_image_classification(self, config, pixel_values):
|
|
config.num_labels = self.num_labels
|
|
model = FlaxRegNetForImageClassification(config=config)
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
|
|
|
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
|
|
|
|
|
|
@require_flax
|
|
class FlaxResNetModelTest(FlaxModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
|
|
|
|
is_encoder_decoder = False
|
|
test_head_masking = False
|
|
has_attentions = False
|
|
|
|
def setUp(self) -> None:
|
|
self.model_tester = FlaxRegNetModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=RegNetConfig, has_text_modality=False)
|
|
|
|
def test_config(self):
|
|
self.create_and_test_config_common_properties()
|
|
self.config_tester.create_and_test_config_to_json_string()
|
|
self.config_tester.create_and_test_config_to_json_file()
|
|
self.config_tester.create_and_test_config_from_and_save_pretrained()
|
|
self.config_tester.create_and_test_config_with_num_labels()
|
|
self.config_tester.check_config_can_be_init_without_params()
|
|
self.config_tester.check_config_arguments_init()
|
|
|
|
def create_and_test_config_common_properties(self):
|
|
return
|
|
|
|
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)
|
|
|
|
@unittest.skip(reason="RegNet does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="RegNet does not support input and output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
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)
|
|
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
|
|
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_stages = self.model_tester.num_stages
|
|
self.assertEqual(len(hidden_states), expected_num_stages + 1)
|
|
|
|
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_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 will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_flax
|
|
class FlaxRegNetModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_image_classification_head(self):
|
|
model = FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="np")
|
|
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = (1, 1000)
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = jnp.array([-0.4180, -1.5051, -3.4836])
|
|
|
|
self.assertTrue(jnp.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
|