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Resnet flax (#21472)

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* [WIP] flax resnet

* added pretrained flax models, results reproducible

* Added pretrained flax models, results reproducible

* working on tests

* no real code change, just some comments

* [flax] adding support for batch norm layers

* fixing bugs related to pt+flax integration

* removing loss from modeling flax output class

* fixing classifier tests

* fixing comments, model output

* cleaning comments

* review changes

* review changes

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* renaming Flax to PyTorch

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Shubhamai
2023-03-25 01:15:57 +05:30
committed by GitHub
parent 88dae78f4d
commit a0cbbba31f
15 changed files with 1057 additions and 8 deletions
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tests/models/resnet/test_modeling_flax_resnet.py Normal file
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# Copyright 2023 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
from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoFeatureExtractor
class FlaxResNetModelTester(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 ResNetConfig(
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 = FlaxResNetModel(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 = FlaxResNetForImageClassification(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 = (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()
is_encoder_decoder = False
test_head_masking = False
has_attentions = False
def setUp(self) -> None:
self.model_tester = FlaxResNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=ResNetConfig, 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="ResNet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ResNet 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)
@unittest.skip(reason="ResNet does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
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 FlaxResNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(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([-11.1069, -9.7877, -8.3777])
self.assertTrue(jnp.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))