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Deprecate TF + JAX (#38758)

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* Scatter deprecation warnings around

* Delete the tests

* Make logging work properly!
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Matt
2025-06-11 17:28:06 +01:00
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tests/models/mobilevit/test_modeling_tf_mobilevit.py
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# Copyright 2022 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.
"""Testing suite for the TensorFlow MobileViT model."""
from __future__ import annotations
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class TFMobileViTConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
class TFMobileViTModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=32,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
output_stride=32,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
classifier_dropout_prob=0.1,
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=10,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.last_hidden_size = last_hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.conv_kernel_size = conv_kernel_size
self.output_stride = output_stride
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.classifier_dropout_prob = classifier_dropout_prob
self.use_labels = use_labels
self.is_training = is_training
self.num_labels = num_labels
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return MobileViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
conv_kernel_size=self.conv_kernel_size,
output_stride=self.output_stride,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
hidden_sizes=[12, 16, 20],
neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32],
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = TFMobileViTModel(config=config)
result = model(pixel_values, training=False)
expected_height = expected_width = self.image_size // self.output_stride
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.last_hidden_size, expected_height, expected_width)
)
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = TFMobileViTForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = TFMobileViTForSemanticSegmentation(config)
expected_height = expected_width = self.image_size // self.output_stride
result = model(pixel_values, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
)
result = model(pixel_values, labels=pixel_labels, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFMobileViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
if is_tf_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
test_onnx = False
def setUp(self):
self.model_tester = TFMobileViTModelTester(self)
self.config_tester = TFMobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MobileViT does not output attentions")
def test_attention_outputs(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_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
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.hidden_states
expected_num_stages = 5
self.assertEqual(len(hidden_states), expected_num_stages)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
divisor = 2
for i in range(len(hidden_states)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
)
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2)
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_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)
def test_for_semantic_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
if model_class.__name__ != "TFMobileViTModel":
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
super().test_keras_fit()
# The default test_loss_computation() uses -100 as a proxy ignore_index
# to test masked losses. Overriding to avoid -100 since semantic segmentation
# models use `semantic_loss_ignore_index` from the config.
def test_loss_computation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# set an ignore index to correctly test the masked loss used in
# `TFMobileViTForSemanticSegmentation`.
if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
config.semantic_loss_ignore_index = 5
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
# The number of elements in the loss should be the same as the number of elements in the label
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
added_label = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
]
expected_loss_size = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
loss = model(model_input, **prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss when we mask some positions
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
if "labels" in prepared_for_class:
labels = prepared_for_class["labels"].numpy()
if len(labels.shape) > 1 and labels.shape[1] != 1:
# labels[0] = -100
prepared_for_class["labels"] = tf.convert_to_tensor(labels)
loss = model(model_input, **prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
self.assertTrue(not np.any(np.isnan(loss.numpy())))
# Test that model correctly compute the loss with a dict
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
loss = model(prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss with a tuple
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
# Get keys that were added with the _prepare_for_class function
label_keys = prepared_for_class.keys() - inputs_dict.keys()
signature = inspect.signature(model.call).parameters
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
# Initialize a list with their default values, update the values and convert to a tuple
list_input = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default)
for index, value in sorted_tuple_index_mapping:
list_input[index] = prepared_for_class[value]
tuple_input = tuple(list_input)
# Send to model
loss = model(tuple_input[:-1])[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
@slow
def test_model_from_pretrained(self):
model_name = "apple/mobilevit-small"
model = TFMobileViTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# 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_tf
class TFMobileViTModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_head(self):
model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")
image_processor = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs, training=False)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-1.9364, -1.2327, -0.4653])
tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4, rtol=1e-04)
@slow
def test_inference_semantic_segmentation(self):
# `from_pt` will be removed
model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(inputs.pixel_values, training=False)
logits = outputs.logits
# verify the logits
expected_shape = tf.TensorShape((1, 21, 32, 32))
self.assertEqual(logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
]
)
tf.debugging.assert_near(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)