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# coding=utf-8 | |
# 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 Data2VecVision model. """ | |
from __future__ import annotations | |
import collections.abc | |
import inspect | |
import unittest | |
import numpy as np | |
from transformers import Data2VecVisionConfig | |
from transformers.file_utils import cached_property, is_tf_available, is_vision_available | |
from transformers.testing_utils import require_tf, require_vision, 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 tensorflow as tf | |
from transformers import ( | |
TFData2VecVisionForImageClassification, | |
TFData2VecVisionForSemanticSegmentation, | |
TFData2VecVisionModel, | |
) | |
from transformers.models.data2vec.modeling_tf_data2vec_vision import ( | |
TF_DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, | |
) | |
if is_vision_available(): | |
from PIL import Image | |
from transformers import BeitImageProcessor | |
class TFData2VecVisionModelTester: | |
def __init__( | |
self, | |
parent, | |
vocab_size=100, | |
batch_size=13, | |
image_size=30, | |
patch_size=2, | |
num_channels=3, | |
is_training=True, | |
use_labels=True, | |
hidden_size=32, | |
num_hidden_layers=2, | |
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, | |
num_labels=3, | |
scope=None, | |
out_indices=[0, 1, 2, 3], | |
): | |
self.parent = parent | |
self.vocab_size = 100 | |
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 | |
self.scope = scope | |
self.out_indices = out_indices | |
self.num_labels = num_labels | |
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.type_sequence_label_size) | |
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 Data2VecVisionConfig( | |
vocab_size=self.vocab_size, | |
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, | |
out_indices=self.out_indices, | |
) | |
def create_and_check_model(self, config, pixel_values, labels, pixel_labels): | |
model = TFData2VecVisionModel(config=config) | |
result = model(pixel_values, training=False) | |
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) | |
image_size = ( | |
self.image_size | |
if isinstance(self.image_size, collections.abc.Iterable) | |
else (self.image_size, self.image_size) | |
) | |
patch_size = ( | |
self.patch_size | |
if isinstance(self.image_size, collections.abc.Iterable) | |
else (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, labels, pixel_labels): | |
config.num_labels = self.type_sequence_label_size | |
model = TFData2VecVisionForImageClassification(config) | |
result = model(pixel_values, labels=labels, training=False) | |
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) | |
def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): | |
config.num_labels = self.num_labels | |
model = TFData2VecVisionForSemanticSegmentation(config) | |
result = model(pixel_values, training=False) | |
self.parent.assertEqual( | |
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) | |
) | |
result = model(pixel_values, labels=pixel_labels) | |
self.parent.assertEqual( | |
result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) | |
) | |
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 | |
def prepare_config_and_inputs_for_keras_fit(self): | |
config_and_inputs = self.prepare_config_and_inputs() | |
config, pixel_values, _, _ = config_and_inputs | |
inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} | |
return config, inputs_dict | |
class TFData2VecVisionModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): | |
""" | |
Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, | |
attention_mask and seq_length. | |
""" | |
all_model_classes = ( | |
(TFData2VecVisionModel, TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation) | |
if is_tf_available() | |
else () | |
) | |
pipeline_model_mapping = ( | |
{"feature-extraction": TFData2VecVisionModel, "image-classification": TFData2VecVisionForImageClassification} | |
if is_tf_available() | |
else {} | |
) | |
test_pruning = False | |
test_onnx = False | |
test_resize_embeddings = False | |
test_head_masking = False | |
def setUp(self): | |
self.model_tester = TFData2VecVisionModelTester(self) | |
self.config_tester = ConfigTester( | |
self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 | |
) | |
def test_config(self): | |
self.config_tester.run_common_tests() | |
def test_inputs_embeds(self): | |
# Data2VecVision does not use inputs_embeds | |
pass | |
def test_model_common_attributes(self): | |
config, _ = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
model = model_class(config) | |
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer)) | |
x = model.get_output_embeddings() | |
self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) | |
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_for_image_segmentation(self): | |
config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) | |
def test_attention_outputs(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
config.return_dict = True | |
# in Data2VecVision, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) | |
image_size = ( | |
self.model_tester.image_size | |
if isinstance(self.model_tester.image_size, collections.abc.Iterable) | |
else (self.model_tester.image_size, self.model_tester.image_size) | |
) | |
patch_size = ( | |
self.model_tester.patch_size | |
if isinstance(self.model_tester.patch_size, collections.abc.Iterable) | |
else (self.model_tester.patch_size, self.model_tester.patch_size) | |
) | |
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) | |
seq_len = num_patches + 1 | |
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) | |
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) | |
chunk_length = getattr(self.model_tester, "chunk_length", None) | |
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): | |
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes | |
for model_class in self.all_model_classes: | |
inputs_dict["output_attentions"] = True | |
inputs_dict["output_hidden_states"] = False | |
config.return_dict = True | |
model = model_class(config) | |
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) | |
attentions = outputs.encoder_attentions if config.is_encoder_decoder else 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), training=False) | |
attentions = outputs.encoder_attentions if config.is_encoder_decoder else 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, encoder_seq_length, encoder_key_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), training=False) | |
self.assertEqual(out_len + 1, len(outputs)) | |
self_attentions = outputs.attentions | |
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) | |
self.assertListEqual( | |
list(self_attentions[0].shape[-3:]), | |
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], | |
) | |
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_layers = getattr( | |
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 | |
) | |
self.assertEqual(len(hidden_states), expected_num_layers) | |
# Data2VecVision has a different seq_length | |
image_size = ( | |
self.model_tester.image_size | |
if isinstance(self.model_tester.image_size, collections.abc.Iterable) | |
else (self.model_tester.image_size, self.model_tester.image_size) | |
) | |
patch_size = ( | |
self.model_tester.patch_size | |
if isinstance(self.model_tester.patch_size, collections.abc.Iterable) | |
else (self.model_tester.patch_size, self.model_tester.patch_size) | |
) | |
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) | |
seq_length = num_patches + 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) | |
# Overriding this method since the base method won't be compatible with Data2VecVision. | |
def test_keras_fit(self): | |
config, _ = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
# Since `TFData2VecVisionModel` cannot operate with the default `fit()` method. | |
if model_class.__name__ != "TFData2VecVisionModel": | |
model = model_class(config) | |
if getattr(model, "hf_compute_loss", None): | |
# Test that model correctly compute the loss with kwargs | |
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit() | |
label_names = {"labels"} | |
self.assertGreater(len(label_names), 0, msg="No matching label names found!") | |
labels = {key: val for key, val in prepared_for_class.items() if key in label_names} | |
inputs_minus_labels = { | |
key: val for key, val in prepared_for_class.items() if key not in label_names | |
} | |
self.assertGreater(len(inputs_minus_labels), 0) | |
model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True) | |
# Make sure the model fits without crashing regardless of where we pass the labels | |
history1 = model.fit( | |
prepared_for_class, | |
validation_data=prepared_for_class, | |
steps_per_epoch=1, | |
validation_steps=1, | |
shuffle=False, | |
) | |
val_loss1 = history1.history["val_loss"][0] | |
history2 = model.fit( | |
inputs_minus_labels, | |
labels, | |
validation_data=(inputs_minus_labels, labels), | |
steps_per_epoch=1, | |
validation_steps=1, | |
shuffle=False, | |
) | |
val_loss2 = history2.history["val_loss"][0] | |
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3)) | |
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): | |
# We override with a slightly higher tol value, as semseg models tend to diverge a bit more | |
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) | |
# Overriding this method since the base method won't be compatible with Data2VecVision. | |
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: | |
# Since `TFData2VecVisionModel` won't have labels against which we | |
# could compute loss. | |
if model_class.__name__ != "TFData2VecVisionModel": | |
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.model_tester.prepare_config_and_inputs_for_keras_fit() | |
added_label = prepared_for_class[ | |
sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0] | |
] | |
loss_size = tf.size(added_label) | |
# Test that model correctly compute the loss with kwargs | |
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.assertEqual(loss.shape, [loss_size]) | |
# Test that model correctly compute the loss with a dict | |
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit() | |
loss = model(**prepared_for_class)[0] | |
self.assertEqual(loss.shape, [loss_size]) | |
# Test that model correctly compute the loss with a tuple | |
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.assertEqual(loss.shape, [loss_size]) | |
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_model_from_pretrained(self): | |
for model_name in TF_DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: | |
model = TFData2VecVisionModel.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 | |
class TFData2VecVisionModelIntegrationTest(unittest.TestCase): | |
def default_image_processor(self): | |
return ( | |
BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None | |
) | |
def test_inference_image_classification_head_imagenet_1k(self): | |
model = TFData2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k") | |
image_processor = self.default_image_processor | |
image = prepare_img() | |
inputs = image_processor(images=image, return_tensors="tf") | |
# forward pass | |
outputs = model(**inputs) | |
logits = outputs.logits | |
# verify the logits | |
expected_shape = tf.convert_to_tensor([1, 1000]) | |
self.assertEqual(logits.shape, expected_shape) | |
expected_slice = tf.convert_to_tensor([0.3277, -0.1395, 0.0911]) | |
tf.debugging.assert_near(logits[0, :3], expected_slice, atol=1e-4) | |
expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]] | |
self.assertEqual(tf.nn.top_k(outputs.logits[0], 2).indices.numpy().tolist(), expected_top2) | |