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""" Testing suite for the TensorFlow Data2VecVision model. """ |
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from __future__ import annotations |
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|
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import collections.abc |
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import inspect |
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import unittest |
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|
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import numpy as np |
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|
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from transformers import Data2VecVisionConfig |
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from transformers.file_utils import cached_property, is_tf_available, is_vision_available |
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from transformers.testing_utils import require_tf, require_vision, slow |
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from ...test_configuration_common import ConfigTester |
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from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor |
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from ...test_pipeline_mixin import PipelineTesterMixin |
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|
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if is_tf_available(): |
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import tensorflow as tf |
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|
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from transformers import ( |
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TFData2VecVisionForImageClassification, |
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TFData2VecVisionForSemanticSegmentation, |
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TFData2VecVisionModel, |
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) |
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from transformers.models.data2vec.modeling_tf_data2vec_vision import ( |
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TF_DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, |
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) |
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|
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if is_vision_available(): |
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from PIL import Image |
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from transformers import BeitImageProcessor |
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class TFData2VecVisionModelTester: |
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def __init__( |
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self, |
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parent, |
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vocab_size=100, |
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batch_size=13, |
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image_size=30, |
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patch_size=2, |
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num_channels=3, |
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is_training=True, |
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use_labels=True, |
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hidden_size=32, |
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num_hidden_layers=2, |
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num_attention_heads=4, |
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intermediate_size=37, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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type_sequence_label_size=10, |
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initializer_range=0.02, |
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num_labels=3, |
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scope=None, |
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out_indices=[0, 1, 2, 3], |
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): |
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self.parent = parent |
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self.vocab_size = 100 |
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self.batch_size = batch_size |
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self.image_size = image_size |
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self.patch_size = patch_size |
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self.num_channels = num_channels |
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self.is_training = is_training |
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self.use_labels = use_labels |
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self.hidden_size = hidden_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.type_sequence_label_size = type_sequence_label_size |
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self.initializer_range = initializer_range |
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self.scope = scope |
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self.out_indices = out_indices |
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self.num_labels = num_labels |
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|
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def prepare_config_and_inputs(self): |
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) |
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labels = None |
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pixel_labels = None |
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if self.use_labels: |
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labels = ids_tensor([self.batch_size], self.type_sequence_label_size) |
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pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) |
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config = self.get_config() |
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return config, pixel_values, labels, pixel_labels |
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def get_config(self): |
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return Data2VecVisionConfig( |
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vocab_size=self.vocab_size, |
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image_size=self.image_size, |
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patch_size=self.patch_size, |
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num_channels=self.num_channels, |
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hidden_size=self.hidden_size, |
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num_hidden_layers=self.num_hidden_layers, |
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num_attention_heads=self.num_attention_heads, |
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intermediate_size=self.intermediate_size, |
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hidden_act=self.hidden_act, |
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hidden_dropout_prob=self.hidden_dropout_prob, |
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attention_probs_dropout_prob=self.attention_probs_dropout_prob, |
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is_decoder=False, |
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initializer_range=self.initializer_range, |
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out_indices=self.out_indices, |
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) |
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|
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def create_and_check_model(self, config, pixel_values, labels, pixel_labels): |
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model = TFData2VecVisionModel(config=config) |
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result = model(pixel_values, training=False) |
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image_size = ( |
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self.image_size |
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if isinstance(self.image_size, collections.abc.Iterable) |
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else (self.image_size, self.image_size) |
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) |
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patch_size = ( |
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self.patch_size |
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if isinstance(self.image_size, collections.abc.Iterable) |
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else (self.patch_size, self.patch_size) |
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) |
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) |
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) |
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def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): |
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config.num_labels = self.type_sequence_label_size |
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model = TFData2VecVisionForImageClassification(config) |
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result = model(pixel_values, labels=labels, training=False) |
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) |
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def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): |
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config.num_labels = self.num_labels |
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model = TFData2VecVisionForSemanticSegmentation(config) |
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result = model(pixel_values, training=False) |
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self.parent.assertEqual( |
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) |
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) |
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result = model(pixel_values, labels=pixel_labels) |
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self.parent.assertEqual( |
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result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) |
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) |
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def prepare_config_and_inputs_for_common(self): |
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config_and_inputs = self.prepare_config_and_inputs() |
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config, pixel_values, labels, pixel_labels = config_and_inputs |
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inputs_dict = {"pixel_values": pixel_values} |
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return config, inputs_dict |
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def prepare_config_and_inputs_for_keras_fit(self): |
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config_and_inputs = self.prepare_config_and_inputs() |
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config, pixel_values, _, _ = config_and_inputs |
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inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} |
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return config, inputs_dict |
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@require_tf |
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class TFData2VecVisionModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): |
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""" |
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Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, |
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attention_mask and seq_length. |
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""" |
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all_model_classes = ( |
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(TFData2VecVisionModel, TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation) |
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if is_tf_available() |
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else () |
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) |
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pipeline_model_mapping = ( |
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{"feature-extraction": TFData2VecVisionModel, "image-classification": TFData2VecVisionForImageClassification} |
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if is_tf_available() |
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else {} |
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) |
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test_pruning = False |
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test_onnx = False |
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test_resize_embeddings = False |
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test_head_masking = False |
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def setUp(self): |
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self.model_tester = TFData2VecVisionModelTester(self) |
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self.config_tester = ConfigTester( |
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self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 |
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) |
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def test_config(self): |
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self.config_tester.run_common_tests() |
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@unittest.skip(reason="Data2VecVision does not use inputs_embeds") |
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def test_inputs_embeds(self): |
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pass |
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def test_model_common_attributes(self): |
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config, _ = self.model_tester.prepare_config_and_inputs_for_common() |
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for model_class in self.all_model_classes: |
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model = model_class(config) |
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self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer)) |
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x = model.get_output_embeddings() |
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self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) |
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def test_forward_signature(self): |
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config, _ = self.model_tester.prepare_config_and_inputs_for_common() |
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for model_class in self.all_model_classes: |
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model = model_class(config) |
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signature = inspect.signature(model.call) |
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arg_names = [*signature.parameters.keys()] |
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expected_arg_names = ["pixel_values"] |
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self.assertListEqual(arg_names[:1], expected_arg_names) |
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def test_model(self): |
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config_and_inputs = self.model_tester.prepare_config_and_inputs() |
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self.model_tester.create_and_check_model(*config_and_inputs) |
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|
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def test_for_image_segmentation(self): |
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config_and_inputs = self.model_tester.prepare_config_and_inputs() |
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self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) |
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def test_attention_outputs(self): |
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() |
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config.return_dict = True |
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image_size = ( |
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self.model_tester.image_size |
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if isinstance(self.model_tester.image_size, collections.abc.Iterable) |
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else (self.model_tester.image_size, self.model_tester.image_size) |
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) |
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patch_size = ( |
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self.model_tester.patch_size |
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if isinstance(self.model_tester.patch_size, collections.abc.Iterable) |
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else (self.model_tester.patch_size, self.model_tester.patch_size) |
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) |
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) |
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seq_len = num_patches + 1 |
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) |
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) |
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chunk_length = getattr(self.model_tester, "chunk_length", None) |
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): |
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes |
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|
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for model_class in self.all_model_classes: |
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inputs_dict["output_attentions"] = True |
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inputs_dict["output_hidden_states"] = False |
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config.return_dict = True |
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model = model_class(config) |
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outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) |
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions |
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) |
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|
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del inputs_dict["output_attentions"] |
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config.output_attentions = True |
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model = model_class(config) |
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outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) |
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions |
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) |
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self.assertListEqual( |
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list(attentions[0].shape[-3:]), |
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], |
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) |
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out_len = len(outputs) |
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inputs_dict["output_attentions"] = True |
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inputs_dict["output_hidden_states"] = True |
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model = model_class(config) |
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outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) |
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self.assertEqual(out_len + 1, len(outputs)) |
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self_attentions = outputs.attentions |
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) |
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self.assertListEqual( |
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list(self_attentions[0].shape[-3:]), |
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], |
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) |
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|
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def test_hidden_states_output(self): |
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def check_hidden_states_output(inputs_dict, config, model_class): |
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model = model_class(config) |
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|
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outputs = model(**self._prepare_for_class(inputs_dict, model_class)) |
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|
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states |
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|
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expected_num_layers = getattr( |
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 |
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) |
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self.assertEqual(len(hidden_states), expected_num_layers) |
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image_size = ( |
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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 = ( |
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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) |
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) |
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) |
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seq_length = num_patches + 1 |
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self.assertListEqual( |
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list(hidden_states[0].shape[-2:]), |
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[seq_length, self.model_tester.hidden_size], |
|
) |
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|
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() |
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|
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for model_class in self.all_model_classes: |
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inputs_dict["output_hidden_states"] = True |
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check_hidden_states_output(inputs_dict, config, model_class) |
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|
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|
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del inputs_dict["output_hidden_states"] |
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config.output_hidden_states = True |
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check_hidden_states_output(inputs_dict, config, model_class) |
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@slow |
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def test_keras_fit(self): |
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config, _ = self.model_tester.prepare_config_and_inputs_for_common() |
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for model_class in self.all_model_classes: |
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|
|
if model_class.__name__ != "TFData2VecVisionModel": |
|
model = model_class(config) |
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if getattr(model, "hf_compute_loss", None): |
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|
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_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit() |
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|
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label_names = {"labels"} |
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self.assertGreater(len(label_names), 0, msg="No matching label names found!") |
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labels = {key: val for key, val in prepared_for_class.items() if key in label_names} |
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inputs_minus_labels = { |
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key: val for key, val in prepared_for_class.items() if key not in label_names |
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} |
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self.assertGreater(len(inputs_minus_labels), 0) |
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model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True) |
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history1 = model.fit( |
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prepared_for_class, |
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validation_data=prepared_for_class, |
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steps_per_epoch=1, |
|
validation_steps=1, |
|
shuffle=False, |
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) |
|
val_loss1 = history1.history["val_loss"][0] |
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history2 = model.fit( |
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inputs_minus_labels, |
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labels, |
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validation_data=(inputs_minus_labels, labels), |
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steps_per_epoch=1, |
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validation_steps=1, |
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shuffle=False, |
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) |
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val_loss2 = history2.history["val_loss"][0] |
|
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3)) |
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|
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def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): |
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|
|
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) |
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|
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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: |
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|
|
|
|
if model_class.__name__ != "TFData2VecVisionModel": |
|
model = model_class(config) |
|
if getattr(model, "hf_compute_loss", None): |
|
|
|
_, 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] |
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] |
|
loss_size = tf.size(added_label) |
|
|
|
|
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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) |
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loss = model(model_input, **prepared_for_class)[0] |
|
self.assertEqual(loss.shape, [loss_size]) |
|
|
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_, 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]) |
|
|
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|
|
label_keys = prepared_for_class.keys() - inputs_dict.keys() |
|
signature = inspect.signature(model.call).parameters |
|
signature_names = list(signature.keys()) |
|
|
|
|
|
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()) |
|
|
|
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) |
|
|
|
|
|
loss = model(tuple_input[:-1])[0] |
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|
|
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) |
|
|
|
@slow |
|
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) |
|
|
|
|
|
|
|
def prepare_img(): |
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") |
|
return image |
|
|
|
|
|
@require_tf |
|
@require_vision |
|
class TFData2VecVisionModelIntegrationTest(unittest.TestCase): |
|
@cached_property |
|
def default_image_processor(self): |
|
return ( |
|
BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None |
|
) |
|
|
|
@slow |
|
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") |
|
|
|
|
|
outputs = model(**inputs) |
|
logits = outputs.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) |
|
|