497 lines
22 KiB
Python
497 lines
22 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the TensorFlow Data2VecVision model. """
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from __future__ import annotations
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import collections.abc
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import inspect
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import unittest
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import numpy as np
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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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if is_tf_available():
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import tensorflow as tf
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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.modeling_tf_utils import keras
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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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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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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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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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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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# Data2VecVision does not use inputs_embeds
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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(), (keras.layers.Layer))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, 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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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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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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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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# in Data2VecVision, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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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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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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# check that output_attentions also work using config
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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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# Check attention is always last and order is fine
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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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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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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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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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# Data2VecVision has a different seq_length
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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_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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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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# check that output_hidden_states also work using config
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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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# Overriding this method since the base method won't be compatible with Data2VecVision.
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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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# Since `TFData2VecVisionModel` cannot operate with the default `fit()` method.
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if model_class.__name__ != "TFData2VecVisionModel":
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model = model_class(config)
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if getattr(model, "hf_compute_loss", None):
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# Test that model correctly compute the loss with kwargs
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_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit()
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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=keras.optimizers.SGD(0.0), run_eagerly=True)
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# Make sure the model fits without crashing regardless of where we pass the labels
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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,
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validation_steps=1,
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shuffle=False,
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)
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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]
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self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3))
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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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# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
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super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
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# Overriding this method since the base method won't be compatible with Data2VecVision.
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def test_loss_computation(self):
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config, inputs_dict = 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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# Since `TFData2VecVisionModel` won't have labels against which we
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# could compute loss.
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if model_class.__name__ != "TFData2VecVisionModel":
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model = model_class(config)
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if getattr(model, "hf_compute_loss", None):
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# The number of elements in the loss should be the same as the number of elements in the label
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_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit()
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added_label = prepared_for_class[
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sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
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]
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loss_size = tf.size(added_label)
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# Test that model correctly compute the loss with kwargs
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possible_input_names = {"input_ids", "pixel_values", "input_features"}
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input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
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model_input = prepared_for_class.pop(input_name)
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loss = model(model_input, **prepared_for_class)[0]
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self.assertEqual(loss.shape, [loss_size])
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# Test that model correctly compute the loss with a dict
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_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit()
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loss = model(**prepared_for_class)[0]
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self.assertEqual(loss.shape, [loss_size])
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# Test that model correctly compute the loss with a tuple
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label_keys = prepared_for_class.keys() - inputs_dict.keys()
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signature = inspect.signature(model.call).parameters
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signature_names = list(signature.keys())
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# Create a dictionary holding the location of the tensors in the tuple
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tuple_index_mapping = {0: input_name}
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for label_key in label_keys:
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label_key_index = signature_names.index(label_key)
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tuple_index_mapping[label_key_index] = label_key
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sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
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# Initialize a list with their default values, update the values and convert to a tuple
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list_input = []
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for name in signature_names:
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if name != "kwargs":
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list_input.append(signature[name].default)
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for index, value in sorted_tuple_index_mapping:
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list_input[index] = prepared_for_class[value]
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|
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tuple_input = tuple(list_input)
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|
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# Send to model
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loss = model(tuple_input[:-1])[0]
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|
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self.assertEqual(loss.shape, [loss_size])
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|
|
|
def test_for_image_classification(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_classification(*config_and_inputs)
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|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "facebook/data2vec-vision-base-ft1k"
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|
model = TFData2VecVisionModel.from_pretrained(model_name)
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|
self.assertIsNotNone(model)
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|
|
|
|
|
# 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
|
|
@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")
|
|
|
|
# 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)
|