Add TFVisionTextDualEncoder (#21873)
* Temporary commit to stash everything so far * Temporary commit to stash everything so far * stash commit * Refactor from_pretrained * Fix final test, make fixup * Update dummies * Add model to TEST_FILES_WITH_NO_COMMON_TESTS * Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Add TFVisionTextDualEncoder to utils/documentation_tests.txt * make fixup --------- Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
This commit is contained in:
Matt
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# coding=utf-8
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# Copyright 2021 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 PyTorch VisionTextDualEncoder model. """
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import collections
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import tempfile
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import unittest
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import numpy as np
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from transformers.testing_utils import require_tf, require_vision, slow
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from transformers.utils import is_tf_available, is_vision_available
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from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
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from ..bert.test_modeling_tf_bert import TFBertModelTester
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from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
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from ..deit.test_modeling_tf_deit import TFDeiTModelTester
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from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
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from ..vit.test_modeling_tf_vit import TFViTModelTester
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if is_tf_available():
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from transformers import (
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TFBertModel,
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TFCLIPVisionModel,
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TFDeiTModel,
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TFRobertaModel,
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TFVisionTextDualEncoderModel,
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TFViTModel,
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VisionTextDualEncoderConfig,
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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 VisionTextDualEncoderProcessor
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# Inspired by
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# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
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# From PyTorch internals
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def to_2tuple(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return (x, x)
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@require_tf
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class TFVisionTextDualEncoderMixin:
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def get_vision_text_model(self, config, text_config):
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pass
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def prepare_config_and_inputs(self):
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pass
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def get_pretrained_model_and_inputs(self):
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pass
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def check_model_from_pretrained_configs(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config)
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model = TFVisionTextDualEncoderModel(config)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim))
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self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim))
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def check_vision_text_dual_encoder_model(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
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self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
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def check_vision_text_dual_encoder_from_pretrained(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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kwargs = {"vision_model": vision_model, "text_model": text_model}
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model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim))
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self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim))
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def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
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output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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out_1 = output[0].numpy()
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = TFVisionTextDualEncoderModel.from_pretrained(tmpdirname)
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after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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out_2 = after_output[0].numpy()
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max_diff = np.amax(np.abs(out_2 - out_1))
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self.assertLessEqual(max_diff, 1e-5)
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def check_vision_text_output_attention(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
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output = model(
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input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
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)
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vision_attentions = output.vision_model_output.attentions
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self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
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# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = to_2tuple(vision_model.config.image_size)
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patch_size = to_2tuple(vision_model.config.patch_size)
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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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self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
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text_attentions = output.text_model_output.attentions
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self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
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self.assertEqual(
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text_attentions[0].shape[-3:],
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(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
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)
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def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
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diff = np.abs((a - b)).max()
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self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
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def test_vision_text_dual_encoder_model(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_vision_text_dual_encoder_model(**inputs_dict)
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def test_model_from_pretrained_configs(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_model_from_pretrained_configs(**inputs_dict)
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def test_vision_text_dual_encoder_from_pretrained(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict)
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def test_save_load(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_save_load(**inputs_dict)
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def test_vision_text_output_attention(self):
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inputs_dict = self.prepare_config_and_inputs()
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self.check_vision_text_output_attention(**inputs_dict)
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@slow
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def test_real_model_save_load_from_pretrained(self):
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model_2, inputs = self.get_pretrained_model_and_inputs()
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outputs = model_2(**inputs)
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out_2 = outputs[0].numpy()
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with tempfile.TemporaryDirectory() as tmp_dirname:
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model_2.save_pretrained(tmp_dirname)
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model_1 = TFVisionTextDualEncoderModel.from_pretrained(tmp_dirname)
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after_outputs = model_1(**inputs)
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out_1 = after_outputs[0].numpy()
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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@require_tf
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class TFViTBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
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def get_pretrained_model_and_inputs(self):
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model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
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"hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-random-bert"
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)
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batch_size = 13
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pixel_values = floats_tensor(
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[
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batch_size,
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model.vision_model.config.num_channels,
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model.vision_model.config.image_size,
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model.vision_model.config.image_size,
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]
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)
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input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
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attention_mask = random_attention_mask([batch_size, 4])
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inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
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return model, inputs
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def get_vision_text_model(self, vision_config, text_config):
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vision_model = TFViTModel(vision_config, name="vision_model")
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text_model = TFBertModel(text_config, name="text_model")
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return vision_model, text_model
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def prepare_config_and_inputs(self):
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vit_model_tester = TFViTModelTester(self)
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bert_model_tester = TFBertModelTester(self)
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vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
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text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values, _ = vision_config_and_inputs
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(
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text_config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = text_config_and_inputs
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return {
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"text_config": text_config,
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"vision_config": vision_config,
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"pixel_values": pixel_values,
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"attention_mask": input_mask,
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"input_ids": input_ids,
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"text_token_type_ids": token_type_ids,
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"text_sequence_labels": sequence_labels,
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"text_token_labels": token_labels,
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"text_choice_labels": choice_labels,
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}
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@require_tf
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class TFDeiTRobertaModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
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def get_pretrained_model_and_inputs(self):
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# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
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# just reinitialize it.
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model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
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"Rocketknight1/tiny-random-deit-tf", "hf-internal-testing/tiny-random-roberta"
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)
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batch_size = 13
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pixel_values = floats_tensor(
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[
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batch_size,
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model.vision_model.config.num_channels,
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model.vision_model.config.image_size,
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model.vision_model.config.image_size,
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]
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)
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input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
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attention_mask = random_attention_mask([batch_size, 4])
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inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
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return model, inputs
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def check_vision_text_output_attention(
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self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs
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):
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vision_model, text_model = self.get_vision_text_model(vision_config, text_config)
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model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model)
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output = model(
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input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True
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)
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vision_attentions = output.vision_model_output.attentions
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self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers)
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# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
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image_size = to_2tuple(vision_model.config.image_size)
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patch_size = to_2tuple(vision_model.config.patch_size)
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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 + 2
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self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len))
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text_attentions = output.text_model_output.attentions
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self.assertEqual(len(text_attentions), text_config.num_hidden_layers)
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self.assertEqual(
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text_attentions[0].shape[-3:],
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(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
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)
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def get_vision_text_model(self, vision_config, text_config):
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vision_model = TFDeiTModel(vision_config, name="vision_model")
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text_model = TFRobertaModel(text_config, name="text_model")
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return vision_model, text_model
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def prepare_config_and_inputs(self):
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vit_model_tester = TFDeiTModelTester(self)
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bert_model_tester = TFRobertaModelTester(self)
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vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs()
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text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values, _ = vision_config_and_inputs
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(
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text_config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = text_config_and_inputs
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return {
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"text_config": text_config,
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"vision_config": vision_config,
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"pixel_values": pixel_values,
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"attention_mask": input_mask,
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"input_ids": input_ids,
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"text_token_type_ids": token_type_ids,
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"text_sequence_labels": sequence_labels,
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"text_token_labels": token_labels,
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"text_choice_labels": choice_labels,
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}
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@require_tf
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class TFCLIPVisionBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase):
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def get_pretrained_model_and_inputs(self):
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model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
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"Rocketknight1/tiny-random-clip-tf", "hf-internal-testing/tiny-random-bert"
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)
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batch_size = 13
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pixel_values = floats_tensor(
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[
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batch_size,
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model.vision_model.config.num_channels,
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model.vision_model.config.image_size,
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model.vision_model.config.image_size,
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]
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)
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input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size)
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attention_mask = random_attention_mask([batch_size, 4])
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inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
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return model, inputs
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def get_vision_text_model(self, vision_config, text_config):
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vision_model = TFCLIPVisionModel(vision_config, name="vision_model")
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text_model = TFBertModel(text_config, name="text_model")
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return vision_model, text_model
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def prepare_config_and_inputs(self):
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clip_model_tester = TFCLIPVisionModelTester(self)
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bert_model_tester = TFBertModelTester(self)
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vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs()
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text_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = vision_config_and_inputs
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(
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text_config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = text_config_and_inputs
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return {
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"text_config": text_config,
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"vision_config": vision_config,
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"pixel_values": pixel_values,
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"attention_mask": input_mask,
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"input_ids": input_ids,
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"text_token_type_ids": token_type_ids,
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"text_sequence_labels": sequence_labels,
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"text_token_labels": token_labels,
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"text_choice_labels": choice_labels,
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}
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@require_vision
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@require_tf
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class TFVisionTextDualEncoderIntegrationTest(unittest.TestCase):
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@slow
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def test_inference(self):
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model = TFVisionTextDualEncoderModel.from_pretrained(
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"clip-italian/clip-italian", logit_scale_init_value=1, from_pt=True
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)
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processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")
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|
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
inputs = processor(
|
||||
text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="np"
|
||||
)
|
||||
|
||||
outputs = model(**inputs)
|
||||
|
||||
# verify the logits
|
||||
self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
|
||||
self.assertEqual(
|
||||
outputs.logits_per_text.shape,
|
||||
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
|
||||
)
|
||||
|
||||
expected_logits = np.array([[1.2284727, 0.3104122]])
|
||||
|
||||
self.assertTrue(np.allclose(outputs.logits_per_image.numpy(), expected_logits, atol=1e-3))
|
||||
Reference in New Issue
Block a user