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527 lines
21 KiB
Python
527 lines
21 KiB
Python
# Copyright 2026 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 TIPSv2 model."""
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import inspect
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import tempfile
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import unittest
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from functools import cached_property
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from unittest.mock import patch
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from parameterized import parameterized
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from transformers import Tipsv2Config, Tipsv2TextConfig, Tipsv2VisionConfig
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from transformers.conversion_mapping import get_model_conversion_mapping
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from transformers.testing_utils import (
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Expectations,
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is_flaky,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available
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from ... import test_modeling_common
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from ...test_backbone_common import BackboneTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_pipeline_mixin import PipelineTesterMixin
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from ...test_processing_common import url_to_local_path
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import (
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Tipsv2Model,
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Tipsv2Processor,
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Tipsv2TextModel,
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Tipsv2VisionBackbone,
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Tipsv2VisionModel,
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)
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from transformers.image_utils import load_image_as_tensor
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class Tipsv2VisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=4,
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image_size=28,
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patch_size=14,
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num_channels=3,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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mlp_ratio=2,
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num_register_tokens=1,
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initializer_range=0.02,
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is_training=True,
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scope=None,
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):
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self.parent = parent
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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.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.mlp_ratio = mlp_ratio
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self.num_register_tokens = num_register_tokens
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self.initializer_range = initializer_range
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self.is_training = is_training
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self.scope = scope
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self.num_patches = (image_size // patch_size) ** 2
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self.seq_length = self.num_patches + 1 + num_register_tokens
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self.mask_length = self.num_patches
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self.num_masks = max(1, self.num_patches // 2)
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def get_config(self):
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return Tipsv2VisionConfig(
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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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mlp_ratio=self.mlp_ratio,
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num_register_tokens=self.num_register_tokens,
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initializer_range=self.initializer_range,
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use_swiglu_ffn=False,
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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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config = self.get_config()
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return config, pixel_values
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def create_and_check_model(self, config, pixel_values):
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model = Tipsv2VisionModel(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def prepare_config_and_inputs_for_common(self):
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config, pixel_values = self.prepare_config_and_inputs()
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return config, {"pixel_values": pixel_values}
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@require_torch
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class Tipsv2VisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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TIPSv2VisionModel is a DINOv2-with-registers vision tower. It does not use token input ids, token embeddings, or
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text attention masks.
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"""
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all_model_classes = (Tipsv2VisionModel,) if is_torch_available() else ()
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pipeline_model_mapping = {"image-feature-extraction": Tipsv2VisionModel} if is_torch_available() else {}
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test_resize_embeddings = False
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has_attentions = False
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def setUp(self):
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self.model_tester = Tipsv2VisionModelTester(self)
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self.config_tester = ConfigTester(
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self,
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config_class=Tipsv2VisionConfig,
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has_text_modality=False,
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hidden_size=16,
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num_attention_heads=4,
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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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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_model_get_set_embeddings(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(), nn.Module)
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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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.forward)
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arg_names = [*signature.parameters.keys()]
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self.assertListEqual(arg_names[:1], ["pixel_values"])
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@parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
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@is_flaky()
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def test_eager_matches_sdpa_inference(self, *args):
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return getattr(ModelTesterMixin, self._testMethodName)(self)
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def test_reverse_loading_mapping(self, check_keys_were_modified=True, skip_base_model=False):
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# Depending on config.use_swiglu_ffn, module names are different. They are mlp.fc1/mlp.fc2 in one case and
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# mlp.weights_in/mlp.weights_out in the other. This results in only one of the two weight conversions matching
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# for each model type, resulting in the test failing as it expects all weight converters to match. This doesn't
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# affect the model's ability to load and save weights correctly.
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# Because we only use config.use_swiglu_ffn=False in the tests, we patch get_model_conversion_mapping to drop
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# the swiglu-only FFN renamings.
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def get_model_conversion_mapping_without_swiglu_ffn(*args, **kwargs):
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dropped_targets = {".mlp.weights_in.", ".mlp.weights_out."}
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return [
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conversion
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for conversion in get_model_conversion_mapping(*args, **kwargs)
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if not dropped_targets.intersection(conversion._original_target_patterns)
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]
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with patch.object(
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test_modeling_common,
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"get_model_conversion_mapping",
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get_model_conversion_mapping_without_swiglu_ffn,
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):
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super().test_reverse_loading_mapping(
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check_keys_were_modified=check_keys_were_modified, skip_base_model=skip_base_model
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)
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@require_torch
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class Tipsv2VisionBackboneTest(unittest.TestCase, BackboneTesterMixin):
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all_model_classes = (Tipsv2VisionBackbone,) if is_torch_available() else ()
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config_class = Tipsv2VisionConfig
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has_attentions = False
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def setUp(self):
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self.model_tester = Tipsv2VisionModelTester(self)
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class Tipsv2TextModelTester:
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def __init__(
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self,
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parent,
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batch_size=4,
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seq_length=7,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=32,
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max_position_embeddings=16,
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initializer_range=0.02,
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is_training=True,
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use_input_mask=True,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.vocab_size = vocab_size
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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.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.scope = scope
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def get_config(self):
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return Tipsv2TextConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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intermediate_size=self.intermediate_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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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 3) + 3
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attention_mask = None
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if self.use_input_mask:
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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attention_mask[:, 0] = 1
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attention_mask[:, -1] = 0
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input_ids = input_ids.masked_fill(attention_mask == 0, 0)
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config = self.get_config()
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return config, input_ids, attention_mask
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def create_and_check_model(self, config, input_ids, attention_mask):
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model = Tipsv2TextModel(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(input_ids, attention_mask=attention_mask)
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result_without_mask = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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self.parent.assertEqual(
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result_without_mask.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)
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)
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def prepare_config_and_inputs_for_common(self):
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config, input_ids, attention_mask = self.prepare_config_and_inputs()
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return config, {"input_ids": input_ids, "attention_mask": attention_mask}
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@require_torch
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class Tipsv2TextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (Tipsv2TextModel,) if is_torch_available() else ()
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test_resize_embeddings = False
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model_split_percents = [0.5, 0.8, 0.9]
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def setUp(self):
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self.model_tester = Tipsv2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Tipsv2TextConfig, hidden_size=16, num_attention_heads=4)
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def test_config(self):
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self.config_tester.run_common_tests()
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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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class Tipsv2ModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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text_kwargs = {} if text_kwargs is None else text_kwargs
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vision_kwargs = {} if vision_kwargs is None else vision_kwargs
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self.parent = parent
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self.text_model_tester = Tipsv2TextModelTester(parent, **text_kwargs)
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self.vision_model_tester = Tipsv2VisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size
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self.hidden_size = self.text_model_tester.hidden_size
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self.num_hidden_layers = self.text_model_tester.num_hidden_layers
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self.num_attention_heads = self.text_model_tester.num_attention_heads
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self.seq_length = self.text_model_tester.seq_length
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self.is_training = is_training
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def get_config(self):
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return Tipsv2Config(
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text_config=self.text_model_tester.get_config().to_dict(),
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vision_config=self.vision_model_tester.get_config().to_dict(),
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temperature_init_value=0.01,
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)
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def prepare_config_and_inputs(self):
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_, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = Tipsv2Model(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.logits_per_image.shape,
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(self.vision_model_tester.batch_size, self.text_model_tester.batch_size),
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)
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self.parent.assertEqual(
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result.logits_per_text.shape,
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(self.text_model_tester.batch_size, self.vision_model_tester.batch_size),
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)
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self.parent.assertEqual(result.image_embeds.shape, (self.vision_model_tester.batch_size, self.hidden_size))
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self.parent.assertEqual(result.text_embeds.shape, (self.text_model_tester.batch_size, self.hidden_size))
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def prepare_config_and_inputs_for_common(self):
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config, input_ids, attention_mask, pixel_values = self.prepare_config_and_inputs()
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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}
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return config, inputs_dict
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@require_torch
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class Tipsv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (Tipsv2Model,) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": Tipsv2Model} if is_torch_available() else {}
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additional_model_inputs = ["pixel_values", "attention_mask", "return_loss"]
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test_resize_embeddings = False
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has_attentions = False
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_is_composite = True
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def setUp(self):
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self.model_tester = Tipsv2ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Tipsv2Config, has_text_modality=False)
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def test_config(self):
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self.config_tester.run_common_tests()
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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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@unittest.skip(reason="Hidden states are tested in the individual TIPSv2 text and vision tower tests.")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Retained gradients for hidden states are tested in individual tower tests.")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(
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reason="Inputs_embeds behavior is covered by the text tower; the composite model has image inputs too."
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)
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def test_inputs_embeds(self):
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pass
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@unittest.skip(
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reason="Inputs_embeds behavior is covered by the text tower; the composite model has image inputs too."
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)
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def test_inputs_embeds_matches_input_ids(self):
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pass
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def test_load_vision_text_config(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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vision_config = Tipsv2VisionConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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text_config = Tipsv2TextConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
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def prepare_img():
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image = load_image_as_tensor(
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url_to_local_path(
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"https://huggingface.co/datasets/hf-internal-testing/fixtures-coco/resolve/main/val2017/000000039769.jpg"
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)
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)
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return image
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@require_torch
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@require_vision
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class Tipsv2ModelIntegrationTest(unittest.TestCase):
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model_id = "google/tipsv2-b14"
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@cached_property
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def default_processor(self):
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return Tipsv2Processor.from_pretrained(self.model_id)
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@slow
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def test_inference(self):
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model = Tipsv2Model.from_pretrained(self.model_id, device_map=torch_device).eval()
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text_queries = ["two cats on a sofa", "a cat lying down", "a dog on a couch", "an empty room"]
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processor = self.default_processor
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inputs = processor(images=prepare_img(), text=text_queries, return_tensors="pt").to(torch_device)
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|
|
|
with torch.no_grad():
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outputs = model(**inputs)
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|
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|
vision_cfg = model.config.vision_config
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num_register_tokens = vision_cfg.num_register_tokens
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patch_tokens = outputs.vision_model_output.last_hidden_state[:, 1 + num_register_tokens :]
|
|
|
|
# Tolerance of 1e-3 for vision outputs because of difference in PIL vs. torch preprocessing.
|
|
EXPECTED_IMAGE_EMBEDS = Expectations(
|
|
{
|
|
("cuda", None): [0.03267, 0.02216, 0.00546, 0.01890, -0.05426],
|
|
("cpu", None): [0.03267, 0.02216, 0.00546, 0.01890, -0.05426],
|
|
}
|
|
)
|
|
expected_image_embeds = torch.tensor(EXPECTED_IMAGE_EMBEDS.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(outputs.image_embeds[0, :5], expected_image_embeds, rtol=1e-3, atol=1e-3)
|
|
|
|
EXPECTED_PATCH_TOKENS = Expectations(
|
|
{
|
|
("cuda", None): [0.25287, -0.01092, -0.57542, 0.09660, -0.04010],
|
|
("cpu", None): [0.25287, -0.01092, -0.57542, 0.09660, -0.04010],
|
|
}
|
|
)
|
|
expected_patch_tokens = torch.tensor(EXPECTED_PATCH_TOKENS.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(patch_tokens[0, 0, :5], expected_patch_tokens, rtol=1e-3, atol=1e-3)
|
|
|
|
EXPECTED_TEXT_EMBEDS = Expectations(
|
|
{
|
|
("cuda", None): [0.69319, 0.03710, 0.01194, 0.02136, -0.04281],
|
|
("cpu", None): [0.69319, 0.03710, 0.01194, 0.02136, -0.04281],
|
|
}
|
|
)
|
|
expected_text_embeds = torch.tensor(EXPECTED_TEXT_EMBEDS.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(outputs.text_embeds[0, :5], expected_text_embeds, rtol=1e-4, atol=1e-4)
|
|
|
|
EXPECTED_LOGITS_PER_IMAGE = Expectations(
|
|
{
|
|
("cuda", None): [31.12190, 26.99341, 20.26748, 17.55544],
|
|
("cpu", None): [31.12190, 26.99341, 20.26748, 17.55544],
|
|
}
|
|
)
|
|
expected_logits_per_image = torch.tensor(EXPECTED_LOGITS_PER_IMAGE.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(outputs.logits_per_image[0], expected_logits_per_image, rtol=1e-3, atol=1e-3)
|
|
|
|
EXPECTED_LOGITS_PER_TEXT = Expectations(
|
|
{
|
|
("cuda", None): [31.12190, 26.99341, 20.26748, 17.55544],
|
|
("cpu", None): [31.12190, 26.99341, 20.26748, 17.55544],
|
|
}
|
|
)
|
|
expected_logits_per_text = torch.tensor(EXPECTED_LOGITS_PER_TEXT.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(outputs.logits_per_text[..., 0], expected_logits_per_text, rtol=1e-3, atol=1e-3)
|
|
|
|
EXPECTED_VISION_POOLER_OUTPUT = Expectations(
|
|
{
|
|
("cuda", None): [0.22055, 0.14960, 0.03689, 0.12756, -0.36632],
|
|
("cpu", None): [0.22055, 0.14960, 0.03689, 0.12756, -0.36632],
|
|
}
|
|
)
|
|
expected_vision_pooler_output = torch.tensor(
|
|
EXPECTED_VISION_POOLER_OUTPUT.get_expectation(), device=torch_device
|
|
)
|
|
torch.testing.assert_close(
|
|
outputs.vision_model_output.pooler_output[0, :5], expected_vision_pooler_output, rtol=1e-3, atol=1e-3
|
|
)
|
|
|
|
EXPECTED_TEXT_POOLER_OUTPUT = Expectations(
|
|
{
|
|
("cuda", None): [12.07207, 0.64612, 0.20788, 0.37204, -0.74551],
|
|
("cpu", None): [12.07207, 0.64612, 0.20788, 0.37204, -0.74551],
|
|
}
|
|
)
|
|
expected_text_pooler_output = torch.tensor(EXPECTED_TEXT_POOLER_OUTPUT.get_expectation(), device=torch_device)
|
|
torch.testing.assert_close(
|
|
outputs.text_model_output.pooler_output[0, :5], expected_text_pooler_output, rtol=1e-4, atol=1e-4
|
|
)
|