717 lines
28 KiB
Python
717 lines
28 KiB
Python
# coding=utf-8
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# Copyright 2024 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 SigLIP model."""
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import inspect
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import os
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import tempfile
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import unittest
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import numpy as np
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import requests
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from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
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from transformers.testing_utils import (
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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, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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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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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 SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel
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if is_vision_available():
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from PIL import Image
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from transformers import SiglipProcessor
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class SiglipVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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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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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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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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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.is_training = is_training
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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.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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# in ViT, the seq length equals the number of patches
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches
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# Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs
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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 get_config(self):
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return SiglipVisionConfig(
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values):
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model = SiglipVisionModel(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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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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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, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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# Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common
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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 = 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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@require_torch
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class SiglipVisionModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP 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 = (SiglipVisionModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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# MP works but offload doesn't work when the MultiheadAttention is offloaded
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# TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
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# in the dispatch_model function
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test_cpu_offload = False
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test_disk_offload_safetensors = False
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test_disk_offload_bin = False
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def setUp(self):
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self.model_tester = SiglipVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=SiglipVisionConfig, 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="SIGLIP does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (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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# 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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@unittest.skip(reason="SiglipVisionModel does not support standalone training")
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def test_training(self):
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pass
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@unittest.skip(reason="SiglipVisionModel does not support standalone training")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="SiglipVisionModel does not support standalone training")
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(reason="SiglipVisionModel does not support standalone training")
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
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def test_initialization(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "google/siglip-base-patch16-224"
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model = SiglipVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class SiglipTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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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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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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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.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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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.dropout = dropout
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self.attention_dropout = attention_dropout
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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.scope = scope
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs
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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)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return SiglipTextConfig(
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vocab_size=self.vocab_size,
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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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 create_and_check_model(self, config, input_ids, input_mask):
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model = SiglipTextModel(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=input_mask)
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result = 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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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common
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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, input_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class SiglipTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (SiglipTextModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_head_masking = False
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model_split_percents = [0.5, 0.8, 0.9]
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip
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def setUp(self):
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self.model_tester = SiglipTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=37)
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config
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def test_config(self):
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self.config_tester.run_common_tests()
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model
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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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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training
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def test_training(self):
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pass
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant_false
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Siglip does not use inputs_embeds")
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING")
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_from_base
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING")
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# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_to_base
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def test_save_load_fast_init_to_base(self):
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pass
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@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
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def test_initialization(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "google/siglip-base-patch16-224"
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model = SiglipTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class SiglipModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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self.is_training = is_training
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, 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 get_config(self):
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return SiglipConfig.from_text_vision_configs(
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self.text_model_tester.get_config(),
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self.vision_model_tester.get_config(),
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)
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = SiglipModel(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values, attention_mask)
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self.parent.assertEqual(
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result.logits_per_image.shape, (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, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
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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, input_ids, attention_mask, pixel_values = 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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"return_loss": False,
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}
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return config, inputs_dict
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@require_torch
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class SiglipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (SiglipModel,) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {}
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fx_compatible = False
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.setUp with CLIP->Siglip
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def setUp(self):
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self.model_tester = SiglipModelTester(self)
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model
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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 is tested in individual model tests")
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output
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|
def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds
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def test_inputs_embeds(self):
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pass
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|
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@unittest.skip(reason="Retain_grad is tested in individual model tests")
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions
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def test_retain_grad_hidden_states_attentions(self):
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pass
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|
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@unittest.skip(reason="SiglipModel does not have input/output embeddings")
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_common_attributes
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="SiglipModel does not support training")
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def test_training(self):
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pass
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@unittest.skip(reason="SiglipModel does not support training")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="SiglipModel does not support training")
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(reason="SiglipModel does not support training")
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
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def test_initialization(self):
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pass
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# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest._create_and_check_torchscript with CLIP->Siglip
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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configs_no_init.return_dict = False
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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model.to(torch_device)
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model.eval()
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try:
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input_ids = inputs_dict["input_ids"]
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pixel_values = inputs_dict["pixel_values"] # Siglip needs pixel_values
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traced_model = torch.jit.trace(model, (input_ids, pixel_values))
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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|
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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|
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|
try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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|
self.fail("Couldn't load module.")
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|
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model.to(torch_device)
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model.eval()
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|
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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|
loaded_model_state_dict = loaded_model.state_dict()
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|
|
|
non_persistent_buffers = {}
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|
for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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|
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|
loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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|
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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|
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|
model_buffers = list(model.buffers())
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|
for non_persistent_buffer in non_persistent_buffers.values():
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|
found_buffer = False
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|
for i, model_buffer in enumerate(model_buffers):
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|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
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|
break
|
|
|
|
self.assertTrue(found_buffer)
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|
model_buffers.pop(i)
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|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
p2 = loaded_model_state_dict[layer_name]
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|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
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|
|
|
self.assertTrue(models_equal)
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|
|
|
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip
|
|
def test_load_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save SiglipConfig and check if we can load SiglipVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save SiglipConfig and check if we can load SiglipTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = SiglipTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "google/siglip-base-patch16-224"
|
|
model = SiglipModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class SiglipForImageClassificationModelTester(SiglipModelTester):
|
|
def __init__(self, parent):
|
|
super().__init__(parent)
|
|
self.batch_size = self.vision_model_tester.batch_size
|
|
self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
|
|
self.hidden_size = self.vision_model_tester.hidden_size
|
|
self.seq_length = self.vision_model_tester.seq_length
|
|
|
|
def prepare_config_and_inputs(self):
|
|
_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
|
|
config = self.get_config()
|
|
|
|
return config, pixel_values
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, pixel_values = config_and_inputs
|
|
inputs_dict = {"pixel_values": pixel_values}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class SiglipForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (SiglipForImageClassification,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {}
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = SiglipForImageClassificationModelTester(self)
|
|
|
|
@unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
|
|
def test_initialization(self):
|
|
pass
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw)
|
|
return image
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class SiglipModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "google/siglip-base-patch16-224"
|
|
model = SiglipModel.from_pretrained(model_name).to(torch_device)
|
|
processor = SiglipProcessor.from_pretrained(model_name)
|
|
|
|
image = prepare_img()
|
|
inputs = processor(
|
|
text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
logits_per_image = outputs.logits_per_image
|
|
logits_per_text = outputs.logits_per_text
|
|
|
|
# verify the logits
|
|
self.assertEqual(
|
|
logits_per_image.shape,
|
|
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
logits_per_text.shape,
|
|
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
|
)
|
|
|
|
expected_logits = torch.tensor([[-0.7567, -10.3354]], device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|
|
|
|
# verify the probs
|
|
probs = torch.sigmoid(logits_per_image) # these are the probabilities
|
|
expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
|
|
self.assertTrue(torch.allclose(probs, expected_probs, atol=1e-3))
|
|
|
|
@slow
|
|
def test_inference_interpolate_pos_encoding(self):
|
|
model_name = "google/siglip-base-patch16-224"
|
|
model = SiglipModel.from_pretrained(model_name).to(torch_device)
|
|
|
|
# 640 x 480 image
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640})
|
|
|
|
inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs, interpolate_pos_encoding=True)
|
|
|
|
# verify the shape
|
|
# patch size = 16
|
|
# batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size
|
|
expected_shape = torch.Size((1, 1200, 768))
|
|
|
|
self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
|