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tests/models/slanet/test_modeling_slanet.py

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+# coding = utf-8
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the SLANet model."""
+
+import inspect
+import unittest
+
+from transformers import (
+    AutoImageProcessor,
+    AutoModelForTableRecognition,
+    SLANetConfig,
+    SLANetForTableRecognition,
+    is_torch_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+
+class SLANetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        image_size=488,
+        num_channels=3,
+        post_conv_out_channels=16,
+        out_channels=1,
+        hidden_size=16,
+        max_text_length=1,
+        num_stages=5,
+        is_training=False,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.post_conv_out_channels = post_conv_out_channels
+        self.out_channels = out_channels
+        self.hidden_size = hidden_size
+        self.max_text_length = max_text_length
+        self.num_stages = num_stages
+        self.is_training = is_training
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self) -> SLANetConfig:
+        backbone_config = {
+            "model_type": "pp_lcnet",
+            "scale": 1,
+            "out_features": ["stage2", "stage3", "stage4", "stage5"],
+            "out_indices": [2, 3, 4, 5],
+            "block_configs": [
+                [[3, 16, 16, 1, False]],
+                [[3, 16, 16, 2, False], [3, 16, 16, 1, False]],
+                [[3, 16, 16, 2, False], [3, 16, 16, 1, False]],
+                [
+                    [3, 16, 16, 2, False],
+                    [5, 16, 16, 1, False],
+                    [5, 16, 16, 1, False],
+                    [5, 16, 16, 1, False],
+                    [5, 16, 16, 1, False],
+                    [5, 16, 16, 1, False],
+                ],
+                [[5, 16, 16, 2, True], [5, 16, 16, 1, True]],
+            ],
+        }
+        config = SLANetConfig(
+            backbone_config=backbone_config,
+            out_channels=self.out_channels,
+            hidden_size=self.hidden_size,
+            max_text_length=self.max_text_length,
+            post_conv_out_channels=self.post_conv_out_channels,
+        )
+
+        return config
+
+
+@require_torch
+class SLANetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (SLANetForTableRecognition,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-feature-extraction": SLANetForTableRecognition} if is_torch_available() else {}
+
+    has_attentions = False
+    test_resize_embeddings = False
+    test_torch_exportable = False
+
+    def setUp(self):
+        self.model_tester = SLANetModelTester(
+            self,
+            batch_size=1,
+            image_size=488,
+        )
+        self.config_tester = ConfigTester(
+            self,
+            config_class=SLANetConfig,
+            has_text_modality=False,
+            common_properties=[],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SLANet does not use inputs_embeds")
+    def test_enable_input_require_grads(self):
+        pass
+
+    @unittest.skip(reason="SLANet does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="SLANet does not use test_inputs_embeds_matches_input_ids")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip(reason="SLANet does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            arg_names = [*signature.parameters.keys()]
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    # SLANet have no seq_length
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.hidden_states
+            expected_num_stages = self.model_tester.num_stages
+
+            self.assertEqual(len(hidden_states), expected_num_stages + 1)
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict.copy(), config, model_class)
+
+            # Check that output_hidden_states also works via config (including backbone subconfig)
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            if config.backbone_config is not None:
+                config.backbone_config.output_hidden_states = True
+            check_hidden_states_output(inputs_dict.copy(), config, model_class)
+
+
+@require_torch
+@require_vision
+@slow
+class SLANetModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        model_path = "PaddlePaddle/SLANet_plus_safetensors"
+        self.model = AutoModelForTableRecognition.from_pretrained(model_path, dtype=torch.float32).to(torch_device)
+        self.image_processor = AutoImageProcessor.from_pretrained(model_path)
+        img_url = url_to_local_path(
+            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg"
+        )
+        self.image = load_image(img_url)
+
+    def test_inference_table_recognition_head(self):
+        inputs = self.image_processor(images=self.image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        pred_table_structure = self.image_processor.post_process_table_recognition(outputs)["structure"]
+        expected_table_structure = [
+            "<html>",
+            "<body>",
+            "<table>",
+            "<tr>",
+            "<td",
+            ' colspan="4"',
+            ">",
+            "</td>",
+            "</tr>",
+            "<tr>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "</tr>",
+            "<tr>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "</tr>",
+            "<tr>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "<td></td>",
+            "</tr>",
+            "</table>",
+            "</body>",
+            "</html>",
+        ]
+
+        self.assertEqual(pred_table_structure, expected_table_structure)