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tests/models/pp_ocrv6_small_det/test_modeling_pp_ocrv6_small_det.py

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+# coding = utf-8
+# Copyright 2026 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 PP-OCRV6SmallDet model."""
+
+import inspect
+import unittest
+
+from parameterized import parameterized
+
+from transformers import (
+    PPOCRV5ServerDetImageProcessor,
+    PPOCRV6SmallDetConfig,
+    PPOCRV6SmallDetForObjectDetection,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    require_cv2,
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+
+class PPOCRV6SmallDetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        image_size=128,
+        num_channels=3,
+        num_stages=4,
+        is_training=False,
+        reduction=4,
+        hidden_act="hardswish",
+        layer_list_out_channels=[16, 16, 16, 16],
+        neck_out_channels=16,
+        kernel_list=[3, 2, 2],
+        interpolate_mode="nearest",
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.num_stages = num_stages
+        self.is_training = is_training
+        self.reduction = reduction
+        self.hidden_act = hidden_act
+        self.layer_list_out_channels = layer_list_out_channels
+        self.neck_out_channels = neck_out_channels
+        self.kernel_list = kernel_list
+        self.interpolate_mode = interpolate_mode
+
+    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) -> PPOCRV6SmallDetConfig:
+        backbone_config = {
+            "model_type": "pp_lcnet_v4",
+            "out_features": ["stage1", "stage2", "stage3", "stage4"],
+            "out_indices": [1, 2, 3, 4],
+            "stem_channels": [3, 16, 16],
+            "stem_type": "large",
+            "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],
+                ],
+            ],
+        }
+        config = PPOCRV6SmallDetConfig(
+            backbone_config=backbone_config,
+            reduction=self.reduction,
+            hidden_act=self.hidden_act,
+            layer_list_out_channels=self.layer_list_out_channels,
+            neck_out_channels=self.neck_out_channels,
+            kernel_list=self.kernel_list,
+            interpolate_mode=self.interpolate_mode,
+        )
+        return config
+
+    def create_and_check_pp_ocrv6_small_det_object_detection(self, config, pixel_values):
+        model = PPOCRV6SmallDetForObjectDetection(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size))
+
+
+@require_torch
+class PPOCRV6SmallDetModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (PPOCRV6SmallDetForObjectDetection,) if is_torch_available() else ()
+    pipeline_model_mapping = {"object-detection": PPOCRV6SmallDetForObjectDetection} if is_torch_available() else {}
+
+    has_attentions = False
+    test_inputs_embeds = False
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = PPOCRV6SmallDetModelTester(
+            self,
+            batch_size=3,
+            is_training=False,
+            image_size=128,
+        )
+        self.config_tester = ConfigTester(
+            self, config_class=PPOCRV6SmallDetConfig, has_text_modality=False, common_properties=[]
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="PPOCRV6SmallDet does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="PPOCRV6SmallDet does not support attention")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="PPOCRV6SmallDet does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="PPOCRV6SmallDet does not support.")
+    def test_multi_gpu_data_parallel_forward(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)
+
+    # PPOCRV6SmallDet 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, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            self._set_subconfig_attributes(config, "output_hidden_states", True)
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @parameterized.expand(["float32", "float16", "bfloat16"])
+    @require_torch_accelerator
+    @slow
+    def test_inference_with_different_dtypes(self, dtype_str):
+        dtype = {
+            "float32": torch.float32,
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+        }[dtype_str]
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device).to(dtype)
+            model.eval()
+            for key, tensor in inputs_dict.items():
+                if tensor.dtype == torch.float32:
+                    inputs_dict[key] = tensor.to(dtype)
+            with torch.no_grad():
+                _ = model(**self._prepare_for_class(inputs_dict, model_class))
+
+
+# TODO: vasqu
+@require_cv2
+@require_torch
+@require_vision
+@slow
+@unittest.skip(reason="PP-OCRv6_small_det_safetensors weights have not been uploaded yet.")
+class PPOCRV6SmallDetModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        model_path = "PaddlePaddle/PP-OCRv6_small_det_safetensors"
+        self.model = PPOCRV6SmallDetForObjectDetection.from_pretrained(model_path).to(torch_device)
+        self.image_processor = (
+            PPOCRV5ServerDetImageProcessor.from_pretrained(model_path) if is_vision_available() else None
+        )
+        img_url = url_to_local_path(
+            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_001.png"
+        )
+        self.image = load_image(img_url)
+
+    def test_inference_object_detection_head(self):
+        inputs = self.image_processor(images=self.image, return_tensors="pt").to(torch_device)
+        bs, c, h, w = inputs["pixel_values"].shape
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        results = self.image_processor.post_process_object_detection(outputs, target_sizes=inputs["target_sizes"])
+
+        expected_shape_logits = torch.Size((bs, c // 3, h, w))
+
+        expected_logits = torch.tensor(
+            [
+                [4.7810e-07, 5.0727e-07, 4.7810e-07],
+                [5.7200e-07, 6.0746e-07, 5.7200e-07],
+                [4.7810e-07, 5.0727e-07, 4.7810e-07],
+            ],
+            device=torch_device,
+        )
+
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape_logits)
+        torch.testing.assert_close(outputs.last_hidden_state[0, 0, :3, :3], expected_logits, rtol=2e-4, atol=2e-4)
+
+        expected_shape_boxes = torch.Size((4, 4, 2))
+        expected_boxes = torch.tensor(
+            [
+                [[76, 550], [451, 539], [452, 576], [77, 587]],
+                [[11, 504], [518, 483], [520, 534], [13, 555]],
+                [[189, 452], [401, 445], [402, 482], [190, 490]],
+                [[38, 408], [488, 387], [490, 433], [40, 454]],
+            ],
+            dtype=torch.short,
+            device=torch_device,
+        )
+
+        self.assertEqual(results[0]["boxes"].shape, expected_shape_boxes)
+        torch.testing.assert_close(results[0]["boxes"], expected_boxes, rtol=2e-2, atol=2e-2)
+
+        expected_scores = torch.tensor([0.8363, 0.8170, 0.8746, 0.8694]).to(torch_device)
+        self.assertEqual(len(results[0]["scores"]), 4)
+        torch.testing.assert_close(
+            torch.tensor(results[0]["scores"]).to(device=torch_device), expected_scores, rtol=2e-2, atol=2e-2
+        )