first commit

tests/models/pp_lcnet/test_image_processing_pp_lcnet.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.
+
+import unittest
+
+from transformers.testing_utils import require_torch, require_vision
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+class PPLCNetImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=[0.406, 0.456, 0.485],
+        image_std=[0.225, 0.224, 0.229],
+        rescale_factor=0.00392156862745098,
+        do_rescale=True,
+        do_center_crop=True,
+        crop_size=None,
+        resize_short=256,
+        resample=2,
+    ):
+        size = size if size is not None else {"height": 256, "width": 256}
+        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.rescale_factor = rescale_factor
+        self.do_rescale = do_rescale
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.resize_short = resize_short
+        self.resample = resample
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "rescale_factor": self.rescale_factor,
+            "do_rescale": self.do_rescale,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "resize_short": self.resize_short,
+            "resample": self.resample,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+
+@require_torch
+@require_vision
+class PPLCNetImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PPLCNetImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    @unittest.skip(reason="PPLCNet does not support 4 channel images yet")
+    def test_call_numpy_4_channels(self):
+        pass

tests/models/pp_lcnet/test_modeling_pp_lcnet.py

@@ -0,0 +1,303 @@
+# 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-LCNet model."""
+
+import inspect
+import unittest
+
+from parameterized import parameterized
+
+from transformers import (
+    PPLCNetBackbone,
+    PPLCNetConfig,
+    PPLCNetForImageClassification,
+    PPLCNetImageProcessor,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_backbone_common import BackboneTesterMixin
+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 PPLCNetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        image_size=128,
+        num_channels=3,
+        num_stages=5,
+        is_training=False,
+        scale=1.0,
+        reduction=4,
+        dropout_prob=0.2,
+        class_expand=1280,
+        use_last_convolution=True,
+        hidden_act="hardswish",
+        num_labels=4,
+        out_features=["stage2", "stage3", "stage4"],
+        out_indices=[2, 3, 4],
+        stem_channels=16,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.is_training = is_training
+        self.num_stages = num_stages
+        self.scale = scale
+        self.reduction = reduction
+        self.dropout_prob = dropout_prob
+        self.class_expand = class_expand
+        self.use_last_convolution = use_last_convolution
+        self.hidden_act = hidden_act
+        self.num_labels = num_labels
+        self.out_features = out_features
+        self.out_indices = out_indices
+        self.stem_channels = stem_channels
+        self.block_configs = [
+            [[3, 16, 32, 1, False]],
+            [[3, 32, 32, 2, False], [3, 32, 32, 1, False]],
+            [[3, 32, 32, 2, False], [3, 32, 32, 1, False]],
+            [
+                [3, 32, 32, 2, False],
+                [5, 32, 32, 1, False],
+                [5, 32, 32, 1, False],
+                [5, 32, 32, 1, False],
+                [5, 32, 32, 1, False],
+                [5, 32, 32, 1, False],
+            ],
+            [[5, 32, 32, 2, True], [5, 32, 32, 1, True]],
+        ]
+
+    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) -> PPLCNetConfig:
+        id2label = {"0": "0", "1": "90", "2": "180", "3": "270"}
+        config = PPLCNetConfig(
+            scale=self.scale,
+            reduction=self.reduction,
+            dropout_prob=self.dropout_prob,
+            class_expand=self.class_expand,
+            use_last_conv=self.use_last_convolution,
+            hidden_act=self.hidden_act,
+            id2label=id2label,
+            out_features=self.out_features,
+            out_indices=self.out_indices,
+            block_configs=self.block_configs,
+        )
+
+        return config
+
+
+@require_torch
+class PPLCNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
+    all_model_classes = (PPLCNetBackbone,) if is_torch_available() else ()
+    has_attentions = False
+    config_class = PPLCNetConfig
+
+    def setUp(self):
+        self.model_tester = PPLCNetModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=PPLCNetConfig,
+            has_text_modality=False,
+            common_properties=[],
+        )
+
+
+@require_torch
+class PPLCNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (PPLCNetForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": PPLCNetForImageClassification} if is_torch_available() else {}
+
+    has_attentions = False
+    test_inputs_embeds = False
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = PPLCNetModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=PPLCNetConfig,
+            has_text_modality=False,
+            common_properties=[],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def create_and_check_pp_lcnet_image_classification(self, config, pixel_values):
+        model = PPLCNetForImageClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values)
+
+        self.assertEqual(result.last_hidden_state.shape, (self.model_tester.batch_size, model.config.num_labels))
+
+    def test_pp_lcnet_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.create_and_check_pp_lcnet_image_classification(*config_and_inputs)
+
+    @unittest.skip(reason="PPLCNet does not use test_inputs_embeds_matches_input_ids")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip(reason="PPLCNet does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="PPLCNet 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="PPLCNet does not support attention")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="PPLCNet does not support train")
+    def test_problem_types(self):
+        pass
+
+    @unittest.skip(reason="PPLCNet does not support model parallelism")
+    def test_model_parallelism(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)
+
+    @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))
+
+    # PPLCNet 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
+            scale = self.model_tester.scale
+
+            self.assertEqual(len(hidden_states), expected_num_stages + 1)
+
+            self.assertEqual(hidden_states[0].shape[1], self.model_tester.stem_channels)
+            for i in range(expected_num_stages):
+                self.assertEqual(
+                    hidden_states[i + 1].shape[1],
+                    self.model_tester.block_configs[i][-1][2] * scale,
+                )
+
+        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
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+
+@require_torch
+@require_vision
+@slow
+class PPLCNetModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        model_path = "PaddlePaddle/PP-LCNet_x1_0_doc_ori_safetensors"
+        self.model = PPLCNetForImageClassification.from_pretrained(model_path).to(torch_device)
+        self.image_processor = PPLCNetImageProcessor.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/img_rot180_demo.jpg"
+        )
+        self.image = load_image(img_url)
+
+    def test_inference_image_classification_head(self):
+        inputs = self.image_processor(images=self.image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        expected_shape_logits = torch.Size((1, 4))
+        expected_logits = torch.tensor([[-0.3655, -1.0573, 2.4883, -1.0640]]).to(torch_device)
+
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape_logits)
+        torch.testing.assert_close(outputs.last_hidden_state, expected_logits, rtol=2e-2, atol=2e-2)
+
+        expected_labels = torch.tensor([2]).to(torch_device)
+        predicted_label = outputs.last_hidden_state.argmax(-1).item()
+
+        self.assertEqual(predicted_label, expected_labels)