# Copyright (c) 2026, NVIDIA CORPORATION. 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 PyTorch RADIO model.""" import unittest from transformers import RadioConfig from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor if is_torch_available(): import torch from transformers import RadioModel class RadioModelTester: def __init__( self, parent, batch_size=2, image_size=32, patch_size=4, num_channels=3, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, mlp_ratio=2.0, hidden_act="gelu", layer_norm_eps=1e-6, attention_probs_dropout_prob=0.0, hidden_dropout_prob=0.0, drop_path_rate=0.0, layerscale_value=1.0, max_img_size=32, num_cls_tokens=2, num_registers=3, summary_idxs=None, initializer_range=0.02, is_training=False, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.attention_probs_dropout_prob = attention_probs_dropout_prob self.hidden_dropout_prob = hidden_dropout_prob self.drop_path_rate = drop_path_rate self.layerscale_value = layerscale_value self.max_img_size = max_img_size self.num_cls_tokens = num_cls_tokens self.num_registers = num_registers self.summary_idxs = summary_idxs if summary_idxs is not None else [0, 1] self.initializer_range = initializer_range self.is_training = is_training self.num_patches = (image_size // patch_size) ** 2 self.num_prefix_tokens = num_cls_tokens + num_registers self.seq_length = self.num_prefix_tokens + self.num_patches def get_config(self): return RadioConfig( hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, mlp_ratio=self.mlp_ratio, hidden_act=self.hidden_act, layer_norm_eps=self.layer_norm_eps, attention_probs_dropout_prob=self.attention_probs_dropout_prob, hidden_dropout_prob=self.hidden_dropout_prob, drop_path_rate=self.drop_path_rate, layerscale_value=self.layerscale_value, num_channels=self.num_channels, patch_size=self.patch_size, image_size=self.image_size, max_img_size=self.max_img_size, num_cls_tokens=self.num_cls_tokens, num_registers=self.num_registers, summary_idxs=self.summary_idxs, initializer_range=self.initializer_range, ) 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 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 create_and_check_model(self, config, pixel_values): model = RadioModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) expected_summary_size = len(self.summary_idxs) * self.hidden_size self.parent.assertEqual(result.summary.shape, (self.batch_size, expected_summary_size)) self.parent.assertEqual(result.features.shape, (self.batch_size, self.num_patches, self.hidden_size)) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_layer_scale_init(self, config, pixel_values): model = RadioModel(config=config) for layer in model.encoder.layer: self.parent.assertTrue( torch.allclose(layer.layer_scale1.lambda1, torch.ones_like(layer.layer_scale1.lambda1)), "layer_scale1.lambda1 should be initialized to 1.0", ) self.parent.assertTrue( torch.allclose(layer.layer_scale2.lambda1, torch.ones_like(layer.layer_scale2.lambda1)), "layer_scale2.lambda1 should be initialized to 1.0", ) def create_and_check_variable_resolution(self, config, pixel_values): model = RadioModel(config=config) model.to(torch_device) model.eval() # Test a different resolution (2x): num_patches quadruples large_size = self.image_size * 2 large_pixel_values = floats_tensor([self.batch_size, self.num_channels, large_size, large_size]) large_pixel_values = large_pixel_values.to(torch_device) expected_patches = (large_size // self.patch_size) ** 2 with torch.no_grad(): result = model(large_pixel_values) self.parent.assertEqual(result.features.shape, (self.batch_size, expected_patches, self.hidden_size)) @require_torch class RadioModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as RadioModel does not use input_ids, inputs_embeds, or attention_mask. """ all_model_classes = (RadioModel,) if is_torch_available() else () pipeline_model_mapping = {} test_resize_embeddings = False test_head_masking = False test_pruning = False def setUp(self): self.model_tester = RadioModelTester(self) self.config_tester = ConfigTester(self, config_class=RadioConfig, has_text_modality=False, hidden_size=16) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config, pixel_values = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(config, pixel_values) def test_layer_scale_init(self): config, pixel_values = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_layer_scale_init(config, pixel_values) def test_variable_resolution(self): config, pixel_values = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_variable_resolution(config, pixel_values) @unittest.skip(reason="RadioModel does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="RadioModel does not use inputs_embeds") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="RadioModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass @unittest.skip(reason="RadioModel uses pixel_values, not token embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip( reason="The shared 'radio' conversion mapping includes a video_embedder rename for video-capable " "checkpoints; the image-only RadioModel has no matching key, so the reverse-mapping check does not apply." ) def test_reverse_loading_mapping(self): pass @unittest.skip( reason="RadioModel has no classification head, so the test body is a no-op; its `_config_zero_init` helper " "also sets the `_std`-suffixed `norm_std` config field to a scalar, which the strict RadioConfig rejects." ) def test_can_load_ignoring_mismatched_shapes(self): pass @slow def test_model_from_pretrained(self): model = RadioModel.from_pretrained("nvidia/C-RADIOv4-H") self.assertIsNotNone(model) @slow def test_inference(self): model = RadioModel.from_pretrained("nvidia/C-RADIOv4-H").to(torch_device) model.eval() torch.manual_seed(42) pixel_values = torch.randn(1, 3, 224, 224, device=torch_device) with torch.no_grad(): outputs = model(pixel_values) self.assertEqual(outputs.summary.shape, (1, 2560)) self.assertEqual(outputs.features.shape, (1, 196, 1280)) self.assertFalse(outputs.summary.isnan().any(), "summary contains NaN") self.assertFalse(outputs.features.isnan().any(), "features contain NaN")