06f1fd69a6
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563 lines
26 KiB
Python
563 lines
26 KiB
Python
# Copyright 2026 the HuggingFace 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 VidEoMT model."""
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import unittest
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import numpy as np
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from transformers import VideomtConfig, VideomtForUniversalSegmentation
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from transformers.testing_utils import (
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Expectations,
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require_torch,
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require_torch_gpu,
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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 ModelTesterMixin, floats_tensor
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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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if is_vision_available():
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from PIL import Image
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from transformers import AutoVideoProcessor
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class VideomtForUniversalSegmentationTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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num_frames=1,
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image_size=40,
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patch_size=2,
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num_queries=5,
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num_register_tokens=19,
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num_labels=4,
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hidden_size=8,
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num_attention_heads=2,
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num_hidden_layers=2,
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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.num_frames = num_frames
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self.num_queries = num_queries
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_labels = num_labels
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_hidden_layers
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self.num_register_tokens = num_register_tokens
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self.is_training = False
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1 + self.num_register_tokens
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def get_config(self):
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config = {
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"image_size": self.image_size,
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"patch_size": self.patch_size,
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"num_labels": self.num_labels,
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"hidden_size": self.hidden_size,
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"num_attention_heads": self.num_attention_heads,
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"num_hidden_layers": self.num_hidden_layers,
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"num_register_tokens": self.num_register_tokens,
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"num_queries": self.num_queries,
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"num_blocks": 1,
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"rope_parameters": {"rope_theta": 100.0},
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}
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return VideomtConfig(**config)
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_frames, 3, self.image_size, self.image_size]).to(
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torch_device
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)
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config = self.get_config()
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return config, pixel_values
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def prepare_config_and_inputs_for_common(self):
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config, pixel_values = self.prepare_config_and_inputs()
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inputs_dict = {"pixel_values_videos": pixel_values}
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return config, inputs_dict
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@require_torch
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class VideomtForUniversalSegmentationTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (VideomtForUniversalSegmentation,) if is_torch_available() else ()
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pipeline_model_mapping = {}
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is_encoder_decoder = False
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test_missing_keys = False
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test_torch_exportable = False
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def setUp(self):
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self.model_tester = VideomtForUniversalSegmentationTester(self)
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self.config_tester = ConfigTester(self, config_class=VideomtConfig, has_text_modality=False)
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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="VideoMT 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_get_set_embeddings(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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output_embeddings = model.get_output_embeddings()
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self.assertTrue(output_embeddings is None or isinstance(output_embeddings, nn.Linear))
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@unittest.skip(reason="VideoMT is not a generative model")
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def test_generate_without_input_ids(self):
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pass
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@unittest.skip(reason="VideoMT does not use token embeddings")
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def test_resize_tokens_embeddings(self):
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pass
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def test_image_inputs_raise(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = VideomtForUniversalSegmentation(config).to(torch_device)
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model.eval()
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with self.assertRaisesRegex(ValueError, "only supports 5D video inputs"):
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model(inputs_dict["pixel_values_videos"][:, 0])
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def test_pixel_values_name_raises(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = VideomtForUniversalSegmentation(config).to(torch_device)
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model.eval()
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with self.assertRaisesRegex(ValueError, "Use `pixel_values_videos`"):
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model(pixel_values=inputs_dict["pixel_values_videos"])
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@slow
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@require_torch
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@require_vision
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class VideomtForUniversalSegmentationIntegrationTest(unittest.TestCase):
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instance_model_id = "tue-mps/videomt-dinov2-small-ytvis2019"
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expected_instance_segments_info = [
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{"id": 0, "label_id": 13, "score": 0.907032},
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{"id": 1, "label_id": 7, "score": 0.805882},
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{"id": 2, "label_id": 13, "score": 0.776713},
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]
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expected_instance_segments_info_frame_1 = [
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{"id": 0, "label_id": 13, "score": 0.958435},
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{"id": 1, "label_id": 7, "score": 0.79756},
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{"id": 2, "label_id": 13, "score": 0.893168},
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]
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expected_panoptic_segments_info = [{"id": 0, "label_id": 13, "score": 0.927756}]
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expected_panoptic_segments_info_frame_1 = [
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{"id": 0, "label_id": 13, "score": 0.980277},
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{"id": 1, "label_id": 13, "score": 0.912077},
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]
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def prepare_video(self, num_frames=2):
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frame = np.array(Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").convert("RGB"))
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return [frame.copy() for _ in range(num_frames)]
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def prepare_model_and_inputs(self, model_id, num_frames=2, dtype=None):
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model_kwargs = {"device_map": "auto"}
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if dtype is not None:
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model_kwargs["dtype"] = dtype
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model = VideomtForUniversalSegmentation.from_pretrained(model_id, **model_kwargs)
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processor = AutoVideoProcessor.from_pretrained(model_id)
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video_frames = self.prepare_video(num_frames=num_frames)
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inputs = processor(videos=[video_frames], return_tensors="pt").to(model.device)
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return model, processor, video_frames, inputs
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def run_inference(self, model_id, num_frames=2, dtype=None):
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model, processor, video_frames, inputs = self.prepare_model_and_inputs(
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model_id, num_frames=num_frames, dtype=dtype
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)
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with torch.inference_mode():
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outputs = model(**inputs)
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self.assert_common_video_outputs(outputs, model, len(video_frames))
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return model, processor, video_frames, outputs
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def assert_common_video_outputs(self, outputs, model, num_frames):
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expected_mask_size = (
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(model.config.image_size // model.config.patch_size) * (2**model.config.num_upscale_blocks),
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(model.config.image_size // model.config.patch_size) * (2**model.config.num_upscale_blocks),
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)
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self.assertEqual(
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outputs.class_queries_logits.shape, (num_frames, model.config.num_queries, model.config.num_labels + 1)
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)
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self.assertEqual(
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outputs.masks_queries_logits.shape, (num_frames, model.config.num_queries, *expected_mask_size)
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)
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self.assertTrue(torch.isfinite(outputs.class_queries_logits.float()).all())
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self.assertTrue(torch.isfinite(outputs.masks_queries_logits.float()).all())
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def assert_segments_info_close(self, actual_segments_info, expected_segments_info):
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self.assertEqual(len(actual_segments_info), len(expected_segments_info))
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for actual, expected in zip(actual_segments_info, expected_segments_info):
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self.assertEqual(actual["id"], expected["id"])
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self.assertEqual(actual["label_id"], expected["label_id"])
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self.assertAlmostEqual(actual["score"], expected["score"], delta=1e-3)
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def test_instance_segmentation_inference(self):
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_, processor, video_frames, outputs = self.run_inference(self.instance_model_id)
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target_sizes = [frame.shape[:2] for frame in video_frames]
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results = processor.post_process_instance_segmentation(outputs, target_sizes=target_sizes)
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self.assertEqual(len(results), len(video_frames))
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self.assertEqual(results[0]["segmentation"].shape, video_frames[0].shape[:2])
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self.assertEqual(results[1]["segmentation"].shape, video_frames[1].shape[:2])
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expected_slice = Expectations(
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{
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("cuda", None): torch.tensor(
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[
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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],
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device=results[0]["segmentation"].device,
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),
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("xpu", None): torch.tensor(
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[
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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],
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device=results[0]["segmentation"].device,
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),
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}
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).get_expectation()
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torch.testing.assert_close(results[0]["segmentation"][24:36, 473:485], expected_slice)
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expected_slice = Expectations(
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{
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("cuda", (8, 6)): torch.tensor(
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[
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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],
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device=results[1]["segmentation"].device,
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),
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("cuda", None): torch.tensor(
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[
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 0, 1, 1, 1, 1, 1, 1, 1, 1, -1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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],
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device=results[1]["segmentation"].device,
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),
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("xpu", None): torch.tensor(
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[
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
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[-1, -1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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],
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device=results[1]["segmentation"].device,
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),
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}
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).get_expectation()
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torch.testing.assert_close(results[1]["segmentation"][24:36, 472:484], expected_slice)
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self.assert_segments_info_close(results[0]["segments_info"], self.expected_instance_segments_info)
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self.assert_segments_info_close(results[1]["segments_info"], self.expected_instance_segments_info_frame_1)
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def test_semantic_segmentation_inference(self):
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_, processor, video_frames, outputs = self.run_inference(self.instance_model_id)
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target_sizes = [frame.shape[:2] for frame in video_frames]
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semantic_results = processor.post_process_semantic_segmentation(outputs, target_sizes=target_sizes)
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self.assertEqual(len(semantic_results), len(video_frames))
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self.assertEqual(semantic_results[0].shape, video_frames[0].shape[:2])
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self.assertEqual(semantic_results[1].shape, video_frames[1].shape[:2])
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expected_slice = Expectations(
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{
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("cuda", None): torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
],
|
|
device=semantic_results[0].device,
|
|
),
|
|
("xpu", None): torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13],
|
|
],
|
|
device=semantic_results[0].device,
|
|
),
|
|
}
|
|
).get_expectation()
|
|
torch.testing.assert_close(semantic_results[0][1:13, 487:499], expected_slice)
|
|
|
|
expected_slice = Expectations(
|
|
{
|
|
("cuda", (8, 6)): torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 0, 0, 0, 0],
|
|
[0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=semantic_results[1].device,
|
|
),
|
|
("cuda", None): torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 0, 0, 0, 0],
|
|
[0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 13, 13, 13, 13, 13, 13, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=semantic_results[1].device,
|
|
),
|
|
("xpu", None): torch.tensor(
|
|
[
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 0, 0, 0, 0],
|
|
[0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 13, 13, 13, 13, 13, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 13, 13, 13, 13, 13, 13, 13, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 13, 13, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=semantic_results[1].device,
|
|
),
|
|
}
|
|
).get_expectation()
|
|
torch.testing.assert_close(semantic_results[1][2:14, 488:500], expected_slice)
|
|
|
|
def test_panoptic_segmentation_inference(self):
|
|
_, processor, video_frames, outputs = self.run_inference(self.instance_model_id)
|
|
|
|
target_sizes = [frame.shape[:2] for frame in video_frames]
|
|
panoptic_results = processor.post_process_panoptic_segmentation(outputs, target_sizes=target_sizes)
|
|
|
|
self.assertEqual(len(panoptic_results), len(video_frames))
|
|
self.assertEqual(panoptic_results[0]["segmentation"].shape, video_frames[0].shape[:2])
|
|
self.assertEqual(panoptic_results[1]["segmentation"].shape, video_frames[1].shape[:2])
|
|
|
|
expected_slice = Expectations(
|
|
{
|
|
("cuda", None): torch.tensor(
|
|
[
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=panoptic_results[1]["segmentation"].device,
|
|
),
|
|
("xpu", None): torch.tensor(
|
|
[
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=panoptic_results[1]["segmentation"].device,
|
|
),
|
|
}
|
|
).get_expectation()
|
|
torch.testing.assert_close(panoptic_results[0]["segmentation"][24:36, 473:485], expected_slice)
|
|
|
|
expected_slice = Expectations(
|
|
{
|
|
("cuda", (8, 6)): torch.tensor(
|
|
[
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=panoptic_results[1]["segmentation"].device,
|
|
),
|
|
("cuda", None): torch.tensor(
|
|
[
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=panoptic_results[1]["segmentation"].device,
|
|
),
|
|
("xpu", None): torch.tensor(
|
|
[
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
|
|
[-1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
|
],
|
|
device=panoptic_results[1]["segmentation"].device,
|
|
),
|
|
}
|
|
).get_expectation()
|
|
torch.testing.assert_close(panoptic_results[1]["segmentation"][24:36, 472:484], expected_slice)
|
|
self.assert_segments_info_close(panoptic_results[0]["segments_info"], self.expected_panoptic_segments_info)
|
|
self.assert_segments_info_close(
|
|
panoptic_results[1]["segments_info"], self.expected_panoptic_segments_info_frame_1
|
|
)
|
|
|
|
@require_torch_gpu
|
|
def test_instance_segmentation_inference_bf16(self):
|
|
_, _, _, outputs = self.run_inference(self.instance_model_id, dtype=torch.bfloat16)
|
|
|
|
self.assertEqual(outputs.class_queries_logits.dtype, torch.bfloat16)
|
|
self.assertEqual(outputs.masks_queries_logits.dtype, torch.bfloat16)
|