a3924ae4a8
- Added intelligent quality checkers and auto-retry pipeline for `image-to-3d` and `text-to-3d`. - Added unit tests for quality checkers. - `text-to-3d` now supports more `text-to-image` models, pipeline success rate improved to 94%.
96 lines
2.6 KiB
Python
96 lines
2.6 KiB
Python
# Project EmbodiedGen
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#
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# Copyright (c) 2025 Horizon Robotics. 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
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# implied. See the License for the specific language governing
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# permissions and limitations under the License.
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from unittest.mock import patch
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import pytest
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from PIL import Image
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from embodied_gen.utils.gpt_clients import GPT_CLIENT
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from embodied_gen.validators.quality_checkers import (
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ImageSegChecker,
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MeshGeoChecker,
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SemanticConsistChecker,
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)
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@pytest.fixture(autouse=True)
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def gptclient_query():
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with patch.object(
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GPT_CLIENT, "query", return_value="mocked gpt response"
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) as mock:
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yield mock
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@pytest.fixture()
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def gptclient_query_case2():
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with patch.object(GPT_CLIENT, "query", return_value=None) as mock:
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yield mock
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@pytest.mark.parametrize(
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"input_images",
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[
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"dummy_path/color_grid_6view.png",
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["dummy_path/color_grid_6view.jpg"],
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[
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"dummy_path/color_grid_6view.png",
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"dummy_path/color_grid_6view2.png",
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],
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[
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Image.new("RGB", (64, 64), "red"),
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Image.new("RGB", (64, 64), "blue"),
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],
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],
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)
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def test_geo_checker_varied_inputs(input_images):
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geo_checker = MeshGeoChecker(GPT_CLIENT)
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flag, result = geo_checker(input_images)
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assert isinstance(flag, (bool, type(None)))
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assert isinstance(result, str)
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def test_seg_checker():
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seg_checker = ImageSegChecker(GPT_CLIENT)
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flag, result = seg_checker(
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[
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"dummy_path/sample_0_raw.png", # raw image
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"dummy_path/sample_0_cond.png", # segmented image
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]
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)
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assert isinstance(flag, (bool, type(None)))
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assert isinstance(result, str)
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def test_semantic_checker():
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semantic_checker = SemanticConsistChecker(GPT_CLIENT)
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flag, result = semantic_checker(
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text="pen",
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image=["dummy_path/pen.png"],
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)
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assert isinstance(flag, (bool, type(None)))
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assert isinstance(result, str)
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def test_semantic_checker(gptclient_query_case2):
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semantic_checker = SemanticConsistChecker(GPT_CLIENT)
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flag, result = semantic_checker(
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text="pen",
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image=["dummy_path/pen.png"],
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)
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assert isinstance(flag, (bool, type(None)))
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assert isinstance(result, str)
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