157 lines
6.6 KiB
Python
157 lines
6.6 KiB
Python
import json
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import unittest
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from io import BytesIO
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from unittest.mock import Mock, patch
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import torch
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from PIL import Image
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from nodes.visual_qa_node import IsekaiVisualQA, _qa_image_views
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from utils.ollama_vision_client import OllamaVisionError
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def report(score=90, blocking_issues=None, issues=None):
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return {
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"score": score,
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"model_score": score,
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"summary": "Visual review result.",
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"blocking_issues": blocking_issues or [],
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"issues": issues or [],
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}
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class IsekaiVisualQATests(unittest.TestCase):
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def setUp(self) -> None:
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self.image = torch.zeros((1, 4, 5, 3), dtype=torch.float32)
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self.pil_image = Image.new("RGB", (5, 4), "white")
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def evaluate(self, model_report, **kwargs):
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with patch("nodes.visual_qa_node.tensor_to_pil", return_value=self.pil_image), \
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patch("nodes.visual_qa_node.pil_to_bytes", return_value=BytesIO(b"png")), \
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patch(
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"nodes.visual_qa_node.evaluate_image_with_ollama",
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return_value=model_report,
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) as evaluate_image, \
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patch("nodes.visual_qa_node._try_unload_comfy_models", return_value=[]):
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result = IsekaiVisualQA().evaluate(self.image, **kwargs)
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return result, evaluate_image
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def test_contract_and_defaults(self) -> None:
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inputs = IsekaiVisualQA.INPUT_TYPES()["required"]
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self.assertEqual(IsekaiVisualQA.RETURN_TYPES, ("IMAGE", "BOOLEAN", "INT", "STRING"))
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self.assertEqual(IsekaiVisualQA.RETURN_NAMES, ("image", "approved", "score", "report_json"))
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self.assertEqual(inputs["model"][1]["default"], "qwen3-vl:8b")
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self.assertEqual(inputs["approval_threshold"][1]["default"], 80)
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self.assertIs(inputs["unload_comfy_models"][1]["default"], True)
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def test_full_frame_and_quadrants_are_encoded_as_five_views(self) -> None:
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image = Image.new("RGB", (10, 8), "white")
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views = _qa_image_views(image)
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sizes = []
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for view in views:
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with Image.open(view) as encoded_image:
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sizes.append(encoded_image.size)
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self.assertEqual(sizes, [(10, 8), (5, 4), (5, 4), (5, 4), (5, 4)])
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def test_approves_at_threshold_with_no_blockers(self) -> None:
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result, evaluate_image = self.evaluate(
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report(score=80), approval_threshold=80, unload_comfy_models=False
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)
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output_image, approved, score, report_json = result
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parsed = json.loads(report_json)
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self.assertIs(output_image, self.image)
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self.assertIs(approved, True)
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self.assertEqual(score, 80)
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self.assertIs(parsed["approved"], True)
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self.assertEqual(parsed["batch_size"], 1)
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self.assertEqual(parsed["model_score"], 80)
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evaluate_image.assert_called_once()
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views = evaluate_image.call_args.kwargs["image_bytes"]
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self.assertEqual(len(views), 5)
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self.assertEqual([view.getvalue() for view in views], [b"png"] * 5)
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def test_rejects_high_score_when_any_blocking_issue_exists(self) -> None:
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blocker = {
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"severity": "blocking",
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"category": "anatomy",
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"description": "Fused fingers.",
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"location": "left hand",
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}
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result, _ = self.evaluate(report(score=100, blocking_issues=[blocker]))
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_, approved, score, report_json = result
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parsed = json.loads(report_json)
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self.assertIs(approved, False)
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self.assertEqual(score, 100)
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self.assertEqual(parsed["blocking_issues"][0]["batch_index"], 0)
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def test_batch_uses_lowest_score_and_aggregates_blockers(self) -> None:
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batch = torch.zeros((2, 4, 5, 3), dtype=torch.float32)
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blocker = {
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"severity": "blocking",
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"category": "artifact",
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"description": "Corrupt region.",
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"location": "right edge",
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}
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reports = [report(score=95), report(score=75, blocking_issues=[blocker])]
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with patch("nodes.visual_qa_node.tensor_to_pil", return_value=self.pil_image), \
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patch("nodes.visual_qa_node.pil_to_bytes", return_value=BytesIO(b"png")), \
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patch(
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"nodes.visual_qa_node.evaluate_image_with_ollama",
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side_effect=reports,
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) as evaluate_image, \
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patch("nodes.visual_qa_node._try_unload_comfy_models", return_value=[]):
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output_image, approved, score, report_json = IsekaiVisualQA().evaluate(batch)
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parsed = json.loads(report_json)
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self.assertIs(output_image, batch)
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self.assertIs(approved, False)
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self.assertEqual(score, 75)
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self.assertEqual(parsed["batch_size"], 2)
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self.assertEqual(parsed["model_score"], 75)
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self.assertEqual(parsed["blocking_issues"][0]["batch_index"], 1)
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self.assertEqual(evaluate_image.call_count, 2)
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def test_inference_error_fails_closed(self) -> None:
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with patch("nodes.visual_qa_node.tensor_to_pil", return_value=self.pil_image), \
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patch("nodes.visual_qa_node.pil_to_bytes", return_value=BytesIO(b"png")), \
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patch(
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"nodes.visual_qa_node.evaluate_image_with_ollama",
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side_effect=OllamaVisionError("model unavailable"),
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), patch("nodes.visual_qa_node._try_unload_comfy_models", return_value=[]):
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output_image, approved, score, report_json = IsekaiVisualQA().evaluate(self.image)
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parsed = json.loads(report_json)
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self.assertIs(output_image, self.image)
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self.assertIs(approved, False)
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self.assertEqual(score, 0)
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self.assertEqual(parsed["blocking_issues"][0]["category"], "inference_error")
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self.assertIn("model unavailable", parsed["blocking_issues"][0]["description"])
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def test_model_unload_failure_is_warning_not_gate_failure(self) -> None:
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with patch("nodes.visual_qa_node.tensor_to_pil", return_value=self.pil_image), \
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patch("nodes.visual_qa_node.pil_to_bytes", return_value=BytesIO(b"png")), \
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patch(
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"nodes.visual_qa_node.evaluate_image_with_ollama",
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return_value=report(score=90),
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), patch(
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"nodes.visual_qa_node._try_unload_comfy_models",
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return_value=["Could not unload Comfy models: busy"],
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):
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_, approved, _, report_json = IsekaiVisualQA().evaluate(self.image)
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parsed = json.loads(report_json)
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self.assertIs(approved, True)
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self.assertEqual(
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parsed["runtime_warnings"],
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["Could not unload Comfy models: busy"],
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)
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if __name__ == "__main__":
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unittest.main()
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