99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
# tests/inference/test_inference.py
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import pytest
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import numpy as np
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import torch
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from PIL import Image
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from aiunn import aiuNNInference
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from aiunn.upsampler.aiunn import aiuNN, aiuNNConfig
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from aiia import AIIABase, AIIAConfig
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import os
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import json
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from unittest.mock import patch, MagicMock, mock_open
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@pytest.fixture
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def real_model(tmp_path):
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# Create temporary directory for model
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model_dir = tmp_path / "model"
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model_dir.mkdir()
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config = AIIAConfig()
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ai_config = aiuNNConfig()
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base_model = AIIABase(config)
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# Make sure aiuNN is properly configured with all required attributes
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upsampler = aiuNN(base_model, config=ai_config)
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# Ensure the upsample attribute is properly set if needed
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# upsampler.upsample = ... # Add any necessary initialization
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# Save the model and config to temporary directory
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save_path = str(model_dir / "save")
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os.makedirs(save_path, exist_ok=True)
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# Save config file
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config_data = {
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"model_type": "test_model",
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"scale": 4,
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"in_channels": 3,
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"out_channels": 3
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}
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with open(os.path.join(save_path, "config.json"), "w") as f:
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json.dump(config_data, f)
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# Save model
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upsampler.save(save_path)
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# Load model in inference mode
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inference_model = aiuNNInference(model_path=save_path, precision='fp16', device='cpu')
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return inference_model
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@pytest.fixture
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def inference(real_model):
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return real_model
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def test_preprocess_image(inference):
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# Create a small test image
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test_array = np.zeros((100, 100, 3), dtype=np.uint8)
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test_image = Image.fromarray(test_array)
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# Test with PIL Image
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result = inference.preprocess_image(test_image)
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assert isinstance(result, torch.Tensor)
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assert result.shape[0] == 1 # batch dimension
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assert result.shape[1] == 3 # channels
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assert result.shape[2:] == (100, 100) # height, width
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def test_postprocess_tensor(inference):
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# Create a test tensor
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test_tensor = torch.zeros(1, 3, 100, 100)
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result = inference.postprocess_tensor(test_tensor)
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assert isinstance(result, Image.Image)
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assert result.size == (100, 100)
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assert result.mode == 'RGB'
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def test_save(inference):
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# Create a test image
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test_image = Image.fromarray(np.zeros((100, 100, 3), dtype=np.uint8))
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output_path = "test_output.png"
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with patch('os.makedirs') as mock_makedirs:
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inference.save(test_image, output_path)
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mock_makedirs.assert_called_with(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
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def test_convert_to_binary(inference):
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# Create a test image
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test_image = Image.fromarray(np.zeros((100, 100, 3), dtype=np.uint8))
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result = inference.convert_to_binary(test_image)
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assert isinstance(result, bytes)
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assert len(result) > 0
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def test_process_batch(inference):
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# Create test images
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test_array = np.zeros((100, 100, 3), dtype=np.uint8)
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test_images = [Image.fromarray(test_array) for _ in range(2)]
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results = inference.process_batch(test_images)
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assert len(results) == 2
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assert all(isinstance(img, Image.Image) for img in results) |