| import unittest, json |
| from pathlib import Path |
| import numpy as np, torch |
| from src.model import InsectCNN |
| from src.inference import load_model |
| from src.inference import identify, genus_of |
| from src.audio import logmel, crop_offsets, resample, SR |
| ROOT=Path(__file__).resolve().parents[1] |
| class ModelPackageTests(unittest.TestCase): |
| def test_forward_shape(self): |
| m=InsectCNN(32).eval() |
| with torch.no_grad(): out=m(torch.zeros(2,1,64,201)) |
| self.assertEqual(tuple(out.shape),(2,32)) |
| def test_hdf5_load(self): |
| m,labels,cfg=load_model(ROOT) |
| self.assertEqual(len(labels),32) |
| self.assertEqual(cfg['num_classes'],32) |
| def test_center_crop_default_unchanged(self): |
| y=np.random.default_rng(0).standard_normal(44100*5).astype(np.float32) |
| y16=resample(y,44100) |
| np.testing.assert_allclose(logmel(y,44100),logmel(y16,SR,(len(y16)-2*SR)//2),atol=1e-5) |
| self.assertEqual(crop_offsets(len(y16),1),[None]) |
| offs=crop_offsets(len(y16),5) |
| self.assertEqual((offs[0],offs[-1]),(0,len(y16)-2*SR)) |
| def test_finetuned_bundle_loads(self): |
| d=ROOT/'finetuned_cnn' |
| if not (d/'model.h5').exists(): self.skipTest('finetuned_cnn not trained') |
| m,labels,cfg=load_model(d) |
| self.assertEqual(len(labels),32) |
| def test_identify_levels(self): |
| labels={str(i):n for i,n in enumerate(['Myopsaltaleona','Myopsaltamackinlayi','Chorthippusbrunneus','Pseudochorthippusparallelus'])} |
| self.assertEqual(genus_of('Pseudochorthippusparallelus'),'Pseudochorthippus') |
| self.assertEqual(identify(np.array([.7,.1,.1,.1]),labels)['level'],'species') |
| r=identify(np.array([.4,.35,.15,.1]),labels); self.assertEqual((r['level'],r['name']),('genus','Myopsalta')) |
| self.assertEqual(identify(np.array([.15,.15,.35,.35]),labels,0.6)['name'],'Orthoptera (crickets/grasshoppers)') |
| self.assertEqual(identify(np.array([.3,.2,.3,.2]),labels,0.6)['level'],'unknown') |
| def test_litert_parity(self): |
| tflite_path = ROOT / 'model.tflite' |
| if not tflite_path.exists(): |
| self.skipTest('model.tflite not exported') |
| import tensorflow as tf |
| interpreter = tf.lite.Interpreter(model_path=str(tflite_path)) |
| interpreter.allocate_tensors() |
| in_idx = interpreter.get_input_details()[0]['index'] |
| out_idx = interpreter.get_output_details()[0]['index'] |
|
|
| m, labels, cfg = load_model(ROOT) |
| sample = np.random.default_rng(123).standard_normal((1, 1, 64, 201)).astype(np.float32) |
| with torch.no_grad(): |
| pt_out = m(torch.from_numpy(sample)).cpu().numpy() |
|
|
| interpreter.set_tensor(in_idx, sample) |
| interpreter.invoke() |
| tflite_out = interpreter.get_tensor(out_idx) |
| np.testing.assert_allclose(pt_out, tflite_out, atol=1e-4) |
| if __name__=='__main__': unittest.main() |
|
|
