def test_tflite(self):
input_shape = (1, 3, 64, 64)
tflite_file = 'mobilenet.tflite'
model = torchvision.models.mobilenet_v2(pretrained=True)
nne.cv2tflite(model, input_shape, tflite_file)
input_data = np.array(np.random.random_sample(input_shape),
dtype=np.float32)
out_tflite = nne.infer_tflite(tflite_file, input_data)
model.eval()
out_pytorch = model(
torch.from_numpy(input_data)).detach().cpu().numpy()
np.testing.assert_allclose(out_tflite,
out_pytorch,
rtol=1e-03,
atol=1e-05)
import torchvision import torch import numpy as np import nne input_shape = (10, 3, 224, 224) model = torchvision.models.mobilenet_v2(pretrained=True).cuda() tflite_file = 'mobilenet.tflite' nne.cv2tflite(model, input_shape, tflite_file) input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32) output_data = nne.infer_tflite(tflite_file, input_data) print(output_data)
import torchvision import torch import numpy as np import nne input_shape = (10, 3, 112, 112) model = torchvision.models.mobilenet_v2(pretrained=True) tflite_file = 'mobilenet.tflite' nne.cv2tflite(model, input_shape, tflite_file, edgetpu=True)