Esempi in Python per infer_onnx

Esempio n. 1

    def test_onnx(self):
        input_shape = (1, 3, 64, 64)
        onnx_file = 'resnet.onnx'
        model = torchvision.models.resnet34(pretrained=True)
        nne.cv2onnx(model, input_shape, onnx_file)

        input_data = np.array(np.random.random_sample(input_shape),
                              dtype=np.float32)
        out_onnx = nne.infer_onnx(onnx_file, input_data)
        model.eval()
        out_pytorch = model(
            torch.from_numpy(input_data)).detach().cpu().numpy()
        np.testing.assert_allclose(out_onnx,
                                   out_pytorch,
                                   rtol=1e-03,
                                   atol=1e-05)

Esempio n. 2

import nne
import torchvision
import torch
import numpy as np

input_shape = (1, 3, 64, 64)
onnx_file = 'resnet.onnx'
model = torchvision.models.resnet34(pretrained=True).cuda()

nne.cv2onnx(model, input_shape, onnx_file)

input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)

output_data = nne.infer_onnx(onnx_file, input_data)

print(output_data)

Esempio n. 3

import nne
import torchvision
import torch
import numpy as np

input_shape = (1, 3, 64, 64)
onnx_file = 'resnet.onnx'
model = torchvision.models.resnet34(pretrained=True).cuda()

nne.cv2onnx(model, input_shape, onnx_file)

input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)

onnx_model = nne.load_onnx(onnx_file)

output_data = nne.infer_onnx(onnx_model, input_data)

print(output_data)