Пример #1
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 def test_host_data_copied_to_device(self):
     with Calibrator(generate_data(1)) as calibrator:
         [ptr] = calibrator.get_batch(names=["x"])
         v = cuda.DeviceView(ptr, shape=(1, 1, 2, 2), dtype=np.float32)
         arr = v.numpy()
         assert arr.shape == (1, 1, 2, 2)
         assert np.all(arr == 1)
Пример #2
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 def test_device_view_dynamic_shapes(self, use_view):
     model = ONNX_MODELS["dynamic_identity"]
     profiles = [
         Profile().add("X", (1, 2, 1, 1), (1, 2, 2, 2), (1, 2, 4, 4)),
     ]
     runner = TrtRunner(EngineFromNetwork(NetworkFromOnnxBytes(model.loader), CreateConfig(profiles=profiles)))
     with runner, cuda.DeviceArray(shape=(1, 2, 3, 3), dtype=np.float32) as arr:
         inp = np.random.random_sample(size=(1, 2, 3, 3)).astype(np.float32)
         arr.copy_from(inp)
         outputs = runner.infer({"X": cuda.DeviceView(arr.ptr, arr.shape, arr.dtype) if use_view else arr})
         assert np.all(outputs["Y"] == inp)
         assert outputs["Y"].shape == (1, 2, 3, 3)
Пример #3
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 def generate_dev_data(num_batches):
     with cuda.DeviceArray(shape=(1, ), dtype=np.float32) as x:
         for _ in range(num_batches):
             x.copy_from(np.ones((1, ), dtype=np.float32))
             xdata = {
                 "array": x,
                 "view": cuda.DeviceView(x.ptr, x.shape, x.dtype),
                 "pointer": x.ptr
             }[mode]
             yield {
                 "X0": xdata,
                 "Y0": np.zeros((1, ), dtype=np.float32)
             }
Пример #4
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 def test_device_views(self, use_view):
     model = ONNX_MODELS["reducable"]
     network_loader = NetworkFromOnnxBytes(model.loader)
     with TrtRunner(
             EngineFromNetwork(network_loader)) as runner, cuda.DeviceArray(
                 (1, ), dtype=np.float32) as x:
         x.copy_from(np.ones((1, ), dtype=np.float32))
         outputs = runner.infer({
             "X0":
             cuda.DeviceView(x.ptr, x.shape, x.dtype) if use_view else x,
             "Y0":
             np.ones((1, ), dtype=np.float32)
         })
         assert outputs["identity_out_6"][0] == 2
         assert outputs["identity_out_8"][0] == 2
Пример #5
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    def test_calibrator_data_and_ordering_correct(self):
        def generate_multidata(num_batches):
            for _ in range(num_batches):
                yield {
                    "x0":
                    np.zeros((4, 5), dtype=np.float32),
                    "x1":
                    cuda.DeviceArray(dtype=np.float32).copy_from(
                        np.ones((4, 5), dtype=np.float32)),
                    "x2":
                    cuda.DeviceArray(dtype=np.float32).copy_from(
                        np.ones((4, 5), dtype=np.float32) * 2).ptr,
                }

        NUM_BATCHES = 2
        with Calibrator(generate_multidata(NUM_BATCHES)) as calibrator:
            for _ in range(NUM_BATCHES):
                ptrs = calibrator.get_batch(names=["x0", "x1", "x2"])
                for index, ptr in enumerate(ptrs):
                    v = cuda.DeviceView(ptr, shape=(4, 5), dtype=np.float32)
                    assert np.all(v.numpy() == index)