I've just noticed that s = torch.Size(np.array([1, 2, 3])) type(s[0]) returns <class 'numpy.int64'> whereas s = torch.Size(torch.tensor([1, 2, 3])) type(s[0]) gives a int. These two things are not interchangeable, yet it seems np.ndarray...
Based on the screenshot you've attached, it seems like you're calling.type()method on the model object after.half(). The.half()method converts the data type of the model totorch.float16(or f16). Subsequently, when you callmodel.type(), it shows'torch.cuda.HalfTensor'which is essenti...
ValueError: Can't convert non-rectangular Python sequence to Tensor. 2019-12-16 15:03 −发生此报错的原因可能是python序列是非矩形的数据,即在某个维度上数据不能对齐;或者你在使用pandas的数据时直接调用,如: 1 input_data = pd.DataFrame([[1,5,3], [5,2,9]]) 2 train_data = tf.random.sh...
shape) == 3: tensor_sample[k] = torch.from_numpy(np.transpose(v, [2, 0, 1])) else: tensor_sample[k] = torch.from_numpy(v.copy()[np.newaxis, :, :]) elif isinstance(v, (float, int)): tensor_sample[k] = v else: raise NotImplemented return tensor_sample ...
def convert_to_onnx(net, output_name): input_names = ['data'] input = torch.randn(1, 3, 256, 256) output_names = ['stage_1_output_1_heatmaps', 'stage_2_output_1_heatmaps'] torch.onnx.export(net, input, output_name, verbose=True, input_names=input_names, output_names=outpu...
tensor_aandtensor_cretain the data type used within thenp_array, cast into PyTorch's variant (torch.int32), whiletensor_bautomatically assigns the values tofloats: tensor_a: tensor([5, 7, 1, 2, 4, 4], dtype=torch.int32) tensor_b: tensor([5., 7., 1., 2., 4., 4.]) tenso...
def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor: return self.transpose(axis0, axis1) def to(self, *args, **kwargs): return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs)) @classmethod def __torch_function__(cls, ...
TensorFlow saved_model: export failure: can’t convert cuda:0 device type tensor to numpy. 对于此类问题,作者在issue中的统一回答是:新版本已解决了该问题,请使用新版本。 然而,直接使用新版本毕竟不方便,因为在工程中很可能已经做了很多别的修改,使用新版本会直接覆盖这些修改。因此,解决思路是用新版本的修...
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod def part(self, n_part: int) -> Self: ... @abstractmethod def to_ggml(self) -> GGMLCompatibleTensor: ... def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]...
infer_new_request({0: input_tensor}) #print(results) num_detected=int(results[3][0]) # get number of objects detected, always seems to be 1 with 2024.3 converted model for i in range(num_detected): if results[2][0][i] > 0.75 and int(results[...