Hi everyone, I have an input of size [1, 64, 6, 15, 20], and when I apply torch's ConvTranspose3d on it with the following arguments ConvTranspose3d(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(0, 0...
mindspore2.0版本的conv3dtranspose算子,在文档中目前描述为在Ascend环境中仅支持group参数设置为1,但实际情况是在GPU环境下也不支持设置为大于1的值,文档中关于异常情况的说明中,也未针对此情况进行说明,希望能在文档中补充相关说明,并在后续版本中解决此约束,Pytorch版本的conv3dtranspose算子是支持group设置大于1的值,...
🐛 Describe the bug See code snippets: import torch from torch import nn m = nn.ConvTranspose3d(32, 16, bias=False, kernel_size=(4, 4, 4), padding=(1, 1, 1), stride=(2, 2, 2)) input = torch.randn(1, 32, 32, 32, 10) output1 = m(input) outp...
padding参数有效地将dilation * (kernel_size - 1) - padding零填充量添加到输入的两种大小。这样设置是为了当使用相同参数初始化Conv3d和ConvTranspose3d时,它们的输入和输出形状彼此相反。但是,当stride > 1时,Conv3d将多个输入形状映射到同一输出形状。output_padding的提供是为了通过有效增加一侧的计算输出形状来解...
对应的 TensorFlow v2 层是tf.keras.layers.Conv3DTranspose。 到原生 TF2 的结构映射 支持的参数均未更改名称。 前: conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3) 后: conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)...
ConvTranspose3d(in_channels * 4, in_channels * 2, 3, padding=1, output_padding=1, stride=2, bias=False), nn.BatchNorm3d(in_channels * 2)) self.conv6 = nn.Sequential( nn.ConvTranspose3d(in_channels * 2, in_channels, 3, padding=1, output_padding=1, stride=2, bias=False), nn...
conv3d_transpose¶ paddle.static.nn. conv3d_transpose ( input, num_filters, output_size=None, filter_size=None, padding=0, stride=1, dilation=1, groups=None, param_attr=None, bias_attr=None, use_cudnn=True, act=None, name=None, data_format='NCDHW' ) [源代码] ¶ 三维转置卷积...
conv3d_transpose¶ paddle.static.nn. conv3d_transpose ( x, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1, data_format='NCHW', output_size=None, name=None ) [源代码] ¶ 三维转置卷积层(Convlution3d transpose layer) 该层根据输入(input)、卷积核(...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - New improved Conv3D implementation for MPS and support for ConvTranspose3D · pytorch/pytorch@f69bf00
🐛 Describe the bug import torch import numpy as np arg_1 = 3 arg_2 = 68 arg_3 = 23 arg_class = torch.nn.ConvTranspose3d(arg_1, arg_2, kernel_size=arg_3,) arg_4_0_tensor = torch.rand([8, 3, 16, 16, 16], dtype=torch.float32) arg_4_0 = arg_...