def register_op(self): self.op_meta = { 'op_type': 'DepthwiseConv{}d'.format(len(self.kernel_size)), 'arguments': { 'num_output': self.weight.shape[0], 'kernel_shape': self.weight.shape[2:], 'strides': _pair(self.stride), 'pads': _pair(self.padding), 'dilations': _pair(self.dilation), 'data_format': 'NCHW', } }
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) super(DepthwiseConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, _pair(0), bias)
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias)
def register_op(self): self.op_meta = { 'op_type': 'Pool2d', 'arguments': { 'kernel_shape': _pair(self.kernel_size), 'strides': _pair(self.stride) if self.stride else _pair(self.kernel_size), 'pads': _pair(self.padding), 'mode': 'AVG', 'data_format': 'NCHW', 'ceil_mode': self.ceil_mode, } }
def register_op(self): self.op_meta = { 'op_type': 'Conv{}d{}'.format(len(self.kernel_size), 'Transpose' if self.transposed else ''), 'n_inputs': 3 if self.bias else 2, 'n_outputs': 1, 'arguments': { 'num_output': self.weight.shape[0], 'kernel_size': self.weight.shape[2:], 'stride': _pair(self.stride), 'pad': _pair(self.padding), 'dilation': _pair(self.dilation), 'group': self.groups, 'data_format': 'NCHW', } }
def register_op(self): self.op_meta = { 'op_type': 'Pooling2d', 'n_inputs': 1, 'n_outputs': 1, 'arguments': { 'kernel_size': _pair(self.kernel_size), 'stride': _pair(self.stride) if self.stride else _pair(self.kernel_size), 'pad': _pair(self.padding), 'mode': 'AVG', 'data_format': 'NCHW', 'ceil': self.ceil_mode } }
def register_op(self): self.op_meta = { 'op_type': 'Conv{}{}d'.format('Transpose' if self.transposed else '', len(self.kernel_size)), 'arguments': { 'num_output': self.weight.shape[1] if self.transposed else self.weight.shape[0], 'kernel_shape': self.weight.shape[2:], 'strides': _pair(self.stride), 'pads': _pair(self.padding), 'dilations': _pair(self.dilation), 'group': self.groups, 'data_format': 'NCHW', } }