def __init__(self,
in_channels,
out_channels,
stride=1,
down_sample=None,
use_se=False,
platform="Ascend",
**kwargs):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride=stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = P.ReLU()
self.conv2 = conv3x3(out_channels, out_channels, stride=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.use_se = use_se
if self.use_se:
self.se = SEBlock(out_channels)
self.down_sample_flag = False
if down_sample is not None:
self.down_sample = down_sample
self.down_sample_flag = True
self.add = TensorAdd()
def __init__(self, in_channels, out_channels, stride=1, down_sample=None,
base_width=64, groups=1, use_se=False, **kwargs):
super(Bottleneck, self).__init__()
width = int(out_channels * (base_width / 64.0)) * groups
self.groups = groups
self.conv1 = conv1x1(in_channels, width, stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.relu = P.ReLU()
self.conv3x3s = nn.CellList()
self.conv2 = GroupConv(width, width, 3, stride, pad=1, groups=groups)
self.op_split = Split(axis=1, output_num=self.groups)
self.op_concat = Concat(axis=1)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = conv1x1(width, out_channels * self.expansion, stride=1)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.use_se = use_se
if self.use_se:
self.se = SEBlock(out_channels * self.expansion)
self.down_sample_flag = False
if down_sample is not None:
self.down_sample = down_sample
self.down_sample_flag = True
self.cast = P.Cast()
self.add = TensorAdd()
def __init__(self,
in_channels,
out_channels,
stride=1,
down_sample=None,
base_width=64,
groups=1,
use_se=False,
platform="Ascend",
**kwargs):
super(Bottleneck, self).__init__()
width = int(out_channels * (base_width / 64.0)) * groups
self.groups = groups
self.conv1 = conv1x1(in_channels, width, stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.relu = P.ReLU()
self.conv3x3s = nn.CellList()
if platform == "GPU":
self.conv2 = nn.Conv2d(width,
width,
3,
stride,
pad_mode='pad',
padding=1,
group=groups)
else:
self.conv2 = GroupConv(width,
width,
3,
stride,
pad=1,
groups=groups)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = conv1x1(width, out_channels * self.expansion, stride=1)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.use_se = use_se
if self.use_se:
self.se = SEBlock(out_channels * self.expansion)
self.down_sample_flag = False
if down_sample is not None:
self.down_sample = down_sample
self.down_sample_flag = True
self.cast = P.Cast()
self.add = TensorAdd()