mirror of https://github.com/Jittor/Jittor
185 lines
6.7 KiB
Python
185 lines
6.7 KiB
Python
import jittor as jt
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from jittor import nn
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from jittor import Module
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from jittor import init
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from jittor.contrib import concat, argmax_pool
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import math
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class Bottle2neck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, baseWidth=26, scale = 4, stype='normal'):
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""" Constructor
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Args:
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inplanes: input channel dimensionality
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planes: output channel dimensionality
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stride: conv stride. Replaces pooling layer.
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downsample: None when stride = 1
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baseWidth: basic width of conv3x3
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scale: number of scale.
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type: 'normal': normal set. 'stage': first block of a new stage.
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"""
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super(Bottle2neck, self).__init__()
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width = int(math.floor(planes * (baseWidth/64.0)))
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self.conv1 = nn.Conv(inplanes, width*scale, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm(width*scale)
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assert scale > 1, 'Res2Net degenerates to ResNet when scales = 1.'
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if scale == 1:
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self.nums = 1
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else:
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self.nums = scale -1
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if stype == 'stage':
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self.pool = nn.Pool(kernel_size=3, stride = stride, padding=1, op='mean')
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self.convs = nn.ModuleList()
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self.bns = nn.ModuleList()
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for i in range(self.nums):
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self.convs.append(nn.Conv(width, width, kernel_size=3, stride = stride, dilation=dilation, padding=dilation, bias=False))
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self.bns.append(nn.BatchNorm(width))
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self.conv3 = nn.Conv(width*scale, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm(planes * self.expansion)
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stype = stype
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self.scale = scale
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self.width = width
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self.stride = stride
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self.dilation = dilation
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def execute(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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spx = out
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outs = []
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for i in range(self.nums):
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if i==0 or self.stype=='stage':
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sp = spx[:, i*self.width: (i+1)*self.width]
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else:
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sp = sp + spx[:, i*self.width: (i+1)*self.width]
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sp = self.convs[i](sp)
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sp = self.relu(self.bns[i](sp))
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outs.append(sp)
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if self.stype=='normal' or self.stride==1:
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outs.append(spx[:, self.nums*self.width: (self.nums+1)*self.width])
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elif self.stype=='stage':
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outs.append(self.pool(spx[:, self.nums*self.width: (self.nums+1)*self.width]))
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out = concat(outs, 1)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Res2Net(Module):
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def __init__(self, block, layers, output_stride, baseWidth = 26, scale = 4):
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super(Res2Net, self).__init__()
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self.baseWidth = baseWidth
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self.scale = scale
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self.inplanes = 64
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blocks = [1, 2, 4]
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if output_stride == 16:
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strides = [1, 2, 2, 1]
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dilations = [1, 1, 1, 2]
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elif output_stride == 8:
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strides = [1, 2, 1, 1]
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dilations = [1, 1, 2, 4]
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else:
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raise NotImplementedError
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# Modules
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self.conv1 = nn.Sequential(
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nn.Conv(3, 32, 3, 2, 1, bias=False),
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nn.BatchNorm(32),
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nn.ReLU(),
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nn.Conv(32, 32, 3, 1, 1, bias=False),
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nn.BatchNorm(32),
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nn.ReLU(),
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nn.Conv(32, 64, 3, 1, 1, bias=False)
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)
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self.bn1 = nn.BatchNorm(64)
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self.relu = nn.ReLU()
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# self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
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self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
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self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])
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def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Pool(kernel_size=stride, stride=stride,
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ceil_mode=True, op='mean'),
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nn.Conv(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=1, bias=False),
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nn.BatchNorm(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, dilation, downsample,
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stype='stage', baseWidth = self.baseWidth, scale=self.scale))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, dilation=dilation, baseWidth = self.baseWidth, scale=self.scale))
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return nn.Sequential(*layers)
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def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Pool(kernel_size=stride, stride=stride,
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ceil_mode=True, op='mean'),
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nn.Conv(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=1, bias=False),
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nn.BatchNorm(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
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downsample=downsample, stype='stage', baseWidth = self.baseWidth, scale=self.scale))
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self.inplanes = planes * block.expansion
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for i in range(1, len(blocks)):
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layers.append(block(self.inplanes, planes, stride=1,
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dilation=blocks[i]*dilation, baseWidth = self.baseWidth, scale=self.scale))
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return nn.Sequential(*layers)
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def execute(self, input):
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x = self.conv1(input)
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x = self.bn1(x)
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x = self.relu(x)
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x = argmax_pool(x, 2, 2)
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x = self.layer1(x)
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low_level_feat = x
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x, low_level_feat
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def res2net50(output_stride=16):
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model = Res2Net(Bottle2neck, [3,4,6,3], output_stride)
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return model
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def res2net101(output_stride=16):
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model = Res2Net(Bottle2neck, [3,4,23,3], output_stride)
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return model
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