mirror of https://github.com/Jittor/Jittor
183 lines
7.1 KiB
Python
183 lines
7.1 KiB
Python
# ***************************************************************
|
|
# Copyright (c) 2020 Jittor. Authors:
|
|
# Guowei Yang <471184555@qq.com>
|
|
# Wenyang Zhou <576825820@qq.com>
|
|
# Dun Liang <randonlang@gmail.com>.
|
|
# All Rights Reserved.
|
|
# This file is subject to the terms and conditions defined in
|
|
# file 'LICENSE.txt', which is part of this source code package.
|
|
# ***************************************************************
|
|
# This model is generated by pytorch converter.
|
|
import jittor as jt
|
|
from jittor import nn
|
|
|
|
__all__ = ['ResNet', 'Resnet18', 'Resnet34', 'Resnet50', 'Resnet101', 'Resnet152', 'Resnext50_32x4d', 'Resnext101_32x8d', 'Wide_resnet50_2', 'Wide_resnet101_2']
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
|
|
return nn.Conv(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation)
|
|
|
|
def conv1x1(in_planes, out_planes, stride=1):
|
|
return nn.Conv(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
|
|
|
class BasicBlock(nn.Module):
|
|
expansion = 1
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None):
|
|
super(BasicBlock, self).__init__()
|
|
if (norm_layer is None):
|
|
norm_layer = nn.BatchNorm
|
|
if ((groups != 1) or (base_width != 64)):
|
|
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
|
|
if (dilation > 1):
|
|
raise NotImplementedError('Dilation > 1 not supported in BasicBlock')
|
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
|
self.bn1 = norm_layer(planes)
|
|
self.relu = nn.Relu()
|
|
self.conv2 = conv3x3(planes, planes)
|
|
self.bn2 = norm_layer(planes)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def execute(self, x):
|
|
identity = x
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
if (self.downsample is not None):
|
|
identity = self.downsample(x)
|
|
out += identity
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
class Bottleneck(nn.Module):
|
|
expansion = 4
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None):
|
|
super(Bottleneck, self).__init__()
|
|
if (norm_layer is None):
|
|
norm_layer = nn.BatchNorm
|
|
width = (int((planes * (base_width / 64.0))) * groups)
|
|
self.conv1 = conv1x1(inplanes, width)
|
|
self.bn1 = norm_layer(width)
|
|
self.conv2 = conv3x3(width, width, stride, groups, dilation)
|
|
self.bn2 = norm_layer(width)
|
|
self.conv3 = conv1x1(width, (planes * self.expansion))
|
|
self.bn3 = norm_layer((planes * self.expansion))
|
|
self.relu = nn.Relu()
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def execute(self, x):
|
|
identity = x
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
out = self.relu(out)
|
|
out = self.conv3(out)
|
|
out = self.bn3(out)
|
|
if (self.downsample is not None):
|
|
identity = self.downsample(x)
|
|
out += identity
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
class ResNet(nn.Module):
|
|
|
|
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
|
|
super(ResNet, self).__init__()
|
|
if (norm_layer is None):
|
|
norm_layer = nn.BatchNorm
|
|
self._norm_layer = norm_layer
|
|
self.inplanes = 64
|
|
self.dilation = 1
|
|
if (replace_stride_with_dilation is None):
|
|
replace_stride_with_dilation = [False, False, False]
|
|
if (len(replace_stride_with_dilation) != 3):
|
|
raise ValueError('replace_stride_with_dilation should be None or a 3-element tuple, got {}'.format(replace_stride_with_dilation))
|
|
self.groups = groups
|
|
self.base_width = width_per_group
|
|
self.conv1 = nn.Conv(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
|
|
self.bn1 = norm_layer(self.inplanes)
|
|
self.relu = nn.Relu()
|
|
self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1, op='maximum')
|
|
self.layer1 = self._make_layer(block, 64, layers[0])
|
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
|
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
|
|
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
|
|
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.fc = nn.Linear((512 * block.expansion), num_classes)
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
|
|
norm_layer = self._norm_layer
|
|
downsample = None
|
|
previous_dilation = self.dilation
|
|
if dilate:
|
|
self.dilation *= stride
|
|
stride = 1
|
|
if ((stride != 1) or (self.inplanes != (planes * block.expansion))):
|
|
downsample = nn.Sequential(conv1x1(self.inplanes, (planes * block.expansion), stride), norm_layer((planes * block.expansion)))
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer))
|
|
self.inplanes = (planes * block.expansion)
|
|
for _ in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer))
|
|
return nn.Sequential(*layers)
|
|
|
|
def _forward_impl(self, x):
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
x = self.avgpool(x)
|
|
x = jt.reshape(x, (x.shape[0], (- 1)))
|
|
x = self.fc(x)
|
|
return x
|
|
|
|
def execute(self, x):
|
|
return self._forward_impl(x)
|
|
|
|
def _resnet(block, layers, **kwargs):
|
|
model = ResNet(block, layers, **kwargs)
|
|
return model
|
|
|
|
def Resnet18(**kwargs):
|
|
return _resnet(BasicBlock, [2, 2, 2, 2], **kwargs)
|
|
|
|
def Resnet34(**kwargs):
|
|
return _resnet( BasicBlock, [3, 4, 6, 3], **kwargs)
|
|
|
|
def Resnet50(**kwargs):
|
|
return _resnet(Bottleneck, [3, 4, 6, 3], **kwargs)
|
|
|
|
def Resnet101(**kwargs):
|
|
return _resnet(Bottleneck, [3, 4, 23, 3], **kwargs)
|
|
|
|
def Resnet152(**kwargs):
|
|
return _resnet(Bottleneck, [3, 8, 36, 3], **kwargs)
|
|
|
|
def Resnext50_32x4d(**kwargs):
|
|
kwargs['groups'] = 32
|
|
kwargs['width_per_group'] = 4
|
|
return _resnet(Bottleneck, [3, 4, 6, 3], **kwargs)
|
|
|
|
def Resnext101_32x8d(**kwargs):
|
|
kwargs['groups'] = 32
|
|
kwargs['width_per_group'] = 8
|
|
return _resnet(Bottleneck, [3, 4, 23, 3], **kwargs)
|
|
|
|
def Wide_resnet50_2(**kwargs):
|
|
kwargs['width_per_group'] = (64 * 2)
|
|
return _resnet(Bottleneck, [3, 4, 6, 3], **kwargs)
|
|
|
|
def Wide_resnet101_2(**kwargs):
|
|
kwargs['width_per_group'] = (64 * 2)
|
|
return _resnet(Bottleneck, [3, 4, 23, 3], **kwargs)
|