def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
self.inplanes = 64
super(Res2Net, self).__init__()
self.baseWidth = baseWidth
self.scale = scale
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self, c1, c2, k=(5, 9, 13)):
super(SPP, self).__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
self.m = nn.ModuleList(
[nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def test_unpool(self):
from jittor import nn
pool = nn.MaxPool2d(2, stride=2, return_indices=True)
unpool = nn.MaxUnpool2d(2, stride=2)
input = jt.array([[[[1., 2, 3, 4, 0], [5, 6, 7, 8, 0],
[9, 10, 11, 12, 0], [13, 14, 15, 16, 0],
[0, 0, 0, 0, 0]]]])
output, indices = pool(input)
out = unpool(output, indices, output_size=input.shape)
assert (out == jt.array([[[[0., 0., 0., 0., 0.], [0., 6., 0., 8., 0.],
[0., 0., 0., 0.,
0.], [0., 14., 0., 16., 0.],
[0., 0., 0., 0., 0.]]]])).all()
def __init__(self,
arch,
block,
layers,
output_stride,
BatchNorm,
pretrained=True):
self.inplanes = 64
self.layers = layers
self.arch = arch
super(ResNet, self).__init__()
blocks = [1, 2, 4]
if output_stride == 16:
strides = [1, 2, 2, 1]
dilations = [1, 1, 1, 2]
elif output_stride == 8:
strides = [1, 2, 1, 1]
dilations = [1, 1, 2, 4]
else:
strides = [1, 2, 2, 2]
dilations = [1, 1, 1, 1]
if arch == 'resnext50':
self.base_width = 4
self.groups = 32
else:
self.base_width = 64
self.groups = 1
# Modules
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = BatchNorm(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block,
64,
layers[0],
stride=strides[0],
dilation=dilations[0],
BatchNorm=BatchNorm)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=strides[1],
dilation=dilations[1],
BatchNorm=BatchNorm)
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=strides[2],
dilation=dilations[2],
BatchNorm=BatchNorm)
if self.arch == 'resnet18':
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=strides[3],
dilation=dilations[3],
BatchNorm=BatchNorm)
else:
self.layer4 = self._make_MG_unit(block,
512,
blocks=blocks,
stride=strides[3],
dilation=dilations[3],
BatchNorm=BatchNorm)
# self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
if self.arch == 'resnet18':
self.toplayer = nn.Conv2d(512,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer1 = nn.Conv2d(256,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer2 = nn.Conv2d(128,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer3 = nn.Conv2d(64,
256,
kernel_size=1,
stride=1,
padding=0)
else:
self.toplayer = nn.Conv2d(2048,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer1 = nn.Conv2d(1024,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer2 = nn.Conv2d(512,
256,
kernel_size=1,
stride=1,
padding=0)
self.latlayer3 = nn.Conv2d(256,
256,
kernel_size=1,
stride=1,
padding=0)
self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
if pretrained:
self._load_pretrained_model()
def __init__(self, num_labels, depth_nc):
super(LinkNet_BackBone_FuseNet, self).__init__()
##### RGB ENCODER ####
self.CBR1_RGB_ENC = make_layers_from_size([[3, 64], [64, 64]])
self.pool1 = jt.nn.Pool(2, return_indices=True)
self.CBR2_RGB_ENC = make_layers_from_size([[64, 128], [128, 128]])
self.pool2 = jt.nn.Pool(2, return_indices=True)
self.CBR3_RGB_ENC = make_layers_from_size([[128, 256], [256, 256],
[256, 256]])
self.pool3 = jt.nn.Pool(2, return_indices=True)
self.dropout3 = nn.Dropout(0.4)
self.CBR4_RGB_ENC = make_layers_from_size([[256, 512], [512, 512],
[512, 512]])
self.pool4 = jt.nn.Pool(2, return_indices=True)
self.dropout4 = nn.Dropout(0.4)
self.CBR5_RGB_ENC = make_layers_from_size([[512, 512], [512, 512],
[512, 512]])
self.dropout5 = nn.Dropout(0.4)
self.pool5 = jt.nn.Pool(2, return_indices=True)
##### 3D DEPTH/DHAC ENCODER ####
self.CBR1_DEPTH_ENC = make_layers_from_size([[depth_nc, 64], [64, 64]])
self.pool1_d = nn.MaxPool2d(kernel_size=2, stride=2)
self.CBR2_DEPTH_ENC = make_layers_from_size([[64, 128], [128, 128]])
self.pool2_d = nn.MaxPool2d(kernel_size=2, stride=2)
self.CBR3_DEPTH_ENC = make_layers_from_size([[128, 256], [256, 256],
[256, 256]])
self.pool3_d = nn.MaxPool2d(kernel_size=2, stride=2)
self.dropout3_d = nn.Dropout(0.4)
self.CBR4_DEPTH_ENC = make_layers_from_size([[256, 512], [512, 512],
[512, 512]])
self.pool4_d = nn.MaxPool2d(kernel_size=2, stride=2)
self.dropout4_d = nn.Dropout(0.4)
self.CBR5_DEPTH_ENC = make_layers_from_size([[512, 512], [512, 512],
[512, 512]])
#### RGB DECODER ####
self.unpool5 = jt.nn.MaxUnpool2d(2)
self.CBR5_RGB_DEC = make_layers_from_size([[512, 512], [512, 512],
[512, 512]])
self.dropout5_dec = nn.Dropout(0.4)
self.unpool4 = jt.nn.MaxUnpool2d(2)
self.CBR4_RGB_DEC = make_layers_from_size([[512, 512], [512, 512],
[512, 256]])
self.dropout4_dec = nn.Dropout(0.4)
self.unpool3 = jt.nn.MaxUnpool2d(2)
self.CBR3_RGB_DEC = make_layers_from_size([[256, 256], [256, 256],
[256, 128]])
self.dropout3_dec = nn.Dropout(0.4)
self.unpool2 = jt.nn.MaxUnpool2d(2)
self.CBR2_RGB_DEC = make_layers_from_size([[128, 128], [128, 64]])
self.unpool1 = jt.nn.MaxUnpool2d(2)
self.CBR1_RGB_DEC = make_layers_from_size([[64, 64], [64, num_labels]],
isFinal=True)
import jittor as jt
from jittor import nn
import numpy as np
from jittor import Module
OPS = {
'none':
lambda C, stride, affine: Zero(stride),
'avg_pool_3x3':
lambda C, stride, affine: nn.AvgPool2d(
3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3':
lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect':
lambda C, stride, affine: Identity()
if stride == 1 else FactorizedReduce(C, C, affine=affine),
'sep_conv_3x3':
lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
'sep_conv_5x5':
lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
'dil_conv_3x3':
lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
'dil_conv_5x5':
lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
}
class ReLUConvBN(Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(ReLUConvBN, self).__init__()
self.op = nn.Sequential(