def __init__(self, in_channels=3, out_channels=5):
super(VGG3D, self).__init__()
self.conv1 = Sequential(
Conv3d(in_channels, 64, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(64, True), ReLU(True),
Conv3d(64, 64, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(64, True), ReLU(True),
Conv3d(64, 64, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(64, True), ReLU(True),
MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)))
self.conv2 = Sequential(
Conv3d(64, 128, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(128, True), ReLU(True),
Conv3d(128, 128, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(128, True), ReLU(True),
Conv3d(128, 128, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
InstanceNorm3d(128, True), ReLU(True),
MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)))
self.conv3 = Sequential(Conv3d(128, 256, kernel_size=3, padding=1),
InstanceNorm3d(256, True), ReLU(True),
Conv3d(256, 256, kernel_size=3, padding=1),
InstanceNorm3d(256, True), ReLU(True),
Conv3d(256, 256, kernel_size=3, padding=1),
InstanceNorm3d(256, True), ReLU(True),
MaxPool3d(2, stride=2))
self.avgpool = AdaptiveAvgPool3d(1, 1, 1)
self.fc = Linear(256, out_channels)
def __init__(self, pv):
super(AlexNetExplicitTaco, self).__init__()
self.conv1 = ConvTTN3d(in_channels=3,
out_channels=64,
kernel_size=11,
stride=(1, 3, 4),
padding=2,
project_variable=pv,
bias=True,
ksize=(0, 0),
fc_in=1,
hw=(150, 224))
self.pool1 = MaxPool3d(kernel_size=3, stride=2)
self.conv2 = ConvTTN3d(in_channels=64,
out_channels=192,
kernel_size=5,
padding=2,
project_variable=pv,
bias=True,
ksize=(0, 0),
fc_in=1,
hw=(23, 27))
self.pool2 = MaxPool3d(kernel_size=3, stride=2)
self.conv3 = ConvTTN3d(in_channels=192,
out_channels=384,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True,
ksize=(0, 0),
fc_in=1,
hw=(11, 13))
self.conv4 = ConvTTN3d(in_channels=384,
out_channels=256,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True,
ksize=(0, 0),
fc_in=1,
hw=(11, 13))
self.conv5 = ConvTTN3d(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True,
ksize=(0, 0),
fc_in=1,
hw=(11, 13))
self.pool3 = MaxPool3d(kernel_size=3, stride=2)
# self.pool4 = AdaptiveAvgPool3d(output_size=1)
self.pool4 = AdaptiveAvgPool3d((1, 6, 6))
self.fc1 = Linear(256 * 1 * 6 * 6, 4096)
self.fc2 = Linear(4096, 4096)
self.fc3 = Linear(4096, pv.label_size)
def __init__(self, in_channels, norm_layer, up_kwargs):
super(PyramidPooling, self).__init__()
self.pool1 = AdaptiveAvgPool3d(1)
self.pool2 = AdaptiveAvgPool3d(2)
self.pool3 = AdaptiveAvgPool3d(3)
self.pool4 = AdaptiveAvgPool3d(6)
out_channels = int(in_channels/4)
self.conv1 = Sequential(Conv3d(in_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
ReLU(True))
self.conv2 = Sequential(Conv3d(in_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
ReLU(True))
self.conv3 = Sequential(Conv3d(in_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
ReLU(True))
self.conv4 = Sequential(Conv3d(in_channels, out_channels, 1, bias=False),
norm_layer(out_channels),
ReLU(True))
# bilinear upsample options
self._up_kwargs = up_kwargs
def __init__(self, pv):
super(VGG19BN_Explicit_3T , self).__init__()
self.conv1 = ConvTTN3d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn1 = BatchNorm3d(64)
self.conv2 = ConvTTN3d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn2 = BatchNorm3d(64)
self.maxpool1 = MaxPool3d(kernel_size=2, padding=0, stride=2)
self.conv3 = ConvTTN3d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn3 = BatchNorm3d(128)
self.conv4 = ConvTTN3d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn4 = BatchNorm3d(128)
self.maxpool2 = MaxPool3d(kernel_size=2, padding=0, stride=2)
self.conv5 = ConvTTN3d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn5 = BatchNorm3d(256)
self.conv6 = ConvTTN3d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn6 = BatchNorm3d(256)
self.conv7 = ConvTTN3d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn7 = BatchNorm3d(256)
self.conv8 = ConvTTN3d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn8 = BatchNorm3d(256)
self.maxpool3 = MaxPool3d(kernel_size=2, padding=0, stride=2)
self.conv9 = ConvTTN3d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn9 = BatchNorm3d(512)
self.conv10 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn10 = BatchNorm3d(512)
self.conv11 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn11 = BatchNorm3d(512)
self.conv12 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn12 = BatchNorm3d(512)
self.maxpool4 = MaxPool3d(kernel_size=2, padding=0, stride=1)
self.conv13 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn13 = BatchNorm3d(512)
self.conv14 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn14 = BatchNorm3d(512)
self.conv15 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn15 = BatchNorm3d(512)
self.conv16 = ConvTTN3d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, project_variable=pv, bias=True)
self.bn16 = BatchNorm3d(512)
self.maxpool5 = MaxPool3d(kernel_size=2, padding=0, stride=2)
self.avgpool = AdaptiveAvgPool3d(output_size=(1, 7, 7))
self.fc1 = Linear(25088, 4096)
self.dropout1 = Dropout(p=0.5)
self.fc2 = Linear(4096, 4096)
self.dropout2 = Dropout(p=0.5)
self.fc3 = Linear(4096, 27)
def __init__(self, pv):
super(ResNet18, self).__init__()
self.conv1_relu = ConvolutionBlock(3, 64, pv)
self.maxpool = MaxPool3d(kernel_size=3,
padding=1,
stride=2,
dilation=1)
self.res2a_relu = ResidualBlock(64, 64, pv)
self.res2b_relu = ResidualBlock(64, 64, pv)
self.res3a_relu = ResidualBlockB(64, 128, pv)
self.res3b_relu = ResidualBlock(128, 128, pv)
self.res4a_relu = ResidualBlockB(128, 256, pv)
self.res4b_relu = ResidualBlock(256, 256, pv)
self.res5a_relu = ResidualBlockB(256, 512, pv)
self.res5b_relu = ResidualBlock(512, 512, pv)
self.avgpool = AdaptiveAvgPool3d(output_size=1)
self.fc = torch.nn.Linear(512, 27)
def __init__(self, pv):
super(AlexNetExplicit3T, self).__init__()
self.conv1 = classic_3TConv(in_channels=3,
out_channels=64,
kernel_size=11,
stride=(1, 3, 4),
padding=2,
project_variable=pv,
bias=True)
self.pool1 = MaxPool3d(kernel_size=3, stride=2)
self.conv2 = classic_3TConv(in_channels=64,
out_channels=192,
kernel_size=5,
padding=2,
project_variable=pv,
bias=True)
self.pool2 = MaxPool3d(kernel_size=3, stride=2)
self.conv3 = classic_3TConv(in_channels=192,
out_channels=384,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True)
self.conv4 = classic_3TConv(in_channels=384,
out_channels=256,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True)
self.conv5 = classic_3TConv(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
project_variable=pv,
bias=True)
self.pool3 = MaxPool3d(kernel_size=3, stride=2)
# self.pool4 = AdaptiveAvgPool3d(output_size=1)
self.pool4 = AdaptiveAvgPool3d((1, 6, 6))
self.fc1 = Linear(256 * 1 * 6 * 6, 4096)
self.fc2 = Linear(4096, 4096)
self.fc3 = Linear(4096, pv.label_size)
},
{
"input_fn":
lambda: rand(2, 2, 6, 8),
"module_fn":
lambda: Sequential(Linear(8, 8), AdaptiveAvgPool2d((3, 4)), Flatten()),
"loss_function_fn":
lambda: MSELoss(),
"target_fn":
lambda: regression_targets((2, 2 * 3 * 4)),
},
{
"input_fn":
lambda: rand(2, 2, 9, 5, 4),
"module_fn":
lambda: Sequential(Linear(4, 4), AdaptiveAvgPool3d(
(3, 5, 2)), Flatten()),
"loss_function_fn":
lambda: MSELoss(),
"target_fn":
lambda: regression_targets((2, 2 * 3 * 5 * 2)),
},
]
##################################################################
# BatchNorm settings #
##################################################################
LOCAL_SETTINGS += [
{
"input_fn":
lambda: rand(2, 3, 4),
"module_fn":
lambda: initialize_training_false_recursive(
def __init__(self, pv):
super(Googlenet3TConv_explicit, self).__init__()
self.conv1 = ConvTTN3d(in_channels=3, out_channels=64, kernel_size=7, padding=3, stride=2, project_variable=pv, bias=False)
self.bn1 = BatchNorm3d(64)
self.maxpool1 = MaxPool3d(kernel_size=(1, 3, 3), padding=0, stride=(1, 2, 2))
self.conv2 = Conv3d(in_channels=64, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn2 = BatchNorm3d(64)
self.conv3 = ConvTTN3d(in_channels=64, out_channels=192, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn3 = BatchNorm3d(192)
self.maxpool2 = MaxPool3d(kernel_size=(1, 3, 3), padding=0, stride=(1, 2, 2))
# inception 3a
self.conv4 = Conv3d(in_channels=192, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn4 = BatchNorm3d(64)
self.conv5 = Conv3d(in_channels=192, out_channels=96, kernel_size=1, padding=0, stride=1, bias=False)
self.bn5 = BatchNorm3d(96)
self.conv6 = ConvTTN3d(in_channels=96, out_channels=128, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn6 = BatchNorm3d(128)
self.conv7 = Conv3d(in_channels=192, out_channels=16, kernel_size=1, padding=0, stride=1, bias=False)
self.bn7 = BatchNorm3d(16)
self.conv8 = ConvTTN3d(in_channels=16, out_channels=32, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn8 = BatchNorm3d(32)
self.maxpool3 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv9 = Conv3d(in_channels=192, out_channels=32, kernel_size=1, padding=0, stride=1, bias=False)
self.bn9 = BatchNorm3d(32)
# inception 3b
self.conv10 = Conv3d(in_channels=256, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn10 = BatchNorm3d(128)
self.conv11 = Conv3d(in_channels=256, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn11 = BatchNorm3d(128)
self.conv12 = ConvTTN3d(in_channels=128, out_channels=192, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn12 = BatchNorm3d(192)
self.conv13 = Conv3d(in_channels=256, out_channels=32, kernel_size=1, padding=0, stride=1, bias=False)
self.bn13 = BatchNorm3d(32)
self.conv14 = ConvTTN3d(in_channels=32, out_channels=96, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn14 = BatchNorm3d(96)
self.maxpool4 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv15 = Conv3d(in_channels=256, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn15 = BatchNorm3d(64)
self.maxpool5 = MaxPool3d(kernel_size=3, padding=0, stride=2)
# inception 4a
self.conv16 = Conv3d(in_channels=480, out_channels=192, kernel_size=1, padding=0, stride=1, bias=False)
self.bn16 = BatchNorm3d(192)
self.conv17 = Conv3d(in_channels=480, out_channels=96, kernel_size=1, padding=0, stride=1, bias=False)
self.bn17 = BatchNorm3d(96)
self.conv18 = ConvTTN3d(in_channels=96, out_channels=208, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn18 = BatchNorm3d(208)
self.conv19 = Conv3d(in_channels=480, out_channels=16, kernel_size=1, padding=0, stride=1, bias=False)
self.bn19 = BatchNorm3d(16)
self.conv20 = ConvTTN3d(in_channels=16, out_channels=48, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn20 = BatchNorm3d(48)
self.maxpool6 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv21 = Conv3d(in_channels=480, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn21 = BatchNorm3d(64)
# inception 4b
self.conv22 = Conv3d(in_channels=512, out_channels=160, kernel_size=1, padding=0, stride=1, bias=False)
self.bn22 = BatchNorm3d(160)
self.conv23 = Conv3d(in_channels=512, out_channels=112, kernel_size=1, padding=0, stride=1, bias=False)
self.bn23 = BatchNorm3d(112)
self.conv24 = ConvTTN3d(in_channels=112, out_channels=224, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn24 = BatchNorm3d(224)
self.conv25 = Conv3d(in_channels=512, out_channels=24, kernel_size=1, padding=0, stride=1, bias=False)
self.bn25 = BatchNorm3d(24)
self.conv26 = ConvTTN3d(in_channels=24, out_channels=64, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn26 = BatchNorm3d(64)
self.maxpool7 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv27 = Conv3d(in_channels=512, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn27 = BatchNorm3d(64)
self.avgpool1 = AvgPool3d(kernel_size=5, padding=0, stride=3)
self.conv28 = Conv3d(in_channels=512, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn28 = BatchNorm3d(128)
# self.fc1 = Linear(in_features=2304, out_features=1024)
self.fc1 = Linear(in_features=768, out_features=1024) # 768
self.dropout1 = Dropout3d(p=0.7)
self.fc2 = Linear(in_features=1024, out_features=pv.label_size)
# inception 4c
self.conv29 = Conv3d(in_channels=512, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn29 = BatchNorm3d(128)
self.conv30 = Conv3d(in_channels=512, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn30 = BatchNorm3d(128)
self.conv31 = ConvTTN3d(in_channels=128, out_channels=256, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn31 = BatchNorm3d(256)
self.conv32 = Conv3d(in_channels=512, out_channels=24, kernel_size=1, padding=0, stride=1, bias=False)
self.bn32 = BatchNorm3d(24)
self.conv33 = ConvTTN3d(in_channels=24, out_channels=64, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn33 = BatchNorm3d(64)
self.maxpool8 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv34 = Conv3d(in_channels=512, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn34 = BatchNorm3d(64)
# inception 4d
self.conv35 = Conv3d(in_channels=512, out_channels=112, kernel_size=1, padding=0, stride=1, bias=False)
self.bn35 = BatchNorm3d(112)
self.conv36 = Conv3d(in_channels=512, out_channels=144, kernel_size=1, padding=0, stride=1, bias=False)
self.bn36 = BatchNorm3d(144)
self.conv37 = ConvTTN3d(in_channels=144, out_channels=288, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn37 = BatchNorm3d(288)
self.conv38 = Conv3d(in_channels=512, out_channels=32, kernel_size=1, padding=0, stride=1, bias=False)
self.bn38 = BatchNorm3d(32)
self.conv39 = ConvTTN3d(in_channels=32, out_channels=64, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn39 = BatchNorm3d(64)
self.maxpool9 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv40 = Conv3d(in_channels=512, out_channels=64, kernel_size=1, padding=0, stride=1, bias=False)
self.bn40 = BatchNorm3d(64)
# inception 4e
self.conv41 = Conv3d(in_channels=528, out_channels=256, kernel_size=1, padding=0, stride=1, bias=False)
self.bn41 = BatchNorm3d(256)
self.conv42 = Conv3d(in_channels=528, out_channels=160, kernel_size=1, padding=0, stride=1, bias=False)
self.bn42 = BatchNorm3d(160)
self.conv43 = ConvTTN3d(in_channels=160, out_channels=320, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn43 = BatchNorm3d(320)
self.conv44 = Conv3d(in_channels=528, out_channels=32, kernel_size=1, padding=0, stride=1, bias=False)
self.bn44 = BatchNorm3d(32)
self.conv45 = ConvTTN3d(in_channels=32, out_channels=128, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn45 = BatchNorm3d(128)
self.maxpool10 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv46 = Conv3d(in_channels=528, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn46 = BatchNorm3d(128)
self.avgpool2 = AvgPool3d(kernel_size=5, padding=0, stride=3)
self.conv47 = Conv3d(in_channels=528, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn47 = BatchNorm3d(128)
# self.fc3 = Linear(in_features=2304, out_features=1024)
self.fc3 = Linear(in_features=768, out_features=1024)
self.dropout2 = Dropout3d(p=0.7)
self.fc4 = Linear(in_features=1024, out_features=pv.label_size)
self.maxpool11 = MaxPool3d(kernel_size=3, padding=0, stride=2)
# inception 5a
self.conv48 = Conv3d(in_channels=832, out_channels=256, kernel_size=1, padding=0, stride=1, bias=False)
self.bn48 = BatchNorm3d(256)
self.conv49 = Conv3d(in_channels=832, out_channels=160, kernel_size=1, padding=0, stride=1, bias=False)
self.bn49 = BatchNorm3d(160)
self.conv50 = ConvTTN3d(in_channels=160, out_channels=320, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn50 = BatchNorm3d(320)
self.conv51 = Conv3d(in_channels=832, out_channels=32, kernel_size=1, padding=0, stride=1, bias=False)
self.bn51 = BatchNorm3d(32)
self.conv52 = ConvTTN3d(in_channels=32, out_channels=128, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn52 = BatchNorm3d(128)
self.maxpool12 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv53 = Conv3d(in_channels=832, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn53 = BatchNorm3d(128)
# inception 5b
self.conv54 = Conv3d(in_channels=832, out_channels=384, kernel_size=1, padding=0, stride=1, bias=False)
self.bn54 = BatchNorm3d(384)
self.conv55 = Conv3d(in_channels=832, out_channels=192, kernel_size=1, padding=0, stride=1, bias=False)
self.bn55 = BatchNorm3d(192)
self.conv56 = ConvTTN3d(in_channels=192, out_channels=384, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn56 = BatchNorm3d(384)
self.conv57 = Conv3d(in_channels=832, out_channels=48, kernel_size=1, padding=0, stride=1, bias=False)
self.bn57 = BatchNorm3d(48)
self.conv58 = ConvTTN3d(in_channels=48, out_channels=128, kernel_size=3, padding=1, stride=1, project_variable=pv, bias=False)
self.bn58 = BatchNorm3d(128)
self.maxpool13 = MaxPool3d(kernel_size=3, padding=1, stride=1)
self.conv59 = Conv3d(in_channels=832, out_channels=128, kernel_size=1, padding=0, stride=1, bias=False)
self.bn59 = BatchNorm3d(128)
self.avgpool3 = AdaptiveAvgPool3d(1)
self.dropout3 = Dropout3d(p=0.4)
self.fc5 = Linear(in_features=1024, out_features=pv.label_size)
def __init__(self):
super(ResNet18Explicit3DConvReduced, self).__init__()
# self.conv1_relu = ConvolutionBlock(3, 64, pv)
self.conv1 = Conv3d(in_channels=3,
out_channels=int(64 / 1.718),
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = BatchNorm3d(int(64 / 1.718))
self.maxpool = MaxPool3d(kernel_size=3,
padding=1,
stride=2,
dilation=1)
# self.res2a_relu = ResidualBlock(int(64/1.718), int(64/1.718), pv)
self.conv2 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(64 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn2 = BatchNorm3d(int(64 / 1.718))
self.conv3 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(64 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn3 = BatchNorm3d(int(64 / 1.718))
# self.res2b_relu = ResidualBlock(int(64/1.718), int(64/1.718), pv)
self.conv4 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(64 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn4 = BatchNorm3d(int(64 / 1.718))
self.conv5 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(64 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn5 = BatchNorm3d(int(64 / 1.718))
# self.res3a_relu = ResidualBlockB(int(64/1.718), int(128/1.718), pv)
self.conv6 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(128 / 1.718),
kernel_size=1,
stride=2,
bias=False)
self.bn6 = BatchNorm3d(int(128 / 1.718))
self.conv7 = Conv3d(in_channels=int(64 / 1.718),
out_channels=int(128 / 1.718),
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn7 = BatchNorm3d(int(128 / 1.718))
self.conv8 = Conv3d(in_channels=int(128 / 1.718),
out_channels=int(128 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn8 = BatchNorm3d(int(128 / 1.718))
# self.res3b_relu = ResidualBlock(int(128/1.718), int(128/1.718), pv)
self.conv9 = Conv3d(in_channels=int(128 / 1.718),
out_channels=int(128 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn9 = BatchNorm3d(int(128 / 1.718))
self.conv10 = Conv3d(in_channels=int(128 / 1.718),
out_channels=int(128 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn10 = BatchNorm3d(int(128 / 1.718))
# self.res4a_relu = ResidualBlockB(int(128/1.718), int(256/1.718), pv)
self.conv11 = Conv3d(in_channels=int(128 / 1.718),
out_channels=int(256 / 1.718),
kernel_size=1,
stride=2,
bias=False)
self.bn11 = BatchNorm3d(int(256 / 1.718))
self.conv12 = Conv3d(in_channels=int(128 / 1.718),
out_channels=int(256 / 1.718),
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn12 = BatchNorm3d(int(256 / 1.718))
self.conv13 = Conv3d(in_channels=int(256 / 1.718),
out_channels=int(256 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn13 = BatchNorm3d(int(256 / 1.718))
# self.res4b_relu = ResidualBlock(int(256/1.718), int(256/1.718), pv)
self.conv14 = Conv3d(in_channels=int(256 / 1.718),
out_channels=int(256 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn14 = BatchNorm3d(int(256 / 1.718))
self.conv15 = Conv3d(in_channels=int(256 / 1.718),
out_channels=int(256 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn15 = BatchNorm3d(int(256 / 1.718))
# self.res5a_relu = ResidualBlockB(int(256/1.718), int(512/1.718), pv)
self.conv16 = Conv3d(in_channels=int(256 / 1.718),
out_channels=int(512 / 1.718),
kernel_size=1,
stride=2,
bias=False)
self.bn16 = BatchNorm3d(int(512 / 1.718))
self.conv17 = Conv3d(in_channels=int(256 / 1.718),
out_channels=int(512 / 1.718),
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn17 = BatchNorm3d(int(512 / 1.718))
self.conv18 = Conv3d(in_channels=int(512 / 1.718),
out_channels=int(512 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn18 = BatchNorm3d(int(512 / 1.718))
# self.res5b_relu = ResidualBlock(int(512/1.718), int(512/1.718), pv)
self.conv19 = Conv3d(in_channels=int(512 / 1.718),
out_channels=int(512 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn19 = BatchNorm3d(int(512 / 1.718))
self.conv20 = Conv3d(in_channels=int(512 / 1.718),
out_channels=int(512 / 1.718),
kernel_size=3,
padding=1,
bias=False)
self.bn20 = BatchNorm3d(int(512 / 1.718))
self.avgpool = AdaptiveAvgPool3d(output_size=1)
self.fc = torch.nn.Linear(int(512 / 1.718), 27)
def __init__(self, pv):
super(ResNet18Explicit3DConv, self).__init__()
# self.conv1_relu = ConvolutionBlock(3, 64, pv)
self.conv1 = Conv3d(in_channels=3,
out_channels=64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = BatchNorm3d(64)
self.maxpool = MaxPool3d(kernel_size=3,
padding=1,
stride=2,
dilation=1)
# self.res2a_relu = ResidualBlock(64, 64, pv)
self.conv2 = Conv3d(in_channels=64,
out_channels=64,
kernel_size=3,
padding=1,
bias=False)
self.bn2 = BatchNorm3d(64)
self.conv3 = Conv3d(in_channels=64,
out_channels=64,
kernel_size=3,
padding=1,
bias=False)
self.bn3 = BatchNorm3d(64)
# self.res2b_relu = ResidualBlock(64, 64, pv)
self.conv4 = Conv3d(in_channels=64,
out_channels=64,
kernel_size=3,
padding=1,
bias=False)
self.bn4 = BatchNorm3d(64)
self.conv5 = Conv3d(in_channels=64,
out_channels=64,
kernel_size=3,
padding=1,
bias=False)
self.bn5 = BatchNorm3d(64)
# self.res3a_relu = ResidualBlockB(64, 128, pv)
self.conv6 = Conv3d(in_channels=64,
out_channels=128,
kernel_size=1,
stride=2,
bias=False)
self.bn6 = BatchNorm3d(128)
self.conv7 = Conv3d(in_channels=64,
out_channels=128,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn7 = BatchNorm3d(128)
self.conv8 = Conv3d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=1,
bias=False)
self.bn8 = BatchNorm3d(128)
# self.res3b_relu = ResidualBlock(128, 128, pv)
self.conv9 = Conv3d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=1,
bias=False)
self.bn9 = BatchNorm3d(128)
self.conv10 = Conv3d(in_channels=128,
out_channels=128,
kernel_size=3,
padding=1,
bias=False)
self.bn10 = BatchNorm3d(128)
# self.res4a_relu = ResidualBlockB(128, 256, pv)
self.conv11 = Conv3d(in_channels=128,
out_channels=256,
kernel_size=1,
stride=2,
bias=False)
self.bn11 = BatchNorm3d(256)
self.conv12 = Conv3d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn12 = BatchNorm3d(256)
self.conv13 = Conv3d(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
bias=False)
self.bn13 = BatchNorm3d(256)
# self.res4b_relu = ResidualBlock(256, 256, pv)
self.conv14 = Conv3d(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
bias=False)
self.bn14 = BatchNorm3d(256)
self.conv15 = Conv3d(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
bias=False)
self.bn15 = BatchNorm3d(256)
# self.res5a_relu = ResidualBlockB(256, 512, pv)
self.conv16 = Conv3d(in_channels=256,
out_channels=512,
kernel_size=1,
stride=2,
bias=False)
self.bn16 = BatchNorm3d(512)
self.conv17 = Conv3d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.bn17 = BatchNorm3d(512)
self.conv18 = Conv3d(in_channels=512,
out_channels=512,
kernel_size=3,
padding=1,
bias=False)
self.bn18 = BatchNorm3d(512)
# self.res5b_relu = ResidualBlock(512, 512, pv)
self.conv19 = Conv3d(in_channels=512,
out_channels=512,
kernel_size=3,
padding=1,
bias=False)
self.bn19 = BatchNorm3d(512)
self.conv20 = Conv3d(in_channels=512,
out_channels=512,
kernel_size=3,
padding=1,
bias=False)
self.bn20 = BatchNorm3d(512)
self.avgpool = AdaptiveAvgPool3d(output_size=1)
self.fc = torch.nn.Linear(512, pv.label_size)