def __init__(self, input_channels, output_channels, kernel_size, stride, N, activation = True, deconv = False, last_deconv = False):
super(rot_conv2d, self).__init__()
r2_act = gspaces.Rot2dOnR2(N = N)
feat_type_in = nn.FieldType(r2_act, input_channels*[r2_act.regular_repr])
feat_type_hid = nn.FieldType(r2_act, output_channels*[r2_act.regular_repr])
if not deconv:
if activation:
self.layer = nn.SequentialModule(
nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, stride = stride, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(feat_type_hid),
nn.ReLU(feat_type_hid)
)
else:
self.layer = nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, stride = stride,padding = (kernel_size - 1)//2)
else:
if last_deconv:
feat_type_in = nn.FieldType(r2_act, input_channels*[r2_act.regular_repr])
feat_type_hid = nn.FieldType(r2_act, output_channels*[r2_act.irrep(1)])
self.layer = nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, stride = stride, padding = 0)
else:
self.layer = nn.SequentialModule(
nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, stride = stride, padding = 0),
nn.InnerBatchNorm(feat_type_hid),
nn.ReLU(feat_type_hid)
)
def __init__(self,
input_channels,
hidden_dim,
kernel_size,
N # Group size
):
super(rot_resblock, self).__init__()
# Specify symmetry transformation
r2_act = gspaces.Rot2dOnR2(N = N)
feat_type_in = nn.FieldType(r2_act, input_channels*[r2_act.regular_repr])
feat_type_hid = nn.FieldType(r2_act, hidden_dim*[r2_act.regular_repr])
self.layer1 = nn.SequentialModule(
nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(feat_type_hid),
nn.ReLU(feat_type_hid)
)
self.layer2 = nn.SequentialModule(
nn.R2Conv(feat_type_hid, feat_type_hid, kernel_size = kernel_size, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(feat_type_hid),
nn.ReLU(feat_type_hid)
)
self.upscale = nn.SequentialModule(
nn.R2Conv(feat_type_in, feat_type_hid, kernel_size = kernel_size, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(feat_type_hid),
nn.ReLU(feat_type_hid)
)
self.input_channels = input_channels
self.hidden_dim = hidden_dim
def __init__(self, in_type, inner_type, out_type, stride=1):
super(BottleneckBlock, self).__init__()
self.bn1 = enn.InnerBatchNorm(in_type)
self.relu1 = enn.ReLU(in_type,inplace=True)
self.conv1 = conv1x1(in_type, inner_type)
self.bn2 = enn.InnerBatchNorm(inner_type)
self.relu2 = enn.ReLU(inner_type,inplace=True)
self.conv2 = conv3x3(inner_type, out_type)
def __init__(self, n_classes=6):
super(SteerCNN, self).__init__()
# the model is equivariant under rotations by 45 degrees, modelled by C8
self.r2_act = gspaces.Rot2dOnR2(N=4)
# the input image is a scalar field, corresponding to the trivial representation
input_type = nn_e2.FieldType(self.r2_act,
3 * [self.r2_act.trivial_repr])
# we store the input type for wrapping the images into a geometric tensor during the forward pass
self.input_type = input_type
# convolution 1
# first specify the output type of the convolutional layer
# we choose 24 feature fields, each transforming under the regular representation of C8
out_type = nn_e2.FieldType(self.r2_act,
24 * [self.r2_act.regular_repr])
self.block1 = nn_e2.SequentialModule(
nn_e2.R2Conv(input_type,
out_type,
kernel_size=7,
padding=3,
bias=False), nn_e2.InnerBatchNorm(out_type),
nn_e2.ReLU(out_type, inplace=True))
self.pool1 = nn_e2.PointwiseAvgPool(out_type, 4)
# convolution 2
# the old output type is the input type to the next layer
in_type = self.block1.out_type
# the output type of the second convolution layer are 48 regular feature fields of C8
#out_type = nn_e2.FieldType(self.r2_act, 48 * [self.r2_act.regular_repr])
self.block2 = nn_e2.SequentialModule(
nn_e2.R2Conv(in_type,
out_type,
kernel_size=7,
padding=3,
bias=False), nn_e2.InnerBatchNorm(out_type),
nn_e2.ReLU(out_type, inplace=True))
self.pool2 = nn_e2.SequentialModule(
nn_e2.PointwiseAvgPoolAntialiased(out_type,
sigma=0.66,
stride=1,
padding=0),
nn_e2.PointwiseAvgPool(out_type, 4), nn_e2.GroupPooling(out_type))
# PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=7)
# number of output channels
c = 24 * 13 * 13 #self.gpool.out_type.size
# Fully Connected
self.fully_net = torch.nn.Sequential(
torch.nn.Linear(c, 64),
torch.nn.BatchNorm1d(64),
torch.nn.ELU(inplace=True),
torch.nn.Linear(64, n_classes),
)
def __init__(self, channel_in=3, n_classes=4, rot_n=4):
super(SmallE2, self).__init__()
r2_act = gspaces.Rot2dOnR2(N=rot_n)
self.feat_type_in = nn.FieldType(r2_act,
channel_in * [r2_act.trivial_repr])
feat_type_hid = nn.FieldType(r2_act, 8 * [r2_act.regular_repr])
feat_type_out = nn.FieldType(r2_act, 2 * [r2_act.regular_repr])
self.bn = nn.InnerBatchNorm(feat_type_hid)
self.relu = nn.ReLU(feat_type_hid)
self.convin = nn.R2Conv(self.feat_type_in,
feat_type_hid,
kernel_size=3)
self.convhid = nn.R2Conv(feat_type_hid, feat_type_hid, kernel_size=3)
self.convout = nn.R2Conv(feat_type_hid, feat_type_out, kernel_size=3)
self.avgpool = nn.PointwiseAvgPool(feat_type_out, 3)
self.invariant_map = nn.GroupPooling(feat_type_out)
c = self.invariant_map.out_type.size
self.lin_in = torch.nn.Linear(c, 64)
self.elu = torch.nn.ELU()
self.lin_out = torch.nn.Linear(64, n_classes)
def __init__(self, input_frames, output_frames, kernel_size, N):
super(Unet_Rot, self).__init__()
r2_act = gspaces.Rot2dOnR2(N = N)
self.feat_type_in = nn.FieldType(r2_act, input_frames*[r2_act.irrep(1)])
self.feat_type_in_hid = nn.FieldType(r2_act, 32*[r2_act.regular_repr])
self.feat_type_hid_out = nn.FieldType(r2_act, (16 + input_frames)*[r2_act.irrep(1)])
self.feat_type_out = nn.FieldType(r2_act, output_frames*[r2_act.irrep(1)])
self.conv1 = nn.SequentialModule(
nn.R2Conv(self.feat_type_in, self.feat_type_in_hid, kernel_size = kernel_size, stride = 2, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(self.feat_type_in_hid),
nn.ReLU(self.feat_type_in_hid)
)
self.conv2 = rot_conv2d(32, 64, kernel_size = kernel_size, stride = 1, N = N)
self.conv2_1 = rot_conv2d(64, 64, kernel_size = kernel_size, stride = 1, N = N)
self.conv3 = rot_conv2d(64, 128, kernel_size = kernel_size, stride = 2, N = N)
self.conv3_1 = rot_conv2d(128, 128, kernel_size = kernel_size, stride = 1, N = N)
self.conv4 = rot_conv2d(128, 256, kernel_size = kernel_size, stride = 2, N = N)
self.conv4_1 = rot_conv2d(256, 256, kernel_size = kernel_size, stride = 1, N = N)
self.deconv3 = rot_deconv2d(256, 64, N)
self.deconv2 = rot_deconv2d(192, 32, N)
self.deconv1 = rot_deconv2d(96, 16, N, last_deconv = True)
self.output_layer = nn.R2Conv(self.feat_type_hid_out, self.feat_type_out, kernel_size = kernel_size, padding = (kernel_size - 1)//2)
def __init__(
self,
in_type: enn.FieldType,
inner_type: enn.FieldType,
dropout_rate: float,
stride: int = 1,
out_type: enn.FieldType = None,
):
super(WideBasic, self).__init__()
if out_type is None:
out_type = in_type
self.in_type = in_type
inner_type = inner_type
self.out_type = out_type
if isinstance(in_type.gspace, gspaces.FlipRot2dOnR2):
rotations = in_type.gspace.fibergroup.rotation_order
elif isinstance(in_type.gspace, gspaces.Rot2dOnR2):
rotations = in_type.gspace.fibergroup.order()
else:
rotations = 0
if rotations in [0, 2, 4]:
conv = conv3x3
else:
conv = conv5x5
self.bn1 = enn.InnerBatchNorm(self.in_type)
self.relu1 = enn.ReLU(self.in_type, inplace=True)
self.conv1 = conv(self.in_type, inner_type)
self.bn2 = enn.InnerBatchNorm(inner_type)
self.relu2 = enn.ReLU(inner_type, inplace=True)
self.dropout = enn.PointwiseDropout(inner_type, p=dropout_rate)
self.conv2 = conv(inner_type, self.out_type, stride=stride)
self.shortcut = None
if stride != 1 or self.in_type != self.out_type:
self.shortcut = conv1x1(self.in_type,
self.out_type,
stride=stride,
bias=False)
def __init__(self, in_type, out_type):
super(BasicBlock, self).__init__()
self.in_type = in_type
self.out_type = out_type
self.bn1 = enn.InnerBatchNorm(self.in_type)
self.relu1 = enn.ReLU(self.in_type, inplace=True)
self.conv1 = conv3x3(self.in_type, self.out_type)
def __init__(self, in_type, out_type, gspace):
super(TransitionBlock, self).__init__()
self.gspace = gspace
self.in_type = FIELD_TYPE["regular"](self.gspace, in_type, fixparams=False)
self.out_type = FIELD_TYPE["regular"](self.gspace, out_type, fixparams=False)
self.bn1 = enn.InnerBatchNorm(self.in_type)
self.relu1 = enn.ReLU(self.in_type,inplace=True)
self.conv1 = conv1x1(self.in_type,self.out_type)
self.avgpool = enn.PointwiseAvgPool(self.out_type, kernel_size=2)
def __init__(self, in_type,inner_type, out_type, stride=1):
super(ResBlock, self).__init__()
self.in_type = in_type
self.inner_type = inner_type
self.out_type = out_type
self.conv1 = conv1x1(self.in_type, self.inner_type, stride = 1, bias = False)
self.bn1 = enn.InnerBatchNorm(self.inner_type)
self.relu1 = enn.ReLU(self.inner_type)
self.conv2 = conv3x3(self.inner_type, self.inner_type, padding=1, stride = stride, bias = False)
self.bn2 = enn.InnerBatchNorm(self.inner_type)
self.relu2 = enn.ReLU(self.inner_type, inplace=True)
self.conv3 = conv1x1(self.inner_type, self.out_type, stride = 1, bias = False)
self.bn3 = enn.InnerBatchNorm(self.out_type)
self.relu3 = enn.ReLU(self.out_type, inplace=True)
self.shortcut = None
if stride != 1 or self.in_type != self.out_type:
self.shortcut = enn.R2Conv(self.in_type, self.out_type, kernel_size=1, stride=stride, bias=False)
def __init__(self, nclasses=1):
super(ResNet50, self).__init__()
self.gspace = gspaces.Rot2dOnR2(N=8)
reg_field64 = FIELD_TYPE["regular"](self.gspace, 64, fixparams=False)
reg_field256 = FIELD_TYPE["regular"](self.gspace, 256, fixparams=False)
reg_field128 = FIELD_TYPE["regular"](self.gspace, 128, fixparams=False)
reg_field512 = FIELD_TYPE["regular"](self.gspace, 512, fixparams=False)
reg_field1024 = FIELD_TYPE["regular"](self.gspace, 1024, fixparams=False)
reg_field2048 = FIELD_TYPE["regular"](self.gspace, 2048, fixparams=False)
self.conv1 = enn.R2Conv(FIELD_TYPE["trivial"](self.gspace, 3, fixparams=False),
reg_field64, kernel_size=7, stride=2, padding=3)
self.bn1 = enn.InnerBatchNorm(reg_field64)
self.relu1 = enn.ELU(reg_field64)
self.maxpool1 = enn.PointwiseMaxPoolAntialiased(reg_field64, kernel_size=2)
layer1 = []
layer1.append(ResBlock(stride=2, in_type = reg_field64, inner_type = reg_field64, out_type = reg_field256))
layer1.append(ResBlock(stride=1, in_type = reg_field256, inner_type = reg_field64, out_type = reg_field256))
layer1.append(ResBlock(stride=1, in_type = reg_field256, inner_type = reg_field64, out_type = reg_field256))
self.layer1 = torch.nn.Sequential(*layer1)
layer2 = []
layer2.append(ResBlock(stride=2, in_type = reg_field256, inner_type = reg_field128, out_type = reg_field512))
layer2.append(ResBlock(stride=1, in_type = reg_field512, inner_type = reg_field128, out_type = reg_field512))
layer2.append(ResBlock(stride=1, in_type = reg_field512, inner_type = reg_field128, out_type = reg_field512))
layer2.append(ResBlock(stride=1, in_type = reg_field512, inner_type = reg_field128, out_type = reg_field512))
self.layer2 = torch.nn.Sequential(*layer2)
layer3 = []
layer3.append(ResBlock(stride=2, in_type = reg_field512, inner_type = reg_field256, out_type = reg_field1024))
layer3.append(ResBlock(stride=1, in_type = reg_field1024, inner_type = reg_field256, out_type = reg_field1024))
layer3.append(ResBlock(stride=1, in_type = reg_field1024, inner_type = reg_field256, out_type = reg_field1024))
layer3.append(ResBlock(stride=1, in_type = reg_field1024, inner_type = reg_field256, out_type = reg_field1024))
layer3.append(ResBlock(stride=1, in_type = reg_field1024, inner_type = reg_field256, out_type = reg_field1024))
layer3.append(ResBlock(stride=1, in_type = reg_field1024, inner_type = reg_field256, out_type = reg_field1024))
self.layer3 = torch.nn.Sequential(*layer3)
layer4 = []
layer4.append(ResBlock(stride=2, in_type = reg_field1024, inner_type = reg_field512, out_type = reg_field2048))
layer4.append(ResBlock(stride=1, in_type = reg_field2048, inner_type = reg_field512, out_type = reg_field2048))
layer4.append(ResBlock(stride=1, in_type = reg_field2048, inner_type = reg_field512, out_type = reg_field2048))
self.layer4 = torch.nn.Sequential(*layer4)
self.pool = torch.nn.AdaptiveAvgPool2d((1, 1))
self.fc = torch.nn.Linear(2048, nclasses)
def __init__(self, growth_rate, list_layer, nclasses):
super(DenseNet161, self).__init__()
self.gspace = gspaces.Rot2dOnR2(N=8)
in_type = 2*growth_rate
self.conv1 = conv7x7(FIELD_TYPE["trivial"](self.gspace, 3, fixparams=False),
FIELD_TYPE["regular"](self.gspace, in_type, fixparams=False))
self.pool1 = enn.PointwiseMaxPool(FIELD_TYPE["regular"](self.gspace, in_type, fixparams=False),
kernel_size=2, stride=2)
#1st block
self.block1 = DenseBlock(in_type, growth_rate, self.gspace, list_layer[0])
in_type = in_type +list_layer[0]*growth_rate
self.trans1 = TransitionBlock(in_type, int(in_type/2), self.gspace)
in_type = int(in_type/2)
#2nd block
self.block2 = DenseBlock(in_type, growth_rate, self.gspace, list_layer[1])
in_type = in_type +list_layer[1]*growth_rate
self.trans2 = TransitionBlock(in_type, int(in_type/2), self.gspace)
in_type = int(in_type/2)
#3rd block
self.block3 = DenseBlock(in_type, growth_rate, self.gspace, list_layer[2])
in_type = in_type +list_layer[2]*growth_rate
self.trans3 = TransitionBlock(in_type, int(in_type/2), self.gspace)
in_type = int(in_type/2)
#4th block
self.block4 = DenseBlock(in_type, growth_rate, self.gspace, list_layer[3])
in_type = in_type +list_layer[3]*growth_rate
self.bn = enn.InnerBatchNorm(FIELD_TYPE["regular"](self.gspace, in_type, fixparams=False))
self.relu = enn.ReLU(FIELD_TYPE["regular"](self.gspace, in_type, fixparams=False),inplace=True)
self.pool2 = torch.nn.AdaptiveAvgPool2d((1, 1))
self.classifier = torch.nn.Linear(in_type, nclasses)
def __init__(self, input_frames, output_frames, kernel_size, N):
super(ResNet_Rot, self).__init__()
r2_act = gspaces.Rot2dOnR2(N = N)
# we use rho_1 representation since the input is velocity fields
self.feat_type_in = nn.FieldType(r2_act, input_frames*[r2_act.irrep(1)])
# we use regular representation for middle layers
self.feat_type_in_hid = nn.FieldType(r2_act, 16*[r2_act.regular_repr])
self.feat_type_hid_out = nn.FieldType(r2_act, 192*[r2_act.regular_repr])
self.feat_type_out = nn.FieldType(r2_act, output_frames*[r2_act.irrep(1)])
self.input_layer = nn.SequentialModule(
nn.R2Conv(self.feat_type_in, self.feat_type_in_hid, kernel_size = kernel_size, padding = (kernel_size - 1)//2),
nn.InnerBatchNorm(self.feat_type_in_hid),
nn.ReLU(self.feat_type_in_hid)
)
layers = [self.input_layer]
layers += [rot_resblock(16, 32, kernel_size, N), rot_resblock(32, 32, kernel_size, N)]
layers += [rot_resblock(32, 64, kernel_size, N), rot_resblock(64, 64, kernel_size, N)]
layers += [rot_resblock(64, 128, kernel_size, N), rot_resblock(128, 128, kernel_size, N)]
layers += [rot_resblock(128, 192, kernel_size, N), rot_resblock(192, 192, kernel_size, N)]
layers += [nn.R2Conv(self.feat_type_hid_out, self.feat_type_out, kernel_size = kernel_size, padding = (kernel_size - 1)//2)]
self.model = torch.nn.Sequential(*layers)
def __init__(self, depth, widen_factor, dropout_rate, num_classes=100,
N: int = 8,
r: int = 1,
f: bool = True,
deltaorth: bool = False,
fixparams: bool = True,
initial_stride: int = 1,
):
r"""
Build and equivariant Wide ResNet.
The parameter ``N`` controls rotation equivariance and the parameter ``f`` reflection equivariance.
More precisely, ``N`` is the number of discrete rotations the model is initially equivariant to.
``N = 1`` means the model is only reflection equivariant from the beginning.
``f`` is a boolean flag specifying whether the model should be reflection equivariant or not.
If it is ``False``, the model is not reflection equivariant.
``r`` is the restriction level:
- ``0``: no restriction. The model is equivariant to ``N`` rotations from the input to the output
- ``1``: restriction before the last block. The model is equivariant to ``N`` rotations before the last block
(i.e. in the first 2 blocks). Then it is restricted to ``N/2`` rotations until the output.
- ``2``: restriction after the first block. The model is equivariant to ``N`` rotations in the first block.
Then it is restricted to ``N/2`` rotations until the output (i.e. in the last 3 blocks).
- ``3``: restriction after the first and the second block. The model is equivariant to ``N`` rotations in the first
block. It is restricted to ``N/2`` rotations before the second block and to ``1`` rotations before the last
block.
NOTICE: if restriction to ``N/2`` is performed, ``N`` needs to be even!
"""
super(Wide_ResNet, self).__init__()
assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
n = int((depth - 4) / 6)
k = widen_factor
print(f'| Wide-Resnet {depth}x{k}')
nStages = [16, 16 * k, 32 * k, 64 * k]
self._fixparams = fixparams
self._layer = 0
# number of discrete rotations to be equivariant to
self._N = N
# if the model is [F]lip equivariant
self._f = f
if self._f:
if N != 1:
self.gspace = gspaces.FlipRot2dOnR2(N)
else:
self.gspace = gspaces.Flip2dOnR2()
else:
if N != 1:
self.gspace = gspaces.Rot2dOnR2(N)
else:
self.gspace = gspaces.TrivialOnR2()
# level of [R]estriction:
# r = 0: never do restriction, i.e. initial group (either DN or CN) preserved for the whole network
# r = 1: restrict before the last block, i.e. initial group (either DN or CN) preserved for the first
# 2 blocks, then restrict to N/2 rotations (either D{N/2} or C{N/2}) in the last block
# r = 2: restrict after the first block, i.e. initial group (either DN or CN) preserved for the first
# block, then restrict to N/2 rotations (either D{N/2} or C{N/2}) in the last 2 blocks
# r = 3: restrict after each block. Initial group (either DN or CN) preserved for the first
# block, then restrict to N/2 rotations (either D{N/2} or C{N/2}) in the second block and to 1 rotation
# in the last one (D1 or C1)
assert r in [0, 1, 2, 3]
self._r = r
# the input has 3 color channels (RGB).
# Color channels are trivial fields and don't transform when the input is rotated or flipped
r1 = enn.FieldType(self.gspace, [self.gspace.trivial_repr] * 3)
# input field type of the model
self.in_type = r1
# in the first layer we always scale up the output channels to allow for enough independent filters
r2 = FIELD_TYPE["regular"](self.gspace, nStages[0], fixparams=True)
# dummy attribute keeping track of the output field type of the last submodule built, i.e. the input field type of
# the next submodule to build
self._in_type = r2
self.conv1 = conv5x5(r1, r2)
self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=initial_stride)
if self._r >= 2:
N_new = N//2
id = (0, N_new) if self._f else N_new
self.restrict1 = self._restrict_layer(id)
else:
self.restrict1 = lambda x: x
self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2)
if self._r == 3:
id = (0, 1) if self._f else 1
self.restrict2 = self._restrict_layer(id)
elif self._r == 1:
N_new = N // 2
id = (0, N_new) if self._f else N_new
self.restrict2 = self._restrict_layer(id)
else:
self.restrict2 = lambda x: x
# last layer maps to a trivial (invariant) feature map
self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2, totrivial=True)
self.bn = enn.InnerBatchNorm(self.layer3.out_type, momentum=0.9)
self.relu = enn.ReLU(self.bn.out_type, inplace=True)
self.linear = torch.nn.Linear(self.bn.out_type.size, num_classes)
for name, module in self.named_modules():
if isinstance(module, enn.R2Conv):
if deltaorth:
init.deltaorthonormal_init(module.weights, module.basisexpansion)
elif isinstance(module, torch.nn.BatchNorm2d):
module.weight.data.fill_(1)
module.bias.data.zero_()
elif isinstance(module, torch.nn.Linear):
module.bias.data.zero_()
print("MODEL TOPOLOGY:")
for i, (name, mod) in enumerate(self.named_modules()):
print(f"\t{i} - {name}")
def __init__(self, n_classes=10):
super(C8SteerableCNN, self).__init__()
# the model is equivariant under rotations by 45 degrees, modelled by C8
self.r2_act = gspaces.Rot2dOnR2(N=8)
# the input image is a scalar field, corresponding to the trivial representation
in_type = nn.FieldType(self.r2_act, [self.r2_act.trivial_repr])
# we store the input type for wrapping the images into a geometric tensor during the forward pass
self.input_type = in_type
# convolution 1
# first specify the output type of the convolutional layer
# we choose 16 feature fields, each transforming under the regular representation of C8
out_type = nn.FieldType(self.r2_act, 24 * [self.r2_act.regular_repr])
self.block1 = nn.SequentialModule(
# nn.MaskModule(in_type, 29, margin=1),
nn.R2Conv(in_type, out_type, kernel_size=7, padding=1, bias=False),
nn.InnerBatchNorm(out_type),
nn.ReLU(out_type, inplace=True))
# convolution 2
# the old output type is the input type to the next layer
in_type = self.block1.out_type
# the output type of the second convolution layer are 32 regular feature fields of C8
out_type = nn.FieldType(self.r2_act, 48 * [self.r2_act.regular_repr])
self.block2 = nn.SequentialModule(
nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),
nn.InnerBatchNorm(out_type), nn.ReLU(out_type, inplace=True))
self.pool1 = nn.SequentialModule(
nn.PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=2))
# convolution 3
# the old output type is the input type to the next layer
in_type = self.block2.out_type
# the output type of the third convolution layer are 32 regular feature fields of C8
out_type = nn.FieldType(self.r2_act, 48 * [self.r2_act.regular_repr])
self.block3 = nn.SequentialModule(
nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),
nn.InnerBatchNorm(out_type), nn.ReLU(out_type, inplace=True))
# convolution 4
# the old output type is the input type to the next layer
in_type = self.block3.out_type
# the output type of the fourth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act, 96 * [self.r2_act.regular_repr])
self.block4 = nn.SequentialModule(
nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),
nn.InnerBatchNorm(out_type), nn.ReLU(out_type, inplace=True))
self.pool2 = nn.SequentialModule(
nn.PointwiseAvgPoolAntialiased(out_type, sigma=0.66, stride=2))
# convolution 5
# the old output type is the input type to the next layer
in_type = self.block4.out_type
# the output type of the fifth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act, 96 * [self.r2_act.regular_repr])
self.block5 = nn.SequentialModule(
nn.R2Conv(in_type, out_type, kernel_size=5, padding=2, bias=False),
nn.InnerBatchNorm(out_type), nn.ReLU(out_type, inplace=True))
# convolution 6
# the old output type is the input type to the next layer
in_type = self.block5.out_type
# the output type of the sixth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act, 64 * [self.r2_act.regular_repr])
self.block6 = nn.SequentialModule(
nn.R2Conv(in_type, out_type, kernel_size=5, padding=1, bias=False),
nn.InnerBatchNorm(out_type), nn.ReLU(out_type, inplace=True))
self.pool3 = nn.PointwiseAvgPool(out_type, kernel_size=4)
self.gpool = nn.GroupPooling(out_type)
# number of output channels
c = self.gpool.out_type.size
# Fully Connected
self.fully_net = torch.nn.Sequential(
torch.nn.Linear(c, 64),
torch.nn.BatchNorm1d(64),
torch.nn.ELU(inplace=True),
torch.nn.Linear(64, n_classes),
)
def __init__(self,
base = 'DNSteerableAGRadGalNet',
attention_module='SelfAttention',
attention_gates=3,
attention_aggregation='ft',
n_classes=2,
attention_normalisation='sigmoid',
quiet=True,
number_rotations=8,
imsize=150,
kernel_size=3,
group="D"
):
super(DNSteerableAGRadGalNet, self).__init__()
aggregation_mode = attention_aggregation
normalisation = attention_normalisation
AG = int(attention_gates)
N = int(number_rotations)
kernel_size = int(kernel_size)
imsize = int(imsize)
n_classes = int(n_classes)
assert aggregation_mode in ['concat', 'mean', 'deep_sup', 'ft'], 'Aggregation mode not recognised. Valid inputs include concat, mean, deep_sup or ft.'
assert normalisation in ['sigmoid','range_norm','std_mean_norm','tanh','softmax'], f'Nomralisation not implemented. Can be any of: sigmoid, range_norm, std_mean_norm, tanh, softmax'
assert AG in [0,1,2,3], f'Number of Attention Gates applied (AG) must be an integer in range [0,3]. Currently AG={AG}'
assert group.lower() in ["d","c"], f"group parameter must either be 'D' for DN, or 'C' for CN, steerable networks. (currently {group})."
filters = [6,16,32,64,128]
self.attention_out_sizes = []
self.ag = AG
self.n_classes = n_classes
self.filters = filters
self.aggregation_mode = aggregation_mode
# Setting up e2
if group.lower() == "d":
self.r2_act = gspaces.FlipRot2dOnR2(N=int(number_rotations))
else:
self.r2_act = gspaces.Rot2dOnR2(N=int(number_rotations))
in_type = e2nn.FieldType(self.r2_act, [self.r2_act.trivial_repr])
out_type = e2nn.FieldType(self.r2_act, 6*[self.r2_act.regular_repr])
self.in_type = in_type
self.mask = e2nn.MaskModule(in_type, imsize, margin=0)
self.conv1a = e2nn.R2Conv(in_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu1a = e2nn.ReLU(out_type); self.bnorm1a= e2nn.InnerBatchNorm(out_type)
self.conv1b = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu1b = e2nn.ReLU(out_type); self.bnorm1b= e2nn.InnerBatchNorm(out_type)
self.conv1c = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu1c = e2nn.ReLU(out_type); self.bnorm1c= e2nn.InnerBatchNorm(out_type)
self.mpool1 = e2nn.PointwiseMaxPool(out_type, kernel_size=(2,2), stride=2)
self.gpool1 = e2nn.GroupPooling(out_type)
in_type = out_type
out_type = e2nn.FieldType(self.r2_act, 16*[self.r2_act.regular_repr])
self.conv2a = e2nn.R2Conv(in_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu2a = e2nn.ReLU(out_type); self.bnorm2a= e2nn.InnerBatchNorm(out_type)
self.conv2b = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu2b = e2nn.ReLU(out_type); self.bnorm2b= e2nn.InnerBatchNorm(out_type)
self.conv2c = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu2c = e2nn.ReLU(out_type); self.bnorm2c= e2nn.InnerBatchNorm(out_type)
self.mpool2 = e2nn.PointwiseMaxPool(out_type, kernel_size=(2,2), stride=2)
self.gpool2 = e2nn.GroupPooling(out_type)
in_type = out_type
out_type = e2nn.FieldType(self.r2_act, 32*[self.r2_act.regular_repr])
self.conv3a = e2nn.R2Conv(in_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu3a = e2nn.ReLU(out_type); self.bnorm3a= e2nn.InnerBatchNorm(out_type)
self.conv3b = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu3b = e2nn.ReLU(out_type); self.bnorm3b= e2nn.InnerBatchNorm(out_type)
self.conv3c = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu3c = e2nn.ReLU(out_type); self.bnorm3c= e2nn.InnerBatchNorm(out_type)
self.mpool3 = e2nn.PointwiseMaxPool(out_type, kernel_size=(2,2), stride=2)
self.gpool3 = e2nn.GroupPooling(out_type)
in_type = out_type
out_type = e2nn.FieldType(self.r2_act, 64*[self.r2_act.regular_repr])
self.conv4a = e2nn.R2Conv(in_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu4a = e2nn.ReLU(out_type); self.bnorm4a= e2nn.InnerBatchNorm(out_type)
self.conv4b = e2nn.R2Conv(out_type, out_type, kernel_size=kernel_size, padding=kernel_size//2, stride=1, bias=False); self.relu4b = e2nn.ReLU(out_type); self.bnorm4b= e2nn.InnerBatchNorm(out_type)
self.mpool4 = e2nn.PointwiseMaxPool(out_type, kernel_size=(2,2), stride=2)
self.gpool4 = e2nn.GroupPooling(out_type)
self.flatten = nn.Flatten(1)
self.dropout = nn.Dropout(p=0.5)
if self.ag == 0:
pass
if self.ag >= 1:
self.attention1 = GridAttentionBlock2D(in_channels=32, gating_channels=64, inter_channels=64, input_size=[imsize//4,imsize//4], normalisation=normalisation)
if self.ag >= 2:
self.attention2 = GridAttentionBlock2D(in_channels=16, gating_channels=64, inter_channels=64, input_size=[imsize//2,imsize//2], normalisation=normalisation)
if self.ag >= 3:
self.attention3 = GridAttentionBlock2D(in_channels=6, gating_channels=64, inter_channels=64, input_size=[imsize,imsize], normalisation=normalisation)
self.fc1 = nn.Linear(16*5*5,256) #channel_size * width * height
self.fc2 = nn.Linear(256,256)
self.fc3 = nn.Linear(256, self.n_classes)
self.dummy = nn.Parameter(torch.empty(0))
self.module_order = ['conv1a', 'relu1a', 'bnorm1a', #1->6
'conv1b', 'relu1b', 'bnorm1b', #6->6
'conv1c', 'relu1c', 'bnorm1c', #6->6
'mpool1',
'conv2a', 'relu2a', 'bnorm2a', #6->16
'conv2b', 'relu2b', 'bnorm2b', #16->16
'conv2c', 'relu2c', 'bnorm2c', #16->16
'mpool2',
'conv3a', 'relu3a', 'bnorm3a', #16->32
'conv3b', 'relu3b', 'bnorm3b', #32->32
'conv3c', 'relu3c', 'bnorm3c', #32->32
'mpool3',
'conv4a', 'relu4a', 'bnorm4a', #32->64
'conv4b', 'relu4b', 'bnorm4b', #64->64
'compatibility_score1',
'compatibility_score2']
#########################
# Aggreagation Strategies
if self.ag != 0:
self.attention_filter_sizes = [32, 16, 6]
concat_length = 0
for i in range(self.ag):
concat_length += self.attention_filter_sizes[i]
if aggregation_mode == 'concat':
self.classifier = nn.Linear(concat_length, self.n_classes)
self.aggregate = self.aggregation_concat
else:
# Not able to initialise in a loop as the modules will not change device with remaining model.
self.classifiers = nn.ModuleList()
if self.ag>=1:
self.classifiers.append(nn.Linear(self.attention_filter_sizes[0], self.n_classes))
if self.ag>=2:
self.classifiers.append(nn.Linear(self.attention_filter_sizes[1], self.n_classes))
if self.ag>=3:
self.classifiers.append(nn.Linear(self.attention_filter_sizes[2], self.n_classes))
if aggregation_mode == 'mean':
self.aggregate = self.aggregation_sep
elif aggregation_mode == 'deep_sup':
self.classifier = nn.Linear(concat_length, self.n_classes)
self.aggregate = self.aggregation_ds
elif aggregation_mode == 'ft':
self.classifier = nn.Linear(self.n_classes*self.ag, self.n_classes)
self.aggregate = self.aggregation_ft
else:
raise NotImplementedError
else:
self.classifier = nn.Linear((150//16)**2*64, self.n_classes)
self.aggregate = lambda x: self.classifier(self.flatten(x))
def __init__(self, in_type, out_type):
super(Conv, self).__init__()
self.conv = enn.SequentialModule(
enn.R2Conv(in_type, out_type, kernel_size=3, stride=1, padding=0),
enn.InnerBatchNorm(out_type), enn.ReLU(out_type))
def build_norm_layer(cfg, num_features, postfix=''):
in_type = FIELD_TYPE['regular'](gspace, num_features)
return 'bn' + str(postfix), enn.InnerBatchNorm(in_type)
def create_equivariant_real_nvp_blocks(input_size,
in_type,
field_type,
out_fiber,
activation_fn,
hidden_size,
n_blocks,
n_hidden,
group_action_type,
kernel_size=3,
padding=1,
only_t=False):
nets, nett = [], []
# we store the input type for wrapping the images into a geometric tensor during the forward pass
input_type = in_type
_, c, h, w = input_size
out_type = enn.FieldType(group_action_type,
c * [group_action_type.trivial_repr])
inter_block_out_type = FIBERS[out_fiber](group_action_type,
hidden_size,
field_type,
fixparams=True)
for i in range(n_blocks):
if not only_t:
s_block = [
enn.SequentialModule(
enn.R2Conv(in_type,
inter_block_out_type,
kernel_size=kernel_size,
padding=padding,
bias=True),
enn.InnerBatchNorm(inter_block_out_type),
activation_fn(inter_block_out_type, inplace=True))
]
t_block = [
enn.SequentialModule(
enn.R2Conv(in_type,
inter_block_out_type,
kernel_size=kernel_size,
padding=padding,
bias=True),
enn.InnerBatchNorm(inter_block_out_type),
activation_fn(inter_block_out_type, inplace=True))
]
for _ in range(n_hidden):
if not only_t:
s_block += [
enn.SequentialModule(
enn.R2Conv(s_block[-1].out_type,
inter_block_out_type,
kernel_size=kernel_size,
padding=padding,
bias=True),
enn.InnerBatchNorm(inter_block_out_type),
activation_fn(inter_block_out_type, inplace=True))
]
t_block += [
enn.SequentialModule(
enn.R2Conv(t_block[-1].out_type,
inter_block_out_type,
kernel_size=kernel_size,
padding=padding,
bias=True),
enn.InnerBatchNorm(inter_block_out_type),
activation_fn(inter_block_out_type, inplace=True))
]
if not only_t:
s_block += [
enn.SequentialModule(
enn.R2Conv(s_block[-1].out_type,
in_type,
kernel_size=kernel_size,
padding=padding,
bias=True), enn.InnerBatchNorm(out_type),
activation_fn(out_type, inplace=True))
]
nets += [MultiInputSequential(*s_block)]
t_block += [
enn.SequentialModule(
enn.R2Conv(t_block[-1].out_type,
in_type,
kernel_size=kernel_size,
padding=padding,
bias=True), enn.InnerBatchNorm(out_type),
activation_fn(out_type, inplace=True))
]
nett += [MultiInputSequential(*t_block)]
t = nett = MultiInputSequential(*nett)
if not only_t:
s = nets = MultiInputSequential(*nets)
return s, t
else:
return t
def __init__(self, input_size, in_type, field_type, out_fiber,
activation_fn, hidden_size, group_action_type):
super(InvariantCNNBlock, self).__init__()
_, self.c, self.h, self.w = input_size
ngf = 16
self.group_action_type = group_action_type
feat_type_in = enn.FieldType(
self.group_action_type,
self.c * [self.group_action_type.trivial_repr])
feat_type_hid = FIBERS[out_fiber](group_action_type,
hidden_size,
field_type,
fixparams=True)
feat_type_out = enn.FieldType(
self.group_action_type,
128 * [self.group_action_type.regular_repr])
# we store the input type for wrapping the images into a geometric tensor during the forward pass
self.input_type = feat_type_in
self.block1 = enn.SequentialModule(
enn.R2Conv(feat_type_in, feat_type_hid, kernel_size=5, padding=0),
enn.InnerBatchNorm(feat_type_hid),
activation_fn(feat_type_hid, inplace=True),
)
self.pool1 = enn.SequentialModule(
enn.PointwiseAvgPoolAntialiased(feat_type_hid,
sigma=0.66,
stride=2))
self.block2 = enn.SequentialModule(
enn.R2Conv(feat_type_hid, feat_type_hid, kernel_size=5),
enn.InnerBatchNorm(feat_type_hid),
activation_fn(feat_type_hid, inplace=True),
)
self.pool2 = enn.SequentialModule(
enn.PointwiseAvgPoolAntialiased(feat_type_hid,
sigma=0.66,
stride=2))
self.block3 = enn.SequentialModule(
enn.R2Conv(feat_type_hid, feat_type_out, kernel_size=3, padding=1),
enn.InnerBatchNorm(feat_type_out),
activation_fn(feat_type_out, inplace=True),
)
self.pool3 = enn.PointwiseAvgPoolAntialiased(feat_type_out,
sigma=0.66,
stride=1,
padding=0)
self.gpool = enn.GroupPooling(feat_type_out)
self.gc = self.gpool.out_type.size
self.gen = torch.nn.Sequential(
torch.nn.ConvTranspose2d(self.gc,
ngf,
kernel_size=4,
stride=1,
padding=0), torch.nn.BatchNorm2d(ngf),
torch.nn.LeakyReLU(0.2),
torch.nn.ConvTranspose2d(ngf,
ngf,
kernel_size=4,
stride=2,
padding=1), torch.nn.BatchNorm2d(ngf),
torch.nn.LeakyReLU(0.2),
torch.nn.ConvTranspose2d(ngf,
int(ngf / 2),
kernel_size=4,
stride=2,
padding=1),
torch.nn.BatchNorm2d(int(ngf / 2)), torch.nn.LeakyReLU(0.2),
torch.nn.ConvTranspose2d(int(ngf / 2),
self.c,
kernel_size=4,
stride=2,
padding=1), torch.nn.Tanh())
