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, 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, 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):
super(DenseFeatureExtractionModuleE2Inv, self).__init__()
filters = np.array([32,32, 64,64, 128,128,128, 256,256,256, 512,512,512], dtype=np.int32)*2
# number of rotations to consider for rotation invariance
N = 8
self.gspace = gspaces.Rot2dOnR2(N)
self.input_type = enn.FieldType(self.gspace, [self.gspace.trivial_repr] * 3)
ip_op_types = [
self.input_type,
]
self.num_channels = 64
for filter_ in filters[:10]:
ip_op_types.append(FIELD_TYPE['regular'](self.gspace, filter_, fixparams=False))
self.model = enn.SequentialModule(*[
conv3x3(ip_op_types[0], ip_op_types[1]),
enn.ReLU(ip_op_types[1], inplace=True),
conv3x3(ip_op_types[1], ip_op_types[2]),
enn.ReLU(ip_op_types[2], inplace=True),
enn.PointwiseMaxPool(ip_op_types[2], 2),
conv3x3(ip_op_types[2], ip_op_types[3]),
enn.ReLU(ip_op_types[3], inplace=True),
conv3x3(ip_op_types[3], ip_op_types[4]),
enn.ReLU(ip_op_types[4], inplace=True),
enn.PointwiseMaxPool(ip_op_types[4], 2),
conv3x3(ip_op_types[4], ip_op_types[5]),
enn.ReLU(ip_op_types[5], inplace=True),
conv3x3(ip_op_types[5], ip_op_types[6]),
enn.ReLU(ip_op_types[6], inplace=True),
conv3x3(ip_op_types[6], ip_op_types[7]),
enn.ReLU(ip_op_types[7], inplace=True),
enn.PointwiseAvgPool(ip_op_types[7], kernel_size=2, stride=1),
conv5x5(ip_op_types[7], ip_op_types[8]),
enn.ReLU(ip_op_types[8], inplace=True),
conv5x5(ip_op_types[8], ip_op_types[9]),
enn.ReLU(ip_op_types[9], inplace=True),
conv5x5(ip_op_types[9], ip_op_types[10]),
enn.ReLU(ip_op_types[10], inplace=True),
# enn.PointwiseMaxPool(ip_op_types[7], 2),
# conv3x3(ip_op_types[7], ip_op_types[8]),
# enn.ReLU(ip_op_types[8], inplace=True),
# conv3x3(ip_op_types[8], ip_op_types[9]),
# enn.ReLU(ip_op_types[9], inplace=True),
# conv3x3(ip_op_types[9], ip_op_types[10]),
# enn.ReLU(ip_op_types[10], inplace=True),
enn.GroupPooling(ip_op_types[10])
])
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, input_shape, num_actions, dueling_DQN):
super(D4_steerable_DQN_Snake, self).__init__()
self.input_shape = input_shape
self.num_actions = num_actions
self.dueling_DQN = dueling_DQN
self.r2_act = gspaces.FlipRot2dOnR2(N=4)
self.input_type = nn.FieldType(
self.r2_act, input_shape[0] * [self.r2_act.trivial_repr])
feature1_type = nn.FieldType(self.r2_act,
8 * [self.r2_act.regular_repr])
feature2_type = nn.FieldType(self.r2_act,
12 * [self.r2_act.regular_repr])
feature3_type = nn.FieldType(self.r2_act,
12 * [self.r2_act.regular_repr])
feature4_type = nn.FieldType(self.r2_act,
32 * [self.r2_act.regular_repr])
self.feature_field1 = nn.SequentialModule(
nn.R2Conv(self.input_type,
feature1_type,
kernel_size=7,
padding=2,
stride=2,
bias=False), nn.ReLU(feature1_type, inplace=True))
self.feature_field2 = nn.SequentialModule(
nn.R2Conv(feature1_type,
feature2_type,
kernel_size=5,
padding=1,
stride=2,
bias=False), nn.ReLU(feature2_type, inplace=True))
self.feature_field3 = nn.SequentialModule(
nn.R2Conv(feature2_type,
feature3_type,
kernel_size=5,
padding=1,
stride=1,
bias=False), nn.ReLU(feature3_type, inplace=True))
self.equivariant_features = nn.SequentialModule(
nn.R2Conv(feature3_type,
feature4_type,
kernel_size=5,
stride=1,
bias=False), nn.ReLU(feature4_type, inplace=True))
self.gpool = nn.GroupPooling(feature4_type)
self.feature_shape()
if self.dueling_DQN:
print("You are using Dueling DQN")
self.advantage = torch.nn.Linear(
self.equivariant_features.out_type.size, self.num_actions)
#self.value = torch.nn.Linear(self.gpool.out_type.size, 1)
self.value = torch.nn.Linear(
self.equivariant_features.out_type.size, 1)
else:
self.actionvalue = torch.nn.Linear(
self.equivariant_features.out_type.size, self.num_actions)
def __init__(self,
base='DNSteerableLeNet',
in_chan=1,
n_classes=2,
imsize=150,
kernel_size=5,
N=8,
quiet=True,
number_rotations=None):
super(DNSteerableLeNet, self).__init__()
kernel_size = int(kernel_size)
out_chan = int(n_classes)
if number_rotations != None:
N = int(number_rotations)
z = imsize // 2 // 2
self.r2_act = gspaces.FlipRot2dOnR2(N)
in_type = e2nn.FieldType(self.r2_act, [self.r2_act.trivial_repr])
self.input_type = in_type
out_type = e2nn.FieldType(self.r2_act, 6 * [self.r2_act.regular_repr])
self.mask = e2nn.MaskModule(in_type, imsize, margin=1)
self.conv1 = e2nn.R2Conv(in_type,
out_type,
kernel_size=kernel_size,
padding=kernel_size // 2,
bias=False)
self.relu1 = e2nn.ReLU(out_type, inplace=True)
self.pool1 = e2nn.PointwiseMaxPoolAntialiased(out_type, kernel_size=2)
in_type = self.pool1.out_type
out_type = e2nn.FieldType(self.r2_act, 16 * [self.r2_act.regular_repr])
self.conv2 = e2nn.R2Conv(in_type,
out_type,
kernel_size=kernel_size,
padding=kernel_size // 2,
bias=False)
self.relu2 = e2nn.ReLU(out_type, inplace=True)
self.pool2 = e2nn.PointwiseMaxPoolAntialiased(out_type, kernel_size=2)
self.gpool = e2nn.GroupPooling(out_type)
self.fc1 = nn.Linear(16 * z * z, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, out_chan)
self.drop = nn.Dropout(p=0.5)
# dummy parameter for tracking device
self.dummy = nn.Parameter(torch.empty(0))
def __init__(self, in_type, conv_func):
super(RegressionHead, self).__init__()
gspace = in_type.gspace
self.add_module('gpool', nn.PointwiseAdaptiveMaxPool(in_type, (1, 1)))
if isinstance(in_type.gspace, e2cnn.gspaces.Rot2dOnR2):
base = 8
elif isinstance(in_type.gspace, e2cnn.gspaces.FlipRot2dOnR2):
base = 4
# number of output channels
# Fully Connected
in_type = in_type
out_type = nn.FieldType(gspace, 2 * base * [gspace.regular_repr])
self.add_module(
'block1',
nn.SequentialModule(
conv_func(in_type,
out_type,
kernel_size=1,
padding=0,
bias=False), nn.ReLU(out_type, inplace=True)))
in_type = out_type
if isinstance(gspace, gspaces.Rot2dOnR2):
out_type = nn.FieldType(gspace, [gspace.irrep(1)])
elif isinstance(gspace, gspaces.FlipRot2dOnR2):
out_type = nn.FieldType(gspace, [gspace.irrep(1, 1)])
else:
raise NotImplementedError
self.add_module(
'block2',
conv_func(in_type, out_type, kernel_size=1, padding=0, bias=False))
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_chan, out_chan, imsize, kernel_size=5, N=8):
super(DNRestrictedLeNet, self).__init__()
z = imsize // 2 // 2
self.r2_act = gspaces.FlipRot2dOnR2(N)
in_type = e2nn.FieldType(self.r2_act, [self.r2_act.trivial_repr])
self.input_type = in_type
out_type = e2nn.FieldType(self.r2_act, 6 * [self.r2_act.regular_repr])
self.mask = e2nn.MaskModule(in_type, imsize, margin=1)
self.conv1 = e2nn.R2Conv(in_type,
out_type,
kernel_size=kernel_size,
padding=kernel_size // 2,
bias=False)
self.relu1 = e2nn.ReLU(out_type, inplace=True)
self.pool1 = e2nn.PointwiseMaxPoolAntialiased(out_type, kernel_size=2)
self.gpool = e2nn.GroupPooling(out_type)
self.conv2 = nn.Conv2d(6, 16, kernel_size, padding=kernel_size // 2)
self.fc1 = nn.Linear(16 * z * z, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, out_chan)
self.drop = nn.Dropout(p=0.5)
# dummy parameter for tracking device
self.dummy = nn.Parameter(torch.empty(0))
def __init__(self, in_type, num_classes=10):
super(ClassificationHead, self).__init__()
gspace = in_type.gspace
self.add_module('gpool', nn.GroupPooling(in_type))
# number of output channels
# Fully Connected
in_type = self.gpool.out_type
out_type = nn.FieldType(gspace, 64 * [gspace.trivial_repr])
self.add_module(
'linear1',
sscnn.e2cnn.PlainConv(in_type,
out_type,
kernel_size=1,
padding=0,
bias=False))
self.add_module('relu1', nn.ReLU(out_type, inplace=True))
in_type = out_type
out_type = nn.FieldType(gspace, num_classes * [gspace.trivial_repr])
self.add_module(
'linear2',
sscnn.e2cnn.PlainConv(in_type,
out_type,
kernel_size=1,
padding=0,
bias=False))
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 build_multiscale_classifier(self, input_size):
n, c, h, w = input_size
hidden_shapes = []
for i in range(self.n_scale):
if i < self.n_scale - 1:
c *= 2 if self.factor_out else 4
h //= 2
w //= 2
hidden_shapes.append((n, c, h, w))
classification_heads = []
feat_type_out = FIBERS['regular'](self.group_action_type,
self.classification_hdim,
self.field_type, fixparams=True)
feat_type_mid = FIBERS['regular'](self.group_action_type,
int(self.classification_hdim // 2),
self.field_type, fixparams=True)
feat_type_last = FIBERS['regular'](self.group_action_type,
int(self.classification_hdim // 4),
self.field_type, fixparams=True)
# feat_type_out = enn.FieldType(self.group_action_type,
# self.classification_hdim*[self.group_action_type.regular_repr])
for i, hshape in enumerate(hidden_shapes):
classification_heads.append(
nn.Sequential(
enn.R2Conv(self.input_type, feat_type_out, 5, stride=2),
layers.EquivariantActNorm2d(feat_type_out.size),
enn.ReLU(feat_type_out, inplace=True),
enn.PointwiseAvgPoolAntialiased(feat_type_out, sigma=0.66, stride=2),
enn.R2Conv(feat_type_out, feat_type_mid, kernel_size=3),
layers.EquivariantActNorm2d(feat_type_mid.size),
enn.ReLU(feat_type_mid, inplace=True),
enn.PointwiseAvgPoolAntialiased(feat_type_mid, sigma=0.66, stride=1),
enn.R2Conv(feat_type_mid, feat_type_last, kernel_size=3),
layers.EquivariantActNorm2d(feat_type_last.size),
enn.ReLU(feat_type_last, inplace=True),
enn.PointwiseAvgPoolAntialiased(feat_type_last, sigma=0.66, stride=2),
enn.GroupPooling(feat_type_last),
)
)
self.classification_heads = nn.ModuleList(classification_heads)
self.logit_layer = nn.Linear(classification_heads[-1][-1].out_type.size, self.n_classes)
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, 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, input_shape, num_actions, dueling_DQN, rest_lev):
super(steerable_DQN_Pacman, self).__init__()
self.input_shape = input_shape
self.num_actions = num_actions
self.dueling_DQN = dueling_DQN
self.rest_lev = rest_lev
self.feature_factor = 1
# Scales up the num of fields
self.num_feature_fields = [8, 16, 16, 128]
# Symmetry group
self.r2_act = gspaces.FlipRot2dOnR2(N=4)
# 1st E-Conv
self.input_type = nn.FieldType(
self.r2_act, input_shape[0] * [self.r2_act.trivial_repr])
feature1_type = nn.FieldType(
self.r2_act,
self.num_feature_fields[0] * [self.r2_act.regular_repr])
self.feature_field1 = nn.SequentialModule(
nn.R2Conv(self.input_type,
feature1_type,
kernel_size=7,
padding=2,
stride=2,
bias=False), nn.ReLU(feature1_type, inplace=True),
nn.PointwiseAvgPoolAntialiased(feature1_type, sigma=0.66,
stride=2))
# 2nd E-Conv
self.input_feature_type = feature1_type
if self.rest_lev == 3:
self.restrict1 = self.restrict_layer((0, 2), feature1_type)
self.feature_factor = 2
else:
self.restrict1 = lambda x: x
feature2_type = nn.FieldType(
self.r2_act, self.num_feature_fields[1] * self.feature_factor *
[self.r2_act.regular_repr])
self.feature_field2 = nn.SequentialModule(
nn.R2Conv(self.input_feature_type,
feature2_type,
kernel_size=5,
padding=2,
stride=2,
bias=False), nn.ReLU(feature2_type, inplace=True))
# 3rd E-Conv
self.input_feature_type = feature2_type
if self.rest_lev == 2:
self.restrict2 = self.restrict_layer((0, 1), feature2_type)
self.feature_factor = 4
else:
self.restrict2 = lambda x: x
feature3_type = nn.FieldType(
self.r2_act, self.num_feature_fields[2] * self.feature_factor *
[self.r2_act.regular_repr])
self.feature_field3 = nn.SequentialModule(
nn.R2Conv(self.input_feature_type,
feature3_type,
kernel_size=5,
padding=1,
stride=2,
bias=False), nn.ReLU(feature3_type, inplace=True))
# 4th E-Conv
self.input_feature_type = feature3_type
if rest_lev == 1:
self.restrict_extra = self.restrict_layer((0, 1), feature3_type)
self.feature_factor = 4
else:
self.restrict_extra = lambda x: x
feature4_type = nn.FieldType(
self.r2_act, self.num_feature_fields[3] * self.feature_factor *
[self.r2_act.regular_repr])
self.feature_field_extra = nn.SequentialModule(
nn.R2Conv(self.input_feature_type,
feature4_type,
kernel_size=5,
padding=0,
bias=True), nn.ReLU(feature4_type, inplace=True))
_, _ = self.feature_shape()
self.out_size = self.feature_field_extra.out_type.size
# Final linear layer
if self.dueling_DQN:
print("You are using Dueling DQN")
self.advantage = torch.nn.Linear(self.out_size, self.num_actions)
self.value = torch.nn.Linear(self.out_size, 1)
else:
self.actionvalue = torch.nn.Linear(self.out_size, self.num_actions)
def __init__(self,
hidden_reps_ids,
kernel_sizes,
dim_cov_est,
context_rep_ids=[1],
N=4,
flip=False,
non_linearity=["NormReLU"],
max_frequency=30):
'''
Input: hidden_reps_ids - list: encoding the hidden fiber representation (see give_fib_reps_from_ids)
kernel_sizes - list of ints - sizes of kernels for convolutional layers
dim_cov_est - dimension of covariance estimation, either 1,2,3 or 4
context_rep_ids - list: gives the input fiber representation (see give_fib_reps_from_ids)
non_linearity - list of strings - gives names of non-linearity to be used
Either length 1 (then same non-linearity for all)
or length is the number of layers (giving a custom non-linearity for every
layer)
N - int - gives the group order, -1 is infinite
flip - Bool - indicates whether we have a flip in the rotation group (i.e.O(2) vs SO(2), D_N vs C_N)
max_frequency - int - maximum irrep frequency to computed, only relevant if N=-1
'''
super(SteerDecoder, self).__init__()
#Save the rotation group, if flip is true, then include all corresponding reflections:
self.flip = flip
self.max_frequency = max_frequency
if self.flip:
self.G_act = gspaces.FlipRot2dOnR2(
N=N) if N != -1 else gspaces.FlipRot2dOnR2(
N=N, maximum_frequency=self.max_frequency)
#The output fiber representation is the identity:
self.target_rep = self.G_act.irrep(1, 1)
else:
self.G_act = gspaces.Rot2dOnR2(
N=N) if N != -1 else gspaces.Rot2dOnR2(
N=N, maximum_frequency=self.max_frequency)
#The output fiber representation is the identity:
self.target_rep = self.G_act.irrep(1)
#Save the N defining D_N or C_N (if N=-1 it is infinity):
self.polygon_corners = N
#Save the id's for the context representation and extract the context fiber representation:
self.context_rep_ids = context_rep_ids
self.context_rep = group.directsum(
self.give_reps_from_ids(self.context_rep_ids))
#Save the parameters:
self.kernel_sizes = kernel_sizes
self.n_layers = len(hidden_reps_ids) + 2
self.hidden_reps_ids = hidden_reps_ids
self.dim_cov_est = dim_cov_est
#-----CREATE LIST OF NON-LINEARITIES----
if len(non_linearity) == 1:
self.non_linearity = (self.n_layers - 2) * non_linearity
elif len(non_linearity) != (self.n_layers - 2):
sys.exit(
"List of non-linearities invalid: must have either length 1 or n_layers-2"
)
else:
self.non_linearity = non_linearity
#-----ENDE LIST OF NON-LINEARITIES----
#-----------CREATE DECODER-----------------
'''
Create a list of layers based on the kernel sizes. Compute the padding such
that the height h and width w of a tensor with shape (batch_size,n_channels,h,w) does not change
while being passed through the decoder
'''
#Create list of feature types:
feat_types = self.give_feat_types()
self.feature_emb = feat_types[0]
self.feature_out = feat_types[-1]
#Create layers list and append it:
layers_list = [
G_CNN.R2Conv(feat_types[0],
feat_types[1],
kernel_size=kernel_sizes[0],
padding=(kernel_sizes[0] - 1) // 2)
]
for it in range(self.n_layers - 2):
if self.non_linearity[it] == "ReLU":
layers_list.append(G_CNN.ReLU(feat_types[it + 1],
inplace=True))
elif self.non_linearity[it] == "NormReLU":
layers_list.append(G_CNN.NormNonLinearity(feat_types[it + 1]))
else:
sys.exit("Unknown non-linearity.")
layers_list.append(
G_CNN.R2Conv(feat_types[it + 1],
feat_types[it + 2],
kernel_size=kernel_sizes[it],
padding=(kernel_sizes[it] - 1) // 2))
#Create a steerable decoder out of the layers list:
self.decoder = G_CNN.SequentialModule(*layers_list)
#-----------END CREATE DECODER---------------
#-----------CONTROL INPUTS------------------
#Control that all kernel sizes are odd (otherwise output shape is not correct):
if any([j % 2 - 1 for j in kernel_sizes]):
sys.exit("All kernels need to have odd sizes")
if len(kernel_sizes) != (self.n_layers - 1):
sys.exit("Number of layers and number kernels do not match.")
if len(self.non_linearity) != (self.n_layers - 2):
sys.exit(
"Number of layers and number of non-linearities do not match.")
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 ennReLU(inplanes):
in_type = FIELD_TYPE['regular'](gspace, inplanes)
return enn.ReLU(in_type, inplace=True)
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, 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}")
return x
if __name__ == "__main__":
r2_act = gspaces.Rot2dOnR2(N=8)
# the input image is a scalar field, corresponding to the trivial representation
in_type = nn.FieldType(r2_act, 3 * [r2_act.trivial_repr])
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dummy = torch.rand(64, 3, 32, 32).to(device)
y = nn.GeometricTensor(dummy, in_type)
# TEST 2
r2_act = gspaces.Rot2dOnR2(N=16) # 5
print(r2_act.trivial_repr)
feat_type_in = nn.FieldType(r2_act, 3 * [r2_act.trivial_repr]) # 6
feat_type_out = nn.FieldType(r2_act, 10 * [r2_act.regular_repr]) # 7
# 8
conv = nn.R2Conv(feat_type_in, feat_type_out, kernel_size=5) # 9
relu = nn.ReLU(feat_type_out) # 10
# 11
x = torch.randn(16, 3, 32, 32) # 12
x = nn.GeometricTensor(x, feat_type_in) # 13
# 14
conv_x = conv(x)
print(conv_x.size())
y = relu(conv_x)
print(y.size())
def __init__(self, conv_func, group, in_channels):
super(Backbone5x5, self).__init__()
# the model is equivariant under rotations by 45 degrees, modelled by C8
# self.r2_act = gspaces.Rot2dOnR2(N=8)
self.r2_act = group
# the input image is a scalar field, corresponding to the trivial representation
in_type = nn.FieldType(self.r2_act,
in_channels * [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
if isinstance(in_type.gspace, e2cnn.gspaces.Rot2dOnR2):
base = 8
elif isinstance(in_type.gspace, e2cnn.gspaces.FlipRot2dOnR2):
base = 4
# 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, 16 * [self.r2_act.regular_repr])
self.add_module(
'block1',
nn.SequentialModule(
# nn.MaskModule(in_type, 29, margin=1),
conv_func(in_type,
out_type,
kernel_size=5,
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 = out_type
# the output type of the second convolution layer are 32 regular feature fields of C8
out_type = nn.FieldType(self.r2_act,
3 * base * [self.r2_act.regular_repr])
self.add_module(
'block2',
nn.SequentialModule(
conv_func(in_type,
out_type,
kernel_size=5,
padding=2,
bias=False),
# nn.InnerBatchNorm(out_type),
nn.ReLU(out_type, inplace=True)))
self.add_module(
'pool1',
nn.SequentialModule(
nn.PointwiseMaxPool(out_type, kernel_size=3, stride=2)))
# convolution 3
# the old output type is the input type to the next layer
in_type = out_type
# the output type of the third convolution layer are 32 regular feature fields of C8
out_type = nn.FieldType(self.r2_act,
4 * base * [self.r2_act.regular_repr])
self.add_module(
'block3',
nn.SequentialModule(
conv_func(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 = out_type
# the output type of the fourth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act,
6 * base * [self.r2_act.regular_repr])
self.add_module(
'block4',
nn.SequentialModule(
conv_func(in_type,
out_type,
kernel_size=5,
padding=2,
bias=False),
# nn.InnerBatchNorm(out_type),
nn.ReLU(out_type, inplace=True)))
self.add_module(
'pool2',
nn.SequentialModule(
nn.PointwiseMaxPool(out_type, kernel_size=3, stride=2)))
# convolution 5
# the old output type is the input type to the next layer
in_type = out_type
# the output type of the fifth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act,
8 * base * [self.r2_act.regular_repr])
self.add_module(
'block5',
nn.SequentialModule(
conv_func(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 = out_type
# the output type of the sixth convolution layer are 64 regular feature fields of C8
out_type = nn.FieldType(self.r2_act,
12 * base * [self.r2_act.regular_repr])
self.add_module(
'block6',
nn.SequentialModule(
conv_func(in_type,
out_type,
kernel_size=5,
padding=1,
bias=False),
# nn.InnerBatchNorm(out_type),
nn.ReLU(out_type, inplace=True)))
self.add_module(
'pool3',
nn.PointwiseMaxPool(out_type, kernel_size=3, stride=1, padding=0))
self.out_type = out_type
