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, 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, 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, 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):
super(ModelDilated, self).__init__()
N = 8
self.gspace = gspaces.Rot2dOnR2(N)
self.in_type = enn.FieldType(self.gspace,
[self.gspace.trivial_repr] * 3)
self.out_type = enn.FieldType(self.gspace,
[self.gspace.regular_repr] * 16)
self.layer = enn.R2Conv(
self.in_type,
self.out_type,
3,
stride=1,
padding=2,
dilation=2,
bias=True,
)
self.invariant = enn.GroupPooling(self.out_type)
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,
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):
super(UNet, self).__init__()
self.r2_act = gspaces.Rot2dOnR2(N=8)
self.field_type_1 = enn.FieldType(self.r2_act,
1 * [self.r2_act.regular_repr])
self.field_type_3 = enn.FieldType(self.r2_act,
3 * [self.r2_act.trivial_repr])
self.field_type_8 = enn.FieldType(self.r2_act,
8 * [self.r2_act.regular_repr])
self.field_type_16 = enn.FieldType(self.r2_act,
16 * [self.r2_act.regular_repr])
self.field_type_32 = enn.FieldType(self.r2_act,
32 * [self.r2_act.regular_repr])
self.field_type_64 = enn.FieldType(self.r2_act,
64 * [self.r2_act.regular_repr])
self.field_type_128 = enn.FieldType(self.r2_act,
128 * [self.r2_act.regular_repr])
self.conv1 = Conv(in_type=self.field_type_3,
out_type=self.field_type_8)
self.conv2 = Conv(in_type=self.field_type_8,
out_type=self.field_type_8)
self.down1 = DownSample(in_type=self.field_type_8,
out_type=self.field_type_16)
self.conv12 = Conv(in_type=self.field_type_16,
out_type=self.field_type_16)
self.down2 = DownSample(in_type=self.field_type_16,
out_type=self.field_type_32)
self.conv22 = Conv(in_type=self.field_type_32,
out_type=self.field_type_32)
self.down3 = DownSample(in_type=self.field_type_32,
out_type=self.field_type_32)
self.conv32 = Conv(in_type=self.field_type_32,
out_type=self.field_type_32)
self.up41 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_32,
mid_type=self.field_type_32)
self.up31 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_16,
mid_type=self.field_type_32)
self.up21 = UpSample(in_type=self.field_type_32,
out_type=self.field_type_8,
mid_type=self.field_type_16)
self.up11 = LastConcat(in_type=self.field_type_16,
out_type=self.field_type_1,
mid_type=self.field_type_8)
self.up42 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_32,
mid_type=self.field_type_32)
self.up32 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_16,
mid_type=self.field_type_32)
self.up22 = UpSample(in_type=self.field_type_32,
out_type=self.field_type_8,
mid_type=self.field_type_16)
self.up12 = LastConcat(in_type=self.field_type_16,
out_type=self.field_type_1,
mid_type=self.field_type_8)
self.up43 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_32,
mid_type=self.field_type_32)
self.up33 = UpSample(in_type=self.field_type_64,
out_type=self.field_type_16,
mid_type=self.field_type_32)
self.up23 = UpSample(in_type=self.field_type_32,
out_type=self.field_type_8,
mid_type=self.field_type_16)
self.up13 = LastConcat(in_type=self.field_type_16,
out_type=self.field_type_1,
mid_type=self.field_type_8)
self.gpool1 = enn.GroupPooling(self.field_type_1)
self.gpool2 = enn.GroupPooling(self.field_type_1)
self.gpool3 = enn.GroupPooling(self.field_type_1)
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, 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())
