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())