Restore ChainedEmbeddingGenWithStructure

Still using this guy, after all

codes/models/archs/ChainedEmbeddingGen.py

@@ -52,6 +52,75 @@ class BasicEmbeddingPyramid(nn.Module):
         return x, p
 
 
+
+
+class ChainedEmbeddingGenWithStructure(nn.Module):
+    def __init__(self, in_nc=3, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2):
+        super(ChainedEmbeddingGenWithStructure, self).__init__()
+        self.recurrent = recurrent
+        self.initial_conv = ConvGnLelu(in_nc, 64, kernel_size=7, bias=True, norm=False, activation=False)
+        if recurrent:
+            self.recurrent_nf = recurrent_nf
+            self.recurrent_stride = recurrent_stride
+            self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False)
+            self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
+        self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
+        self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
+        self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)])
+        self.structure_blocks = nn.ModuleList([ConvGnLelu(64, 64, kernel_size=3, bias=False, norm=False, activation=False, weight_init_factor=.1) for i in range(3)])
+        self.structure_upsample = FinalUpsampleBlock2x(64)
+        self.grad_extract = ImageGradientNoPadding()
+        self.upsample = FinalUpsampleBlock2x(64)
+        self.ref_join_std = 0
+
+    def forward(self, x, recurrent=None):
+        fea = self.initial_conv(x)
+        if self.recurrent:
+            if recurrent is None:
+                if self.recurrent_nf == 3:
+                    recurrent = torch.zeros_like(x)
+                    if self.recurrent_stride != 1:
+                        recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest')
+                else:
+                    recurrent = torch.zeros_like(fea)
+            rec = self.recurrent_process(recurrent)
+            fea, recstd = self.recurrent_join(fea, rec)
+            self.ref_join_std = recstd.item()
+        if self.spine is not None:
+            emb = checkpoint(self.spine, fea)
+        else:
+            b,f,h,w = fea.shape
+            emb = (torch.zeros((b,f,h//2,w//2), device=fea.device),
+                   torch.zeros((b,f,h//4,w//4), device=fea.device))
+        grad = fea
+        for i, block in enumerate(self.blocks):
+            fea = fea + checkpoint(block, fea, *emb)[0]
+            if i < 3:
+                structure_br = checkpoint(self.structure_joins[i], grad, fea)
+                grad = grad + checkpoint(self.structure_blocks[i], structure_br)
+        out = checkpoint(self.upsample, fea)
+        return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out), fea
+
+    def get_debug_values(self, step, net_name):
+        return { 'ref_join_std': self.ref_join_std }
+
+
+# This is a structural block that learns to mute regions of a residual transformation given a signal.
+class OptionalPassthroughBlock(nn.Module):
+    def __init__(self, nf, initial_bias=10):
+        super(OptionalPassthroughBlock, self).__init__()
+        self.switch_process = nn.Sequential(ConvGnLelu(nf, nf // 2, 1, activation=False, norm=False, bias=False),
+                                       ConvGnLelu(nf // 2, nf // 4, 1, activation=False, norm=False, bias=False),
+                                       ConvGnLelu(nf // 4, 1, 1, activation=False, norm=False, bias=False))
+        self.bias = nn.Parameter(torch.tensor(initial_bias, dtype=torch.float), requires_grad=True)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, x, switch_signal):
+        switch = self.switch_process(switch_signal)
+        bypass_map = self.activation(self.bias + switch)
+        return x * bypass_map, bypass_map
+
+
 class MultifacetedChainedEmbeddingGen(nn.Module):
     def __init__(self, depth=10, scale=2):
         super(MultifacetedChainedEmbeddingGen, self).__init__()

codes/models/networks.py

@@ -76,6 +76,12 @@ def define_G(opt, net_key='network_G', scale=None):
         netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
                                  multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
                                  init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10, recurrent=recurrent)
+    elif which_model == 'chained_gen_structured':
+        rec = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
+        recnf = opt_net['recurrent_nf'] if 'recurrent_nf' in opt_net.keys() else 3
+        recstd = opt_net['recurrent_stride'] if 'recurrent_stride' in opt_net.keys() else 2
+        in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
+        netG = chained.ChainedEmbeddingGenWithStructure(depth=opt_net['depth'], recurrent=rec, recurrent_nf=recnf, recurrent_stride=recstd, in_nc=in_nc)
     elif which_model == 'multifaceted_chained':
         scale = opt_net['scale'] if 'scale' in opt_net.keys() else 2
         netG = chained.MultifacetedChainedEmbeddingGen(depth=opt_net['depth'], scale=scale)