New switched_conv

codes/models/RRDBNet_arch.py

@@ -6,10 +6,14 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
+from torchvision.models.resnet import Bottleneck
 
 from models.arch_util import make_layer, default_init_weights, ConvGnSilu, ConvGnLelu
+from models.pixel_level_contrastive_learning.resnet_unet_2 import UResNet50_2
+from models.pixel_level_contrastive_learning.resnet_unet_3 import UResNet50_3
 from trainer.networks import register_model
 from utils.util import checkpoint, sequential_checkpoint, opt_get
+from models.switched_conv import SwitchedConv
 
 
 class ResidualDenseBlock(nn.Module):
@@ -303,82 +307,91 @@ class RRDBNet(nn.Module):
                 torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
 
 
-
+class RRDBNetSwitchedConv(nn.Module):
-class DiscRDB(nn.Module):
-    def __init__(self, mid_channels=64, growth_channels=32):
-        super(DiscRDB, self).__init__()
-        for i in range(5):
-            out_channels = mid_channels if i == 4 else growth_channels
-            actnorm = i != 5
-            self.add_module(
-                f'conv{i+1}',
-                ConvGnLelu(mid_channels + i * growth_channels, out_channels, kernel_size=3, norm=actnorm, activation=actnorm, bias=True)
-            )
-            self.lrelu = nn.LeakyReLU(negative_slope=.2)
-        for i in range(5):
-            default_init_weights(getattr(self, f'conv{i+1}'), 1)
-
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return self.lrelu(x5 + x)
-
-
-class DiscRRDB(nn.Module):
-    def __init__(self, mid_channels, growth_channels=32):
-        super(DiscRRDB, self).__init__()
-        self.rdb1 = DiscRDB(mid_channels, growth_channels)
-        self.rdb2 = DiscRDB(mid_channels, growth_channels)
-        self.rdb3 = DiscRDB(mid_channels, growth_channels)
-        self.gn = nn.GroupNorm(num_groups=8, num_channels=mid_channels)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        return self.gn(out + x)
-
-
-class RRDBDiscriminator(nn.Module):
     def __init__(self,
                  in_channels,
+                 out_channels,
                  mid_channels=64,
                  num_blocks=23,
                  growth_channels=32,
-                 blocks_per_checkpoint=1
+                 body_block=RRDB,
+                 blocks_per_checkpoint=1,
+                 scale=4,
+                 initial_stride=1,
+                 use_ref=False,  # When set, a reference image is expected as input and synthesized if not found. Useful for video SR.
+                 resnet_encoder_dict=None
                  ):
-        super(RRDBDiscriminator, self).__init__()
+        super().__init__()
         self.num_blocks = num_blocks
         self.blocks_per_checkpoint = blocks_per_checkpoint
+        self.scale = scale
         self.in_channels = in_channels
-        self.conv_first = ConvGnLelu(in_channels, mid_channels, 3, stride=4, activation=False, norm=False, bias=True)
+        self.use_ref = use_ref
+        first_conv_stride = initial_stride if not self.use_ref else scale
+        first_conv_ksize = 3 if first_conv_stride == 1 else 7
+        first_conv_padding = 1 if first_conv_stride == 1 else 3
+        self.conv_first = nn.Conv2d(in_channels, mid_channels, first_conv_ksize, first_conv_stride, first_conv_padding)
+        self.reduce_ch = mid_channels
+        reduce_to = None
         self.body = make_layer(
-            DiscRRDB,
+            body_block,
             num_blocks,
             mid_channels=mid_channels,
-            growth_channels=growth_channels)
+            growth_channels=growth_channels,
-        self.tail = nn.Sequential(
+            reduce_to=reduce_to)
-            ConvGnLelu(mid_channels, mid_channels // 2, kernel_size=1, activation=True, norm=False, bias=True),
+        self.conv_body = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
-            ConvGnLelu(mid_channels // 2, mid_channels // 4, kernel_size=1, activation=True, norm=False, bias=True),
+        # upsample
-            ConvGnLelu(mid_channels // 4, 1, kernel_size=1, activation=False, norm=False, bias=True)
+        self.conv_up1 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
-        )
+        self.conv_up2 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
-        self.pred_ = None
+        if scale >= 8:
+            self.conv_up3 = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
+        else:
+            self.conv_up3 = None
+        self.conv_hr = SwitchedConv(self.reduce_ch, self.reduce_ch, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
+        self.conv_last = SwitchedConv(self.reduce_ch, out_channels, 3, 8, 1, 1, include_coupler=True, coupler_dim_in=64)
+        
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
 
-    def forward(self, x):
+        self.resnet_encoder = UResNet50_3(Bottleneck, [3, 4, 6, 3], out_dim=64)
-        feat = self.conv_first(x)
+        if resnet_encoder_dict:
+            self.resnet_encoder.load_state_dict(torch.load(resnet_encoder_dict))
+
+        for m in [
+            self.conv_first, self.conv_body, self.conv_up1,
+            self.conv_up2, self.conv_up3, self.conv_hr, self.conv_last
+        ]:
+            if m is not None:
+                default_init_weights(m, 0.1)
+
+    def forward(self, x, ref=None):
+        switch_enc = checkpoint(self.resnet_encoder, F.interpolate(x, scale_factor=2, mode="bilinear"))
+
+        x_lg = x
+        feat = self.conv_first(x_lg)
         feat = sequential_checkpoint(self.body, self.num_blocks // self.blocks_per_checkpoint, feat)
-        pred = checkpoint(self.tail, feat)
+        feat = feat[:, :self.reduce_ch]
-        self.pred_ = pred.detach().clone()
+        body_feat = checkpoint(self.conv_body, feat, switch_enc)
-        return pred
+        feat = feat + body_feat
+
+        # upsample
+        out = self.lrelu(
+            checkpoint(self.conv_up1, F.interpolate(feat, scale_factor=2, mode='nearest'), switch_enc))
+        if self.scale >= 4:
+            out = self.lrelu(
+                checkpoint(self.conv_up2, F.interpolate(out, scale_factor=2, mode='nearest'), switch_enc))
+            if self.scale >= 8:
+                out = self.lrelu(
+                    self.conv_up3(F.interpolate(out, scale_factor=2, mode='nearest'), switch_enc))
+        else:
+            out = self.lrelu(checkpoint(self.conv_up2, out, switch_enc))
+        out = checkpoint(self.conv_hr, out, switch_enc)
+        out = checkpoint(self.conv_last, self.lrelu(out), switch_enc)
+        return out
 
     def visual_dbg(self, step, path):
-        if self.pred_ is not None:
+        for i, bm in enumerate(self.body):
-            self.pred_ = F.sigmoid(self.pred_)
+            if hasattr(bm, 'bypass_map'):
-            torchvision.utils.save_image(self.pred_.cpu().float(), os.path.join(path, "%i_predictions.png" % (step,)))
+                torchvision.utils.save_image(bm.bypass_map.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
 
 
 @register_model
@@ -404,4 +417,16 @@ def register_RRDBNet(opt_net, opt):
     return RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
                                 mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode,
                                 output_mode=output_mode, body_block=RRDB, scale=opt_net['scale'], growth_channels=gc,
-                                initial_stride=initial_stride)
+                                initial_stride=initial_stride)
+
+
+@register_model
+def register_rrdb_switched_conv(opt_net, opt):
+    gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32
+    initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1
+    bypass_noise = opt_get(opt_net, ['bypass_noise'], False)
+    block = functools.partial(RRDBWithBypass, randomly_add_noise_to_bypass=bypass_noise)
+    return RRDBNetSwitchedConv(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
+                                mid_channels=opt_net['nf'], num_blocks=opt_net['nb'],
+                                body_block=block, scale=opt_net['scale'], growth_channels=gc,
+                                initial_stride=initial_stride, resnet_encoder_dict=opt_net['switch_encoder'])

codes/models/switched_conv.py

@@ -0,0 +1,125 @@
+import functools
+import math
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from torch.nn import init, Conv2d
+import torch.nn.functional as F
+
+
+class SwitchedConv(nn.Module):
+    def __init__(self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        switch_breadth: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = 'zeros',
+        include_coupler: bool = False,  # A 'coupler' is a latent converter which can make any bxcxhxw tensor a compatible switchedconv selector by performing a linear 1x1 conv, softmax and interpolate.
+        coupler_dim_in: int = 0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.padding_mode = padding_mode
+        self.groups = groups
+
+        if include_coupler:
+            self.coupler = Conv2d(coupler_dim_in, switch_breadth, kernel_size=1)
+        else:
+            self.coupler = None
+
+        self.weights = nn.ParameterList([nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size)) for _ in range(switch_breadth)])
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        for w in self.weights:
+            init.kaiming_uniform_(w, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weights[0])
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, inp, selector):
+        if self.coupler:
+            selector = F.softmax(self.coupler(selector), dim=1)
+            out_shape = [s // self.stride for s in inp.shape[2:]]
+            if selector.shape[2] != out_shape[0] or selector.shape[3] != out_shape[1]:
+                selector = F.interpolate(selector, size=out_shape, mode="nearest")
+
+        conv_results = []
+        for i, w in enumerate(self.weights):
+            conv_results.append(F.conv2d(inp, w, self.bias, self.stride, self.padding, self.dilation, self.groups) * selector[:, i].unsqueeze(1))
+        return torch.stack(conv_results, dim=-1).sum(dim=-1)
+
+
+
+# Given a state_dict and the module that that sd belongs to, strips out all Conv2d.weight parameters and replaces them
+# with the equivalent SwitchedConv.weight parameters. Does not create coupler params.
+def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_list=[]):
+    state_dict = module.state_dict()
+    for name, m in module.named_modules():
+        ignored = False
+        for smod in ignore_list:
+            if smod in name:
+                ignored = True
+                continue
+        if ignored:
+            continue
+        if isinstance(m, nn.Conv2d):
+            if name == '':
+                basename = 'weight'
+                modname = 'weights'
+            else:
+                basename = f'{name}.weight'
+                modname = f'{name}.weights'
+            cnv_weights = state_dict[basename]
+            del state_dict[basename]
+            for j in range(switch_breadth):
+                state_dict[f'{modname}.{j}'] = cnv_weights.clone()
+    return state_dict
+
+
+def test_net():
+    base_conv = Conv2d(32, 64, 3, stride=2, padding=1, bias=True).to('cuda')
+    mod_conv = SwitchedConv(32, 64, 3, switch_breadth=8, stride=2, padding=1, bias=True, include_coupler=True, coupler_dim_in=128).to('cuda')
+    mod_sd = convert_conv_net_state_dict_to_switched_conv(base_conv, 8)
+    mod_conv.load_state_dict(mod_sd, strict=False)
+    inp = torch.randn((8,32,128,128), device='cuda')
+    sel = torch.randn((8,128,32,32), device='cuda')
+    out1 = base_conv(inp)
+    out2 = mod_conv(inp, sel)
+    assert(torch.max(torch.abs(out1-out2)) < 1e-6)
+
+def perform_conversion():
+    sd = torch.load("../experiments/rrdb_imgset_226500_generator.pth")
+    load_net_clean = OrderedDict()  # remove unnecessary 'module.'
+    for k, v in sd.items():
+        if k.startswith('module.'):
+            load_net_clean[k.replace('module.', '')] = v
+        else:
+            load_net_clean[k] = v
+    sd = load_net_clean
+    import models.RRDBNet_arch as rrdb
+    block = functools.partial(rrdb.RRDBWithBypass)
+    mod = rrdb.RRDBNet(in_channels=3, out_channels=3,
+                                mid_channels=64, num_blocks=23, body_block=block, scale=2, initial_stride=2)
+    mod.load_state_dict(sd)
+    converted = convert_conv_net_state_dict_to_switched_conv(mod, 8, ['body.','conv_first','resnet_encoder'])
+    torch.save(converted, "../experiments/rrdb_imgset_226500_generator_converted.pth")
+
+
+if __name__ == '__main__':
+    perform_conversion()