forked from mrq/DL-Art-School
136 lines
5.4 KiB
Python
136 lines
5.4 KiB
Python
import functools
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import math
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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from lambda_networks import LambdaLayer
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from torch.nn import init, Conv2d
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import torch.nn.functional as F
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class SwitchedConv(nn.Module):
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def __init__(self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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switch_breadth: int,
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stride: int = 1,
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padding: int = 0,
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dilation: int = 1,
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros',
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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.
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coupler_mode: str = 'standard',
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coupler_dim_in: int = 0):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.padding_mode = padding_mode
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self.groups = groups
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if include_coupler:
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if coupler_mode == 'standard':
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self.coupler = Conv2d(coupler_dim_in, switch_breadth, kernel_size=1)
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elif coupler_mode == 'lambda':
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self.coupler = LambdaLayer(dim=coupler_dim_in, dim_out=switch_breadth, r=23, dim_k=16, heads=2, dim_u=1)
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else:
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self.coupler = None
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self.weights = nn.ParameterList([nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size)) for _ in range(switch_breadth)])
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if bias:
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self.bias = nn.Parameter(torch.Tensor(out_channels))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self) -> None:
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for w in self.weights:
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init.kaiming_uniform_(w, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = init._calculate_fan_in_and_fan_out(self.weights[0])
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bound = 1 / math.sqrt(fan_in)
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init.uniform_(self.bias, -bound, bound)
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def forward(self, inp, selector=None):
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if self.coupler:
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if selector is None: # A coupler can convert from any input to a selector, so 'None' is allowed.
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selector = inp
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selector = F.softmax(self.coupler(selector), dim=1)
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self.last_select = selector.detach().clone()
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out_shape = [s // self.stride for s in inp.shape[2:]]
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if selector.shape[2] != out_shape[0] or selector.shape[3] != out_shape[1]:
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selector = F.interpolate(selector, size=out_shape, mode="nearest")
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assert selector is not None
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conv_results = []
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for i, w in enumerate(self.weights):
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conv_results.append(F.conv2d(inp, w, self.bias, self.stride, self.padding, self.dilation, self.groups) * selector[:, i].unsqueeze(1))
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return torch.stack(conv_results, dim=-1).sum(dim=-1)
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# Given a state_dict and the module that that sd belongs to, strips out all Conv2d.weight parameters and replaces them
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# with the equivalent SwitchedConv.weight parameters. Does not create coupler params.
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def convert_conv_net_state_dict_to_switched_conv(module, switch_breadth, ignore_list=[]):
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state_dict = module.state_dict()
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for name, m in module.named_modules():
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ignored = False
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for smod in ignore_list:
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if smod in name:
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ignored = True
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continue
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if ignored:
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continue
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if isinstance(m, nn.Conv2d):
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if name == '':
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basename = 'weight'
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modname = 'weights'
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else:
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basename = f'{name}.weight'
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modname = f'{name}.weights'
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cnv_weights = state_dict[basename]
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del state_dict[basename]
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for j in range(switch_breadth):
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state_dict[f'{modname}.{j}'] = cnv_weights.clone()
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return state_dict
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def test_net():
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base_conv = Conv2d(32, 64, 3, stride=2, padding=1, bias=True).to('cuda')
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mod_conv = SwitchedConv(32, 64, 3, switch_breadth=8, stride=2, padding=1, bias=True, include_coupler=True, coupler_dim_in=128).to('cuda')
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mod_sd = convert_conv_net_state_dict_to_switched_conv(base_conv, 8)
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mod_conv.load_state_dict(mod_sd, strict=False)
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inp = torch.randn((8,32,128,128), device='cuda')
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sel = torch.randn((8,128,32,32), device='cuda')
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out1 = base_conv(inp)
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out2 = mod_conv(inp, sel)
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assert(torch.max(torch.abs(out1-out2)) < 1e-6)
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def perform_conversion():
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sd = torch.load("../experiments/rrdb_imgset_226500_generator.pth")
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load_net_clean = OrderedDict() # remove unnecessary 'module.'
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for k, v in sd.items():
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if k.startswith('module.'):
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load_net_clean[k.replace('module.', '')] = v
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else:
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load_net_clean[k] = v
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sd = load_net_clean
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import models.RRDBNet_arch as rrdb
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block = functools.partial(rrdb.RRDBWithBypass)
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mod = rrdb.RRDBNet(in_channels=3, out_channels=3,
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mid_channels=64, num_blocks=23, body_block=block, scale=2, initial_stride=2)
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mod.load_state_dict(sd)
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converted = convert_conv_net_state_dict_to_switched_conv(mod, 8, ['body.','conv_first','resnet_encoder'])
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torch.save(converted, "../experiments/rrdb_imgset_226500_generator_converted.pth")
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if __name__ == '__main__':
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perform_conversion()
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