forked from mrq/DL-Art-School
294 lines
10 KiB
Python
294 lines
10 KiB
Python
import os
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import torch
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import torchvision
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from torch import nn
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from torch.nn import functional as F
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import torch.distributed as distributed
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from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \
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convert_conv_net_state_dict_to_switched_conv
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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# Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper.
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class UpsampleConv(nn.Module):
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def __init__(self, in_filters, out_filters, breadth, kernel_size, padding):
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super().__init__()
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self.conv = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth, include_coupler=True, coupler_mode='standard', coupler_dim_in=in_filters, dropout_rate=0.4)
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def forward(self, x):
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up = torch.nn.functional.interpolate(x, scale_factor=2)
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return self.conv(up)
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class Quantize(nn.Module):
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def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
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super().__init__()
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self.dim = dim
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self.n_embed = n_embed
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self.decay = decay
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self.eps = eps
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embed = torch.randn(dim, n_embed)
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self.register_buffer("embed", embed)
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self.register_buffer("cluster_size", torch.zeros(n_embed))
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self.register_buffer("embed_avg", embed.clone())
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def forward(self, input):
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flatten = input.reshape(-1, self.dim)
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dist = (
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flatten.pow(2).sum(1, keepdim=True)
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- 2 * flatten @ self.embed
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+ self.embed.pow(2).sum(0, keepdim=True)
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)
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_, embed_ind = (-dist).max(1)
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embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
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embed_ind = embed_ind.view(*input.shape[:-1])
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quantize = self.embed_code(embed_ind)
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if self.training:
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embed_onehot_sum = embed_onehot.sum(0)
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embed_sum = flatten.transpose(0, 1) @ embed_onehot
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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distributed.all_reduce(embed_onehot_sum)
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distributed.all_reduce(embed_sum)
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self.cluster_size.data.mul_(self.decay).add_(
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embed_onehot_sum, alpha=1 - self.decay
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)
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self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
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n = self.cluster_size.sum()
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cluster_size = (
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(self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
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)
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embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
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self.embed.data.copy_(embed_normalized)
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diff = (quantize.detach() - input).pow(2).mean()
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quantize = input + (quantize - input).detach()
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return quantize, diff, embed_ind
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def embed_code(self, embed_id):
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return F.embedding(embed_id, self.embed.transpose(0, 1))
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class ResBlock(nn.Module):
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def __init__(self, in_channel, channel, breadth):
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super().__init__()
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self.conv = nn.Sequential(
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channel, channel, 3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(channel, in_channel, 1),
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)
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def forward(self, input):
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out = self.conv(input)
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out += input
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return out
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class Encoder(nn.Module):
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def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, breadth):
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super().__init__()
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if stride == 4:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 5, breadth, stride=2, include_coupler=True, coupler_mode='standard', coupler_dim_in=channel // 2, dropout_rate=0.4),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel, channel, 3, breadth, include_coupler=True, coupler_mode='standard', coupler_dim_in=channel, dropout_rate=0.4),
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]
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elif stride == 2:
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blocks = [
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nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
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nn.ReLU(inplace=True),
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SwitchedConvHardRouting(channel // 2, channel, 3, breadth, include_coupler=True, coupler_mode='standard', coupler_dim_in=channel // 2, dropout_rate=0.4),
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]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, breadth))
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blocks.append(nn.ReLU(inplace=True))
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class Decoder(nn.Module):
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def __init__(
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self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, breadth
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):
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super().__init__()
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blocks = [SwitchedConvHardRouting(in_channel, channel, 3, breadth, include_coupler=True, coupler_mode='standard', coupler_dim_in=in_channel, dropout_rate=0.4)]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, breadth))
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blocks.append(nn.ReLU(inplace=True))
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if stride == 4:
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blocks.extend(
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[
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UpsampleConv(channel, channel // 2, breadth, 5, padding=2),
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nn.ReLU(inplace=True),
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UpsampleConv(
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channel // 2, out_channel, breadth, 5, padding=2
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),
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]
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)
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elif stride == 2:
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blocks.append(
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UpsampleConv(channel, out_channel, breadth, 5, padding=2)
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)
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class VQVAE(nn.Module):
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def __init__(
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self,
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in_channel=3,
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channel=128,
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n_res_block=2,
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n_res_channel=32,
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codebook_dim=64,
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codebook_size=512,
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decay=0.99,
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breadth=8,
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):
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super().__init__()
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self.breadth = breadth
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self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, breadth=breadth)
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self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, breadth=breadth)
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self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
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self.quantize_t = Quantize(codebook_dim, codebook_size)
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self.dec_t = Decoder(
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codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, breadth=breadth
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)
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self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
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self.quantize_b = Quantize(codebook_dim, codebook_size*2)
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self.upsample_t = UpsampleConv(
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codebook_dim, codebook_dim, breadth, 5, padding=2
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)
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self.dec = Decoder(
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codebook_dim + codebook_dim,
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in_channel,
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channel,
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n_res_block,
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n_res_channel,
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stride=4,
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breadth=breadth
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)
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def forward(self, input):
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quant_t, quant_b, diff, _, _ = self.encode(input)
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dec = self.decode(quant_t, quant_b)
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return dec, diff
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def save_attention_to_image_rgb(self, output_file, attention_out, attention_size, cmap_discrete_name='viridis'):
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from matplotlib import cm
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magnitude, indices = torch.topk(attention_out, 3, dim=1)
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indices = indices.cpu()
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colormap = cm.get_cmap(cmap_discrete_name, attention_size)
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img = torch.tensor(colormap(indices[:, 0, :, :].detach().numpy())) # TODO: use other k's
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img = img.permute((0, 3, 1, 2))
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torchvision.utils.save_image(img, output_file)
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def visual_dbg(self, step, path):
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convs = [self.dec.blocks[-1].conv, self.dec_t.blocks[-1].conv, self.enc_b.blocks[-4], self.enc_t.blocks[-4]]
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for i, c in enumerate(convs):
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self.save_attention_to_image_rgb(os.path.join(path, "%i_selector_%i.png" % (step, i+1)), c.last_select, self.breadth)
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def get_debug_values(self, step, __):
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switched_convs = [('enc_b_blk2', self.enc_b.blocks[2]),
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('enc_b_blk4', self.enc_b.blocks[4]),
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('enc_t_blk2', self.enc_t.blocks[2]),
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('dec_t_blk0', self.dec_t.blocks[0]),
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('dec_t_blk-1', self.dec_t.blocks[-1].conv),
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('dec_blk0', self.dec.blocks[0]),
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('dec_blk-1', self.dec.blocks[-1].conv),
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('dec_blk-3', self.dec.blocks[-3].conv)]
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logs = {}
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for name, swc in switched_convs:
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logs[f'{name}_histogram_switch_usage'] = swc.latest_masks
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return logs
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def encode(self, input):
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enc_b = checkpoint(self.enc_b, input)
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enc_t = checkpoint(self.enc_t, enc_b)
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quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
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quant_t, diff_t, id_t = self.quantize_t(quant_t)
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quant_t = quant_t.permute(0, 3, 1, 2)
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diff_t = diff_t.unsqueeze(0)
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dec_t = checkpoint(self.dec_t, quant_t)
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enc_b = torch.cat([dec_t, enc_b], 1)
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quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
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quant_b, diff_b, id_b = self.quantize_b(quant_b)
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quant_b = quant_b.permute(0, 3, 1, 2)
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diff_b = diff_b.unsqueeze(0)
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return quant_t, quant_b, diff_t + diff_b, id_t, id_b
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def decode(self, quant_t, quant_b):
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upsample_t = self.upsample_t(quant_t)
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quant = torch.cat([upsample_t, quant_b], 1)
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dec = checkpoint(self.dec, quant)
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return dec
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def decode_code(self, code_t, code_b):
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quant_t = self.quantize_t.embed_code(code_t)
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quant_t = quant_t.permute(0, 3, 1, 2)
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quant_b = self.quantize_b.embed_code(code_b)
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quant_b = quant_b.permute(0, 3, 1, 2)
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dec = self.decode(quant_t, quant_b)
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return dec
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def convert_weights(weights_file):
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sd = torch.load(weights_file)
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import models.vqvae.vqvae_no_conv_transpose as stdvq
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std_model = stdvq.VQVAE()
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std_model.load_state_dict(sd)
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nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 8, ['quantize_conv_t', 'quantize_conv_b',
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'enc_b.blocks.0', 'enc_t.blocks.0',
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'conv.1', 'conv.3'])
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torch.save(nsd, "converted.pth")
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@register_model
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def register_vqvae_norm_hard_switched_conv_lambda(opt_net, opt):
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kw = opt_get(opt_net, ['kwargs'], {})
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return VQVAE(**kw)
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if __name__ == '__main__':
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v = VQVAE(breadth=8).cuda()
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print(v(torch.randn(1,3,128,128).cuda())[0].shape)
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#convert_weights("../../../experiments/50000_generator.pth")
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