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lambda nets in switched_conv and a vqvae to use it

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This commit is contained in:
James Betker 2021-01-23 14:57:57 -07:00
parent b374dcdd46
commit 10ec6bda1d
2 changed files with 76 additions and 76 deletions
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11
codes/models/switched_conv.py
View File

@ -4,6 +4,7 @@ from collections import OrderedDict
import torch
import torch.nn as nn
from lambda_networks import LambdaLayer
from torch.nn import init, Conv2d
import torch.nn.functional as F
@ -21,6 +22,7 @@ class SwitchedConv(nn.Module):
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_mode: str = 'standard',
coupler_dim_in: int = 0):
super().__init__()
self.in_channels = in_channels
@ -33,7 +35,11 @@ class SwitchedConv(nn.Module):
self.groups = groups
if include_coupler:
if coupler_mode == 'standard':
self.coupler = Conv2d(coupler_dim_in, switch_breadth, kernel_size=1)
elif coupler_mode == 'lambda':
self.coupler = LambdaLayer(dim=coupler_dim_in, dim_out=switch_breadth, r=23, dim_k=16, heads=2, dim_u=1)
else:
self.coupler = None
@ -52,12 +58,15 @@ class SwitchedConv(nn.Module):
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, inp, selector):
def forward(self, inp, selector=None):
if self.coupler:
if selector is None: # A coupler can convert from any input to a selector, so 'None' is allowed.
selector = inp
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")
assert selector is not None
conv_results = []
for i, w in enumerate(self.weights):

139
codes/models/vqvae/weighted_conv_vqvae.py → codes/models/vqvae/vqvae_no_conv_transpose_switched_lambda.py
View File

@ -1,29 +1,25 @@
# Copyright 2018 The Sonnet Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import torch
from torch import nn
from torch.nn import functional as F
import torch.distributed as distributed
from models.vqvae.scaled_weight_conv import ScaledWeightConv, ScaledWeightConvTranspose
from models.switched_conv import SwitchedConv, convert_conv_net_state_dict_to_switched_conv
from trainer.networks import register_model
from utils.util import checkpoint, opt_get
# Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper.
class UpsampleConv(nn.Module):
def __init__(self, in_filters, out_filters, breadth, kernel_size, padding):
super().__init__()
self.conv = SwitchedConv(in_filters, out_filters, kernel_size, breadth, padding=padding, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_filters)
def forward(self, x):
up = torch.nn.functional.interpolate(x, scale_factor=2)
return self.conv(up)
class Quantize(nn.Module):
def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
super().__init__()
@ -82,20 +78,15 @@ class ResBlock(nn.Module):
def __init__(self, in_channel, channel, breadth):
super().__init__()
self.conv = nn.ModuleList([
self.conv = nn.Sequential(
nn.ReLU(inplace=True),
ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth),
SwitchedConv(in_channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
nn.ReLU(inplace=True),
ScaledWeightConv(channel, in_channel, 1, breadth=breadth),
])
SwitchedConv(channel, in_channel, 1, breadth, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel),
)
def forward(self, input, masks):
out = input
for m in self.conv:
if isinstance(m, ScaledWeightConv):
out = m(out, masks)
else:
out = m(out)
def forward(self, input):
out = self.conv(input)
out += input
return out
@ -107,34 +98,29 @@ class Encoder(nn.Module):
if stride == 4:
blocks = [
ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
SwitchedConv(in_channel, channel // 2, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
nn.ReLU(inplace=True),
ScaledWeightConv(channel // 2, channel, 4, stride=2, padding=1, breadth=breadth),
SwitchedConv(channel // 2, channel, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2),
nn.ReLU(inplace=True),
ScaledWeightConv(channel, channel, 3, padding=1, breadth=breadth),
SwitchedConv(channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel),
]
elif stride == 2:
blocks = [
ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
SwitchedConv(in_channel, channel // 2, 5, breadth, stride=2, padding=2, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel),
nn.ReLU(inplace=True),
ScaledWeightConv(channel // 2, channel, 3, padding=1, breadth=breadth),
SwitchedConv(channel // 2, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=channel // 2),
]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
blocks.append(ResBlock(channel, n_res_channel, breadth))
blocks.append(nn.ReLU(inplace=True))
self.blocks = nn.ModuleList(blocks)
self.blocks = nn.Sequential(*blocks)
def forward(self, input):
for block in self.blocks:
if isinstance(block, ScaledWeightConv) or isinstance(block, ResBlock):
input = block(input, self.masks)
else:
input = block(input)
return input
return self.blocks(input)
class Decoder(nn.Module):
@ -143,39 +129,33 @@ class Decoder(nn.Module):
):
super().__init__()
blocks = [ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth)]
blocks = [SwitchedConv(in_channel, channel, 3, breadth, padding=1, include_coupler=True, coupler_mode='lambda', coupler_dim_in=in_channel)]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
blocks.append(ResBlock(channel, n_res_channel, breadth))
blocks.append(nn.ReLU(inplace=True))
if stride == 4:
blocks.extend(
[
ScaledWeightConvTranspose(channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
UpsampleConv(channel, channel // 2, breadth, 5, padding=2),
nn.ReLU(inplace=True),
ScaledWeightConvTranspose(
channel // 2, out_channel, 4, stride=2, padding=1, breadth=breadth
UpsampleConv(
channel // 2, out_channel, breadth, 5, padding=2
),
]
)
elif stride == 2:
blocks.append(
ScaledWeightConvTranspose(channel, out_channel, 4, stride=2, padding=1, breadth=breadth)
UpsampleConv(channel, out_channel, breadth, 5, padding=2)
)
self.blocks = nn.ModuleList(blocks)
self.blocks = nn.Sequential(*blocks)
def forward(self, input):
for block in self.blocks:
if isinstance(block, ScaledWeightConvTranspose) or isinstance(block, ResBlock) \
or isinstance(block, ScaledWeightConv):
input = block(input, self.masks)
else:
input = block(input)
return input
return self.blocks(input)
class VQVAE(nn.Module):
@ -187,22 +167,22 @@ class VQVAE(nn.Module):
n_res_channel=32,
codebook_dim=64,
codebook_size=512,
breadth=8,
decay=0.99,
breadth=4,
):
super().__init__()
self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, breadth=breadth)
self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, breadth=breadth)
self.quantize_conv_t = ScaledWeightConv(channel, codebook_dim, 1, breadth=breadth)
self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
self.quantize_t = Quantize(codebook_dim, codebook_size)
self.dec_t = Decoder(
codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, breadth=breadth
)
self.quantize_conv_b = ScaledWeightConv(codebook_dim + channel, codebook_dim, 1, breadth=breadth)
self.quantize_b = Quantize(codebook_dim, codebook_size)
self.upsample_t = ScaledWeightConvTranspose(
codebook_dim, codebook_dim, 4, stride=2, padding=1, breadth=breadth
self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
self.quantize_b = Quantize(codebook_dim, codebook_size*2)
self.upsample_t = UpsampleConv(
codebook_dim, codebook_dim, breadth, 5, padding=2
)
self.dec = Decoder(
codebook_dim + codebook_dim,
@ -214,21 +194,17 @@ class VQVAE(nn.Module):
breadth=breadth
)
def forward(self, input, masks):
# This awkward injection point is necessary to enable checkpointing to work.
for m in [self.enc_b, self.enc_t, self.dec_t, self.dec]:
m.masks = masks
quant_t, quant_b, diff, _, _ = self.encode(input, masks)
dec = self.decode(quant_t, quant_b, masks)
def forward(self, input):
quant_t, quant_b, diff, _, _ = self.encode(input)
dec = self.decode(quant_t, quant_b)
return dec, diff
def encode(self, input, masks):
def encode(self, input):
enc_b = checkpoint(self.enc_b, input)
enc_t = checkpoint(self.enc_t, enc_b)
quant_t = self.quantize_conv_t(enc_t, masks).permute(0, 2, 3, 1)
quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
quant_t, diff_t, id_t = self.quantize_t(quant_t)
quant_t = quant_t.permute(0, 3, 1, 2)
diff_t = diff_t.unsqueeze(0)
@ -236,15 +212,15 @@ class VQVAE(nn.Module):
dec_t = checkpoint(self.dec_t, quant_t)
enc_b = torch.cat([dec_t, enc_b], 1)
quant_b = self.quantize_conv_b(enc_b, masks).permute(0, 2, 3, 1)
quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
quant_b, diff_b, id_b = self.quantize_b(quant_b)
quant_b = quant_b.permute(0, 3, 1, 2)
diff_b = diff_b.unsqueeze(0)
return quant_t, quant_b, diff_t + diff_b, id_t, id_b
def decode(self, quant_t, quant_b, masks):
upsample_t = self.upsample_t(quant_t, masks)
def decode(self, quant_t, quant_b):
upsample_t = self.upsample_t(quant_t)
quant = torch.cat([upsample_t, quant_b], 1)
dec = checkpoint(self.dec, quant)
@ -256,12 +232,27 @@ class VQVAE(nn.Module):
quant_b = self.quantize_b.embed_code(code_b)
quant_b = quant_b.permute(0, 3, 1, 2)
dec = self.decode(quant_t, quant_b, masks)
dec = self.decode(quant_t, quant_b)
return dec
def convert_weights(weights_file):
sd = torch.load(weights_file)
import models.vqvae.vqvae_no_conv_transpose as stdvq
std_model = stdvq.VQVAE()
std_model.load_state_dict(sd)
nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 4, ['quantize_conv_t', 'quantize_conv_b'])
torch.save(nsd, "converted.pth")
@register_model
def register_weighted_vqvae(opt_net, opt):
def register_vqvae_norm_switched_conv_lambda(opt_net, opt):
kw = opt_get(opt_net, ['kwargs'], {})
return VQVAE(**kw)
if __name__ == '__main__':
#v = VQVAE()
#print(v(torch.randn(1,3,128,128))[0].shape)
convert_weights("../../../experiments/4000_generator.pth")