File size: 6,389 Bytes
3ce19a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | import os
import sys
from collections import defaultdict
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from mapping_network import MappingNetowrk, AdaptiveInstanceNorm, NoiseInjection
from helpers.imle_helpers import (
get_1x1,
get_3x3,
draw_gaussian_diag_samples,
gaussian_analytical_kl,
)
from rtm_core import RTMMappingNetwork
class Block(nn.Module):
def __init__(self, in_width, middle_width, out_width, down_rate=None, residual=False, use_3x3=True, zero_last=False):
super().__init__()
self.down_rate = down_rate
self.residual = residual
self.c1 = get_1x1(in_width, middle_width)
self.c2 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1(
middle_width, middle_width)
self.c3 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1(
middle_width, middle_width)
self.c4 = get_1x1(middle_width, out_width, zero_weights=zero_last)
def forward(self, x):
xhat = self.c1(F.gelu(x))
xhat = self.c2(F.gelu(xhat))
xhat = self.c3(F.gelu(xhat))
xhat = self.c4(F.gelu(xhat))
out = x + xhat if self.residual else xhat
if self.down_rate is not None:
out = F.avg_pool2d(
out, kernel_size=self.down_rate, stride=self.down_rate)
return out
def parse_layer_string(s):
layers = []
for ss in s.split(','):
if 'x' in ss:
res, num = ss.split('x')
count = int(num)
layers += [(int(res), None) for _ in range(count)]
elif 'm' in ss:
res, mixin = [int(a) for a in ss.split('m')]
layers.append((res, mixin))
elif 'd' in ss:
res, down_rate = [int(a) for a in ss.split('d')]
layers.append((res, down_rate))
else:
res = int(ss)
layers.append((res, None))
return layers
def pad_channels(t, width):
d1, d2, d3, d4 = t.shape
empty = torch.zeros(d1, width, d3, d4, device=t.device)
empty[:, :d2, :, :] = t
return empty
def get_width_settings(width, s):
mapping = defaultdict(lambda: width)
if s:
s = s.split(',')
for ss in s:
k, v = ss.split(':')
mapping[int(k)] = int(v)
return mapping
class DecBlock(nn.Module):
def __init__(self, H, res, mixin, n_blocks):
super().__init__()
self.base = res
self.mixin = mixin
self.H = H
self.widths = get_width_settings(H.width, H.custom_width_str)
width = self.widths[res]
if res <= H.max_hierarchy:
self.noise = NoiseInjection(width)
self.adaIN = AdaptiveInstanceNorm(width, H.latent_dim)
use_3x3 = res > 2
cond_width = int(width * H.bottleneck_multiple)
self.resnet = Block(width, cond_width, width,
residual=True, use_3x3=use_3x3)
self.resnet.c4.weight.data *= np.sqrt(1 / n_blocks)
def forward(self, x, w, spatial_noise):
if self.mixin is not None:
x = F.interpolate(x, scale_factor=self.base // self.mixin)
if self.base <= self.H.max_hierarchy:
x = self.noise(x, spatial_noise)
x = self.adaIN(x, w)
x = self.resnet(x)
return x
class Decoder(nn.Module):
def __init__(self, H):
super().__init__()
self.H = H
self.use_rtm = getattr(H, "use_rtm", False)
if self.use_rtm:
self.mapping_network = RTMMappingNetwork(
code_dim=H.latent_dim,
num_tokens=getattr(H, "num_tokens", 1),
H_cycles=getattr(H, "H_cycles", 1),
L_cycles=getattr(H, "L_cycles", 1),
H_layers=getattr(H, "H_layers", 2),
L_layers=getattr(H, "L_layers", 2),
hidden_size=getattr(H, "rtm_hidden_size", 256),
expansion=getattr(H, "rtm_expansion", 4.0),
refinement_steps=getattr(H, "refinement_steps", 1),
with_grad=getattr(H, "rtm_with_grad", False),
cycle_noise_std=getattr(H, "rtm_cycle_noise_std", 0.0),
)
else:
self.mapping_network = MappingNetowrk(
code_dim=H.latent_dim, n_mlp=H.n_mpl)
resos = set()
cond_width = int(H.width * H.bottleneck_multiple)
dec_blocks = []
self.widths = get_width_settings(H.width, H.custom_width_str)
blocks = parse_layer_string(H.dec_blocks)
for idx, (res, mixin) in enumerate(blocks):
dec_blocks.append(DecBlock(H, res, mixin, n_blocks=len(blocks)))
resos.add(res)
self.resolutions = sorted(resos)
self.dec_blocks = nn.ModuleList(dec_blocks)
first_res = self.resolutions[0]
self.constant = nn.Parameter(torch.randn(
1, self.widths[first_res], first_res, first_res))
self.resnet = get_1x1(H.width, H.image_channels)
self.gain = nn.Parameter(torch.ones(1, H.image_channels, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, H.image_channels, 1, 1))
def forward(self, latent_code, spatial_noise, input_is_w=False):
if not input_is_w:
ws = self.mapping_network(latent_code)
else:
ws = [latent_code]
x = self.constant.repeat(latent_code.shape[0], 1, 1, 1)
num_blocks = len(self.dec_blocks)
if len(ws) == 1:
w = ws[0]
for idx, block in enumerate(self.dec_blocks):
x = block(x, w, None)
else:
# Multi-scale w injection: reversed so refined (last cycle) goes
# to early/low-res blocks, diverse (first cycle) to late/high-res.
ws_rev = list(reversed(ws))
num_ws = len(ws_rev)
for idx, block in enumerate(self.dec_blocks):
w_idx = min(int(idx * num_ws / num_blocks), num_ws - 1)
x = block(x, ws_rev[w_idx], None)
x = self.resnet(x)
x = self.gain * x + self.bias
return x
class IMLE(nn.Module):
def __init__(self, H):
super().__init__()
self.dci_db = None
self.decoder = Decoder(H)
def forward(self, latents, spatial_noise=None, input_is_w=False):
return self.decoder.forward(latents, spatial_noise, input_is_w)
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