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# Copyright (c) SenseTime Research. All rights reserved.
from legacy import save_obj, load_pkl
import torch
from torch.nn import functional as F
import pandas as pd
from .edit_config import attr_dict
import os
def conv_warper(layer, input, style, noise):
# the conv should change
conv = layer.conv
batch, in_channel, height, width = input.shape
style = style.view(batch, 1, in_channel, 1, 1)
weight = conv.scale * conv.weight * style
if conv.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
weight = weight * demod.view(batch, conv.out_channel, 1, 1, 1)
weight = weight.view(
batch * conv.out_channel, in_channel, conv.kernel_size, conv.kernel_size
)
if conv.upsample:
input = input.view(1, batch * in_channel, height, width)
weight = weight.view(
batch, conv.out_channel, in_channel, conv.kernel_size, conv.kernel_size
)
weight = weight.transpose(1, 2).reshape(
batch * in_channel, conv.out_channel, conv.kernel_size, conv.kernel_size
)
out = F.conv_transpose2d(
input, weight, padding=0, stride=2, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, conv.out_channel, height, width)
out = conv.blur(out)
elif conv.downsample:
input = conv.blur(input)
_, _, height, width = input.shape
input = input.view(1, batch * in_channel, height, width)
out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, conv.out_channel, height, width)
else:
input = input.view(1, batch * in_channel, height, width)
out = F.conv2d(input, weight, padding=conv.padding, groups=batch)
_, _, height, width = out.shape
out = out.view(batch, conv.out_channel, height, width)
out = layer.noise(out, noise=noise)
out = layer.activate(out)
return out
def decoder(G, style_space, latent, noise):
# an decoder warper for G
out = G.input(latent)
out = conv_warper(G.conv1, out, style_space[0], noise[0])
skip = G.to_rgb1(out, latent[:, 1])
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(
G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
):
out = conv_warper(conv1, out, style_space[i], noise=noise1)
out = conv_warper(conv2, out, style_space[i+1], noise=noise2)
skip = to_rgb(out, latent[:, i + 2], skip)
i += 2
image = skip
return image
def encoder_ifg(G, noise, attr_name, truncation=1, truncation_latent=None,
latent_dir='latent_direction/ss/',
step=0, total=0, real=False):
if not real:
styles = [noise]
styles = [G.style(s) for s in styles]
style_space = []
if truncation < 1:
if not real:
style_t = []
for style in styles:
style_t.append(truncation_latent + truncation *
(style - truncation_latent))
styles = style_t
else: # styles are latent (tensor: 1,18,512), for real PTI output
truncation_latent = truncation_latent.repeat(
18, 1).unsqueeze(0) # (1,512) --> (1,18,512)
styles = torch.add(truncation_latent, torch.mul(
torch.sub(noise, truncation_latent), truncation))
noise = [getattr(G.noises, 'noise_{}'.format(i))
for i in range(G.num_layers)]
if not real:
inject_index = G.n_latent
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else:
latent = styles
style_space.append(G.conv1.conv.modulation(latent[:, 0]))
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(
G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
):
style_space.append(conv1.conv.modulation(latent[:, i]))
style_space.append(conv2.conv.modulation(latent[:, i+1]))
i += 2
# get layer, strength by dict
strength = attr_dict['interface_gan'][attr_name][0]
if step != 0 and total != 0:
strength = step / total * strength
for i in range(15):
style_vect = load_pkl(os.path.join(
latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, i)))
style_vect = torch.from_numpy(style_vect).to(latent.device).float()
style_space[i] += style_vect * strength
return style_space, latent, noise
def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None,
statics_dir="latent_direction/ss_statics",
latent_dir="latent_direction/ss/",
step=0, total=0, real=False):
if not real:
styles = [noise]
styles = [G.style(s) for s in styles]
style_space = []
if truncation < 1:
if not real:
style_t = []
for style in styles:
style_t.append(
truncation_latent + truncation *
(style - truncation_latent)
)
styles = style_t
else: # styles are latent (tensor: 1,18,512), for real PTI output
truncation_latent = truncation_latent.repeat(
18, 1).unsqueeze(0) # (1,512) --> (1,18,512)
styles = torch.add(truncation_latent, torch.mul(
torch.sub(noise, truncation_latent), truncation))
noise = [getattr(G.noises, 'noise_{}'.format(i))
for i in range(G.num_layers)]
if not real:
inject_index = G.n_latent
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else:
latent = styles
style_space.append(G.conv1.conv.modulation(latent[:, 0]))
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(
G.convs[::2], G.convs[1::2], noise[1::2], noise[2::2], G.to_rgbs
):
style_space.append(conv1.conv.modulation(latent[:, i]))
style_space.append(conv2.conv.modulation(latent[:, i+1]))
i += 2
# get threshold, layer, strength by dict
layer, strength, threshold = attr_dict['stylespace'][attr_name]
statis_dir = os.path.join(
statics_dir, "{}_statis/{}".format(attr_name, layer))
statis_csv_path = os.path.join(statis_dir, "statis.csv")
statis_df = pd.read_csv(statis_csv_path)
statis_df = statis_df.sort_values(by='channel', ascending=True)
ch_mask = statis_df['strength'].values
ch_mask = torch.from_numpy(ch_mask).to(latent.device).float()
ch_mask = (ch_mask.abs() > threshold).float()
style_vect = load_pkl(os.path.join(
latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, layer)))
style_vect = torch.from_numpy(style_vect).to(latent.device).float()
style_vect = style_vect * ch_mask
if step != 0 and total != 0:
strength = step / total * strength
style_space[layer] += style_vect * strength
return style_space, latent, noise
def encoder_sefa(G, noise, attr_name, truncation=1, truncation_latent=None,
latent_dir='latent_direction/sefa/',
step=0, total=0, real=False):
if not real:
styles = [noise]
styles = [G.style(s) for s in styles]
if truncation < 1:
if not real:
style_t = []
for style in styles:
style_t.append(
truncation_latent + truncation *
(style - truncation_latent)
)
styles = style_t
else:
truncation_latent = truncation_latent.repeat(
18, 1).unsqueeze(0) # (1,512) --> (1,18,512)
styles = torch.add(truncation_latent, torch.mul(
torch.sub(noise, truncation_latent), truncation))
noise = [getattr(G.noises, 'noise_{}'.format(i))
for i in range(G.num_layers)]
if not real:
inject_index = G.n_latent
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else:
latent = styles
layer, strength = attr_dict['sefa'][attr_name]
sefa_vect = torch.load(os.path.join(
latent_dir, '{}.pt'.format(attr_name))).to(latent.device).float()
if step != 0 and total != 0:
strength = step / total * strength
for l in layer:
latent[:, l, :] += (sefa_vect * strength * 2)
return latent, noise
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