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from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.auto_encoder import AutoEncoder
class RGBPartNet(nn.Module):
def __init__(
self,
ae_in_channels: int = 3,
ae_in_size: Tuple[int, int] = (64, 48),
ae_feature_channels: int = 64,
f_a_c_p_dims: Tuple[int, int, int] = (128, 128, 64),
image_log_on: bool = False
):
super().__init__()
self.h, self.w = ae_in_size
(self.f_a_dim, self.f_c_dim, self.f_p_dim) = f_a_c_p_dims
self.image_log_on = image_log_on
self.ae = AutoEncoder(
ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
)
def forward(self, x_c1, x_c2=None):
losses, features, images = self._disentangle(x_c1, x_c2)
if self.training:
losses = torch.stack(losses)
return losses, features, images
else:
return features
def _disentangle(self, x_c1_t2, x_c2_t2=None):
n, t, c, h, w = x_c1_t2.size()
if self.training:
x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :]
((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
f_a = f_a_.view(n, t, -1)
f_c = f_c_.view(n, t, -1)
f_p = f_p_.view(n, t, -1)
i_a, i_c, i_p = None, None, None
if self.image_log_on:
with torch.no_grad():
x_a, i_a = self._separate_decode(
f_a.mean(1),
torch.zeros_like(f_c[:, 0, :]),
torch.zeros_like(f_p[:, 0, :])
)
x_c, i_c = self._separate_decode(
torch.zeros_like(f_a[:, 0, :]),
f_c.mean(1),
torch.zeros_like(f_p[:, 0, :]),
)
x_p_, i_p_ = self._separate_decode(
torch.zeros_like(f_a_),
torch.zeros_like(f_c_),
f_p_
)
x_p = tuple(_x_p.view(n, t, *_x_p.size()[1:]) for _x_p in x_p_)
i_p = i_p_.view(n, t, c, h, w)
return losses, (x_a, x_c, x_p), (i_a, i_c, i_p)
else: # evaluating
f_c_, f_p_ = self.ae(x_c1_t2)
f_c = f_c_.view(n, t, -1)
f_p = f_p_.view(n, t, -1)
return (f_c, f_p), None, None
def _separate_decode(self, f_a, f_c, f_p):
x_1 = torch.cat((f_a, f_c, f_p), dim=1)
x_1 = self.ae.decoder.fc(x_1).view(
-1,
self.ae.decoder.feature_channels * 8,
self.ae.decoder.h_0,
self.ae.decoder.w_0
)
x_1 = F.relu(x_1, inplace=True)
x_2 = self.ae.decoder.trans_conv1(x_1)
x_3 = self.ae.decoder.trans_conv2(x_2)
x_4 = self.ae.decoder.trans_conv3(x_3)
image = torch.sigmoid(self.ae.decoder.trans_conv4(x_4))
x = (x_1, x_2, x_3, x_4)
return x, image
|
