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import torch
import torch.nn as nn
from models.auto_encoder import AutoEncoder
from models.hpm import HorizontalPyramidMatching
from models.part_net import PartNet
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),
hpm_use_1x1conv: bool = False,
hpm_scales: tuple[int, ...] = (1, 2, 4),
hpm_use_avg_pool: bool = True,
hpm_use_max_pool: bool = True,
tfa_squeeze_ratio: int = 4,
tfa_num_parts: int = 16,
embedding_dims: int = 256,
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.hpm_num_parts = sum(hpm_scales)
self.image_log_on = image_log_on
self.ae = AutoEncoder(
ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
)
self.pn_in_channels = ae_feature_channels * 2
self.pn = PartNet(
self.pn_in_channels, tfa_squeeze_ratio, tfa_num_parts
)
self.hpm = HorizontalPyramidMatching(
ae_feature_channels * 2, self.pn_in_channels, hpm_use_1x1conv,
hpm_scales, hpm_use_avg_pool, hpm_use_max_pool
)
self.num_total_parts = self.hpm_num_parts + tfa_num_parts
empty_fc = torch.empty(self.num_total_parts,
self.pn_in_channels, embedding_dims)
self.fc_mat = nn.Parameter(empty_fc)
def fc(self, x):
return x @ self.fc_mat
def forward(self, x_c1, x_c2=None):
# Step 1: Disentanglement
# n, t, c, h, w
((x_c, x_p), ae_losses, images) = self._disentangle(x_c1, x_c2)
# Step 2.a: Static Gait Feature Aggregation & HPM
# n, c, h, w
x_c = self.hpm(x_c)
# p, n, c
# Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation)
# n, t, c, h, w
x_p = self.pn(x_p)
# p, n, c
# Step 3: Cat feature map together and fc
x = torch.cat((x_c, x_p))
x = self.fc(x)
if self.training:
return x.transpose(0, 1), ae_losses, images
else:
return x.unsqueeze(1).view(-1)
def _disentangle(self, x_c1_t2, x_c2_t2=None):
n, t, c, h, w = x_c1_t2.size()
device = x_c1_t2.device
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)
# Decode features
x_c = self._decode_cano_feature(f_c_, n, t, device)
x_p_ = self._decode_pose_feature(f_p_, n, t, device)
x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
i_a, i_c, i_p = None, None, None
if self.image_log_on:
with torch.no_grad():
i_a = self._decode_appr_feature(f_a_, n, t, device)
# Continue decoding canonical features
i_c = self.ae.decoder.trans_conv3(x_c)
i_c = torch.sigmoid(self.ae.decoder.trans_conv4(i_c))
i_p_ = self.ae.decoder.trans_conv3(x_p_)
i_p_ = torch.sigmoid(self.ae.decoder.trans_conv4(i_p_))
i_p = i_p_.view(n, t, c, h, w)
return (x_c, x_p), losses, (i_a, i_c, i_p)
else: # evaluating
f_c_, f_p_ = self.ae(x_c1_t2)
x_c = self._decode_cano_feature(f_c_, n, t, device)
x_p_ = self._decode_pose_feature(f_p_, n, t, device)
x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
return (x_c, x_p), None, None
def _decode_appr_feature(self, f_a_, n, t, device):
# Decode appearance features
f_a = f_a_.view(n, t, -1)
x_a = self.ae.decoder(
f_a.mean(1),
torch.zeros((n, self.f_c_dim), device=device),
torch.zeros((n, self.f_p_dim), device=device)
)
return x_a
def _decode_cano_feature(self, f_c_, n, t, device):
# Decode average canonical features to higher dimension
f_c = f_c_.view(n, t, -1)
x_c = self.ae.decoder(
torch.zeros((n, self.f_a_dim), device=device),
f_c.mean(1),
torch.zeros((n, self.f_p_dim), device=device),
is_feature_map=True
)
return x_c
def _decode_pose_feature(self, f_p_, n, t, device):
# Decode pose features to images
x_p_ = self.ae.decoder(
torch.zeros((n * t, self.f_a_dim), device=device),
torch.zeros((n * t, self.f_c_dim), device=device),
f_p_,
is_feature_map=True
)
return x_p_
|
