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import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.auto_encoder import AutoEncoder
from models.hpm import HorizontalPyramidMatching
from models.part_net import PartNet
from utils.triplet_loss import BatchAllTripletLoss
class RGBPartNet(nn.Module):
def __init__(
self,
ae_in_channels: int = 3,
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,
fpfe_feature_channels: int = 32,
fpfe_kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
fpfe_paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
fpfe_halving: tuple[int, ...] = (0, 2, 3),
tfa_squeeze_ratio: int = 4,
tfa_num_parts: int = 16,
embedding_dims: int = 256,
triplet_margins: tuple[float, float] = (0.2, 0.2),
image_log_on: bool = False
):
super().__init__()
(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_feature_channels, f_a_c_p_dims
)
self.pn = PartNet(
ae_in_channels, fpfe_feature_channels, fpfe_kernel_sizes,
fpfe_paddings, fpfe_halving, tfa_squeeze_ratio, tfa_num_parts
)
out_channels = self.pn.tfa_in_channels
self.hpm = HorizontalPyramidMatching(
ae_feature_channels * 2, out_channels, hpm_use_1x1conv,
hpm_scales, hpm_use_avg_pool, hpm_use_max_pool
)
empty_fc = torch.empty(self.hpm_num_parts + tfa_num_parts,
out_channels, embedding_dims)
self.fc_mat = nn.Parameter(empty_fc)
(hpm_margin, pn_margin) = triplet_margins
self.hpm_ba_trip = BatchAllTripletLoss(hpm_margin)
self.pn_ba_trip = BatchAllTripletLoss(pn_margin)
def fc(self, x):
return x @ self.fc_mat
def forward(self, x_c1, x_c2=None, y=None):
# Step 0: Swap batch_size and time dimensions for next step
# n, t, c, h, w
x_c1 = x_c1.transpose(0, 1)
if self.training:
x_c2 = x_c2.transpose(0, 1)
# Step 1: Disentanglement
# t, n, c, h, w
((x_c_c1, x_p_c1), images, losses) = self._disentangle(x_c1, x_c2)
# Step 2.a: Static Gait Feature Aggregation & HPM
# n, c, h, w
x_c = self.hpm(x_c_c1)
# p, n, c
# Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation)
# t, n, c, h, w
x_p = self.pn(x_p_c1)
# 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:
hpm_ba_trip = self.hpm_ba_trip(x[:self.hpm_num_parts], y)
pn_ba_trip = self.pn_ba_trip(x[self.hpm_num_parts:], y)
losses = torch.stack((*losses, hpm_ba_trip, pn_ba_trip))
return losses, images
else:
return x.unsqueeze(1).view(-1)
def _disentangle(self, x_c1, x_c2=None):
t, n, c, h, w = x_c1.size()
device = x_c1.device
if self.training:
# Encoded appearance, canonical and pose features
f_a_c1, f_c_c1, f_p_c1 = [], [], []
# Features required to calculate losses
f_p_c2 = []
xrecon_loss, cano_cons_loss = [], []
for t2 in range(t):
t1 = random.randrange(t)
output = self.ae(x_c1[t2], x_c1[t1], x_c2[t2])
(f_c1_t2, f_p_t2, losses) = output
(f_a_c1_t2, f_c_c1_t2, f_p_c1_t2) = f_c1_t2
if self.image_log_on:
f_a_c1.append(f_a_c1_t2)
# Save canonical features and pose features
f_c_c1.append(f_c_c1_t2)
f_p_c1.append(f_p_c1_t2)
# Losses per time step
# Used in pose similarity loss
(_, f_p_c2_t2) = f_p_t2
f_p_c2.append(f_p_c2_t2)
# Cross reconstruction loss and canonical loss
(xrecon_loss_t2, cano_cons_loss_t2) = losses
xrecon_loss.append(xrecon_loss_t2)
cano_cons_loss.append(cano_cons_loss_t2)
if self.image_log_on:
f_a_c1 = torch.stack(f_a_c1)
f_c_c1_mean = torch.stack(f_c_c1).mean(0)
f_p_c1 = torch.stack(f_p_c1)
f_p_c2 = torch.stack(f_p_c2)
# Decode features
appearance_image, canonical_image, pose_image = None, None, None
with torch.no_grad():
# Decode average canonical features to higher dimension
x_c_c1 = self.ae.decoder(
torch.zeros((n, self.f_a_dim), device=device),
f_c_c1_mean,
torch.zeros((n, self.f_p_dim), device=device),
cano_only=True
)
# Decode pose features to images
f_p_c1_ = f_p_c1.view(t * n, -1)
x_p_c1_ = self.ae.decoder(
torch.zeros((t * n, self.f_a_dim), device=device),
torch.zeros((t * n, self.f_c_dim), device=device),
f_p_c1_
)
x_p_c1 = x_p_c1_.view(t, n, c, h, w)
if self.image_log_on:
# Decode appearance features
f_a_c1_ = f_a_c1.view(t * n, -1)
appearance_image_ = self.ae.decoder(
f_a_c1_,
torch.zeros((t * n, self.f_c_dim), device=device),
torch.zeros((t * n, self.f_p_dim), device=device)
)
appearance_image = appearance_image_.view(t, n, c, h, w)
# Continue decoding canonical features
canonical_image = self.ae.decoder.trans_conv3(x_c_c1)
canonical_image = torch.sigmoid(
self.ae.decoder.trans_conv4(canonical_image)
)
pose_image = x_p_c1
# Losses
xrecon_loss = torch.sum(torch.stack(xrecon_loss))
pose_sim_loss = self._pose_sim_loss(f_p_c1, f_p_c2) * 10
cano_cons_loss = torch.mean(torch.stack(cano_cons_loss))
return ((x_c_c1, x_p_c1),
(appearance_image, canonical_image, pose_image),
(xrecon_loss, pose_sim_loss, cano_cons_loss))
else: # evaluating
x_c1_ = x_c1.view(t * n, c, h, w)
(f_c_c1_, f_p_c1_) = self.ae(x_c1_)
# Canonical features
f_c_c1 = f_c_c1_.view(t, n, -1)
f_c_c1_mean = f_c_c1.mean(0)
x_c_c1 = self.ae.decoder(
torch.zeros((n, self.f_a_dim)),
f_c_c1_mean,
torch.zeros((n, self.f_p_dim)),
cano_only=True
)
# Pose features
x_p_c1_ = self.ae.decoder(
torch.zeros((t * n, self.f_a_dim)),
torch.zeros((t * n, self.f_c_dim)),
f_p_c1_
)
x_p_c1 = x_p_c1_.view(t, n, c, h, w)
return (x_c_c1, x_p_c1), None, None
@staticmethod
def _pose_sim_loss(f_p_c1: torch.Tensor,
f_p_c2: torch.Tensor) -> torch.Tensor:
f_p_c1_mean = f_p_c1.mean(dim=0)
f_p_c2_mean = f_p_c2.mean(dim=0)
return F.mse_loss(f_p_c1_mean, f_p_c2_mean)
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