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import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from models import AutoEncoder, HorizontalPyramidMatching, PartNet
class RGBPartNet(nn.Module):
def __init__(
self,
num_class: int = 74,
ae_in_channels: int = 3,
ae_feature_channels: int = 64,
f_a_c_p_dims: tuple[int, int, int] = (128, 128, 64),
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_part: int = 16,
):
super().__init__()
self.ae = AutoEncoder(
num_class, 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_part
)
self.hpm = HorizontalPyramidMatching(
ae_feature_channels * 8, self.pn.tfa_in_channels, hpm_scales,
hpm_use_avg_pool, hpm_use_max_pool
)
# TODO Weight inti here
def forward(self, x_c1, x_c2, y=None):
# Step 0: Swap batch_size and time dimensions for next step
# n, t, c, h, w
x_c1, x_c2 = x_c1.transpose(0, 1), x_c2.transpose(0, 1)
# Step 1: Disentanglement
# t, n, c, h, w
((x_c_c1, x_p_c1), losses) = self._disentangle(x_c1, x_c2, y)
# Step 2.a: HPM & Static Gait Feature Aggregation
# t, n, c, h, w
x_c = self.hpm(x_c_c1)
# p, t, n, c
x_c = x_c.mean(dim=1)
# 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 calculate losses
x = torch.cat((x_c, x_p))
if self.training:
# TODO Implement Batch All triplet loss function
batch_all_triplet_loss = torch.tensor(0.)
print(*losses, batch_all_triplet_loss)
loss = torch.sum(torch.stack((*losses, batch_all_triplet_loss)))
return loss
else:
return x
def _disentangle(self, x_c1, x_c2, y):
num_frames = len(x_c1)
# Decoded canonical features and Pose images
x_c_c1, x_p_c1 = [], []
if self.training:
# Features required to calculate losses
f_p_c1, f_p_c2 = [], []
xrecon_loss, cano_cons_loss = [], []
for t2 in range(num_frames):
t1 = random.randrange(num_frames)
output = self.ae(x_c1[t1], x_c1[t2], x_c2[t2], y)
(x_c1_t2, f_p_t2, losses) = output
# Decoded features or image
(x_c_c1_t2, x_p_c1_t2) = x_c1_t2
# Canonical Features for HPM
x_c_c1.append(x_c_c1_t2)
# Pose image for Part Net
x_p_c1.append(x_p_c1_t2)
# Losses per time step
# Used in pose similarity loss
(f_p_c1_t2, f_p_c2_t2) = f_p_t2
f_p_c1.append(f_p_c1_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)
x_c_c1 = torch.stack(x_c_c1)
x_p_c1 = torch.stack(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)
cano_cons_loss = torch.mean(torch.stack(cano_cons_loss))
return ((x_c_c1, x_p_c1),
(xrecon_loss, pose_sim_loss, cano_cons_loss))
else: # evaluating
for t2 in range(num_frames):
t1 = random.randrange(num_frames)
x_c1_t2 = self.ae(x_c1[t1], x_c1[t2], x_c2[t2])
# Decoded features or image
(x_c_c1_t2, x_p_c1_t2) = x_c1_t2
# Canonical Features for HPM
x_c_c1.append(x_c_c1_t2)
# Pose image for Part Net
x_p_c1.append(x_p_c1_t2)
x_c_c1 = torch.stack(x_c_c1)
x_p_c1 = torch.stack(x_p_c1)
return (x_c_c1, x_p_c1), None
@staticmethod
def _pose_sim_loss(f_p_c1: list[torch.Tensor],
f_p_c2: list[torch.Tensor]) -> torch.Tensor:
f_p_c1_mean = torch.stack(f_p_c1).mean(dim=0)
f_p_c2_mean = torch.stack(f_p_c2).mean(dim=0)
return F.mse_loss(f_p_c1_mean, f_p_c2_mean)
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