File size: 2,076 Bytes
84e9a1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, dim, dropout=0.3):
super(ResBlock, self).__init__()
self.block = nn.Sequential(
nn.Linear(dim, dim),
nn.BatchNorm1d(dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(dim, dim),
nn.BatchNorm1d(dim),
nn.GELU(),
nn.Dropout(dropout)
)
def forward(self, x):
return x + self.block(x)
class Yoga3HeadMLP(nn.Module):
def __init__(self, input_dim, num_poses, num_joints=15):
super(Yoga3HeadMLP, self).__init__()
self.input_layer = nn.Sequential(
nn.Linear(input_dim, 256),
nn.BatchNorm1d(256),
nn.GELU()
)
self.res1 = ResBlock(256, dropout=0.3)
self.res2 = ResBlock(256, dropout=0.3)
# Head 1: Pose ID Classification
self.pose_head = nn.Sequential(
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.GELU(),
nn.Dropout(0.2),
nn.Linear(128, num_poses)
)
# Head 2: Correctness Classification (binary probability output)
self.correctness_head = nn.Sequential(
nn.Linear(256, 64),
nn.BatchNorm1d(64),
nn.GELU(),
nn.Dropout(0.2),
nn.Linear(64, 1)
)
# Head 3: Joint Deviation Regression
self.deviation_head = nn.Sequential(
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.GELU(),
nn.Dropout(0.2),
nn.Linear(128, num_joints)
)
def forward(self, x):
features = self.input_layer(x)
features = self.res1(features)
features = self.res2(features)
pose_logits = self.pose_head(features)
correctness_logit = self.correctness_head(features).squeeze(-1)
deviations = self.deviation_head(features)
return pose_logits, correctness_logit, deviations
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