Note
Go to the end to download the full example code.
Tutorial for Human Pose Estimation¶
In[1]:
import yaml
import torch
import torch.nn as nn
from tqdm import tqdm
import os
Dataset with MetaFi:¶
Point cloud Pose reconstruction dataset collected by Ti 6843 mmWave radar. 40 subjects are included and the human poses are obtained by 2 RGB camera. We provide cross-subject experiment settings with all daily activities. In the library, we provide a dataloader to use mmWave PC data, and predict these human poses.
Load the data¶
In[3]:
from pysensing.mmwave.PC.dataset.hpe import load_hpe_dataset
# The path contains the radHAR dataset
train_dataset, test_dataset = load_hpe_dataset("MetaFi")
Try to download MetaFi dateset in /home/kemove/yyz/av-gihub/tutorials/mmwave_PC_source/mmfi
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Extracted to /home/kemove/yyz/av-gihub/tutorials/mmwave_PC_source/
Using default config file.
using dataset: MetaFi DATA
S02 ['A02', 'A03', 'A04', 'A05', 'A14', 'A18', 'A19', 'A21', 'A22', 'A23', 'A27']
S03 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22']
S05 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S06 ['A02', 'A04', 'A05', 'A14', 'A17', 'A18', 'A20', 'A21', 'A22', 'A23', 'A27']
S08 ['A02', 'A03', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A23', 'A27']
S09 ['A02', 'A17', 'A18', 'A20', 'A21', 'A22', 'A23', 'A27']
S11 ['A02', 'A03', 'A04', 'A13', 'A14', 'A18', 'A20', 'A21', 'A22', 'A23']
S12 ['A02', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A27']
S13 ['A02', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A27']
S14 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A23']
S15 ['A02', 'A03', 'A04', 'A05', 'A13', 'A17', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S16 ['A02', 'A05', 'A13', 'A14', 'A18', 'A19', 'A20', 'A22', 'A23', 'A27']
S17 ['A02', 'A03', 'A04', 'A05', 'A13', 'A18', 'A19', 'A20', 'A21', 'A23']
S18 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A23', 'A27']
S19 ['A02', 'A03', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23']
S21 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A18', 'A20', 'A21', 'A22', 'A23', 'A27']
S23 ['A02', 'A03', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A21', 'A22', 'A27']
S25 ['A02', 'A03', 'A04', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S26 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A19', 'A20', 'A23']
S27 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A20', 'A21', 'A22', 'A23']
S28 ['A02', 'A03', 'A04', 'A05', 'A13', 'A17', 'A18', 'A19', 'A21', 'A27']
S29 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A19', 'A22', 'A23', 'A27']
S30 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A22', 'A23', 'A27']
S31 ['A02', 'A03', 'A04', 'A05', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S32 ['A02', 'A03', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S33 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S34 ['A02', 'A03', 'A04', 'A13', 'A14', 'A17', 'A18', 'A19', 'A23', 'A27']
S35 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23']
S36 ['A02', 'A03', 'A05', 'A13', 'A19', 'A20', 'A21', 'A22', 'A27']
S37 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A22', 'A23', 'A27']
S38 ['A02', 'A04', 'A05', 'A13', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S40 ['A02', 'A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A21', 'A22', 'A23', 'A27']
S04 ['A03', 'A04', 'A14', 'A17', 'A20', 'A21', 'A22', 'A27']
S07 ['A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A27']
S20 ['A03', 'A04', 'A13', 'A14', 'A17', 'A20', 'A21', 'A22', 'A23', 'A27']
S22 ['A03', 'A04', 'A13', 'A14', 'A17', 'A18', 'A20', 'A21', 'A22', 'A23', 'A27']
S24 ['A03', 'A04', 'A05', 'A14', 'A17', 'A19', 'A20', 'A21', 'A22', 'A23', 'A27']
S39 ['A03', 'A04', 'A05', 'A13', 'A14', 'A17', 'A18', 'A19', 'A21', 'A22', 'A23', 'A27']
S01 ['A04', 'A14', 'A17', 'A18', 'A19', 'A20', 'A22', 'A23', 'A27']
S10 ['A04', 'A05', 'A18', 'A19', 'A20', 'A22', 'A23', 'A27']
S01 ['A02', 'A03', 'A05', 'A13', 'A21']
S04 ['A02', 'A05', 'A13', 'A18', 'A19', 'A23']
S07 ['A02', 'A23']
S10 ['A02', 'A03', 'A13', 'A14', 'A17', 'A21']
S20 ['A02', 'A05', 'A18', 'A19']
S22 ['A02', 'A05', 'A19']
S24 ['A02', 'A13', 'A18']
S39 ['A02', 'A20']
S06 ['A03', 'A13', 'A19']
S09 ['A03', 'A04', 'A05', 'A13', 'A14', 'A19']
S12 ['A03', 'A04', 'A23']
S13 ['A03', 'A22', 'A23']
S16 ['A03', 'A04', 'A17', 'A21']
S38 ['A03', 'A14']
S08 ['A04', 'A22']
S19 ['A04', 'A27']
S23 ['A04', 'A20', 'A23']
S32 ['A04']
S36 ['A04', 'A14', 'A17', 'A18', 'A23']
S11 ['A05', 'A17', 'A19', 'A27']
S25 ['A05']
S34 ['A05', 'A20', 'A21', 'A22']
S02 ['A13', 'A17', 'A20']
S31 ['A13', 'A14']
S15 ['A14', 'A18']
S17 ['A14', 'A17', 'A22', 'A27']
S28 ['A14', 'A20', 'A22', 'A23']
S21 ['A17', 'A19']
S26 ['A17', 'A18', 'A21', 'A22', 'A27']
S05 ['A18']
S29 ['A18', 'A20', 'A21']
S27 ['A19', 'A27']
S30 ['A20', 'A21']
S40 ['A20']
S37 ['A21']
S14 ['A22', 'A27']
S18 ['A22']
S03 ['A23', 'A27']
S35 ['A27']
Visualize the PC data¶
In[6]:
from matplotlib import pyplot as plt
from pysensing.mmwave.PC.tutorial.plot import plot_3d_graph
# Example of the samples in the dataset
index = 10 # Randomly select an index
pc,pose = train_dataset.__getitem__(index)
print(pc.shape, type(pose))
plot_3d_graph(pose, pc[0])
(5, 150, 5) <class 'torch.Tensor'>
Create model¶
mmFi utilizes PointTransformer model as a baseline hpe method. From model.hpe, we can import desired hpe model designed for mmWave PC. The model parameter for PointTransformer reimplemented for mmFi is as follows:
In[7]:
from pysensing.mmwave.PC.model.hpe import PointTransformerReg
model = PointTransformerReg(
input_dim = 5,
nblocks = 5,
n_p = 17
)
print(model)
PointTransformerReg(
(backbone): Backbone(
(fc1): Sequential(
(0): Linear(in_features=5, out_features=32, bias=True)
(1): ReLU()
(2): Linear(in_features=32, out_features=32, bias=True)
)
(transformer1): TransformerBlock(
(fc1): Linear(in_features=32, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=32, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(transition_downs): ModuleList(
(0): TransitionDown(
(conv1): Sequential(
(0): Conv1d(35, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(1): TransitionDown(
(conv1): Sequential(
(0): Conv1d(67, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(2): TransitionDown(
(conv1): Sequential(
(0): Conv1d(131, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(3): TransitionDown(
(conv1): Sequential(
(0): Conv1d(259, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(transformers): ModuleList(
(0): TransformerBlock(
(fc1): Linear(in_features=64, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=64, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(1): TransformerBlock(
(fc1): Linear(in_features=128, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=128, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(2): TransformerBlock(
(fc1): Linear(in_features=256, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=256, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(3): TransformerBlock(
(fc1): Linear(in_features=512, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=512, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
)
)
(transformer): Transformer(
(layers): ModuleList(
(0-4): 5 x ModuleList(
(0): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): Attention(
(to_k): Linear(in_features=512, out_features=512, bias=False)
(to_v): Linear(in_features=512, out_features=512, bias=False)
(to_q): Linear(in_features=512, out_features=512, bias=False)
(to_out): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
)
)
)
)
(1): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): FeedForward(
(net): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=256, out_features=512, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
)
)
)
)
)
(fc2): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): ReLU()
(3): Linear(in_features=256, out_features=32, bias=True)
)
(fc3): Sequential(
(0): ReLU()
(1): Linear(in_features=32, out_features=64, bias=True)
(2): Dropout(p=0.0, inplace=False)
(3): ReLU()
(4): Linear(in_features=64, out_features=3, bias=True)
)
)
A shortcut for loading the hpe model to avoid the tedious hyper-parameter setting.
In[8]:
from pysensing.mmwave.PC.model.hpe import load_hpe_model
model = load_hpe_model("MetaFi", "PointTransformer")
print(model)
PointTransformerReg(
(backbone): Backbone(
(fc1): Sequential(
(0): Linear(in_features=5, out_features=32, bias=True)
(1): ReLU()
(2): Linear(in_features=32, out_features=32, bias=True)
)
(transformer1): TransformerBlock(
(fc1): Linear(in_features=32, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=32, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(transition_downs): ModuleList(
(0): TransitionDown(
(conv1): Sequential(
(0): Conv1d(35, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(1): TransitionDown(
(conv1): Sequential(
(0): Conv1d(67, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(2): TransitionDown(
(conv1): Sequential(
(0): Conv1d(131, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(3): TransitionDown(
(conv1): Sequential(
(0): Conv1d(259, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(transformers): ModuleList(
(0): TransformerBlock(
(fc1): Linear(in_features=64, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=64, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(1): TransformerBlock(
(fc1): Linear(in_features=128, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=128, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(2): TransformerBlock(
(fc1): Linear(in_features=256, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=256, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(3): TransformerBlock(
(fc1): Linear(in_features=512, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=512, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
)
)
(transformer): Transformer(
(layers): ModuleList(
(0-4): 5 x ModuleList(
(0): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): Attention(
(to_k): Linear(in_features=512, out_features=512, bias=False)
(to_v): Linear(in_features=512, out_features=512, bias=False)
(to_q): Linear(in_features=512, out_features=512, bias=False)
(to_out): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
)
)
)
)
(1): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): FeedForward(
(net): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=256, out_features=512, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
)
)
)
)
)
(fc2): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): ReLU()
(3): Linear(in_features=256, out_features=32, bias=True)
)
(fc3): Sequential(
(0): ReLU()
(1): Linear(in_features=32, out_features=64, bias=True)
(2): Dropout(p=0.0, inplace=False)
(3): ReLU()
(4): Linear(in_features=64, out_features=3, bias=True)
)
)
Model Train¶
pysensing library support quick training of model with the following steps. The training interface incorporates pytorch loss functions, optimizers and dataloaders to facilate training. An example is provided for how to define the aforemetioned terms.
In[11]:
# Create pytorch dataloaders
train_loader = torch.utils.data.DataLoader(train_dataset, shuffle=True, batch_size=16, num_workers=16)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False, num_workers=16)
# Define pytorch loss function as criterion
criterion = nn.CrossEntropyLoss()
# Define pytorch optimizer for training
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
# GPU acceleration with cuda
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
A quick training using hpe_train. The resulted model parameters will be saved into “train_{num_epochs}.pth”.
In[12]:
# Pysensing training interface
from pysensing.mmwave.PC.inference.hpe import hpe_train
# hpe_train(model, train_loader, num_epochs=1, optimizer=optimizer, criterion=criterion, device=device)
Model inference¶
Load the pretrained model, e.g. from https://pysensing.oss-ap-southeast-1.aliyuncs.com/pretrain/mmwave_pc/hpe/MetaFi_PointTransformer.pth , and perform human pose estimation!
In[13]:
# load pretrained model
from pysensing.mmwave.PC.inference import load_pretrain
model = load_pretrain(model, "MetaFi", "PointTransformer").to(device)
model.eval()
Use pretrained model!
PointTransformerReg(
(backbone): Backbone(
(fc1): Sequential(
(0): Linear(in_features=5, out_features=32, bias=True)
(1): ReLU()
(2): Linear(in_features=32, out_features=32, bias=True)
)
(transformer1): TransformerBlock(
(fc1): Linear(in_features=32, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=32, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(transition_downs): ModuleList(
(0): TransitionDown(
(conv1): Sequential(
(0): Conv1d(35, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(1): TransitionDown(
(conv1): Sequential(
(0): Conv1d(67, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(2): TransitionDown(
(conv1): Sequential(
(0): Conv1d(131, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(3): TransitionDown(
(conv1): Sequential(
(0): Conv1d(259, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(transformers): ModuleList(
(0): TransformerBlock(
(fc1): Linear(in_features=64, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=64, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(1): TransformerBlock(
(fc1): Linear(in_features=128, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=128, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(2): TransformerBlock(
(fc1): Linear(in_features=256, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=256, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(3): TransformerBlock(
(fc1): Linear(in_features=512, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=512, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
)
)
(transformer): Transformer(
(layers): ModuleList(
(0-4): 5 x ModuleList(
(0): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): Attention(
(to_k): Linear(in_features=512, out_features=512, bias=False)
(to_v): Linear(in_features=512, out_features=512, bias=False)
(to_q): Linear(in_features=512, out_features=512, bias=False)
(to_out): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
)
)
)
)
(1): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): FeedForward(
(net): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=256, out_features=512, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
)
)
)
)
)
(fc2): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): ReLU()
(3): Linear(in_features=256, out_features=32, bias=True)
)
(fc3): Sequential(
(0): ReLU()
(1): Linear(in_features=32, out_features=64, bias=True)
(2): Dropout(p=0.0, inplace=False)
(3): ReLU()
(4): Linear(in_features=64, out_features=3, bias=True)
)
)
Test the model on testing dataset.
In[14]:
from pysensing.mmwave.PC.inference.hpe import hpe_test
# hpe_test(model, test_loader, criterion=criterion, device=device)
Model inference on sample and deep feature embedding of input modality in HPE task.
In[15]:
# Model inference
idx = 5
points, pose= test_dataset.__getitem__(idx)
points = torch.tensor(points).unsqueeze(0).float().to(device)
predicted_result = model(points)
print("The predicted pose is {}, while the ground truth is {}".format(predicted_result.cpu(),pose))
# Deep feature embedding
from pysensing.mmwave.PC.inference.embedding import embedding
emb = embedding(input = points, model=model, dataset_name = "MetaFi", model_name = "PointTransformer", device=device)
print("The shape of feature embedding is: ", emb.shape)
The predicted pose is tensor([[[-0.1173, 0.0153, 3.0501],
[-0.2115, 0.0202, 3.0561],
[-0.2253, 0.3597, 3.0622],
[-0.2422, 0.7359, 3.0763],
[ 0.0275, -0.0367, 3.0689],
[ 0.0280, 0.3391, 3.0851],
[ 0.0785, 0.7613, 3.1083],
[-0.1009, -0.2694, 3.0435],
[-0.0910, -0.5694, 3.0297],
[-0.0988, -0.6771, 3.0037],
[-0.1014, -0.7289, 3.0262],
[ 0.0349, -0.5211, 3.0320],
[ 0.2234, -0.4633, 2.9764],
[ 0.1241, -0.5345, 2.8527],
[-0.1986, -0.5264, 3.0985],
[-0.3518, -0.4575, 3.0666],
[-0.2247, -0.4778, 2.9471]]], grad_fn=<ToCopyBackward0>), while the ground truth is tensor([[-0.0626, -0.0378, 3.3111],
[-0.1724, -0.0395, 3.3111],
[-0.1786, 0.3689, 3.3083],
[-0.2026, 0.7605, 3.3111],
[ 0.0473, -0.0362, 3.3111],
[ 0.0633, 0.3689, 3.3111],
[ 0.0673, 0.7605, 3.3111],
[-0.0685, -0.3322, 3.3016],
[-0.0744, -0.6267, 3.2920],
[-0.0642, -0.7458, 3.2512],
[-0.0653, -0.8049, 3.2850],
[ 0.0914, -0.5671, 3.3118],
[ 0.3367, -0.5248, 3.3104],
[ 0.2930, -0.5677, 3.0678],
[-0.2505, -0.5671, 3.3131],
[-0.5012, -0.5671, 3.3116],
[-0.4514, -0.5674, 3.0686]])
The shape of feature embedding is: torch.Size([1, 17, 32])
mmDiff: diffusion model for mmWave radar HPE¶
Load Diffusion Runner with model initialized. This process will define the setting for model and dataset. Currently two settings are implemented: 1. “mmBody + P4Transformer”:
Phase 1: Input [b, 4, 5000, 6]; Output: [b, 17, 3] and [b, 17, 64]. Phase 2: GRC, LRC, TMC, SLC
- “MetaFi + PointTransformer”:
Phase 1: Input [b, 5, 150, 5]; Output: [b, 17, 3] and [b, 17, 32]. Phase 2: GRC, TMC, SLC
In[16]:
from pysensing.mmwave.PC.model.hpe.mmDiff.load_mmDiff import load_mmDiff
mmDiffRunner = load_mmDiff("MetaFi")
Self.model_feat vadility passes.
MMdiff using PointTransformer as feature extractor.
- Phase 1 Training: Can train phase 1 from scratch (is_train = True) or load pretrained phase 1 model (is_train = False).
Set is_save = True to facilitate phase 2 training acceleration.
If phase 1 features are saved, set is_save = False.
In[17]:
mmDiffRunner.phase1_train(train_dataset, test_dataset, is_train=False, is_save=False)
Phase 1 use pretrained model!
Phase 1 can also receive self defined model and the model should follow the setting defined above. The Self-defined model should output coarse joints and coarse joint features.
In[18]:
# Self defined model should output coarse joints and coarse joint features
from pysensing.mmwave.PC.model.hpe.pointTrans import PointTransformerReg_feat
model = PointTransformerReg_feat(
input_dim = 5,
nblocks = 5,
n_p = 17
)
print(model)
mmDiffRunner.phase1_train(train_dataset, test_dataset, model_self=model, is_train=False, is_save=False)
PointTransformerReg_feat(
(backbone): Backbone(
(fc1): Sequential(
(0): Linear(in_features=5, out_features=32, bias=True)
(1): ReLU()
(2): Linear(in_features=32, out_features=32, bias=True)
)
(transformer1): TransformerBlock(
(fc1): Linear(in_features=32, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=32, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(transition_downs): ModuleList(
(0): TransitionDown(
(conv1): Sequential(
(0): Conv1d(35, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(1): TransitionDown(
(conv1): Sequential(
(0): Conv1d(67, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(2): TransitionDown(
(conv1): Sequential(
(0): Conv1d(131, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(3): TransitionDown(
(conv1): Sequential(
(0): Conv1d(259, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv2): Sequential(
(0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(transformers): ModuleList(
(0): TransformerBlock(
(fc1): Linear(in_features=64, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=64, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(1): TransformerBlock(
(fc1): Linear(in_features=128, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=128, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(2): TransformerBlock(
(fc1): Linear(in_features=256, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=256, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
(3): TransformerBlock(
(fc1): Linear(in_features=512, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=512, bias=True)
(fc_delta): Sequential(
(0): Linear(in_features=3, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(fc_gamma): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
)
(w_qs): Linear(in_features=128, out_features=128, bias=False)
(w_ks): Linear(in_features=128, out_features=128, bias=False)
(w_vs): Linear(in_features=128, out_features=128, bias=False)
)
)
)
(transformer): Transformer(
(layers): ModuleList(
(0-4): 5 x ModuleList(
(0): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): Attention(
(to_k): Linear(in_features=512, out_features=512, bias=False)
(to_v): Linear(in_features=512, out_features=512, bias=False)
(to_q): Linear(in_features=512, out_features=512, bias=False)
(to_out): Sequential(
(0): Linear(in_features=512, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
)
)
)
)
(1): Residual(
(fn): PreNorm(
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(fn): FeedForward(
(net): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): GELU(approximate='none')
(2): Dropout(p=0.0, inplace=False)
(3): Linear(in_features=256, out_features=512, bias=True)
(4): Dropout(p=0.0, inplace=False)
)
)
)
)
)
)
)
(fc2): Sequential(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): Dropout(p=0.0, inplace=False)
(2): ReLU()
(3): Linear(in_features=256, out_features=32, bias=True)
)
(fc3): Sequential(
(0): ReLU()
(1): Linear(in_features=32, out_features=64, bias=True)
(2): Dropout(p=0.0, inplace=False)
(3): ReLU()
(4): Linear(in_features=64, out_features=3, bias=True)
)
)
Self.model_feat vadility passes.
Phase 1 use self defined model!
Phase 2 Training: Can train from scratch (is_train = True) or load pretrained phase 2 model (is_train = False).
In[19]:
mmDiffRunner.phase2_train(train_loader = None, is_train = False)
Phase 2 use pretrained model!
Testing mmDiff
In[20]:
#mmDiffRunner.test()
Total running time of the script: (4 minutes 45.885 seconds)
