Support HAA4D
This commit is contained in:
Yiming Dou
@ -47,6 +47,7 @@ The SMPL human body layer for Pytorch is from the [smplpytorch](https://github.c
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- [UTD-MHAD](https://personal.utdallas.edu/~kehtar/UTD-MHAD.html)
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- [Human3.6M](http://vision.imar.ro/human3.6m/description.php)
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- [NTU](https://rose1.ntu.edu.sg/dataset/actionRecognition/)
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- [HAA4D](https://cse.hkust.edu.hk/haa4d/dataset.html)
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- Set the **DATASET.PATH** in the corresponding configuration file to the location of dataset.
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5
demo.py
5
demo.py
@ -1,4 +1,6 @@
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import torch
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import random
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import numpy as np
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from smplpytorch.pytorch.smpl_layer import SMPL_Layer
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from display_utils import display_model
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@ -15,7 +17,7 @@ if __name__ == '__main__':
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model_root='smplpytorch/native/models')
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# Generate random pose and shape parameters
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pose_params = torch.rand(batch_size, 72) * 0.2
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pose_params = torch.rand(batch_size, 72) * 0.01
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shape_params = torch.rand(batch_size, 10) * 0.03
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# GPU mode
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@ -26,7 +28,6 @@ if __name__ == '__main__':
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# Forward from the SMPL layer
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verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
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print(Jtr)
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# Draw output vertices and joints
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display_model(
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@ -1,3 +1,4 @@
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from xml.parsers.expat import model
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from matplotlib import pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from mpl_toolkits.mplot3d.art3d import Poly3DCollection
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@ -21,7 +22,8 @@ def display_model(
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if ax is None:
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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verts, joints = model_info['verts'][batch_idx], model_info['joints'][batch_idx]
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verts = model_info['verts'][batch_idx]
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joints = model_info['joints'][batch_idx]
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if model_faces is None:
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ax.scatter(verts[:, 0], verts[:, 1], verts[:, 2], alpha=0.2)
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elif not only_joint:
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24
fit/configs/HAA4D.json
Normal file
24
fit/configs/HAA4D.json
Normal file
@ -0,0 +1,24 @@
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{
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"MODEL": {
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"GENDER": "neutral"
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},
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"TRAIN": {
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"LEARNING_RATE": 1e-2,
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"MAX_EPOCH": 1000,
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"WRITE": 10,
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"OPTIMIZE_SCALE":1,
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"OPTIMIZE_SHAPE":1
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},
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"USE_GPU": 1,
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"DATASET": {
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"NAME": "NTU",
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"PATH": "../NTU RGB+D/result",
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"TARGET_PATH": "",
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"DATA_MAP": [
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[0,0],[1,4],[2,1],[4,5],[5,2],[7,6],[8,3],
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[12,9],[18,12],[19,15],[20,13],[21,16],
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[15,10],[6,1]
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]
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},
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"DEBUG": 0
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}
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@ -20,19 +20,19 @@
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],
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@ -40,52 +40,51 @@
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]
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@ -1156,127 +1156,127 @@ CMU_Mocap = {
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"41_09": "Climb"
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}
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NTU={
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"1":" drink water",
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"2":" eat meal/snack",
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"3":" brushing teeth",
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"4":" brushing hair",
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"5":" drop",
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"6":" pickup",
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"7":" throw",
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"8":" sitting down",
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"9":" standing up (from sitting position)",
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"10":" clapping",
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"11":" reading",
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"12":" writing",
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"13":" tear up paper",
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"14":" wear jacket",
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"15":" take off jacket",
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"16":" wear a shoe",
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"17":" take off a shoe",
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"18":" wear on glasses",
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"19":" take off glasses",
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"20":" put on a hat/cap",
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"21":" take off a hat/cap",
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"22":" cheer up",
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"23":" hand waving",
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"24":" kicking something",
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"25":" reach into pocket",
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"26":" hopping (one foot jumping)",
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"27":" jump up",
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"28":" make a phone call/answer phone",
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"29":" playing with phone/tablet",
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"30":" typing on a keyboard",
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"31":" pointing to something with finger",
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"32":" taking a selfie",
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"33":" check time (from watch)",
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"34":" rub two hands together",
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"35":" nod head/bow",
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"36":" shake head",
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"37":" wipe face",
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"38":" salute",
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"39":" put the palms together",
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"40":" cross hands in front (say stop)",
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"41":" sneeze/cough",
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"42":" staggering",
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"43":" falling",
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"44":" touch head (headache)",
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"45":" touch chest (stomachache/heart pain)",
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"46":" touch back (backache)",
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"47":" touch neck (neckache)",
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"48":" nausea or vomiting condition",
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"49":" use a fan (with hand or paper)/feeling warm",
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"50":" punching/slapping other person",
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"51":" kicking other person",
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"52":" pushing other person",
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"53":" pat on back of other person",
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"54":" point finger at the other person",
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"55":" hugging other person",
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"56":" giving something to other person",
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"57":" touch other person's pocket",
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"58":" handshaking",
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"59":" walking towards each other",
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"60":" walking apart from each other",
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"61":" put on headphone",
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"62":" take off headphone",
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"63":" shoot at the basket",
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"64":" bounce ball",
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"65":" tennis bat swing",
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"66":" juggling table tennis balls",
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"67":" hush (quite)",
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"68":" flick hair",
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"69":" thumb up",
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"70":" thumb down",
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"71":" make ok sign",
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"72":" make victory sign",
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"73":" staple book",
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"74":" counting money",
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"75":" cutting nails",
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"76":" cutting paper (using scissors)",
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"77":" snapping fingers",
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"78":" open bottle",
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"79":" sniff (smell)",
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"80":" squat down",
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"81":" toss a coin",
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"82":" fold paper",
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"83":" ball up paper",
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"84":" play magic cube",
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"85":" apply cream on face",
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"86":" apply cream on hand back",
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"87":" put on bag",
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"88":" take off bag",
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"89":" put something into a bag",
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"90":" take something out of a bag",
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"91":" open a box",
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"92":" move heavy objects",
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"93":" shake fist",
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"94":" throw up cap/hat",
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"95":" hands up (both hands)",
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"96":" cross arms",
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"97":" arm circles",
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"98":" arm swings",
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"99":" running on the spot",
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"100":" butt kicks (kick backward)",
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"101":" cross toe touch",
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"102":" side kick",
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"103":" yawn",
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"104":" stretch oneself",
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"105":" blow nose",
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"106":" hit other person with something",
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"107":" wield knife towards other person",
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"108":" knock over other person (hit with body)",
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"109":" grab other person’s stuff",
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"110":" shoot at other person with a gun",
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"111":" step on foot",
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"112":" high-five",
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"113":" cheers and drink",
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"114":" carry something with other person",
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"115":" take a photo of other person",
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"116":" follow other person",
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"117":" whisper in other person’s ear",
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"118":" exchange things with other person",
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"119":" support somebody with hand",
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"120":" finger-guessing game (playing rock-paper-scissors)",
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NTU = {
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"1":"drink water",
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"2":"eat meal/snack",
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"3":"brushing teeth",
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"4":"brushing hair",
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"5":"drop",
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"6":"pickup",
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"7":"throw",
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"8":"sitting down",
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"9":"standing up (from sitting position)",
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"10":"clapping",
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"11":"reading",
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"12":"writing",
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"13":"tear up paper",
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"14":"wear jacket",
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"15":"take off jacket",
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"16":"wear a shoe",
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"17":"take off a shoe",
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"18":"wear on glasses",
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"19":"take off glasses",
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"20":"put on a hat/cap",
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"21":"take off a hat/cap",
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"22":"cheer up",
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"23":"hand waving",
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"24":"kicking something",
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"25":"reach into pocket",
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"26":"hopping (one foot jumping)",
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"27":"jump up",
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"28":"make a phone call/answer phone",
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"29":"playing with phone/tablet",
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"30":"typing on a keyboard",
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"31":"pointing to something with finger",
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"32":"taking a selfie",
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"33":"check time (from watch)",
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"34":"rub two hands together",
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"35":"nod head/bow",
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"36":"shake head",
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"37":"wipe face",
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"38":"salute",
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"39":"put the palms together",
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"40":"cross hands in front (say stop)",
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"41":"sneeze/cough",
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"42":"staggering",
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"43":"falling",
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"44":"touch head (headache)",
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"45":"touch chest (stomachache/heart pain)",
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"46":"touch back (backache)",
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"47":"touch neck (neckache)",
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"48":"nausea or vomiting condition",
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"49":"use a fan (with hand or paper)/feeling warm",
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"50":"punching/slapping other person",
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"51":"kicking other person",
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"52":"pushing other person",
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"53":"pat on back of other person",
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"54":"point finger at the other person",
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"55":"hugging other person",
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"56":"giving something to other person",
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"57":"touch other person's pocket",
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"58":"handshaking",
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"59":"walking towards each other",
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"60":"walking apart from each other",
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"61":"put on headphone",
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"62":"take off headphone",
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"63":"shoot at the basket",
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"64":"bounce ball",
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"65":"tennis bat swing",
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"66":"juggling table tennis balls",
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"67":"hush (quite)",
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"68":"flick hair",
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"69":"thumb up",
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"70":"thumb down",
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"71":"make ok sign",
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"72":"make victory sign",
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"73":"staple book",
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"74":"counting money",
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"75":"cutting nails",
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"76":"cutting paper (using scissors)",
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"77":"snapping fingers",
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"78":"open bottle",
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"79":"sniff (smell)",
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"80":"squat down",
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"81":"toss a coin",
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"82":"fold paper",
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"83":"ball up paper",
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"84":"play magic cube",
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"85":"apply cream on face",
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"86":"apply cream on hand back",
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"87":"put on bag",
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"88":"take off bag",
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"89":"put something into a bag",
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"90":"take something out of a bag",
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"91":"open a box",
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"92":"move heavy objects",
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"93":"shake fist",
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"94":"throw up cap/hat",
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"95":"hands up (both hands)",
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"96":"cross arms",
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"97":"arm circles",
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"98":"arm swings",
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"99":"running on the spot",
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"100":"butt kicks (kick backward)",
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"101":"cross toe touch",
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"102":"side kick",
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"103":"yawn",
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"104":"stretch oneself",
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"105":"blow nose",
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"106":"hit other person with something",
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"107":"wield knife towards other person",
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"108":"knock over other person (hit with body)",
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"109":"grab other person’s stuff",
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"110":"shoot at other person with a gun",
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"111":"step on foot",
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"112":"high-five",
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"113":"cheers and drink",
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"114":"carry something with other person",
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"115":"take a photo of other person",
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"116":"follow other person",
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"117":"whisper in other person’s ear",
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"118":"exchange things with other person",
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"119":"support somebody with hand",
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"120":"finger-guessing game (playing rock-paper-scissors)",
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}
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def get_label(file_name, dataset_name):
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@ -19,5 +19,7 @@ def load(name, path):
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elif name == "Human3.6M":
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return np.load(path, allow_pickle=True)[0::5] # down_sample
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elif name == "NTU":
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return np.load(path, allow_pickle=True)[0::2]
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elif name == "HAA4D":
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return np.load(path, allow_pickle=True)
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@ -1,32 +1,33 @@
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import os
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import sys
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sys.path.append(os.getcwd())
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from meters import Meters
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from smplpytorch.pytorch.smpl_layer import SMPL_Layer
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from train import train
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from transform import transform
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from save import save_pic, save_params
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from load import load
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import torch
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import numpy as np
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from tensorboardX import SummaryWriter
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from easydict import EasyDict as edict
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import time
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import sys
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import os
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import logging
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import argparse
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import json
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sys.path.append(os.getcwd())
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from load import load
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from save import save_pic, save_params
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from transform import transform
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from train import train
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from smplpytorch.pytorch.smpl_layer import SMPL_Layer
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from meters import Meters
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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def parse_args():
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parser = argparse.ArgumentParser(description='Fit SMPL')
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parser.add_argument('--exp', dest='exp',
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help='Define exp name',
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default=time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time())), type=str)
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parser.add_argument('--dataset_name', dest='dataset_name',
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parser.add_argument('--dataset_name', '-n', dest='dataset_name',
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help='select dataset',
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default='', type=str)
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parser.add_argument('--dataset_path', dest='dataset_path',
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@ -97,15 +98,18 @@ if __name__ == "__main__":
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center_idx=0,
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gender=cfg.MODEL.GENDER,
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model_root='smplpytorch/native/models')
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meters=Meters()
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meters = Meters()
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file_num = 0
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for root, dirs, files in os.walk(cfg.DATASET.PATH):
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for file in files:
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for file in sorted(files):
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if not 'baseball_swing' in file:
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continue
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file_num += 1
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logger.info('Processing file: {} [{} / {}]'.format(file, file_num, len(files)))
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target = torch.from_numpy(transform(args.dataset_name,load(args.dataset_name,
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os.path.join(root, file)))).float()
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logger.info(
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'Processing file: {} [{} / {}]'.format(file, file_num, len(files)))
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target = torch.from_numpy(transform(args.dataset_name, load(args.dataset_name,
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os.path.join(root, file)))).float()
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logger.info("target shape:{}".format(target.shape))
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res = train(smpl_layer, target,
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logger, writer, device,
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@ -115,7 +119,8 @@ if __name__ == "__main__":
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logger.info("avg_loss:{:.4f}".format(meters.avg))
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save_params(res, file, logger, args.dataset_name)
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# save_pic(res,smpl_layer,file,logger,args.dataset_name,target)
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save_pic(res, smpl_layer, file, logger, args.dataset_name, target)
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
logger.info("Fitting finished! Average loss: {:.9f}".format(meters.avg))
|
||||
logger.info(
|
||||
"Fitting finished! Average loss: {:.9f}".format(meters.avg))
|
||||
|
||||
@ -19,7 +19,7 @@ def save_pic(res, smpl_layer, file, logger, dataset_name, target):
|
||||
_, _, verts, Jtr = res
|
||||
file_name = re.split('[/.]', file)[-2]
|
||||
fit_path = "fit/output/{}/picture/{}".format(dataset_name, file_name)
|
||||
create_dir_not_exist(fit_path)
|
||||
os.makedirs(fit_path,exist_ok=True)
|
||||
logger.info('Saving pictures at {}'.format(fit_path))
|
||||
for i in tqdm(range(Jtr.shape[0])):
|
||||
display_model(
|
||||
@ -28,7 +28,7 @@ def save_pic(res, smpl_layer, file, logger, dataset_name, target):
|
||||
model_faces=smpl_layer.th_faces,
|
||||
with_joints=True,
|
||||
kintree_table=smpl_layer.kintree_table,
|
||||
savepath=os.path.join(fit_path+"/frame_{}".format(i)),
|
||||
savepath=os.path.join(fit_path+"/frame_{:0>4d}".format(i)),
|
||||
batch_idx=i,
|
||||
show=False,
|
||||
only_joint=True)
|
||||
@ -55,3 +55,21 @@ def save_params(res, file, logger, dataset_name):
|
||||
with open(os.path.join((fit_path),
|
||||
"{}_params.pkl".format(file_name)), 'wb') as f:
|
||||
pickle.dump(params, f)
|
||||
|
||||
|
||||
def save_single_pic(res, smpl_layer, epoch, logger, dataset_name, target):
|
||||
_, _, verts, Jtr = res
|
||||
fit_path = "fit/output/{}/picture".format(dataset_name)
|
||||
create_dir_not_exist(fit_path)
|
||||
logger.info('Saving pictures at {}'.format(fit_path))
|
||||
display_model(
|
||||
{'verts': verts.cpu().detach(),
|
||||
'joints': Jtr.cpu().detach()},
|
||||
model_faces=smpl_layer.th_faces,
|
||||
with_joints=True,
|
||||
kintree_table=smpl_layer.kintree_table,
|
||||
savepath=fit_path+"/epoch_{:0>4d}".format(epoch),
|
||||
batch_idx=60,
|
||||
show=False,
|
||||
only_joint=False)
|
||||
logger.info('Picture saved')
|
||||
@ -6,6 +6,7 @@ import os
|
||||
|
||||
from tqdm import tqdm
|
||||
sys.path.append(os.getcwd())
|
||||
from save import save_single_pic
|
||||
|
||||
|
||||
|
||||
@ -25,8 +26,10 @@ def init(smpl_layer, target, device, cfg):
|
||||
params["shape_params"].requires_grad = bool(cfg.TRAIN.OPTIMIZE_SHAPE)
|
||||
params["scale"].requires_grad = bool(cfg.TRAIN.OPTIMIZE_SCALE)
|
||||
|
||||
optimizer = optim.Adam([params["pose_params"], params["shape_params"], params["scale"]],
|
||||
lr=cfg.TRAIN.LEARNING_RATE)
|
||||
optim_params = [{'params': params["pose_params"], 'lr': cfg.TRAIN.LEARNING_RATE},
|
||||
{'params': params["shape_params"], 'lr': cfg.TRAIN.LEARNING_RATE},
|
||||
{'params': params["scale"], 'lr': cfg.TRAIN.LEARNING_RATE*10},]
|
||||
optimizer = optim.Adam(optim_params)
|
||||
|
||||
index = {}
|
||||
smpl_index = []
|
||||
@ -50,12 +53,15 @@ def train(smpl_layer, target,
|
||||
pose_params = params["pose_params"]
|
||||
shape_params = params["shape_params"]
|
||||
scale = params["scale"]
|
||||
|
||||
with torch.no_grad():
|
||||
verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
|
||||
params["scale"]*=(torch.max(torch.abs(target))/torch.max(torch.abs(Jtr)))
|
||||
|
||||
for epoch in tqdm(range(cfg.TRAIN.MAX_EPOCH)):
|
||||
# for epoch in range(cfg.TRAIN.MAX_EPOCH):
|
||||
verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
|
||||
loss = F.smooth_l1_loss(Jtr.index_select(1, index["smpl_index"]) * 100,
|
||||
target.index_select(1, index["dataset_index"]) * 100 * scale)
|
||||
loss = F.smooth_l1_loss(scale*Jtr.index_select(1, index["smpl_index"]),
|
||||
target.index_select(1, index["dataset_index"]))
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
@ -67,12 +73,15 @@ def train(smpl_layer, target,
|
||||
logger.info("Early stop at epoch {} !".format(epoch))
|
||||
break
|
||||
|
||||
if epoch % cfg.TRAIN.WRITE == 0:
|
||||
if epoch % cfg.TRAIN.WRITE == 0 or epoch<10:
|
||||
# logger.info("Epoch {}, lossPerBatch={:.6f}, scale={:.4f}".format(
|
||||
# epoch, float(loss),float(scale)))
|
||||
print("Epoch {}, lossPerBatch={:.6f}, scale={:.4f}".format(
|
||||
epoch, float(loss),float(scale)))
|
||||
writer.add_scalar('loss', float(loss), epoch)
|
||||
writer.add_scalar('learning_rate', float(
|
||||
optimizer.state_dict()['param_groups'][0]['lr']), epoch)
|
||||
# save_single_pic(res,smpl_layer,epoch,logger,args.dataset_name,target)
|
||||
|
||||
logger.info('Train ended, min_loss = {:.4f}'.format(
|
||||
float(meters.min_loss)))
|
||||
|
||||
@ -5,7 +5,8 @@ rotate = {
|
||||
'CMU_Mocap': [0.05, 0.05, 0.05],
|
||||
'UTD_MHAD': [-1., 1., -1.],
|
||||
'Human3.6M': [-0.001, -0.001, 0.001],
|
||||
'NTU': [-1., 1., -1.]
|
||||
'NTU': [1., 1., -1.],
|
||||
'HAA4D': [1., -1., -1.],
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import imageio, os
|
||||
images = []
|
||||
filenames = sorted(fn for fn in os.listdir('./fit/output/Human3.6M/picture/fit/s_01_act_09_subact_02_ca_02') )
|
||||
filenames = sorted(fn for fn in os.listdir('D:/OneDrive - sjtu.edu.cn/MVIG/Action-Dataset/Pose_to_SMPL/fit/output/NTU/picture') )
|
||||
for filename in filenames:
|
||||
images.append(imageio.imread('./fit/output/Human3.6M/picture/fit/s_01_act_09_subact_02_ca_02/'+filename))
|
||||
imageio.mimsave('fit_mesh.gif', images, duration=0.2)
|
||||
images.append(imageio.imread('D:/OneDrive - sjtu.edu.cn/MVIG/Action-Dataset/Pose_to_SMPL/fit/output/NTU/picture/'+filename))
|
||||
imageio.mimsave('clapping_example.gif', images, duration=0.2)
|
||||
Reference in New Issue
Block a user