Thanks for your interest.
To train 4 streams on NW-UCLA, you can follow the following steps:

For joint stream, create a file ./config/train_nwucla.yaml. Paste the following code in it.

Experiment_name: ShiftGCN_NW-UCLA_joint

# feeder
feeder: feeders.feeder.Feeder
train_feeder_args:
  data_path: joint
  label_path: train
  debug: False
  random_choose: True
  random_shift: False
  random_move: False
  window_size: 52
  normalization: False

test_feeder_args:
  data_path: joint
  label_path: val
# model
model: model.shift_gcn.Model
model_args:
  num_class: 10
  num_point: 20
  num_person: 1
  graph: graph.ntu_rgb_d.Graph
  graph_args:
    labeling_mode: 'spatial'

#optim
weight_decay: 0.0001
base_lr: 0.1
step: [60, 80, 100]

# training
device: [0]
only_train_epoch: 1
batch_size: 16
test_batch_size: 64
num_epoch: 140
nesterov: True

For "bone", "joint motion" and "bone motion" streams, you should replace "data_path: joint" with the corresponding stream name in the above ./config/train_nwucla.yaml.

Replace the code in ./feeders/feeder.py with the following code. Note that /home/share/NW-UCLA/data/all_sqe/ is your directory for NW-UCLA dataset. The preprocess of NW-UCLA dataset is borrow from Chenyang Si, the author of "An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition".

import numpy as np
import pickle
import torch
from torch.utils.data import Dataset
import sys
import json
import random
import math
sys.path.extend(['../'])
from feeders import tools


class Feeder(Dataset):
    def __init__(self, data_path, label_path,
                 random_choose=False, random_shift=False, random_move=False,
                 window_size=-1, normalization=False, debug=False, use_mmap=True):

        if 'val' in label_path:
            self.train_val = 'val'
            self.data_dict = [{"file_name": "a05_s04_e02_v03", "length": 21, "label": 5}, {"file_name": "a12_s09_e04_v03", "length": 26, "label": 10}, {"file_name": "a03_s03_e04_v03", "length": 35, "label": 3}, {"file_name": "a08_s02_e01_v03", "length": 101, "label": 7}, {"file_name": "a03_s05_e03_v03", "length": 26, "label": 3}, {"file_name": "a12_s10_e01_v03", "length": 21, "label": 10}, {"file_name": "a01_s07_e03_v03", "length": 31, "label": 1}, {"file_name": "a03_s08_e02_v03", "length": 21, "label": 3}, {"file_name": "a11_s10_e03_v03", "length": 51, "label": 9}, {"file_name": "a11_s03_e00_v03", "length": 46, "label": 9}, {"file_name": "a03_s02_e00_v03", "length": 32, "label": 3}, {"file_name": "a11_s01_e04_v03", "length": 16, "label": 9}, {"file_name": "a09_s08_e04_v03", "length": 63, "label": 8}, {"file_name": "a09_s06_e01_v03", "length": 41, "label": 8}, {"file_name": "a09_s07_e01_v03", "length": 51, "label": 8}, {"file_name": "a02_s08_e01_v03", "length": 21, "label": 2}, {"file_name": "a01_s04_e01_v03", "length": 23, "label": 1}, {"file_name": "a02_s02_e02_v03", "length": 31, "label": 2}, {"file_name": "a02_s07_e05_v03", "length": 31, "label": 2}, {"file_name": "a06_s02_e00_v03", "length": 16, "label": 6}, {"file_name": "a03_s02_e02_v03", "length": 22, "label": 3}, {"file_name": "a11_s09_e04_v03", "length": 22, "label": 9}, {"file_name": "a09_s03_e04_v03", "length": 61, "label": 8}, {"file_name": "a04_s01_e02_v03", "length": 23, "label": 4}, {"file_name": "a12_s01_e01_v03", "length": 17, "label": 10}, {"file_name": "a02_s07_e03_v03", "length": 9, "label": 2}, {"file_name": "a05_s08_e04_v03", "length": 19, "label": 5}, {"file_name": "a02_s07_e02_v03", "length": 31, "label": 2}, {"file_name": "a04_s07_e02_v03", "length": 16, "label": 4}, {"file_name": "a01_s08_e03_v03", "length": 27, "label": 1}, {"file_name": "a08_s03_e01_v03", "length": 68, "label": 7}, {"file_name": "a04_s08_e03_v03", "length": 21, "label": 4}, {"file_name": "a03_s10_e00_v03", "length": 17, "label": 3}, {"file_name": "a04_s03_e03_v03", "length": 21, "label": 4}, {"file_name": "a06_s06_e02_v03", "length": 21, "label": 6}, {"file_name": "a09_s03_e00_v03", "length": 81, "label": 8}, {"file_name": "a09_s03_e03_v03", "length": 46, "label": 8}, {"file_name": "a04_s02_e02_v03", "length": 21, "label": 4}, {"file_name": "a08_s01_e02_v03", "length": 78, "label": 7}, {"file_name": "a04_s04_e00_v03", "length": 11, "label": 4}, {"file_name": "a03_s02_e03_v03", "length": 39, "label": 3}, {"file_name": "a05_s04_e00_v03", "length": 21, "label": 5}, {"file_name": "a05_s07_e03_v03", "length": 36, "label": 5}, {"file_name": "a06_s10_e00_v03", "length": 31, "label": 6}, {"file_name": "a11_s07_e00_v03", "length": 31, "label": 9}, {"file_name": "a03_s01_e01_v03", "length": 24, "label": 3}, {"file_name": "a04_s06_e01_v03", "length": 16, "label": 4}, {"file_name": "a08_s02_e04_v03", "length": 96, "label": 7}, {"file_name": "a09_s08_e03_v03", "length": 46, "label": 8}, {"file_name": 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{"file_name": "a06_s01_e01_v02", "length": 21, "label": 6}, {"file_name": "a12_s03_e02_v02", "length": 39, "label": 10}, {"file_name": "a08_s05_e02_v02", "length": 51, "label": 7}, {"file_name": "a03_s04_e00_v02", "length": 26, "label": 3}, {"file_name": "a11_s01_e03_v02", "length": 31, "label": 9}, {"file_name": "a03_s08_e01_v02", "length": 21, "label": 3}, {"file_name": "a11_s04_e00_v02", "length": 32, "label": 9}, {"file_name": "a04_s05_e00_v02", "length": 36, "label": 4}, {"file_name": "a12_s05_e01_v02", "length": 31, "label": 10}, {"file_name": "a02_s05_e02_v02", "length": 26, "label": 2}, {"file_name": "a06_s06_e01_v02", "length": 16, "label": 6}, {"file_name": "a03_s03_e02_v02", "length": 32, "label": 3}, {"file_name": "a11_s07_e02_v02", "length": 21, "label": 9}, {"file_name": "a11_s01_e02_v02", "length": 21, "label": 9}]
        self.nw_ucla_root = '/home/share/NW-UCLA/data/all_sqe/'
        self.time_steps = 52
        self.bone = [(1,2),(2,3),(3,3),(4,3),(5,3),(6,5),(7,6),(8,7),(9,3),(10,9),(11,10),(12,11),(13,1),(14,13),(15,14),(16,15),(17,1),(18,17),(19,18),(20,19)]


        self.label = []
        for index in range(len(self.data_dict)):
            info = self.data_dict[index]
            self.label.append(int(info['label']) - 1)

        self.debug = debug
        self.data_path = data_path
        self.label_path = label_path
        self.random_choose = random_choose
        self.random_shift = random_shift
        self.random_move = random_move
        self.window_size = window_size
        self.normalization = normalization
        self.use_mmap = use_mmap
        self.load_data()
        if normalization:
            self.get_mean_map()

    def load_data(self):
        # data: N C V T M
        pass

    def get_mean_map(self):
        data = self.data
        N, C, T, V, M = data.shape
        self.mean_map = data.mean(axis=2, keepdims=True).mean(axis=4, keepdims=True).mean(axis=0)
        self.std_map = data.transpose((0, 2, 4, 1, 3)).reshape((N * T * M, C * V)).std(axis=0).reshape((C, 1, V, 1))

    def __len__(self):
        return len(self.data_dict)

    def __iter__(self):
        return self

    def rand_view_transform(self,X, agx, agy, s): 
        agx = math.radians(agx)
        agy = math.radians(agy)
        Rx = np.asarray([[1,0,0], [0,math.cos(agx),math.sin(agx)], [0, -math.sin(agx),math.cos(agx)]])
        Ry = np.asarray([[math.cos(agy), 0, -math.sin(agy)], [0,1,0], [math.sin(agy), 0, math.cos(agy)]])
        Ss = np.asarray([[s,0,0],[0,s,0],[0,0,s]])
        X0 = np.dot(np.reshape(X,(-1,3)), np.dot(Ry,np.dot(Rx,Ss)))
        X = np.reshape(X0, X.shape)
        return X

    def __getitem__(self, index):
        info = self.data_dict[index]

        file_name = info['file_name']
        label = int(info['label']) - 1

        file_json = open(self.nw_ucla_root + file_name + '.json', 'r')
        data_dict = json.load(file_json)
        skeletons = data_dict['skeletons']  # data
        value = np.array(skeletons)

        if self.train_val == 'train':
            random.random()
            agx = random.randint(-60, 60)
            agy = random.randint(-60, 60)
            s = random.uniform(0.5, 1.5)

            center = value[0,1,:]
            value = value - center
            scalerValue = self.rand_view_transform(value, agx, agy, s)

            scalerValue = np.reshape(scalerValue, (-1, 3))
            scalerValue = (scalerValue - np.min(scalerValue,axis=0)) / (np.max(scalerValue,axis=0) - np.min(scalerValue,axis=0))
            scalerValue = scalerValue*2-1
            scalerValue = np.reshape(scalerValue, (-1, 20, 3))

            data = np.zeros( (self.time_steps, 20, 3) )

            value = scalerValue[:,:,:]
            length = value.shape[0]

            random_idx = random.sample(list(np.arange(length))*100, self.time_steps)
            random_idx.sort()
            data[:,:,:] = value[random_idx,:,:]

        else:
            random.random()
            agx = 0
            agy = 0
            s = 1.0

            center = value[0,1,:]
            value = value - center
            scalerValue = self.rand_view_transform(value, agx, agy, s)

            scalerValue = np.reshape(scalerValue, (-1, 3))
            scalerValue = (scalerValue - np.min(scalerValue,axis=0)) / (np.max(scalerValue,axis=0) - np.min(scalerValue,axis=0))
            scalerValue = scalerValue*2-1

            scalerValue = np.reshape(scalerValue, (-1, 20, 3))

            data = np.zeros( (self.time_steps, 20, 3) )

            value = scalerValue[:,:,:]
            length = value.shape[0]

            idx = np.linspace(0,length-1,self.time_steps).astype(np.int)
            data[:,:,:] = value[idx,:,:] # T,V,C

        if 'bone' in self.data_path:
            data_bone = np.zeros_like(data)
            for bone_idx in range(20):
                data_bone[:,self.bone[bone_idx][0]-1,:] = data[:,self.bone[bone_idx][0]-1,:] - data[:,self.bone[bone_idx][1]-1,:]
            data = data_bone

        if 'motion' in self.data_path:
            data_motion = np.zeros_like(data)
            data_motion[:-1,:,:] = data[1:,:,:] - data[:-1,:,:]
            data = data_motion

        data = np.transpose(data, (2, 0, 1))
        C,T,V = data.shape
        data = np.reshape(data,(C,T,V,1))

        return data, label, index

    def top_k(self, score, top_k):
        rank = score.argsort()

        hit_top_k = [l in rank[i, -top_k:] for i, l in enumerate(self.label)]
        return sum(hit_top_k) * 1.0 / len(hit_top_k)


def import_class(name):
    components = name.split('.')
    mod = __import__(components[0])
    for comp in components[1:]:
        mod = getattr(mod, comp)
    return mod

Replace 25 with 20 in the class Shift_gcn of ./model/shift_gcn.
Insert self.data_loader[ln].dataset.sample_name = np.arange(len(score)) to the line 451 of main.py.
Run CUDA_VISIBLE_DEVICES="0" python main.py --config ./config/train_nwucla.yaml.

4 stream for NW-UCLA #13

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