Get HumanML3D working on High Performance Computer Rivanna of the University of Virginia.

.gitignore

@@ -0,0 +1,11 @@
+__pycache__
+.ipynb_checkpoints
+Mean.npy
+Std.npy
+amass_data
+animations
+body_models
+joints
+new_joints
+new_joint_vecs
+pose_data

animation.ipynb

@@ -145,7 +145,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This will take a few hours for the whole dataset. Here we show ten animations for an example\n"
+    "This will take a few hours for the whole dataset."
    ]
   },
   {

animation.py

@@ -0,0 +1,141 @@
+import os
+from os.path import join as pjoin
+from tqdm import tqdm
+import numpy as np
+import argparse
+import math
+
+
+import matplotlib
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib.animation import FuncAnimation, PillowWriter
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+import mpl_toolkits.mplot3d.axes3d as p3
+def plot_3d_motion(save_path, kinematic_tree, joints, title, figsize=(10, 10), fps=120, radius=4):
+#     matplotlib.use('Agg')
+
+    title_sp = title.split(' ')
+    if len(title_sp) > 10:
+        title = '\n'.join([' '.join(title_sp[:10]), ' '.join(title_sp[10:])])
+    def init():
+        ax.set_xlim3d([-radius / 2, radius / 2])
+        ax.set_ylim3d([0, radius])
+        ax.set_zlim3d([0, radius])
+        # print(title)
+        fig.suptitle(title, fontsize=20)
+        ax.grid(b=False)
+
+    def plot_xzPlane(minx, maxx, miny, minz, maxz):
+        ## Plot a plane XZ
+        verts = [
+            [minx, miny, minz],
+            [minx, miny, maxz],
+            [maxx, miny, maxz],
+            [maxx, miny, minz]
+        ]
+        xz_plane = Poly3DCollection([verts])
+        xz_plane.set_facecolor((0.5, 0.5, 0.5, 0.5))
+        ax.add_collection3d(xz_plane)
+
+    #         return ax
+
+    # (seq_len, joints_num, 3)
+    data = joints.copy().reshape(len(joints), -1, 3)
+    fig = plt.figure(figsize=figsize)
+    ax = fig.add_subplot(111, projection = "3d")
+    init()
+    MINS = data.min(axis=0).min(axis=0)
+    MAXS = data.max(axis=0).max(axis=0)
+    colors = ['red', 'blue', 'black', 'red', 'blue',  
+              'darkblue', 'darkblue', 'darkblue', 'darkblue', 'darkblue',
+             'darkred', 'darkred','darkred','darkred','darkred']
+    frame_number = data.shape[0]
+    #     print(data.shape)
+
+    height_offset = MINS[1]
+    data[:, :, 1] -= height_offset
+    trajec = data[:, 0, [0, 2]]
+
+    data[..., 0] -= data[:, 0:1, 0]
+    data[..., 2] -= data[:, 0:1, 2]
+
+    #     print(trajec.shape)
+
+    def update(index):
+        #         print(index)
+        ax.cla()
+
+        ax.set_xlim3d([-radius / 2, radius / 2])
+        ax.set_ylim3d([0, radius])
+        ax.set_zlim3d([0, radius])
+        ax.grid(False)
+        ax.view_init(elev = 120, azim = -90)
+        ax.dist = 7.5
+
+        plot_xzPlane(MINS[0]-trajec[index, 0], MAXS[0]-trajec[index, 0], 0, MINS[2]-trajec[index, 1], MAXS[2]-trajec[index, 1])
+#         ax.scatter(data[index, :22, 0], data[index, :22, 1], data[index, :22, 2], color='black', s=3)
+
+        if index > 1:
+            ax.plot3D(trajec[:index, 0]-trajec[index, 0], np.zeros_like(trajec[:index, 0]), trajec[:index, 1]-trajec[index, 1], linewidth=1.0,
+                      color='blue')
+        #             ax = plot_xzPlane(ax, MINS[0], MAXS[0], 0, MINS[2], MAXS[2])
+
+
+        for i, (chain, color) in enumerate(zip(kinematic_tree, colors)):
+#             print(color)
+            if i < 5:
+                linewidth = 4.0
+            else:
+                linewidth = 2.0
+            ax.plot3D(data[index, chain, 0], data[index, chain, 1], data[index, chain, 2], linewidth=linewidth, color=color)
+        #         print(trajec[:index, 0].shape)
+
+        plt.axis('off')
+        ax.set_xticklabels([])
+        ax.set_yticklabels([])
+        ax.set_zticklabels([])
+
+    ani = FuncAnimation(fig, update, frames=frame_number, interval=1000/fps, repeat=False)
+
+    ani.save(save_path, fps=fps)
+    plt.close()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--num-batches", type = int, default = 10)
+    parser.add_argument("--batch-idx", type = int, default = 0, help = "0 based batch index")
+    args = parser.parse_args()
+
+    src_dir = './HumanML3D/new_joints/'
+    tgt_ani_dir = "./HumanML3D/animations/"
+
+
+    kinematic_chain = [[0, 2, 5, 8, 11], [0, 1, 4, 7, 10], [0, 3, 6, 9, 12, 15], [9, 14, 17, 19, 21], [9, 13, 16, 18, 20]]
+    os.makedirs(tgt_ani_dir, exist_ok=True)
+
+    npy_files = os.listdir(src_dir)
+    npy_files = sorted(npy_files)
+    # npy_files = npy_files[:10]
+
+    n = len(npy_files)
+    batch_size = math.ceil(n / args.num_batches)
+    start = args.batch_idx * batch_size
+    end = min(start + batch_size, n)
+    batch = npy_files[start:end]
+    desc = f"batch {args.batch_idx + 1} / {args.num_batches} ({start}:{end} of {n})"
+
+
+    for npy_file in tqdm(batch, desc = desc):
+        src_path = pjoin(src_dir, npy_file)
+        data = np.load(src_path)
+        save_path = pjoin(tgt_ani_dir, npy_file[:-3] + 'mp4')
+        if os.path.exists(save_path):
+            continue
+        #   You may set the title on your own.
+        plot_3d_motion(save_path, kinematic_chain, data, title="None", fps=20, radius=4)
+
+
+if __name__ == "__main__":
+    main()

cal_mean_variance.py

@@ -0,0 +1,61 @@
+import numpy as np
+import sys
+import os
+from os.path import join as pjoin
+
+
+# root_rot_velocity (B, seq_len, 1)
+# root_linear_velocity (B, seq_len, 2)
+# root_y (B, seq_len, 1)
+# ric_data (B, seq_len, (joint_num - 1)*3)
+# rot_data (B, seq_len, (joint_num - 1)*6)
+# local_velocity (B, seq_len, joint_num*3)
+# foot contact (B, seq_len, 4)
+def mean_variance(data_dir, save_dir, joints_num):
+    file_list = os.listdir(data_dir)
+    data_list = []
+
+    for file in file_list:
+        data = np.load(pjoin(data_dir, file))
+        if np.isnan(data).any():
+            print(file)
+            continue
+        data_list.append(data)
+
+    data = np.concatenate(data_list, axis=0)
+    print(data.shape)
+    Mean = data.mean(axis=0)
+    Std = data.std(axis=0)
+    Std[0:1] = Std[0:1].mean() / 1.0
+    Std[1:3] = Std[1:3].mean() / 1.0
+    Std[3:4] = Std[3:4].mean() / 1.0
+    Std[4: 4+(joints_num - 1) * 3] = Std[4: 4+(joints_num - 1) * 3].mean() / 1.0
+    Std[4+(joints_num - 1) * 3: 4+(joints_num - 1) * 9] = Std[4+(joints_num - 1) * 3: 4+(joints_num - 1) * 9].mean() / 1.0
+    Std[4+(joints_num - 1) * 9: 4+(joints_num - 1) * 9 + joints_num*3] = Std[4+(joints_num - 1) * 9: 4+(joints_num - 1) * 9 + joints_num*3].mean() / 1.0
+    Std[4 + (joints_num - 1) * 9 + joints_num * 3: ] = Std[4 + (joints_num - 1) * 9 + joints_num * 3: ].mean() / 1.0
+
+    assert 8 + (joints_num - 1) * 9 + joints_num * 3 == Std.shape[-1]
+
+    np.save(pjoin(save_dir, 'Mean.npy'), Mean)
+    np.save(pjoin(save_dir, 'Std.npy'), Std)
+
+    return Mean, Std
+
+
+# The given data is used to double check if you are on the right track.
+reference1 = np.load('./HumanML3D/Mean_reference.npy')
+reference2 = np.load('./HumanML3D/Std_reference.npy')
+
+
+if __name__ == '__main__':
+    data_dir = './HumanML3D/new_joint_vecs/'
+    save_dir = './HumanML3D/'
+    mean, std = mean_variance(data_dir, save_dir, 22)
+#     print(mean)
+#     print(Std)
+
+
+    print(abs(mean-reference1).sum())
+
+
+    print(abs(std-reference2).sum())

common/quaternion.py

@@ -10,7 +10,7 @@
 
 _EPS4 = np.finfo(float).eps * 4.0
 
-_FLOAT_EPS = np.finfo(np.float).eps
+_FLOAT_EPS = np.finfo(float).eps
 
 # PyTorch-backed implementations
 def qinv(q):

environment.yaml

@@ -134,10 +134,10 @@ dependencies:
   - zstd=1.4.9=haebb681_0
   - pip:
     - absl-py==1.0.0
-    - body-visualizer==1.1.0
+    #- body-visualizer==1.1.0
     - cachetools==4.2.4
     - charset-normalizer==2.0.12
-    - configer==1.4.1
+    #- configer==1.4.1
     - configparser==5.2.0
     - dotmap==1.3.23
     - freetype-py==2.3.0
@@ -147,15 +147,15 @@ dependencies:
     - grpcio==1.46.3
     - idna==3.3
     - imageio==2.19.3
-    - install==1.3.5
+    #- install==1.3.5
     - markdown==3.3.7
     - networkx==2.6.3
     - numpy==1.18.5
     - oauthlib==3.2.0
     - opencv-python==4.5.5.64
     - pillow==9.1.1
     - protobuf==3.20.1
-    - psbody-mesh==0.4
+    #- psbody-mesh==0.4
     - pyasn1==0.4.8
     - pyasn1-modules==0.2.8
     - pyglet==1.5.26