app.py

import gradio as gr
import mathutils
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.cm as cmx
import os.path as osp
import h5py
import random
import torch
import torch.nn as nn

from GDANet_cls import GDANET
from DGCNN import DGCNN

with open('shape_names.txt') as f:
    CLASS_NAME = f.read().splitlines()

model_gda = GDANET()
model_gda = nn.DataParallel(model_gda)
model_gda.load_state_dict(torch.load('./GDANet_WOLFMix.t7', map_location=torch.device('cpu')))
model_gda.eval()

model_dgcnn = DGCNN()
model_dgcnn = nn.DataParallel(model_dgcnn)
model_dgcnn.load_state_dict(torch.load('./dgcnn.t7', map_location=torch.device('cpu')))
model_dgcnn.eval()

def pyplot_draw_point_cloud(points, corruption):
    rot1 = mathutils.Euler([-math.pi / 2, 0, 0]).to_matrix().to_3x3()
    rot2 = mathutils.Euler([0, 0, math.pi]).to_matrix().to_3x3()
    points = np.dot(points, rot1)
    points = np.dot(points, rot2)
    x, y, z = points[:, 0], points[:, 1], points[:, 2]
    colorsMap = 'winter'
    cs = y
    cm = plt.get_cmap(colorsMap)
    cNorm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
    fig = plt.figure(figsize=(5, 5))
    ax = fig.add_subplot(111, projection='3d')
    ax.scatter(x, y, z, c=scalarMap.to_rgba(cs))
    scalarMap.set_array(cs)
    ax.set_xlim(-1, 1)
    ax.set_ylim(-1, 1)
    ax.set_zlim(-1, 1)
    plt.axis('off')
    plt.title(corruption, fontsize=30)
    plt.tight_layout()
    plt.savefig('visualization.png', bbox_inches='tight', dpi=200)
    plt.close()



def load_dataset(corruption_idx, severity):
    corruptions = [
        'clean',
        'scale',
        'jitter',
        'rotate',
        'dropout_global',
        'dropout_local',
        'add_global',
        'add_local',
    ]
    corruption_type = corruptions[corruption_idx]
    if corruption_type == 'clean':
        f = h5py.File(osp.join('modelnet_c', corruption_type + '.h5'))
    else:
        f = h5py.File(osp.join('modelnet_c', corruption_type + '_{}'.format(severity-1) + '.h5'))
    data = f['data'][:].astype('float32')
    label = f['label'][:].astype('int64')
    f.close()
    return data, label

def recognize_pcd(model, pcd):
    pcd = torch.tensor(pcd).unsqueeze(0)
    pcd = pcd.permute(0, 2, 1)
    output = model(pcd)
    prediction = output.softmax(-1).flatten()
    _, top5_idx = torch.topk(prediction, 5)
    return {CLASS_NAME[i]: float(prediction[i]) for i in top5_idx.tolist()}

def run(seed, corruption_idx, severity):
    data, label = load_dataset(corruption_idx, severity)
    sample_indx = int(seed)
    pcd, cls = data[sample_indx], label[sample_indx]
    pyplot_draw_point_cloud(pcd, CLASS_NAME[cls[0]])
    output = 'visualization.png'
    return output, recognize_pcd(model_dgcnn, pcd), recognize_pcd(model_gda, pcd)

if __name__ == '__main__':
    iface = gr.Interface(
        fn=run,
        inputs=[
            gr.components.Number(label='Sample Seed', precision=0),
            gr.components.Radio(
                ['Clean', 'Scale', 'Jitter', 'Rotate', 'Drop Global', 'Drop Local', 'Add Global', 'Add Local'],
                value='Clean', type="index", label='Corruption Type'),
            gr.components.Slider(1, 5, step=1, label='Corruption severity'),
        ],
        outputs=[
            gr.components.Image(type="file", label="Visualization"),
            gr.components.Label(num_top_classes=5, label="Baseline (DGCNN) Prediction"),
            gr.components.Label(num_top_classes=5, label="Ours (GDANet+WolfMix) Prediction")
        ],
        live=False,
        allow_flagging='never',
        title="Benchmarking and Analyzing Point Cloud Classification under Corruptions [ICML 2022]",
        description="Welcome to the demo of ModelNet-C! You can visualize various types of corrupted point clouds in ModelNet-C and see how our proposed techniques contribute to robust predicitions compared to baseline methods.",
        examples=[
            [0, 'Jitter', 5],
            [999, 'Drop Local', 5],
        ],
        # css=".output-image, .image-preview {height: 500px !important}",
        article="<p style='text-align: center'><a href='https://github.com/jiawei-ren/ModelNet-C' target='_blank'>ModelNet-C @ GitHub</a></p> "
    )
    iface.launch()