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="

ModelNet-C @ GitHub

" ) iface.launch()