From 0dec511094f15b6f669e047a60b836f5c4f01d98 Mon Sep 17 00:00:00 2001 From: Claire Chen Date: Wed, 27 Mar 2024 16:17:06 -0700 Subject: [PATCH] Heatmap inference on real data (#2) --- aograsp/aograsp_model/770-network.pth | Bin 0 -> 4023985 bytes aograsp/aograsp_model/conf.pth | Bin 0 -> 3247 bytes aograsp/model_utils.py | 48 +++++++++ aograsp/models/model_pointscore.py | 79 ++++++++++++++ aograsp/models/modules.py | 129 +++++++++++++++++++++++ aograsp/viz_utils.py | 121 ++++++++++++++++++++++ pyproject.toml | 23 ++++ run_pointscore_inference.py | 144 ++++++++++++++++++++++++++ test_data/real/microwave_closed.ply | Bin 0 -> 110799 bytes test_data/real/microwave_open.ply | Bin 0 -> 110799 bytes 10 files changed, 544 insertions(+) create mode 100644 aograsp/aograsp_model/770-network.pth create mode 100644 aograsp/aograsp_model/conf.pth create mode 100644 aograsp/model_utils.py create mode 100644 aograsp/models/model_pointscore.py create mode 100644 aograsp/models/modules.py create mode 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f"\n--------------------------------------------------------------------------------" + ) + print(f"------ Restoring model to {ckpt_path} ----------------") + print( + f"--------------------------------------------------------------------------------\n" + ) + + data_to_restore = torch.load(ckpt_path) + + # Remove training-only parameters + layers_to_remove = [] + for key in data_to_restore: + if "siamese" in key: + layers_to_remove.append(key) + for key in layers_to_remove: + del data_to_restore[key] + + model.load_state_dict(data_to_restore) + else: + raise ValueError("Specified checkpoint cannot be found.") + + return model diff --git a/aograsp/models/model_pointscore.py b/aograsp/models/model_pointscore.py new file mode 100644 index 0000000..c15d939 --- /dev/null +++ b/aograsp/models/model_pointscore.py @@ -0,0 +1,79 @@ +""" + Pointscore model +""" + +import torch +import torch.nn as nn +import os + +from aograsp.models.modules import PointNet2SemSegSSG, PointScore + + +class Model_PointScore(nn.Module): + def __init__(self, conf): + super(Model_PointScore, self).__init__() + self.conf = conf + self.feat_dim = conf.feat_dim + + self.pointnet2 = PointNet2SemSegSSG( + { + "feat_dim": conf.feat_dim, + "pn_radius": conf.pn_radius, + "pn_nsample": conf.pn_nsample, + } + ) + + self.point_score = PointScore( + self.feat_dim, + dropout_p=self.conf.dropout_p, + k=conf.pointscore_k, + weight_loss=conf.pointscore_weight_loss, + ) + + def forward(self, input_dict): + pcs = input_dict["pcs"] + batch_size = pcs.shape[0] + pcs = pcs.repeat(1, 1, 2) + + # push through PointNet++ + whole_feats = self.pointnet2(pcs) + net = whole_feats.permute(0, 2, 1) + point_score_heatmap = self.point_score(net).reshape(batch_size, -1) + # [B, N, 1] --> [B, N] + + output_dict = { + "whole_feats": net, + "point_score_heatmap": point_score_heatmap, + } + return output_dict + + def loss(self, output_dict, input_dict, gt_labels): + pred_heatmap = output_dict["point_score_heatmap"] + gt_heatmap = input_dict["heatmap"] + + # Compute point score loss + point_score_loss = self.point_score.get_topk_mse_loss(pred_heatmap, gt_heatmap) + point_score_loss = point_score_loss.mean() + return { + "total_loss": point_score_loss, + "point_score_loss": point_score_loss, + } + + def test(self, input_dict, gt_labels): + """Run inference with model and get error between predicted and gt heatmaps""" + + pcs = input_dict["pcs"] + batch_size = pcs.shape[0] + pcs = pcs.repeat(1, 1, 2) + + with torch.no_grad(): + whole_feats = self.pointnet2(pcs) + net = whole_feats.permute(0, 2, 1) + pred_point_score_map = self.point_score(net).reshape(batch_size, -1) + + output_dict = { + "whole_feats": net, + "point_score_heatmap": pred_point_score_map, + } + + return output_dict diff --git a/aograsp/models/modules.py b/aograsp/models/modules.py new file mode 100644 index 0000000..06ac0d6 --- /dev/null +++ b/aograsp/models/modules.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +# https://github.com/erikwijmans/Pointnet2_PyTorch +from pointnet2_ops.pointnet2_modules import PointnetFPModule, PointnetSAModule +from pointnet2.models.pointnet2_ssg_cls import PointNet2ClassificationSSG + + +class PointNet2SemSegSSG(PointNet2ClassificationSSG): + def _build_model(self): + radius = self.hparams["pn_radius"] + nsample = self.hparams["pn_nsample"] + + self.SA_modules = nn.ModuleList() + self.SA_modules.append( + PointnetSAModule( + npoint=1024, + radius=radius[0], + nsample=nsample[0], + mlp=[3, 32, 32, 64], + use_xyz=True, + ) + ) + self.SA_modules.append( + PointnetSAModule( + npoint=256, + radius=radius[1], + nsample=nsample[1], + mlp=[64, 64, 64, 128], + use_xyz=True, + ) + ) + self.SA_modules.append( + PointnetSAModule( + npoint=64, + radius=radius[2], + nsample=nsample[2], + mlp=[128, 128, 128, 256], + use_xyz=True, + ) + ) + self.SA_modules.append( + PointnetSAModule( + npoint=16, + radius=radius[3], + nsample=nsample[3], + mlp=[256, 256, 256, 512], + use_xyz=True, + ) + ) + + self.FP_modules = nn.ModuleList() + self.FP_modules.append(PointnetFPModule(mlp=[128 + 3, 128, 128, 128])) + self.FP_modules.append(PointnetFPModule(mlp=[256 + 64, 256, 128])) + self.FP_modules.append(PointnetFPModule(mlp=[256 + 128, 256, 256])) + self.FP_modules.append(PointnetFPModule(mlp=[512 + 256, 256, 256])) + + self.fc_layer = nn.Sequential( + nn.Conv1d(128, self.hparams["feat_dim"], kernel_size=1, bias=False), + nn.BatchNorm1d(self.hparams["feat_dim"]), + nn.ReLU(True), + ) + + def forward(self, pointcloud): + r""" + Forward pass of the network + + Parameters + ---------- + pointcloud: Variable(torch.cuda.FloatTensor) + (B, N, 3 + input_channels) tensor + Point cloud to run predicts on + Each point in the point-cloud MUST + be formated as (x, y, z, features...) + """ + xyz, features = self._break_up_pc(pointcloud) + + l_xyz, l_features = [xyz], [features] + for i in range(len(self.SA_modules)): + li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) + l_xyz.append(li_xyz) + l_features.append(li_features) + + for i in range(-1, -(len(self.FP_modules) + 1), -1): + l_features[i - 1] = self.FP_modules[i]( + l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] + ) + + return self.fc_layer(l_features[0]) + + +class PointScore(nn.Module): + def __init__(self, feat_dim, dropout_p=0.0, k=128, weight_loss=True): + super(PointScore, self).__init__() + + self.mlp1 = nn.Linear(feat_dim, feat_dim) + self.mlp2 = nn.Linear(feat_dim, 1) + self.dropout = nn.Dropout(dropout_p) + + self.MSELoss = nn.MSELoss(reduction="none") + self.K = k + self.weight_loss = weight_loss + + # feats B x F + # output: B + def forward(self, feats): + net = self.dropout(F.leaky_relu(self.mlp1(feats))) + net = torch.sigmoid(self.mlp2(net)) + return net + + def get_topk_mse_loss(self, pred_logits, heatmap): + loss = self.MSELoss(pred_logits, heatmap.float()) + weights = torch.exp(heatmap) + if self.weight_loss: + loss = loss * weights + total_loss = torch.topk(loss, self.K, dim=1).values + return total_loss + + def get_all_pts_err(self, pred_heatmap, gt_heatmap): + """ + Compute error between predicted and gt labels of all points + Args: + pred_labels: predicted labels [B, N] + gt_labels: ground truth heatmap labels [B, N] + """ + err = self.MSELoss(pred_heatmap, gt_heatmap).mean() + + return err diff --git a/aograsp/viz_utils.py b/aograsp/viz_utils.py new file mode 100644 index 0000000..81e9bad --- /dev/null +++ b/aograsp/viz_utils.py @@ -0,0 +1,121 @@ +""" +Visualization functions +""" + +import os +import sys +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.cm +import open3d as o3d + + +def get_o3d_pts(pts): + """ + Get open3d pcd from pts np.array + """ + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(pts) + return pcd + + +def viz_heatmap( + all_pts, + heatmap_labels, + save_path=None, + frame="world", + draw_frame=False, + scale_cmap_to_heatmap_range=False, +): + pcd = get_o3d_pts(all_pts) + cmap = matplotlib.cm.get_cmap("RdYlGn") + if scale_cmap_to_heatmap_range: + # Scale heatmap labels to [0,1] to index into cmap + heatmap_labels = scale_to_0_1(heatmap_labels) + colors = cmap(np.squeeze(heatmap_labels))[:, :3] + pcd.colors = o3d.utility.Vector3dVector(colors) + + # Plot and save without opening a window + vis = o3d.visualization.Visualizer() + vis.create_window() + vis.add_geometry(pcd) + vis.update_geometry(pcd) + + # Draw ref frame + if draw_frame: + mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame( + size=0.1, origin=[0, 0, 0] + ) + vis.add_geometry(mesh_frame) + + if frame == "camera": + # If visualizing in camera frame, view pcd from scene view + ctr = vis.get_view_control() + param = ctr.convert_to_pinhole_camera_parameters() + fov = ctr.get_field_of_view() + H = np.eye(4) + H[2, 3] = 0.2 # Move camera back by 20cm + param.extrinsic = H + ctr.convert_from_pinhole_camera_parameters(param) + else: + # If world frame, place camera accordingly to face object front + ctr = vis.get_view_control() + param = ctr.convert_to_pinhole_camera_parameters() + fov = ctr.get_field_of_view() + H = np.eye(4) + H[2, -1] = 1 + R = Rotation.from_euler("XYZ", [90, 0, 90], degrees=True).as_matrix() + H[:3, :3] = R + param.extrinsic = H + ctr.convert_from_pinhole_camera_parameters(param) + + vis.poll_events() + vis.update_renderer() + + if save_path is None: + vis.run() + else: + vis.capture_screen_image( + save_path, + do_render=True, + ) + vis.destroy_window() + + +def scale_to_0_1(data): + return (data - np.min(data)) / (np.max(data) - np.min(data)) + + +def viz_histogram( + labels, + save_path=None, + scale_cmap_to_heatmap_range=False, +): + # Plot histgram with y axis log scale + if scale_cmap_to_heatmap_range: + # Scale histogram min, max to labels range + n, bins, patches = plt.hist(labels, log=True, range=(min(labels), max(labels))) + else: + # Use histogram range [0,1] + n, bins, patches = plt.hist(labels, log=True, range=(0, 1)) + + # Set each bar according to color map + cmap = matplotlib.cm.get_cmap("RdYlGn") + bin_centers = 0.5 * (bins[:-1] + bins[1:]) + # Scale values to interval [0,1] + col = bin_centers - min(bin_centers) + if scale_cmap_to_heatmap_range: + # Scale heatmap labels to [0,1] to index into cmap + col = scale_to_0_1(col) + for c, p in zip(col, patches): + plt.setp(p, "facecolor", cmap(c)) + + # Label each bar with count + plt.bar_label(patches) + + if save_path is None: + plt.show() + else: + plt.savefig(save_path) + plt.close() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9cf82a9 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,23 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "aograsp" +version = "0.0.1" +authors = [ + { name="Claire Chen" }, + { name="Carlota Pares" }, +] +description = "AO-Grasp" +readme = "README.md" +requires-python = ">=3.7" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[tool.setuptools.packages.find] +include = ["ao-grasp*"] +exclude = ["viz_data*", "test_data*"] \ No newline at end of file diff --git a/run_pointscore_inference.py b/run_pointscore_inference.py new file mode 100644 index 0000000..0228567 --- /dev/null +++ b/run_pointscore_inference.py @@ -0,0 +1,144 @@ +""" +Run pointscore inference on partial pointcloud +""" + +import os +from argparse import ArgumentParser +import numpy as np +import torch +import open3d as o3d + +# from train_model.test import load_conf +import aograsp.viz_utils as v_utils +import aograsp.model_utils as m_utils + + +def get_heatmap(args): + # Load model and weights + print("Loading model and weights") + model = m_utils.load_model() + model.to(args.device) + + # Directory to save output + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + + # Create directories to save heatmaps and heatmap images + point_score_dir = os.path.join(args.output_dir, "point_score") + point_score_img_dir = os.path.join(args.output_dir, "point_score_img") + os.makedirs(point_score_dir, exist_ok=True) + os.makedirs(point_score_img_dir, exist_ok=True) + + # Read from args.pcd_path and put into input_dict for model + if args.pcd_path is None: + raise ValueError("Missing path to input point cloud (--pcd_path)") + + pcd_ext = os.path.splitext(args.pcd_path)[1] + data_name = os.path.splitext(os.path.basename(args.pcd_path))[0] + if pcd_ext == ".ply": + # Open3d pointcloud + pcd = o3d.io.read_point_cloud(args.pcd_path) + pts_arr = np.array(pcd.points) + else: + raise ValueError(f"{pcd_ext} filetype not supported") + + # Recenter pcd to origin + mean = np.mean(pts_arr, axis=0) + pts_arr -= mean + + # Get pts as tensor and create input dict for model + pts = torch.from_numpy(pts_arr).float().to(args.device) + pts = torch.unsqueeze(pts, dim=0) + input_dict = {"pcs": pts} + + # Run inference + print("Computing heatmap") + model.eval() + with torch.no_grad(): + test_dict = model.test(input_dict, None) + + # Save heatmap point cloud + scores = test_dict["point_score_heatmap"][0].cpu().numpy() + + pcd_path = os.path.join(point_score_dir, f"{data_name}.npz") + heatmap_dict = { + "pts": pts_arr + mean, # Save original un-centered data + "labels": scores, + } + np.savez_compressed(pcd_path, data=heatmap_dict) + + # Save image of heatmap + fig_path = os.path.join(point_score_img_dir, f"heatmap_{data_name}.png") + hist_path = os.path.join(point_score_img_dir, f"heatmap_{data_name}_hist.png") + try: + v_utils.viz_heatmap( + heatmap_dict["pts"], + scores, + save_path=fig_path, + frame="camera", + scale_cmap_to_heatmap_range=True, + ) + except Exception as e: + print(e) + + v_utils.viz_histogram( + scores, + save_path=hist_path, + scale_cmap_to_heatmap_range=True, + ) + print(f"Heatmap saved to: {pcd_path}") + print(f"Visualization saved to: {fig_path}") + + +def parse_args(): + parser = ArgumentParser() + + # main parameters (required) + parser.add_argument( + "--output_dir", + type=str, + default="output", + help="Path to directory to write output files", + ) + parser.add_argument("--pcd_path", type=str, help="Path to seg_pcd_clean.ply") + + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="cpu or cuda:x for using cuda on GPU x", + ) + + parser.add_argument( + "--display", + type=str, + default=":3", + choices=[":1", ":2", ":3"], + help="Display number", + ) + + # parse args + conf = parser.parse_args() + + conf.data_path = None + + return conf + + +def main(args): + os.environ["DISPLAY"] = args.display + + ### prepare before training + # make exp_name + + # Load and test single trained model + # Load conf params + # load_conf_path = os.path.join(conf.exp_dir, "conf.pth") + # conf = load_conf(conf, load_conf_path) + + get_heatmap(args) + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/test_data/real/microwave_closed.ply b/test_data/real/microwave_closed.ply new file mode 100644 index 0000000000000000000000000000000000000000..abf8c95e261019f8af0112fcf7b406c1b362476e GIT binary patch literal 110799 zcmZU5cT`i)6K^2&-a8Vh0wRK_DCh?4C`A!LY!Lxb>4FtBD)1EqL3$C53etP88=6Ry z-Vp;T(u>$dc=v98=e%>?`^#gN*`1x8o%u}J>+XD$$I;Ex<-8Zk?vm?y&zt9*FL`-6 zJDhWHwZC-Um87$2GtUJ#7Z(RtFVY@Qhx1+z_9VNTBy)EM*DZT_9Go4{$TbH~FNYf> zz0Ete@wj`sxnuvmNwRnIwsUqM-T420x%vP7a_j&8;(fvCyeG-i!T$gK>7u8DgX{nM zqn)$20}saPIVXqn_70vr{+7D3)QU3r=>w;H`qM~AAQ0#|m*#-5fRHJp&wR`92%OHc z)+qi#2iOqxQ{f_DV`IxudKN2iyb>sS+ZJ)RR^mf+cJr@*lfc!Q#PN0GiC(yQlWp6> zd5BL)NF*)+3yb3u6|7OgCvrjXz~{0J2DSJQL)~Ku3`F1G_u_F&4tHSe?RRa72s54uOHJm8@rF>eE3ICfd39@OWCZ|;Z$T#`T`Il zux(%XTeOmw3r_b=hbMbp#D^H9zy+YqC!{1$G^uiT2s%C#oUd03MiXx7oCmC|tWM`9 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