Corrections

datasets/NCLT.py

@@ -0,0 +1,502 @@
+#
+#
+#      0=================================0
+#      |    Kernel Point Convolutions    |
+#      0=================================0
+#
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Class handling SemanticKitti dataset.
+#      Implements a Dataset, a Sampler, and a collate_fn
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Hugues THOMAS - 11/06/2018
+#
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Imports and global variables
+#       \**********************************/
+#
+
+# Common libs
+import sys
+import struct
+import scipy
+import time
+import numpy as np
+import pickle
+import torch
+import yaml
+#from mayavi import mlab
+from multiprocessing import Lock
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+
+# OS functions
+from os import listdir
+from os.path import exists, join, isdir, getsize
+
+# Dataset parent class
+from datasets.common import *
+from torch.utils.data import Sampler, get_worker_info
+from utils.mayavi_visu import *
+from utils.metrics import fast_confusion
+
+from datasets.common import grid_subsampling
+from utils.config import bcolors
+
+
+def ssc_to_homo(ssc, ssc_in_radians=True):
+
+    # Convert 6-DOF ssc coordinate transformation to 4x4 homogeneous matrix
+    # transformation
+
+    if ssc.ndim == 1:
+        reduce = True
+        ssc = np.expand_dims(ssc, 0)
+    else:
+        reduce = False
+
+    if not ssc_in_radians:
+        ssc[:, 3:] = np.pi / 180.0 * ssc[:, 3:]
+
+    sr = np.sin(ssc[:, 3])
+    cr = np.cos(ssc[:, 3])
+
+    sp = np.sin(ssc[:, 4])
+    cp = np.cos(ssc[:, 4])
+
+    sh = np.sin(ssc[:, 5])
+    ch = np.cos(ssc[:, 5])
+
+    H = np.zeros((ssc.shape[0], 4, 4))
+
+    H[:, 0, 0] = ch*cp
+    H[:, 0, 1] = -sh*cr + ch*sp*sr
+    H[:, 0, 2] = sh*sr + ch*sp*cr
+    H[:, 1, 0] = sh*cp
+    H[:, 1, 1] = ch*cr + sh*sp*sr
+    H[:, 1, 2] = -ch*sr + sh*sp*cr
+    H[:, 2, 0] = -sp
+    H[:, 2, 1] = cp*sr
+    H[:, 2, 2] = cp*cr
+
+    H[:, 0, 3] = ssc[:, 0]
+    H[:, 1, 3] = ssc[:, 1]
+    H[:, 2, 3] = ssc[:, 2]
+
+    H[:, 3, 3] = 1
+
+    if reduce:
+        H = np.squeeze(H)
+
+    return H
+
+
+def verify_magic(s):
+
+    magic = 44444
+
+    m = struct.unpack('<HHHH', s)
+
+    return len(m)>=4 and m[0] == magic and m[1] == magic and m[2] == magic and m[3] == magic
+
+
+def test_read_hits():
+
+    data_path = '../../Data/NCLT'
+    velo_folder = 'velodyne_data'
+    day = '2012-01-08'
+
+    hits_path = join(data_path, velo_folder, day, 'velodyne_hits.bin')
+
+    all_utimes = []
+    all_hits = []
+    all_ints = []
+
+    num_bytes = getsize(hits_path)
+    current_bytes = 0
+
+    with open(hits_path, 'rb') as f_bin:
+
+        total_hits = 0
+        first_utime = -1
+        last_utime = -1
+
+        while True:
+
+            magic = f_bin.read(8)
+            if magic == b'':
+                break
+
+            if not verify_magic(magic):
+                print('Could not verify magic')
+
+            num_hits = struct.unpack('<I', f_bin.read(4))[0]
+            utime = struct.unpack('<Q', f_bin.read(8))[0]
+
+            # Do not convert padding (it is an int always equal to zero)
+            padding = f_bin.read(4)
+
+            total_hits += num_hits
+            if first_utime == -1:
+                first_utime = utime
+            last_utime = utime
+
+            hits = []
+            ints = []
+
+            for i in range(num_hits):
+
+                x = struct.unpack('<H', f_bin.read(2))[0]
+                y = struct.unpack('<H', f_bin.read(2))[0]
+                z = struct.unpack('<H', f_bin.read(2))[0]
+                i = struct.unpack('B', f_bin.read(1))[0]
+                l = struct.unpack('B', f_bin.read(1))[0]
+
+                hits += [[x, y, z]]
+                ints += [i]
+
+            utimes = np.full((num_hits,), utime - first_utime, dtype=np.int32)
+            ints = np.array(ints, dtype=np.uint8)
+            hits = np.array(hits, dtype=np.float32)
+            hits *= 0.005
+            hits += -100.0
+
+            all_utimes.append(utimes)
+            all_hits.append(hits)
+            all_ints.append(ints)
+
+            if 100 * current_bytes / num_bytes > 0.1:
+                break
+
+            current_bytes += 24 + 8 * num_hits
+
+            print('{:d}/{:d}  =>  {:.1f}%'.format(current_bytes, num_bytes, 100 * current_bytes / num_bytes))
+
+        all_utimes = np.hstack(all_utimes)
+        all_hits = np.vstack(all_hits)
+        all_ints = np.hstack(all_ints)
+
+        write_ply('test_hits',
+                  [all_hits, all_ints, all_utimes],
+                  ['x', 'y', 'z', 'intensity', 'utime'])
+
+    print("Read %d total hits from %ld to %ld" % (total_hits, first_utime, last_utime))
+
+    return 0
+
+
+def frames_to_ply(show_frames=False):
+
+    # In files
+    data_path = '../../Data/NCLT'
+    velo_folder = 'velodyne_data'
+
+    days = np.sort([d for d in listdir(join(data_path, velo_folder))])
+
+    for day in days:
+
+        # Out files
+        ply_folder = join(data_path, 'frames_ply', day)
+        if not exists(ply_folder):
+            makedirs(ply_folder)
+
+        day_path = join(data_path, velo_folder, day, 'velodyne_sync')
+        f_names = np.sort([f for f in listdir(day_path) if f[-4:] == '.bin'])
+
+        N = len(f_names)
+        print('Reading', N, 'files')
+
+        for f_i, f_name in enumerate(f_names):
+
+            ply_name = join(ply_folder, f_name[:-4] + '.ply')
+            if exists(ply_name):
+                continue
+
+
+            t1 = time.time()
+
+            hits = []
+            ints = []
+
+            with open(join(day_path, f_name), 'rb') as f_bin:
+
+                while True:
+                    x_str = f_bin.read(2)
+
+                    # End of file
+                    if x_str == b'':
+                        break
+
+                    x = struct.unpack('<H', x_str)[0]
+                    y = struct.unpack('<H', f_bin.read(2))[0]
+                    z = struct.unpack('<H', f_bin.read(2))[0]
+                    intensity = struct.unpack('B', f_bin.read(1))[0]
+                    l = struct.unpack('B', f_bin.read(1))[0]
+
+                    hits += [[x, y, z]]
+                    ints += [intensity]
+
+            ints = np.array(ints, dtype=np.uint8)
+            hits = np.array(hits, dtype=np.float32)
+            hits *= 0.005
+            hits += -100.0
+
+            write_ply(ply_name,
+                      [hits, ints],
+                      ['x', 'y', 'z', 'intensity'])
+
+            t2 = time.time()
+            print('File {:s} {:d}/{:d} Done in {:.1f}s'.format(f_name, f_i, N, t2 - t1))
+
+            if show_frames:
+                fig = plt.figure()
+                ax = fig.add_subplot(111, projection='3d')
+                ax.scatter(hits[:, 0], hits[:, 1], -hits[:, 2], c=-hits[:, 2], s=5, linewidths=0)
+                plt.show()
+
+    return 0
+
+
+def merge_day_pointclouds(show_day_trajectory=False, only_SLAM_nodes=False):
+    """
+    Recreate the whole day point cloud thks to gt pose
+    Generate gt_annotation of mobile objects
+    """
+
+    # In files
+    data_path = '../../Data/NCLT'
+    gt_folder = 'ground_truth'
+    cov_folder = 'ground_truth_cov'
+
+    # Transformation from body to velodyne frame (from NCLT paper)
+    x_body_velo = np.array([0.002, -0.004, -0.957, 0.807, 0.166, -90.703])
+    H_body_velo = ssc_to_homo(x_body_velo, ssc_in_radians=False)
+    H_velo_body = np.linalg.inv(H_body_velo)
+    x_body_lb3 = np.array([0.035, 0.002, -1.23, -179.93, -0.23, 0.50])
+    H_body_lb3 = ssc_to_homo(x_body_lb3, ssc_in_radians=False)
+    H_lb3_body = np.linalg.inv(H_body_lb3)
+
+    # Get gt files and days
+    gt_files = np.sort([gt_f for gt_f in listdir(join(data_path, gt_folder)) if gt_f[-4:] == '.csv'])
+    cov_files = np.sort([cov_f for cov_f in listdir(join(data_path, cov_folder)) if cov_f[-4:] == '.csv'])
+    days = [d[:-4].split('_')[1] for d in gt_files]
+
+    # Load all gt poses
+    print('\nLoading days groundtruth poses...')
+    t0 = time.time()
+    gt_H = []
+    gt_t = []
+    for d, gt_f in enumerate(gt_files):
+
+        t1 = time.time()
+
+        gt_pkl_file = join(data_path, gt_folder, gt_f[:-4] + '.pkl')
+        if exists(gt_pkl_file):
+            # Read pkl
+            with open(gt_pkl_file, 'rb') as f:
+                day_gt_t, day_gt_H = pickle.load(f)
+
+        else:
+            # File paths
+            gt_csv = join(data_path, gt_folder, gt_f)
+
+            # Load gt
+            gt = np.loadtxt(gt_csv, delimiter=',')
+
+            # Convert gt to homogenous rotation/translation matrix
+            day_gt_t = gt[:, 0]
+            day_gt_H = ssc_to_homo(gt[:, 1:])
+
+            # Save pickle
+            with open(gt_pkl_file, 'wb') as f:
+                pickle.dump([day_gt_t, day_gt_H], f)
+
+            t2 = time.time()
+            print('{:s} {:d}/{:d} Done in {:.1f}s'.format(gt_f, d, gt_files.shape[0], t2 - t1))
+
+        gt_t += [day_gt_t]
+        gt_H += [day_gt_H]
+
+        if show_day_trajectory:
+
+            cov_csv = join(data_path, cov_folder, cov_files[d])
+            cov = np.loadtxt(cov_csv, delimiter=',')
+            t_cov = cov[:, 0]
+            t_cov_bool = np.logical_and(t_cov > np.min(day_gt_t), t_cov < np.max(day_gt_t))
+            t_cov = t_cov[t_cov_bool]
+
+            # Note: Interpolation is not needed, this is done as a convinience
+            interp = scipy.interpolate.interp1d(day_gt_t, day_gt_H[:, :3, 3], kind='nearest', axis=0)
+            node_poses = interp(t_cov)
+
+            plt.figure()
+            plt.scatter(day_gt_H[:, 1, 3], day_gt_H[:, 0, 3], 1, c=-day_gt_H[:, 2, 3], linewidth=0)
+            plt.scatter(node_poses[:, 1], node_poses[:, 0], 1, c=-node_poses[:, 2], linewidth=5)
+            plt.axis('equal')
+            plt.title('Ground Truth Position of Nodes in SLAM Graph')
+            plt.xlabel('East (m)')
+            plt.ylabel('North (m)')
+            plt.colorbar()
+
+            plt.show()
+
+    t2 = time.time()
+    print('Done in {:.1f}s\n'.format(t2 - t0))
+
+    # Out files
+    out_folder = join(data_path, 'day_ply')
+    if not exists(out_folder):
+        makedirs(out_folder)
+
+    # Focus on a particular point
+    p0 = np.array([-220, -527, 12])
+    center_radius = 10.0
+    point_radius = 50.0
+
+    # Loop on days
+    for d, day in enumerate(days):
+
+        #if day != '2012-02-05':
+        #    continue
+        day_min_t = gt_t[d][0]
+        day_max_t = gt_t[d][-1]
+
+        frames_folder = join(data_path, 'frames_ply', day)
+        f_times = np.sort([float(f[:-4]) for f in listdir(frames_folder) if f[-4:] == '.ply'])
+
+        # If we want, load only SLAM nodes
+        if only_SLAM_nodes:
+
+            # Load node timestamps
+            cov_csv = join(data_path, cov_folder, cov_files[d])
+            cov = np.loadtxt(cov_csv, delimiter=',')
+            t_cov = cov[:, 0]
+            t_cov_bool = np.logical_and(t_cov > day_min_t, t_cov < day_max_t)
+            t_cov = t_cov[t_cov_bool]
+
+            # Find closest lidar frames
+            t_cov = np.expand_dims(t_cov, 1)
+            diffs = np.abs(t_cov - f_times)
+            inds = np.argmin(diffs, axis=1)
+            f_times = f_times[inds]
+
+        # Is this frame in gt
+        f_t_bool = np.logical_and(f_times > day_min_t, f_times < day_max_t)
+        f_times = f_times[f_t_bool]
+
+        # Interpolation gt poses to frame timestamps
+        interp = scipy.interpolate.interp1d(gt_t[d], gt_H[d], kind='nearest', axis=0)
+        frame_poses = interp(f_times)
+
+        N = len(f_times)
+        world_points = []
+        world_frames = []
+        world_frames_c = []
+        print('Reading', day, ' => ', N, 'files')
+        for f_i, f_t in enumerate(f_times):
+
+            t1 = time.time()
+
+            #########
+            # GT pose
+            #########
+
+            H = frame_poses[f_i].astype(np.float32)
+            # s = '\n'
+            # for cc in H:
+            #     for c in cc:
+            #         s += '{:5.2f} '.format(c)
+            #     s += '\n'
+            # print(s)
+
+            #############
+            # Focus check
+            #############
+
+            if np.linalg.norm(H[:3, 3] - p0) > center_radius:
+                continue
+
+            ###################################
+            # Local frame coordinates for debug
+            ###################################
+
+            # Create artificial frames
+            x = np.linspace(0, 1, 50, dtype=np.float32)
+            points = np.hstack((np.vstack((x, x*0, x*0)), np.vstack((x*0, x, x*0)), np.vstack((x*0, x*0, x)))).T
+            colors = ((points > 0.1).astype(np.float32) * 255).astype(np.uint8)
+
+            hpoints = np.hstack((points, np.ones_like(points[:, :1])))
+            hpoints = np.matmul(hpoints, H.T)
+            hpoints[:, 3] *= 0
+            world_frames += [hpoints[:, :3]]
+            world_frames_c += [colors]
+
+            #######################
+            # Load velo point cloud
+            #######################
+
+            # Load frame ply file
+            f_name = '{:.0f}.ply'.format(f_t)
+            data = read_ply(join(frames_folder, f_name))
+            points = np.vstack((data['x'], data['y'], data['z'])).T
+            #intensity = data['intensity']
+
+            hpoints = np.hstack((points, np.ones_like(points[:, :1])))
+            hpoints = np.matmul(hpoints, H.T)
+            hpoints[:, 3] *= 0
+            hpoints[:, 3] += np.sqrt(f_t - f_times[0])
+
+            # focus check
+            focus_bool = np.linalg.norm(hpoints[:, :3] - p0, axis=1) < point_radius
+            hpoints = hpoints[focus_bool, :]
+
+            world_points += [hpoints]
+
+            t2 = time.time()
+            print('File {:s} {:d}/{:d} Done in {:.1f}s'.format(f_name, f_i, N, t2 - t1))
+
+        if len(world_points) < 2:
+            continue
+
+        world_points = np.vstack(world_points)
+
+
+        ###### DEBUG
+        world_frames = np.vstack(world_frames)
+        world_frames_c = np.vstack(world_frames_c)
+        write_ply('testf.ply',
+                  [world_frames, world_frames_c],
+                  ['x', 'y', 'z', 'red', 'green', 'blue'])
+        ###### DEBUG
+
+        print(world_points.shape, world_points.dtype)
+
+        # Subsample merged frames
+        # world_points, features = grid_subsampling(world_points[:, :3],
+        #                                           features=world_points[:, 3:],
+        #                                           sampleDl=0.1)
+        features = world_points[:, 3:]
+        world_points = world_points[:, :3]
+
+        print(world_points.shape, world_points.dtype)
+
+        write_ply('test' + day + '.ply',
+                  [world_points, features],
+                  ['x', 'y', 'z', 't'])
+
+
+        # Generate gt annotations
+
+        # Subsample day ply (for visualization)
+
+        # Save day ply
+
+        # a = 1/0

datasets/S3DIS.py

@@ -131,7 +131,7 @@ class S3DISDataset(PointCloudDataset):
         # Prepare ply files
         ###################
 
-        self.prepare_S3DIS_ply()
+        #self.prepare_S3DIS_ply()
 
         ################
         # Load ply files
@@ -1037,7 +1037,7 @@ class S3DISSampler(Sampler):
             if breaking:
                 break
 
-    def calibration(self, dataloader, untouched_ratio=0.9, verbose=False):
+    def calibration(self, dataloader, untouched_ratio=0.9, verbose=False, force_redo=False):
         """
         Method performing batch and neighbors calibration.
             Batch calibration: Set "batch_limit" (the maximum number of points allowed in every batch) so that the
@@ -1053,7 +1053,7 @@ class S3DISSampler(Sampler):
         print('\nStarting Calibration (use verbose=True for more details)')
         t0 = time.time()
 
-        redo = False
+        redo = force_redo
 
         # Batch limit
         # ***********
@@ -1075,7 +1075,7 @@ class S3DISSampler(Sampler):
                                                self.dataset.config.in_radius,
                                                self.dataset.config.first_subsampling_dl,
                                                self.dataset.config.batch_num)
-        if key in batch_lim_dict:
+        if not redo and key in batch_lim_dict:
             self.dataset.batch_limit[0] = batch_lim_dict[key]
         else:
             redo = True
@@ -1116,7 +1116,7 @@ class S3DISSampler(Sampler):
             if key in neighb_lim_dict:
                 neighb_limits += [neighb_lim_dict[key]]
 
-        if len(neighb_limits) == self.dataset.config.num_layers:
+        if not redo and len(neighb_limits) == self.dataset.config.num_layers:
             self.dataset.neighborhood_limits = neighb_limits
         else:
             redo = True

datasets/SemanticKitti.py

@@ -1115,7 +1115,7 @@ class SemanticKittiSampler(Sampler):
             # Perform calibration
             #####################
 
-            self.dataset.batch_limit = self.dataset.max_in_p * (self.dataset.batch_num - 1)
+            #self.dataset.batch_limit[0] = self.dataset.max_in_p * (self.dataset.batch_num - 1)
 
             for epoch in range(10):
                 for batch_i, batch in enumerate(dataloader):
@@ -1145,7 +1145,7 @@ class SemanticKittiSampler(Sampler):
                         smooth_errors = smooth_errors[1:]
 
                     # Update batch limit with P controller
-                    self.dataset.batch_limit += Kp * error
+                    self.dataset.batch_limit[0] += Kp * error
 
                     # finer low pass filter when closing in
                     if not finer and np.abs(estim_b - target_b) < 1:
@@ -1166,7 +1166,7 @@ class SemanticKittiSampler(Sampler):
                         message = 'Step {:5d}  estim_b ={:5.2f} batch_limit ={:7d}'
                         print(message.format(i,
                                              estim_b,
-                                             int(self.dataset.batch_limit)))
+                                             int(self.dataset.batch_limit[0])))
 
                 if breaking:
                     break
@@ -1224,7 +1224,7 @@ class SemanticKittiSampler(Sampler):
                                                         self.dataset.config.first_subsampling_dl,
                                                         self.dataset.batch_num,
                                                         self.dataset.max_in_p)
-            batch_lim_dict[key] = float(self.dataset.batch_limit)
+            batch_lim_dict[key] = float(self.dataset.batch_limit[0])
             with open(batch_lim_file, 'wb') as file:
                 pickle.dump(batch_lim_dict, file)
 

datasets/common.py

@@ -228,7 +228,7 @@ class PointCloudDataset(Dataset):
         # Add random symmetries to the scale factor
         symmetries = np.array(self.config.augment_symmetries).astype(np.int32)
         symmetries *= np.random.randint(2, size=points.shape[1])
-        scale = (scale * symmetries * 2 - 1).astype(np.float32)
+        scale = (scale * (1 - symmetries * 2)).astype(np.float32)
 
         #######
         # Noise

models/architectures.py

@@ -183,7 +183,7 @@ class KPCNN(nn.Module):
                     other_KP = torch.cat([KP_locs[:, :i, :], KP_locs[:, i + 1:, :]], dim=1).detach()
                     distances = torch.sqrt(torch.sum((other_KP - KP_locs[:, i:i + 1, :]) ** 2, dim=2))
                     rep_loss = torch.sum(torch.clamp_max(distances - 1.5, max=0.0) ** 2, dim=1)
-                    repulsive_loss += self.l1(rep_loss, torch.zeros_like(rep_loss))
+                    repulsive_loss += self.l1(rep_loss, torch.zeros_like(rep_loss)) / self.K
 
 
 
@@ -218,7 +218,7 @@ class KPFCNN(nn.Module):
         #####################
 
         # Save all block operations in a list of modules
-        self.encoder_blocs = nn.ModuleList()
+        self.encoder_blocks = nn.ModuleList()
         self.encoder_skip_dims = []
         self.encoder_skips = []
 
@@ -239,7 +239,7 @@ class KPFCNN(nn.Module):
                 break
 
             # Apply the good block function defining tf ops
-            self.encoder_blocs.append(block_decider(block,
+            self.encoder_blocks.append(block_decider(block,
                                                     r,
                                                     in_dim,
                                                     out_dim,
@@ -264,7 +264,7 @@ class KPFCNN(nn.Module):
         #####################
 
         # Save all block operations in a list of modules
-        self.decoder_blocs = nn.ModuleList()
+        self.decoder_blocks = nn.ModuleList()
         self.decoder_concats = []
 
         # Find first upsampling block
@@ -283,7 +283,7 @@ class KPFCNN(nn.Module):
                 self.decoder_concats.append(block_i)
 
             # Apply the good block function defining tf ops
-            self.decoder_blocs.append(block_decider(block,
+            self.decoder_blocks.append(block_decider(block,
                                                     r,
                                                     in_dim,
                                                     out_dim,
@@ -331,12 +331,12 @@ class KPFCNN(nn.Module):
 
         # Loop over consecutive blocks
         skip_x = []
-        for block_i, block_op in enumerate(self.encoder_blocs):
+        for block_i, block_op in enumerate(self.encoder_blocks):
             if block_i in self.encoder_skips:
                 skip_x.append(x)
             x = block_op(x, batch)
 
-        for block_i, block_op in enumerate(self.decoder_blocs):
+        for block_i, block_op in enumerate(self.decoder_blocks):
             if block_i in self.decoder_concats:
                 x = torch.cat([x, skip_x.pop()], dim=1)
             x = block_op(x, batch)
@@ -434,9 +434,8 @@ class KPFCNN(nn.Module):
 
                     other_KP = torch.cat([KP_locs[:, :i, :], KP_locs[:, i + 1:, :]], dim=1).detach()
                     distances = torch.sqrt(torch.sum((other_KP - KP_locs[:, i:i + 1, :]) ** 2, dim=2))
-                    rep_loss = torch.sum(torch.clamp_max(distances - 1.5, max=0.0) ** 2, dim=1)
-                    repulsive_loss += self.l1(rep_loss, torch.zeros_like(rep_loss))
-
+                    rep_loss = torch.sum(torch.clamp_max(distances - 0.5, max=0.0) ** 2, dim=1)
+                    repulsive_loss += self.l1(rep_loss, torch.zeros_like(rep_loss)) / self.K
 
 
         return self.offset_decay * (fitting_loss + repulsive_loss)

models/blocks.py

@@ -421,6 +421,7 @@ class BatchNormBlock(nn.Module):
         super(BatchNormBlock, self).__init__()
         self.bn_momentum = bn_momentum
         self.use_bn = use_bn
+        self.in_dim = in_dim
         if self.use_bn:
             self.batch_norm = nn.BatchNorm1d(in_dim, momentum=bn_momentum)
             #self.batch_norm = nn.InstanceNorm1d(in_dim, momentum=bn_momentum)
@@ -442,6 +443,11 @@ class BatchNormBlock(nn.Module):
         else:
             return x + self.bias
 
+    def __repr__(self):
+        return 'BatchNormBlock(in_feat: {:d}, momentum: {:.3f}, only_bias: {:s})'.format(self.in_dim,
+                                                                                         self.bn_momentum,
+                                                                                         str(not self.use_bn))
+
 
 class UnaryBlock(nn.Module):
 
@@ -458,6 +464,8 @@ class UnaryBlock(nn.Module):
         self.bn_momentum = bn_momentum
         self.use_bn = use_bn
         self.no_relu = no_relu
+        self.in_dim = in_dim
+        self.out_dim = out_dim
         self.mlp = nn.Linear(in_dim, out_dim, bias=False)
         self.batch_norm = BatchNormBlock(out_dim, self.use_bn, self.bn_momentum)
         if not no_relu:
@@ -471,6 +479,12 @@ class UnaryBlock(nn.Module):
             x = self.leaky_relu(x)
         return x
 
+    def __repr__(self):
+        return 'UnaryBlock(in_feat: {:d}, out_feat: {:d}, BN: {:s}, ReLU: {:s})'.format(self.in_dim,
+                                                                                        self.out_dim,
+                                                                                        str(self.use_bn),
+                                                                                        str(not self.no_relu))
+
 
 class SimpleBlock(nn.Module):
 
@@ -492,6 +506,8 @@ class SimpleBlock(nn.Module):
         self.use_bn = config.use_batch_norm
         self.layer_ind = layer_ind
         self.block_name = block_name
+        self.in_dim = in_dim
+        self.out_dim = out_dim
 
         # Define the KPConv class
         self.KPConv = KPConv(config.num_kernel_points,
@@ -547,6 +563,8 @@ class ResnetBottleneckBlock(nn.Module):
         self.use_bn = config.use_batch_norm
         self.block_name = block_name
         self.layer_ind = layer_ind
+        self.in_dim = in_dim
+        self.out_dim = out_dim
 
         # First downscaling mlp
         if in_dim != out_dim // 4:
@@ -639,6 +657,10 @@ class NearestUpsampleBlock(nn.Module):
     def forward(self, x, batch):
         return closest_pool(x, batch.upsamples[self.layer_ind - 1])
 
+    def __repr__(self):
+        return 'NearestUpsampleBlock(layer: {:d} -> {:d})'.format(self.layer_ind,
+                                                                  self.layer_ind - 1)
+
 
 class MaxPoolBlock(nn.Module):
 

plot_convergence.py

@@ -1445,12 +1445,14 @@ def S3DIS_go(old_result_limit):
 
 def SemanticKittiFirst(old_result_limit):
     """
-    Test SematicKitti. First exps
+    Test SematicKitti. First exps.
+    Try some class weight strategies. It seems that the final score is not impacted so much. With weights, some classes
+    are better while other are worse, for a final score that remains the same.
     """
 
     # Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
     start = 'Log_2020-04-07_15-30-17'
-    end = 'Log_2020-05-07_15-30-17'
+    end = 'Log_2020-04-11_21-34-16'
 
     if end < old_result_limit:
         res_path = 'old_results'
@@ -1464,8 +1466,43 @@ def SemanticKittiFirst(old_result_limit):
     logs_names = ['R=5.0_dl=0.04',
                   'R=5.0_dl=0.08',
                   'R=10.0_dl=0.08',
-                  'R=10.0_dl=0.08_weigths',
-                  'R=10.0_dl=0.08_sqrt_weigths',
+                  'R=10.0_dl=0.08_20*weigths',
+                  'R=10.0_dl=0.08_20*sqrt_weigths',
+                  'R=10.0_dl=0.08_100*sqrt_w',
+                  'R=10.0_dl=0.08_100*sqrt_w_capped',
+                  'R=10.0_dl=0.08_no_w']
+
+    logs_names = np.array(logs_names[:len(logs)])
+
+    return logs, logs_names
+
+
+def SemanticKitti_scale(old_result_limit):
+    """
+    Test SematicKitti. Try different scales of input raduis / subsampling.
+    """
+
+    # Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
+    start = 'Log_2020-04-11_21-34-15'
+    end = 'Log_2020-04-20_11-52-58'
+
+    if end < old_result_limit:
+        res_path = 'old_results'
+    else:
+        res_path = 'results'
+
+    logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end])
+    logs = logs.astype('<U50')
+
+    # Give names to the logs (for legends)
+    logs_names = ['R=10.0_dl=0.08',
+                  'R=4.0_dl=0.04',
+                  'R=6.0_dl=0.06',
+                  'R=6.0_dl=0.06_inF=2',
+                  'test',
+                  'test',
+                  'test',
+                  'test',
                   'test']
 
     logs_names = np.array(logs_names[:len(logs)])
@@ -1473,6 +1510,41 @@ def SemanticKittiFirst(old_result_limit):
     return logs, logs_names
 
 
+def S3DIS_deform(old_result_limit):
+    """
+    Debug S3DIS deformable.
+    At checkpoint 50, the points seem to start fitting the shape, but then, they just get further away from each other
+    and do not care about input points. The fitting loss seems broken?
+    """
+
+    # Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
+    start = 'Log_2020-04-22_11-52-58'
+    end = 'Log_2020-05-22_11-52-58'
+
+    if end < old_result_limit:
+        res_path = 'old_results'
+    else:
+        res_path = 'results'
+
+    logs = np.sort([join(res_path, l) for l in listdir(res_path) if start <= l <= end])
+    logs = logs.astype('<U50')
+    logs = np.insert(logs, 0, 'results/Log_2020-04-04_10-04-42')
+
+    # Give names to the logs (for legends)
+    logs_names = ['off_d=0.01_baseline',
+                  'off_d=0.01',
+                  'off_d=0.05',
+                  'off_d=0.05_corrected',
+                  'off_d=0.05_norepulsive',
+                  'off_d=0.05_repulsive0.5',
+                  'test']
+
+    logs_names = np.array(logs_names[:len(logs)])
+
+    return logs, logs_names
+
+
+
 
 
 
@@ -1489,7 +1561,7 @@ if __name__ == '__main__':
     old_res_lim = 'Log_2020-03-25_19-30-17'
 
     # My logs: choose the logs to show
-    logs, logs_names = SemanticKittiFirst(old_res_lim)
+    logs, logs_names = S3DIS_deform(old_res_lim)
     #os.environ['QT_DEBUG_PLUGINS'] = '1'
 
     ######################################################

train_NCLT.py

@@ -0,0 +1,344 @@
+#
+#
+#      0=================================0
+#      |    Kernel Point Convolutions    |
+#      0=================================0
+#
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Callable script to start a training on NCLT dataset
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Hugues THOMAS - 06/03/2020
+#
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Imports and global variables
+#       \**********************************/
+#
+
+# Common libs
+import signal
+import os
+import numpy as np
+import sys
+import torch
+
+# Dataset
+from datasets.NCLT import *
+from torch.utils.data import DataLoader
+
+from utils.config import Config
+from utils.trainer import ModelTrainer
+from models.architectures import KPFCNN
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Config Class
+#       \******************/
+#
+
+class NCLTConfig(Config):
+    """
+    Override the parameters you want to modify for this dataset
+    """
+
+    ####################
+    # Dataset parameters
+    ####################
+
+    # Dataset name
+    dataset = 'NCLT'
+
+    # Number of classes in the dataset (This value is overwritten by dataset class when Initializating dataset).
+    num_classes = None
+
+    # Type of task performed on this dataset (also overwritten)
+    dataset_task = ''
+
+    # Number of CPU threads for the input pipeline
+    input_threads = 10
+
+    #########################
+    # Architecture definition
+    #########################
+
+    # Define layers
+    architecture = ['simple',
+                    'resnetb',
+                    'resnetb_strided',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb_strided',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb_strided',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb',
+                    'resnetb_strided',
+                    'resnetb',
+                    'resnetb',
+                    'nearest_upsample',
+                    'unary',
+                    'nearest_upsample',
+                    'unary',
+                    'nearest_upsample',
+                    'unary',
+                    'nearest_upsample',
+                    'unary']
+
+    ###################
+    # KPConv parameters
+    ###################
+
+    # Radius of the input sphere
+    in_radius = 6.0
+    val_radius = 51.0
+    n_frames = 1
+    max_in_points = 100000
+    max_val_points = 200000
+
+    # Number of batch
+    batch_num = 8
+    val_batch_num = 1
+
+    # Number of kernel points
+    num_kernel_points = 15
+
+    # Size of the first subsampling grid in meter
+    first_subsampling_dl = 0.06
+
+    # Radius of convolution in "number grid cell". (2.5 is the standard value)
+    conv_radius = 2.5
+
+    # Radius of deformable convolution in "number grid cell". Larger so that deformed kernel can spread out
+    deform_radius = 6.0
+
+    # Radius of the area of influence of each kernel point in "number grid cell". (1.0 is the standard value)
+    KP_extent = 1.5
+
+    # Behavior of convolutions in ('constant', 'linear', 'gaussian')
+    KP_influence = 'linear'
+
+    # Aggregation function of KPConv in ('closest', 'sum')
+    aggregation_mode = 'sum'
+
+    # Choice of input features
+    first_features_dim = 128
+    in_features_dim = 2
+
+    # Can the network learn modulations
+    modulated = False
+
+    # Batch normalization parameters
+    use_batch_norm = True
+    batch_norm_momentum = 0.02
+
+    # Offset loss
+    # 'permissive' only constrains offsets inside the deform radius (NOT implemented yet)
+    # 'fitting' helps deformed kernels to adapt to the geometry by penalizing distance to input points
+    offsets_loss = 'fitting'
+    offsets_decay = 0.01
+
+    #####################
+    # Training parameters
+    #####################
+
+    # Maximal number of epochs
+    max_epoch = 800
+
+    # Learning rate management
+    learning_rate = 1e-2
+    momentum = 0.98
+    lr_decays = {i: 0.1 ** (1 / 150) for i in range(1, max_epoch)}
+    grad_clip_norm = 100.0
+
+    # Number of steps per epochs
+    epoch_steps = 500
+
+    # Number of validation examples per epoch
+    validation_size = 200
+
+    # Number of epoch between each checkpoint
+    checkpoint_gap = 50
+
+    # Augmentations
+    augment_scale_anisotropic = True
+    augment_symmetries = [True, False, False]
+    augment_rotation = 'vertical'
+    augment_scale_min = 0.8
+    augment_scale_max = 1.2
+    augment_noise = 0.001
+    augment_color = 0.8
+
+    # Choose weights for class (used in segmentation loss). Empty list for no weights
+    # class proportion for R=10.0 and dl=0.08 (first is unlabeled)
+    # 19.1 48.9 0.5  1.1  5.6  3.6  0.7  0.6  0.9 193.2 17.7 127.4 6.7 132.3 68.4 283.8 7.0 78.5 3.3 0.8
+    #
+    #
+
+    # sqrt(Inverse of proportion * 100)
+    # class_w = [1.430, 14.142, 9.535, 4.226, 5.270, 11.952, 12.910, 10.541, 0.719,
+    #            2.377, 0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.505, 11.180]
+
+    # sqrt(Inverse of proportion * 100)  capped (0.5 < X < 5)
+    # class_w = [1.430, 5.000, 5.000, 4.226, 5.000, 5.000, 5.000, 5.000, 0.719, 2.377,
+    #            0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.000, 5.000]
+
+
+    # Do we nee to save convergence
+    saving = True
+    saving_path = None
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Main Call
+#       \***************/
+#
+
+if __name__ == '__main__':
+
+    #test_read_hits()
+
+    #frames_to_ply()
+
+
+    merge_day_pointclouds()
+
+    a = 1/0
+
+
+
+
+
+
+    ############################
+    # Initialize the environment
+    ############################
+
+    # Set which gpu is going to be used
+    GPU_ID = '2'
+
+    # Set GPU visible device
+    os.environ['CUDA_VISIBLE_DEVICES'] = GPU_ID
+
+    ###############
+    # Previous chkp
+    ###############
+
+    # Choose here if you want to start training from a previous snapshot (None for new training)
+    # previous_training_path = 'Log_2020-03-19_19-53-27'
+    previous_training_path = ''
+
+    # Choose index of checkpoint to start from. If None, uses the latest chkp
+    chkp_idx = None
+    if previous_training_path:
+
+        # Find all snapshot in the chosen training folder
+        chkp_path = os.path.join('results', previous_training_path, 'checkpoints')
+        chkps = [f for f in os.listdir(chkp_path) if f[:4] == 'chkp']
+
+        # Find which snapshot to restore
+        if chkp_idx is None:
+            chosen_chkp = 'current_chkp.tar'
+        else:
+            chosen_chkp = np.sort(chkps)[chkp_idx]
+        chosen_chkp = os.path.join('results', previous_training_path, 'checkpoints', chosen_chkp)
+
+    else:
+        chosen_chkp = None
+
+    ##############
+    # Prepare Data
+    ##############
+
+    print()
+    print('Data Preparation')
+    print('****************')
+
+    # Initialize configuration class
+    config = NCLTConfig()
+    if previous_training_path:
+        config.load(os.path.join('results', previous_training_path))
+        config.saving_path = None
+
+    # Get path from argument if given
+    if len(sys.argv) > 1:
+        config.saving_path = sys.argv[1]
+
+    # Initialize datasets
+    training_dataset = NCLTDataset(config, set='training',
+                                            balance_classes=True)
+    test_dataset = NCLTDataset(config, set='validation',
+                                        balance_classes=False)
+
+    # Initialize samplers
+    training_sampler = NCLTSampler(training_dataset)
+    test_sampler = NCLTSampler(test_dataset)
+
+    # Initialize the dataloader
+    training_loader = DataLoader(training_dataset,
+                                 batch_size=1,
+                                 sampler=training_sampler,
+                                 collate_fn=NCLTCollate,
+                                 num_workers=config.input_threads,
+                                 pin_memory=True)
+    test_loader = DataLoader(test_dataset,
+                             batch_size=1,
+                             sampler=test_sampler,
+                             collate_fn=NCLTCollate,
+                             num_workers=config.input_threads,
+                             pin_memory=True)
+
+    # Calibrate max_in_point value
+    training_sampler.calib_max_in(config, training_loader, verbose=True)
+    test_sampler.calib_max_in(config, test_loader, verbose=True)
+
+    # Calibrate samplers
+    training_sampler.calibration(training_loader, verbose=True)
+    test_sampler.calibration(test_loader, verbose=True)
+
+    # debug_timing(training_dataset, training_loader)
+    # debug_timing(test_dataset, test_loader)
+    # debug_class_w(training_dataset, training_loader)
+
+    print('\nModel Preparation')
+    print('*****************')
+
+    # Define network model
+    t1 = time.time()
+    net = KPFCNN(config, training_dataset.label_values, training_dataset.ignored_labels)
+
+    debug = False
+    if debug:
+        print('\n*************************************\n')
+        print(net)
+        print('\n*************************************\n')
+        for param in net.parameters():
+            if param.requires_grad:
+                print(param.shape)
+        print('\n*************************************\n')
+        print("Model size %i" % sum(param.numel() for param in net.parameters() if param.requires_grad))
+        print('\n*************************************\n')
+
+    # Define a trainer class
+    trainer = ModelTrainer(net, config, chkp_path=chosen_chkp)
+    print('Done in {:.1f}s\n'.format(time.time() - t1))
+
+    print('\nStart training')
+    print('**************')
+
+    # Training
+    trainer.train(net, training_loader, test_loader, config)
+
+    print('Forcing exit now')
+    os.kill(os.getpid(), signal.SIGINT)

train_S3DIS.py

@@ -74,22 +74,15 @@ class S3DISConfig(Config):
                     'resnetb_strided',
                     'resnetb',
                     'resnetb',
-                    'resnetb',
                     'resnetb_strided',
                     'resnetb',
                     'resnetb',
-                    'resnetb',
-                    'resnetb',
-                    'resnetb',
                     'resnetb_strided',
-                    'resnetb',
-                    'resnetb',
-                    'resnetb',
-                    'resnetb',
-                    'resnetb',
-                    'resnetb_strided',
-                    'resnetb',
-                    'resnetb',
+                    'resnetb_deformable',
+                    'resnetb_deformable',
+                    'resnetb_deformable_strided',
+                    'resnetb_deformable',
+                    'resnetb_deformable',
                     'nearest_upsample',
                     'unary',
                     'nearest_upsample',
@@ -104,7 +97,7 @@ class S3DISConfig(Config):
     ###################
 
     # Radius of the input sphere
-    in_radius = 1.0
+    in_radius = 1.5
 
     # Number of kernel points
     num_kernel_points = 15
@@ -142,7 +135,7 @@ class S3DISConfig(Config):
     # 'permissive' only constrains offsets inside the deform radius (NOT implemented yet)
     # 'fitting' helps deformed kernels to adapt to the geometry by penalizing distance to input points
     offsets_loss = 'fitting'
-    offsets_decay = 0.01
+    offsets_decay = 0.05
 
     #####################
     # Training parameters
@@ -158,7 +151,7 @@ class S3DISConfig(Config):
     grad_clip_norm = 100.0
 
     # Number of batch
-    batch_num = 8
+    batch_num = 6
 
     # Number of steps per epochs
     epoch_steps = 500

train_SemanticKitti.py

@@ -100,21 +100,21 @@ class SemanticKittiConfig(Config):
     ###################
 
     # Radius of the input sphere
-    in_radius = 10.0
+    in_radius = 6.0
     val_radius = 51.0
     n_frames = 1
     max_in_points = 100000
-    max_val_points = 100000
+    max_val_points = 200000
 
     # Number of batch
-    batch_num = 10
+    batch_num = 8
     val_batch_num = 1
 
     # Number of kernel points
     num_kernel_points = 15
 
     # Size of the first subsampling grid in meter
-    first_subsampling_dl = 0.08
+    first_subsampling_dl = 0.06
 
     # Radius of convolution in "number grid cell". (2.5 is the standard value)
     conv_radius = 2.5
@@ -133,7 +133,7 @@ class SemanticKittiConfig(Config):
 
     # Choice of input features
     first_features_dim = 128
-    in_features_dim = 5
+    in_features_dim = 2
 
     # Can the network learn modulations
     modulated = False
@@ -158,7 +158,7 @@ class SemanticKittiConfig(Config):
     # Learning rate management
     learning_rate = 1e-2
     momentum = 0.98
-    lr_decays = {i: 0.1 ** (1 / 100) for i in range(1, max_epoch)}
+    lr_decays = {i: 0.1 ** (1 / 150) for i in range(1, max_epoch)}
     grad_clip_norm = 100.0
 
     # Number of steps per epochs
@@ -190,8 +190,8 @@ class SemanticKittiConfig(Config):
     #            2.377, 0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.505, 11.180]
 
     # sqrt(Inverse of proportion * 100)  capped (0.5 < X < 5)
-    class_w = [1.430, 5.000, 5.000, 4.226, 5.000, 5.000, 5.000, 5.000, 0.719, 2.377,
-               0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.000, 5.000]
+    # class_w = [1.430, 5.000, 5.000, 4.226, 5.000, 5.000, 5.000, 5.000, 0.719, 2.377,
+    #            0.886, 3.863, 0.869, 1.209, 0.594, 3.780, 1.129, 5.000, 5.000]
 
 
     # Do we nee to save convergence
@@ -212,7 +212,7 @@ if __name__ == '__main__':
     ############################
 
     # Set which gpu is going to be used
-    GPU_ID = '3'
+    GPU_ID = '2'
 
     # Set GPU visible device
     os.environ['CUDA_VISIBLE_DEVICES'] = GPU_ID

utils/tester.py

@@ -181,7 +181,7 @@ class ModelTester:
                     inds = in_inds[i0:i0 + length]
                     c_i = cloud_inds[b_i]
 
-                    if test_radius_ratio < 0.99:
+                    if 0 < test_radius_ratio < 1:
                         mask = np.sum(points ** 2, axis=1) < (test_radius_ratio * config.in_radius) ** 2
                         inds = inds[mask]
                         probs = probs[mask]

utils/trainer.py

@@ -259,7 +259,7 @@ class ModelTrainer:
 
                 # Save checkpoints occasionally
                 if (self.epoch + 1) % config.checkpoint_gap == 0:
-                    checkpoint_path = join(checkpoint_directory, 'chkp_{:04d}.tar'.format(self.epoch))
+                    checkpoint_path = join(checkpoint_directory, 'chkp_{:04d}.tar'.format(self.epoch + 1))
                     torch.save(save_dict, checkpoint_path)
 
             # Validation

utils/visualizer.py

@@ -82,7 +82,7 @@ class ModelVisualizer:
         net.load_state_dict(checkpoint['model_state_dict'])
         self.epoch = checkpoint['epoch']
         net.eval()
-        print("Model and training state restored.")
+        print("\nModel state restored from {:s}.".format(chkp_path))
 
         return
 
@@ -679,139 +679,63 @@ class ModelVisualizer:
             except tf.errors.OutOfRangeError:
                 break
 
-    def show_effective_recep_field(self, model, dataset, relu_idx=0):
+    def show_effective_recep_field(self, net, loader, config, f_idx=0):
 
-        ###################################################
-        # First add a modulation variable on input features
-        ###################################################
+        ##########################################
+        # First choose the visualized deformations
+        ##########################################
 
-        # Tensorflow random seed
-        random_seed = 42
+        blocks = {}
 
-        # Create a modulated input feature op
-        with tf.variable_scope('input_modulations'):
-            initial = tf.constant(0., shape=[200000, 1])
-            input_modulations_var = tf.Variable(initial, name='alphas')
-            input_modulations = 2 * tf.sigmoid(input_modulations_var)
-            assert_op = tf.assert_less(tf.shape(model.inputs['features'])[0], tf.shape(input_modulations)[0])
-            with tf.control_dependencies([assert_op]):
-                modulated_input = model.inputs['features'] * input_modulations[:tf.shape(model.inputs['features'])[0]]
-            modulated_input = tf.identity(modulated_input, name='modulated_features')
+        named_blocks = [(m_name, m) for m_name, m in net.named_modules()
+                        if len(m_name.split('.')) == 2 and m_name.split('.')[0].endswith('_blocks')]
+        chosen_block = named_blocks[-1][0]
 
-        print('*******************************************')
-
-        # Swap the op with the normal input features
-        for op in tf.get_default_graph().get_operations():
-
-            if 'input_modulations' in op.name:
-                continue
-
-            if model.inputs['features'].name in [in_t.name for in_t in op.inputs]:
-                input_list = []
-                for in_t in op.inputs:
-                    if in_t.name == model.inputs['features'].name:
-                        input_list += [modulated_input]
-                    else:
-                        input_list += [in_t]
-                print('swapping op ', op.name)
-                print('old inputs ', [in_t.name for in_t in op.inputs])
-                print('new inputs ', [in_t.name for in_t in input_list])
-                ge.swap_inputs(op, input_list)
-
-        print('*******************************************')
-
-        ##########################
-        # Create the ERF optimizer
-        ##########################
-
-        # This optimizer only computes gradients for the feature modulation variables. We set the ERF loss, which
-        # consists of modifying the features in one location a the wanted layer
-
-        with tf.variable_scope('ERF_loss'):
-
-            # List all relu ops
-            all_ops = [op for op in tf.get_default_graph().get_operations() if op.name.startswith('KernelPointNetwork')
-                       and op.name.endswith('LeakyRelu')]
-
-            # Print the chosen one
-            features_tensor = all_ops[relu_idx].outputs[0]
-
-            # Get parameters
-            layer_idx = int(features_tensor.name.split('/')[1][6:])
-            if 'strided' in all_ops[relu_idx].name and not ('strided' in all_ops[relu_idx + 1].name):
-                layer_idx += 1
-            features_dim = int(features_tensor.shape[1])
-            radius = model.config.first_subsampling_dl * model.config.density_parameter * (2 ** layer_idx)
-
-            print('You chose to visualize the output of operation named: ' + all_ops[relu_idx].name)
-            print('It contains {:d} features.'.format(int(features_tensor.shape[1])))
-
-            print('\nPossible Relu indices:')
-            for i, t in enumerate(all_ops):
-                print(i, ': ', t.name)
-
-            print('\n****************************************************************************')
-
-            # Get the receptive field of a random point
-            N = tf.shape(features_tensor)[0]
-            #random_ind = tf.random_uniform([1], minval=0, maxval=N, dtype=np.int32, seed=random_seed)[0]
-            #chosen_i_holder = tf.placeholder(tf.int32, name='chosen_ind')
-            aimed_coordinates = tf.placeholder(tf.float32, shape=(1, 3), name='aimed_coordinates')
-            d2 = tf.reduce_sum(tf.square(model.inputs['points'][layer_idx] - aimed_coordinates), axis=1)
-            chosen_i_tf = tf.argmin(d2, output_type=tf.int32)
-
-            #test1 = tf.multiply(features_tensor, 2.0, name='test1')
-            #test2 = tf.multiply(features_tensor, 2.0, name='test2')
-
-            #  Gradient scaling operation
-            @tf.custom_gradient
-            def scale_grad_layer(x):
-                def scaled_grad(dy):
-                    p_op = tf.print(x.name,
-                                    tf.reduce_mean(tf.abs(x)),
-                                    tf.reduce_mean(tf.abs(dy)),
-                                    output_stream=sys.stdout)
-                    with tf.control_dependencies([p_op]):
-                        new_dy = 1.0 * dy
-                    return new_dy
-                return tf.identity(x), scaled_grad
-
-            #test2 = scale_grad_layer(test2)
-
-            # Get the tensor of error for these features (one for the chosen point, zero for the rest)
-            chosen_f_tf = tf.placeholder(tf.int32, name='feature_ind')
-            ERF_error = tf.expand_dims(tf.cast(tf.equal(tf.range(N), chosen_i_tf), tf.float32), 1)
-            ERF_error *= tf.expand_dims(tf.cast(tf.equal(tf.range(features_dim), chosen_f_tf), tf.float32), 0)
-
-            # Get objective for the features (with a stop gradient so that we can get a gradient on the loss)
-            objective_features = features_tensor + ERF_error
-            objective_features = tf.stop_gradient(objective_features)
-
-            # Loss is the error but with the features that can be learned to correct it
-            ERF_loss = tf.reduce_sum(tf.square(objective_features - features_tensor))
+        for mi, (m_name, m) in enumerate(named_blocks):
 
 
-        with tf.variable_scope('ERF_optimizer'):
+                c1 = bcolors.OKBLUE
+                c2 = bcolors.BOLD
+                ce = bcolors.ENDC
+                print('{:}{:}{:s}{:}{:} {:s}'.format(c1, c2, m_name, ce, ce, m.__repr__()))
+                blocks[m_name] = m
 
-            # Create the gradient descent optimizer with a dummy learning rate
-            optimizer = tf.train.GradientDescentOptimizer(1.0)
+                if mi == f_idx:
+                    chosen_block = m_name
 
-            # Get the gradients with respect to the modulation variable
-            ERF_var_grads = optimizer.compute_gradients(ERF_loss, var_list=[input_modulations_var])
+        print('\nChoose which block output you want to visualize by entering the block name in blue')
+        override_block = input('Block name: ')
+
+        if len(override_block) > 0:
+            chosen_block = override_block
+        print('{:}{:}{:s}{:}{:} {:s}'.format(c1, c2, chosen_block, ce, ce, blocks[chosen_block].__repr__()))
+        features_dim = blocks[chosen_block].out_dim
+
+        # Fix all the trainable variables in the network (is it needed in eval mode?)
+        print('\n*************************************\n')
+        for p_name, param in net.named_parameters():
+            if param.requires_grad:
+                param.requires_grad = False
+        print('\n*************************************\n')
+
+        # Create modulation variable that requires grad
+        input_modulations = torch.nn.Parameter(torch.zeros((200000, 1),
+                                                           dtype=torch.float32),
+                                               requires_grad=True)
+
+        print('\n*************************************\n')
+        for p_name, param in net.named_parameters():
+            if param.requires_grad:
+                print(p_name, param.shape)
+        print('\n*************************************\n')
+
+        # Create ERF loss
+
+        # Create ERF optimizer
 
-            # Gradient of the modulations
-            ERF_train_op = optimizer.apply_gradients(ERF_var_grads)
 
-        ################################
-        # Run model on all test examples
-        ################################
 
-        # Init our modulation variable
-        self.sess.run(tf.variables_initializer([input_modulations_var]))
 
-        # Initialise iterator with test data
-        self.sess.run(dataset.test_init_op)
-        count = 0
 
         global plots, p_scale, show_in_p, remove_h, aim_point
         aim_point = np.zeros((1, 3), dtype=np.float32)
@@ -841,10 +765,11 @@ class ModelVisualizer:
             global points, in_points, grad_values, chosen_point, aim_point, in_colors
 
             # Generate clouds until we effectively changed
+            batch = None
             if only_points:
-                for i in range(50):
-                    all_points = self.sess.run(model.inputs['points'])
-                    if all_points[0].shape[0] != in_points.shape[0]:
+                # get a new batch (index does not matter given our input pipeline)
+                for batch in loader:
+                    if batch.points[0].shape[0] != in_points.shape[0]:
                         break
 
             sum_grads = 0
@@ -853,11 +778,65 @@ class ModelVisualizer:
             else:
                 num_tries = 10
 
+            #################################################
+            # Apply ERF optim to the same batch several times
+            #################################################
+
+            if 'cuda' in self.device.type:
+                batch.to(self.device)
+
+
+
             for test_i in range(num_tries):
 
                 print('Updating ERF {:.0f}%'.format((test_i + 1) * 100 / num_tries))
                 rand_f_i = np.random.randint(features_dim)
 
+                # Reset input modulation variable
+                torch.nn.init.zeros_(input_modulations)
+
+                reset_op = input_modulations_var.assign(tf.zeros_like(input_modulations_var))
+                self.sess.run(reset_op)
+
+                # zero the parameter gradients
+                ERF_optimizer.zero_grad()
+
+                # Forward pass
+                outputs = net(batch, config)
+
+                loss = net.ERF_loss(outputs)
+
+                # Backward
+                loss.backward()
+
+                # Get result from hook here?
+
+                ERF_optimizer.step()
+                torch.cuda.synchronize(self.device)
+
+
+
+
+
+
+                # Forward pass
+                outputs = net(batch, config)
+                original_KP = deform_convs[deform_idx].kernel_points.cpu().detach().numpy()
+                stacked_deformed_KP = deform_convs[deform_idx].deformed_KP.cpu().detach().numpy()
+                count += batch.lengths[0].shape[0]
+
+                if 'cuda' in self.device.type:
+                    torch.cuda.synchronize(self.device)
+
+
+
+
+
+
+
+
+
+
                 # Reset input modulation variable
                 reset_op = input_modulations_var.assign(tf.zeros_like(input_modulations_var))
                 self.sess.run(reset_op)
@@ -1069,6 +1048,8 @@ class ModelVisualizer:
         fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
         mlab.show()
 
+        return
+
     def show_deformable_kernels(self, net, loader, config, deform_idx=0):
         """
         Show some inference with deformable kernels

visualize_ERFs.py

@@ -0,0 +1,205 @@
+#
+#
+#      0=================================0
+#      |    Kernel Point Convolutions    |
+#      0=================================0
+#
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Callable script to start a training on ModelNet40 dataset
+#
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#      Hugues THOMAS - 06/03/2020
+#
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Imports and global variables
+#       \**********************************/
+#
+
+# Common libs
+import signal
+import os
+import numpy as np
+import sys
+import torch
+
+# Dataset
+from datasets.ModelNet40 import *
+from datasets.S3DIS import *
+from torch.utils.data import DataLoader
+
+from utils.config import Config
+from utils.visualizer import ModelVisualizer
+from models.architectures import KPCNN, KPFCNN
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Main Call
+#       \***************/
+#
+
+def model_choice(chosen_log):
+
+    ###########################
+    # Call the test initializer
+    ###########################
+
+    # Automatically retrieve the last trained model
+    if chosen_log in ['last_ModelNet40', 'last_ShapeNetPart', 'last_S3DIS']:
+
+        # Dataset name
+        test_dataset = '_'.join(chosen_log.split('_')[1:])
+
+        # List all training logs
+        logs = np.sort([os.path.join('results', f) for f in os.listdir('results') if f.startswith('Log')])
+
+        # Find the last log of asked dataset
+        for log in logs[::-1]:
+            log_config = Config()
+            log_config.load(log)
+            if log_config.dataset.startswith(test_dataset):
+                chosen_log = log
+                break
+
+        if chosen_log in ['last_ModelNet40', 'last_ShapeNetPart', 'last_S3DIS']:
+            raise ValueError('No log of the dataset "' + test_dataset + '" found')
+
+    # Check if log exists
+    if not os.path.exists(chosen_log):
+        raise ValueError('The given log does not exists: ' + chosen_log)
+
+    return chosen_log
+
+
+# ----------------------------------------------------------------------------------------------------------------------
+#
+#           Main Call
+#       \***************/
+#
+
+if __name__ == '__main__':
+
+    ###############################
+    # Choose the model to visualize
+    ###############################
+
+    #   Here you can choose which model you want to test with the variable test_model. Here are the possible values :
+    #
+    #       > 'last_XXX': Automatically retrieve the last trained model on dataset XXX
+    #       > '(old_)results/Log_YYYY-MM-DD_HH-MM-SS': Directly provide the path of a trained model
+
+    # chosen_log = 'results/Log_2020-04-04_10-04-42'  # => ModelNet40
+    # chosen_log = 'results/Log_2020-04-04_10-04-42'  # => S3DIS
+    chosen_log = 'results/Log_2020-04-22_12-28-37'  # => S3DIS corrected
+
+    # You can also choose the index of the snapshot to load (last by default)
+    chkp_idx = -1
+
+    # Eventually you can choose which feature is visualized (index of the deform operation in the network)
+    f_idx = -1
+
+    # Deal with 'last_XXX' choices
+    chosen_log = model_choice(chosen_log)
+
+    ############################
+    # Initialize the environment
+    ############################
+
+    # Set which gpu is going to be used
+    GPU_ID = '0'
+
+    # Set GPU visible device
+    os.environ['CUDA_VISIBLE_DEVICES'] = GPU_ID
+
+    ###############
+    # Previous chkp
+    ###############
+
+    # Find all checkpoints in the chosen training folder
+    chkp_path = os.path.join(chosen_log, 'checkpoints')
+    chkps = [f for f in os.listdir(chkp_path) if f[:4] == 'chkp']
+
+    # Find which snapshot to restore
+    if chkp_idx is None:
+        chosen_chkp = 'current_chkp.tar'
+    else:
+        chosen_chkp = np.sort(chkps)[chkp_idx]
+    chosen_chkp = os.path.join(chosen_log, 'checkpoints', chosen_chkp)
+
+    # Initialize configuration class
+    config = Config()
+    config.load(chosen_log)
+
+    ##################################
+    # Change model parameters for test
+    ##################################
+
+    # Change parameters for the test here. For example, you can stop augmenting the input data.
+
+    config.augment_noise = 0.0001
+    #config.augment_symmetries = False
+    config.batch_num = 1
+    config.in_radius = 2.0
+    config.input_threads = 0
+
+    ##############
+    # Prepare Data
+    ##############
+
+    print()
+    print('Data Preparation')
+    print('****************')
+
+    # Initiate dataset
+    if config.dataset.startswith('ModelNet40'):
+        test_dataset = ModelNet40Dataset(config, train=False)
+        test_sampler = ModelNet40Sampler(test_dataset)
+        collate_fn = ModelNet40Collate
+    elif config.dataset == 'S3DIS':
+        test_dataset = S3DISDataset(config, set='validation', use_potentials=True)
+        test_sampler = S3DISSampler(test_dataset)
+        collate_fn = S3DISCollate
+    else:
+        raise ValueError('Unsupported dataset : ' + config.dataset)
+
+    # Data loader
+    test_loader = DataLoader(test_dataset,
+                             batch_size=1,
+                             sampler=test_sampler,
+                             collate_fn=collate_fn,
+                             num_workers=config.input_threads,
+                             pin_memory=True)
+
+    # Calibrate samplers
+    test_sampler.calibration(test_loader, verbose=True)
+
+    print('\nModel Preparation')
+    print('*****************')
+
+    # Define network model
+    t1 = time.time()
+    if config.dataset_task == 'classification':
+        net = KPCNN(config)
+    elif config.dataset_task in ['cloud_segmentation', 'slam_segmentation']:
+        net = KPFCNN(config, test_dataset.label_values, test_dataset.ignored_labels)
+    else:
+        raise ValueError('Unsupported dataset_task for deformation visu: ' + config.dataset_task)
+
+    # Define a visualizer class
+    visualizer = ModelVisualizer(net, config, chkp_path=chosen_chkp, on_gpu=False)
+    print('Done in {:.1f}s\n'.format(time.time() - t1))
+
+    print('\nStart visualization')
+    print('*******************')
+
+    # Training
+    visualizer.show_effective_recep_field(net, test_loader, config, f_idx)
+
+
+

visualize_deformations.py

@@ -30,11 +30,12 @@ import torch
 
 # Dataset
 from datasets.ModelNet40 import *
+from datasets.S3DIS import *
 from torch.utils.data import DataLoader
 
 from utils.config import Config
 from utils.visualizer import ModelVisualizer
-from models.architectures import KPCNN
+from models.architectures import KPCNN, KPFCNN
 
 
 # ----------------------------------------------------------------------------------------------------------------------
@@ -93,10 +94,12 @@ if __name__ == '__main__':
     #       > 'last_XXX': Automatically retrieve the last trained model on dataset XXX
     #       > '(old_)results/Log_YYYY-MM-DD_HH-MM-SS': Directly provide the path of a trained model
 
-    chosen_log = 'results/Log_2020-03-23_22-18-26'  # => ModelNet40
+    # chosen_log = 'results/Log_2020-04-04_10-04-42'  # => ModelNet40
+    # chosen_log = 'results/Log_2020-04-22_11-53-45'  # => S3DIS
+    chosen_log = 'results/Log_2020-04-22_12-28-37'  # => S3DIS corrected
 
     # You can also choose the index of the snapshot to load (last by default)
-    chkp_idx = None
+    chkp_idx = -1
 
     # Eventually you can choose which feature is visualized (index of the deform operation in the network)
     deform_idx = 0
@@ -139,10 +142,11 @@ if __name__ == '__main__':
 
     # Change parameters for the test here. For example, you can stop augmenting the input data.
 
-    #config.augment_noise = 0.0001
+    config.augment_noise = 0.0001
     #config.augment_symmetries = False
-    #config.batch_num = 3
-    #config.in_radius = 4
+    config.batch_num = 1
+    config.in_radius = 2.0
+    config.input_threads = 0
 
     ##############
     # Prepare Data
@@ -152,22 +156,28 @@ if __name__ == '__main__':
     print('Data Preparation')
     print('****************')
 
-    # Initialize datasets
+    # Initiate dataset
+    if config.dataset.startswith('ModelNet40'):
         test_dataset = ModelNet40Dataset(config, train=False)
-
-    # Initialize samplers
         test_sampler = ModelNet40Sampler(test_dataset)
+        collate_fn = ModelNet40Collate
+    elif config.dataset == 'S3DIS':
+        test_dataset = S3DISDataset(config, set='validation', use_potentials=True)
+        test_sampler = S3DISSampler(test_dataset)
+        collate_fn = S3DISCollate
+    else:
+        raise ValueError('Unsupported dataset : ' + config.dataset)
 
-    # Initialize the dataloader
+    # Data loader
     test_loader = DataLoader(test_dataset,
                              batch_size=1,
                              sampler=test_sampler,
-                             collate_fn=ModelNet40Collate,
-                             num_workers=0,
+                             collate_fn=collate_fn,
+                             num_workers=config.input_threads,
                              pin_memory=True)
 
     # Calibrate samplers
-    test_sampler.calibration(test_loader)
+    test_sampler.calibration(test_loader, verbose=True)
 
     print('\nModel Preparation')
     print('*****************')
@@ -176,6 +186,8 @@ if __name__ == '__main__':
     t1 = time.time()
     if config.dataset_task == 'classification':
         net = KPCNN(config)
+    elif config.dataset_task in ['cloud_segmentation', 'slam_segmentation']:
+        net = KPFCNN(config, test_dataset.label_values, test_dataset.ignored_labels)
     else:
         raise ValueError('Unsupported dataset_task for deformation visu: ' + config.dataset_task)