From ea89ccb0e559f2b27e9b7ab09381741011976d18 Mon Sep 17 00:00:00 2001 From: Jing Zhu Date: Mon, 14 Jun 2021 11:43:24 +0800 Subject: [PATCH 1/7] 0614 from hpc.sjtu --- builder/data_builder.py | 3 ++- config/__pycache__/__init__.cpython-37.pyc | Bin 156 -> 145 bytes config/__pycache__/config.cpython-37.pyc | Bin 1954 -> 2021 bytes config/semantickitti.yaml | 4 ++-- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/builder/data_builder.py b/builder/data_builder.py index 127437b..ee388f2 100644 --- a/builder/data_builder.py +++ b/builder/data_builder.py @@ -30,7 +30,8 @@ def build(dataset_config, return_ref=train_ref, label_mapping=label_mapping, nusc=nusc) val_pt_dataset = SemKITTI(data_path, imageset=val_imageset, return_ref=val_ref, label_mapping=label_mapping, nusc=nusc) - + #import pdb + #pdb.set_trace() train_dataset = get_model_class(dataset_config['dataset_type'])( train_pt_dataset, grid_size=grid_size, diff --git a/config/__pycache__/__init__.cpython-37.pyc 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@@ dataset_params: ################### ## Data_loader options train_data_loader: - data_path: "/data/dataset/semantic_kitti/data_semkitti/dataset/sequences/" + data_path: "/lustre/home/acct-stu/stu010/sequences/" imageset: "train" return_ref: True batch_size: 2 @@ -49,7 +49,7 @@ train_data_loader: num_workers: 4 val_data_loader: - data_path: "/data/dataset/semantic_kitti/data_semkitti/dataset/sequences/" + data_path: "/lustre/home/acct-stu/stu010/sequences/" imageset: "val" return_ref: True batch_size: 1 From d42f5801ba270fae1aa598d35b73d1333e0d211e Mon Sep 17 00:00:00 2001 From: Yue Kuang Date: Mon, 14 Jun 2021 12:21:02 +0800 Subject: [PATCH 2/7] 0614 --- .gitignore | 0 NUSCENES-GUIDE.md | 0 README.md | 0 builder/__init__.py | 0 builder/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 146 bytes builder/__pycache__/data_builder.cpython-37.pyc | Bin 0 -> 1572 bytes builder/__pycache__/loss_builder.cpython-37.pyc | Bin 0 -> 527 bytes .../__pycache__/model_builder.cpython-37.pyc | Bin 0 -> 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train.sh | 4 +-- train_cylinder_asym.py | 1 - train_cylinder_asym_nuscenes.py | 0 train_nusc.sh | 0 utils/__init__.py | 0 utils/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 144 bytes .../__pycache__/load_save_util.cpython-37.pyc | Bin 0 -> 1207 bytes .../__pycache__/lovasz_losses.cpython-37.pyc | Bin 0 -> 10313 bytes utils/__pycache__/metric_util.cpython-37.pyc | Bin 0 -> 843 bytes utils/load_save_util.py | 0 utils/log_util.py | 0 utils/lovasz_losses.py | 0 utils/metric_util.py | 0 59 files changed, 39 insertions(+), 13 deletions(-) mode change 100644 => 100755 builder/__init__.py create mode 100755 builder/__pycache__/__init__.cpython-37.pyc create mode 100755 builder/__pycache__/data_builder.cpython-37.pyc create mode 100755 builder/__pycache__/loss_builder.cpython-37.pyc create mode 100755 builder/__pycache__/model_builder.cpython-37.pyc mode change 100644 => 100755 builder/data_builder.py mode change 100644 => 100755 builder/loss_builder.py mode change 100644 => 100755 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(np.floor((np.clip(xyz_pol, min_bound, max_bound) - min_bound) / intervals)).astype(np.int) + # fixed + # grid_ind = (np.floor((np.clip(xyz_pol, min_bound, max_bound) - min_bound) / intervals)).astype(np.int) + + # triple + #x_clip = np.clip(xyz_pol, min_bound, max_bound) + #a = (cur_grid_size[0] - 1) / (triple_func(max_bound[0] - min_bound[0])) + #tmp_y = a * triple_func(x_clip[:, 0] - min_bound[0]) + #grid_ind1 = (np.floor(tmp_y)).astype(np.int) + #grid_ind2 = (np.floor((x_clip[:, 1:] - min_bound[1:]) / ((max_bound[1:] - min_bound[1:]) / (cur_grid_size[1:] - 1)))).astype(np.int) + #grid_ind = np.concatenate((grid_ind1.reshape((grid_ind1.shape[0], 1)), grid_ind2), axis=1) + ################## + + #exponential + x_clip = np.clip(xyz_pol, min_bound, max_bound) + a = (cur_grid_size[0] - 1) / (ln_func(max_bound[0] + 1 - min_bound[0])) + tmp_y = a * ln_func(x_clip[:, 0] + 1 - min_bound[0]) + grid_ind1 = (np.floor(tmp_y)).astype(np.int) + grid_ind2 = (np.floor((x_clip[:, 1:] - 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--- a/train.sh +++ b/train.sh @@ -1,5 +1,5 @@ name=cylinder_asym_networks gpuid=0 -CUDA_VISIBLE_DEVICES=${gpuid} python -u train_cylinder_asym.py \ -2>&1 | tee logs_dir/${name}_logs_tee.txt \ No newline at end of file +CUDA_VISIBLE_DEVICES=${gpuid} python3 -u train_cylinder_asym.py \ +2>&1 | tee logs_dir/${name}_logs_tee.txt diff --git a/train_cylinder_asym.py b/train_cylinder_asym.py old mode 100644 new mode 100755 index aa32d89..ecd7e4d --- a/train_cylinder_asym.py +++ b/train_cylinder_asym.py @@ -87,7 +87,6 @@ def main(args): with torch.no_grad(): for i_iter_val, (_, val_vox_label, val_grid, val_pt_labs, val_pt_fea) in enumerate( val_dataset_loader): - val_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in val_pt_fea] val_grid_ten = [torch.from_numpy(i).to(pytorch_device) for i in val_grid] diff --git a/train_cylinder_asym_nuscenes.py b/train_cylinder_asym_nuscenes.py old mode 100644 new mode 100755 diff --git a/train_nusc.sh b/train_nusc.sh old 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new mode 100755 From de677afdc7a90a559c30a5a90ab772266a0d727d Mon Sep 17 00:00:00 2001 From: Yue Kuang Date: Wed, 23 Jun 2021 10:02:06 +0800 Subject: [PATCH 4/7] balanced plot statistic --- statistic_new_data_coding.py | 166 +++++++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100755 statistic_new_data_coding.py diff --git a/statistic_new_data_coding.py b/statistic_new_data_coding.py new file mode 100755 index 0000000..6fc0880 --- /dev/null +++ b/statistic_new_data_coding.py @@ -0,0 +1,166 @@ +import numpy as np +import os +import tqdm +import matplotlib.mlab as mlab +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +import random +import shutil + +# transformation between Cartesian coordinates and polar coordinates +def cart2polar(input_xyz): + rho = np.sqrt(input_xyz[:, 0] ** 2 + input_xyz[:, 1] ** 2) + phi = np.arctan2(input_xyz[:, 1], input_xyz[:, 0]) + return np.stack((rho, phi, input_xyz[:, 2]), axis=1) + +# quadratic +def quadratic_func(x): + return x ** 0.5 + +# triple +def triple_func(x): + return x ** (1/3) + +# ln +def ln_func(x): + return np.log(x) + +def cal_cell_num(func_type): + file_num = 11 + root_path = '/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences' + + max_bound = np.asarray([50, 3.1415926, 2]) + min_bound = np.asarray([0, -3.1415926, -4]) + crop_range = max_bound - min_bound + cur_grid_size = np.asarray([480, 360, 32]) + + # for plot + dim1 = cur_grid_size[0] + dim2 = cur_grid_size[1] + dim3 = cur_grid_size[2] + + total_cell_list = [0, 0, 0, 0, 0] + full_cell_list = [0, 0, 0, 0, 0] + res = [] + total_file_num = 0 + + for j in range(file_num): + if j == 8: + continue + folder_path = os.path.join(root_path, str(j).zfill(2), 'velodyne') + # folder_path = '/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences/01/velodyne' + for root, dirs, files in os.walk(folder_path): + for f in tqdm.tqdm(files): + # filepath='/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences/00/velodyne/003907.bin' + filepath = os.path.join(root, f) + raw_data = np.fromfile(filepath, dtype=np.float32).reshape((-1, 4)) + xyz_pol = cart2polar(raw_data[:, :3]) + + total_file_num += 1 + if func_type == "quadratic": + x_clip = np.clip(xyz_pol, min_bound, max_bound) + a = (cur_grid_size[0] - 1) / (quadratic_func(max_bound[0] - min_bound[0])) + tmp_y = a * quadratic_func(x_clip[:, 0] - min_bound[0]) + grid_ind1 = (np.floor(tmp_y)).astype(np.int) + grid_ind2 = (np.floor((x_clip[:, 1:] - min_bound[1:]) / ((max_bound[1:] - min_bound[1:]) / ( + cur_grid_size[1:] - 1)))).astype(np.int) + grid_ind = np.concatenate((grid_ind1.reshape((grid_ind1.shape[0], 1)), grid_ind2), axis=1) + + for i in range(5): + left_x = i * 10 + right_x = (i + 1) * 10 + left_y = np.floor(a * quadratic_func(left_x)) + right_y = np.floor(a * quadratic_func(right_x)) + unique_cell = np.unique(grid_ind, axis=0) + full_cell_num = np.sum(np.logical_and((unique_cell[:, 0] >= left_y), (unique_cell[:, 0] < right_y))) + full_cell_list[i] += full_cell_num + + elif func_type == "triple": + x_clip = np.clip(xyz_pol, min_bound, max_bound) + a = (cur_grid_size[0] - 1) / (triple_func(max_bound[0] - min_bound[0])) + tmp_y = a * triple_func(x_clip[:, 0] - min_bound[0]) + grid_ind1 = (np.floor(tmp_y)).astype(np.int) + grid_ind2 = (np.floor((x_clip[:, 1:] - min_bound[1:]) / ((max_bound[1:] - min_bound[1:]) / ( + cur_grid_size[1:] - 1)))).astype(np.int) + grid_ind = np.concatenate((grid_ind1.reshape((grid_ind1.shape[0], 1)), grid_ind2), axis=1) + + for i in range(5): + left_x = i * 10 + right_x = (i + 1) * 10 + left_y = np.floor(a * triple_func(left_x)) + right_y = np.floor(a * triple_func(right_x)) + unique_cell = np.unique(grid_ind, axis=0) + full_cell_num = np.sum(np.logical_and((unique_cell[:, 0] >= left_y), (unique_cell[:, 0] < right_y))) + full_cell_list[i] += full_cell_num + + elif func_type == "ln": + x_clip = np.clip(xyz_pol, min_bound, max_bound) + a = (cur_grid_size[0] - 1) / (ln_func(max_bound[0] + 1 - min_bound[0])) + tmp_y = a * ln_func(x_clip[:, 0] + 1 - min_bound[0]) + grid_ind1 = (np.floor(tmp_y)).astype(np.int) + grid_ind2 = (np.floor((x_clip[:, 1:] - min_bound[1:]) / ((max_bound[1:] - min_bound[1:]) / ( + cur_grid_size[1:] - 1)))).astype(np.int) + grid_ind = np.concatenate((grid_ind1.reshape((grid_ind1.shape[0], 1)), grid_ind2), axis=1) + for i in range(5): + left_x = i * 10 + right_x = (i + 1) * 10 + left_y = np.floor(a * ln_func(left_x)) + right_y = np.floor(a * ln_func(right_x)) + unique_cell = np.unique(grid_ind, axis=0) + full_cell_num = np.sum(np.logical_and((unique_cell[:, 0] >= left_y), (unique_cell[:, 0] < right_y))) + full_cell_list[i] += full_cell_num + + elif func_type == "original": + intervals = crop_range / (cur_grid_size - 1) + if (intervals == 0).any(): print("Zero interval!") + x_clip = np.clip(xyz_pol, min_bound, max_bound) + grid_ind = (np.floor((x_clip - min_bound) / intervals)).astype(np.int) + + for i in range(5): + left_x = i * 10 + right_x = (i + 1) * 10 + left_y = np.floor(left_x / intervals[0]) + right_y = np.floor(right_x / intervals[0]) + unique_cell = np.unique(grid_ind, axis=0) + full_cell_num = np.sum( + np.logical_and((unique_cell[:, 0] >= left_y), (unique_cell[:, 0] < right_y))) + full_cell_list[i] += full_cell_num + + + for j in range(5): + left_x = j * 10 + right_x = (j + 1) * 10 + if func_type == "quadratic": + a = (cur_grid_size[0] - 1) / (quadratic_func(max_bound[0] - min_bound[0])) + left_y = np.floor(a * quadratic_func(left_x)) + right_y = np.floor(a * quadratic_func(right_x)) + total_cell_list[j] = (right_y - left_y) * dim2 * dim3 * total_file_num + + elif func_type == "triple": + a = (cur_grid_size[0] - 1) / (triple_func(max_bound[0] - min_bound[0])) + left_y = np.floor(a * triple_func(left_x)) + right_y = np.floor(a * triple_func(right_x)) + total_cell_list[j] = (right_y - left_y) * dim2 * dim3 * total_file_num + + elif func_type == "ln": + a = (cur_grid_size[0] - 1) / (ln_func(max_bound[0] + 1 - min_bound[0])) + left_y = np.floor(a * ln_func(left_x + 1)) + right_y = np.floor(a * ln_func(right_x + 1)) + total_cell_list[j] = (right_y - left_y) * dim2 * dim3 * total_file_num + + elif func_type == "original": + intervals = crop_range / (cur_grid_size - 1) + left_y = np.floor((np.clip(left_x, min_bound[0], max_bound[0]) - min_bound[0]) / intervals[0]) + right_y = np.floor((np.clip(right_x, min_bound[0], max_bound[0]) - min_bound[0]) / intervals[0]) + total_cell_list[j] = (right_y - left_y) * dim2 * dim3 * total_file_num + + res.append(full_cell_list[j] / total_cell_list[j]) + print("res:", res) + + with open(func_type + '.txt', 'w') as file: + for i in res: + file.write(str(i) + '\n') + +if __name__ == '__main__': + func_type = ["quadratic", "triple", "ln", "original"] + cal_cell_num(func_type[2]) From 12b35418ed7a0aa60d35f0267d4bd2a0331cecbe Mon Sep 17 00:00:00 2001 From: Yue Kuang Date: Wed, 23 Jun 2021 10:07:31 +0800 Subject: [PATCH 5/7] data encoding variations --- dataloader/dataset_semantickitti.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dataloader/dataset_semantickitti.py b/dataloader/dataset_semantickitti.py index dd6e7f1..53337a5 100755 --- a/dataloader/dataset_semantickitti.py +++ b/dataloader/dataset_semantickitti.py @@ -254,7 +254,7 @@ def __getitem__(self, index): # fixed # grid_ind = (np.floor((np.clip(xyz_pol, min_bound, max_bound) - min_bound) / intervals)).astype(np.int) - # triple + # triple (quadratic interval) #x_clip = np.clip(xyz_pol, min_bound, max_bound) #a = (cur_grid_size[0] - 1) / (triple_func(max_bound[0] - min_bound[0])) #tmp_y = a * triple_func(x_clip[:, 0] - min_bound[0]) @@ -263,7 +263,7 @@ def __getitem__(self, index): #grid_ind = np.concatenate((grid_ind1.reshape((grid_ind1.shape[0], 1)), grid_ind2), axis=1) ################## - #exponential + #exponential (exponential interval) x_clip = np.clip(xyz_pol, min_bound, max_bound) a = (cur_grid_size[0] - 1) / (ln_func(max_bound[0] + 1 - min_bound[0])) tmp_y = a * ln_func(x_clip[:, 0] + 1 - min_bound[0]) From 2426e044a539ad4e061a3bbe4bebff6264ce6a1f Mon Sep 17 00:00:00 2001 From: Yue Kuang Date: Thu, 1 Jul 2021 18:57:50 +0800 Subject: [PATCH 6/7] add vanilla CNN version --- network/segmentator_3d_asymm_spconv.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/network/segmentator_3d_asymm_spconv.py b/network/segmentator_3d_asymm_spconv.py index 08c317c..c56db9f 100755 --- a/network/segmentator_3d_asymm_spconv.py +++ b/network/segmentator_3d_asymm_spconv.py @@ -46,6 +46,13 @@ def conv1x1(in_planes, out_planes, stride=1, indice_key=None): class ResContextBlock(nn.Module): def __init__(self, in_filters, out_filters, kernel_size=(3, 3, 3), stride=1, indice_key=None): super(ResContextBlock, self).__init__() + ####no asymconv + # self.conv1 = conv3x3(in_filters, out_filters, indice_key=indice_key + "bef") + # self.bn0 = nn.BatchNorm1d(out_filters) + # self.act1 = nn.LeakyReLU() + # self.weight_initialization() + ################## + self.conv1 = conv1x3(in_filters, out_filters, indice_key=indice_key + "bef") self.bn0 = nn.BatchNorm1d(out_filters) self.act1 = nn.LeakyReLU() @@ -71,6 +78,13 @@ def weight_initialization(self): nn.init.constant_(m.bias, 0) def forward(self, x): + ####noasymconv + # shortcut = self.conv1(x) + # shortcut.features = self.act1(shortcut.features) + # shortcut.features = self.bn0(shortcut.features) + # return shortcut + ############ + shortcut = self.conv1(x) shortcut.features = self.act1(shortcut.features) shortcut.features = self.bn0(shortcut.features) From 0f5562ed11463e9afb03681eda2264f9a860c93b Mon Sep 17 00:00:00 2001 From: yexinyinancy Date: Sun, 4 Jul 2021 15:07:29 +0800 Subject: [PATCH 7/7] statistic instance number per class and split dataset --- statistic_class_num.py | 82 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 statistic_class_num.py diff --git a/statistic_class_num.py b/statistic_class_num.py new file mode 100644 index 0000000..992f4fb --- /dev/null +++ b/statistic_class_num.py @@ -0,0 +1,82 @@ +import numpy as np +import os +import tqdm +import matplotlib.mlab as mlab +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +import random +import shutil + +def plot(): + class_map = {5: 'bus', 4: 'truck', 8: 'motorcyclist', 6: 'person', 7: 'bicyclist', 1: 'car', 0: 'unlabeled', + 19: 'traffic-sign',18: 'pole', 17: 'terrain', 16: 'trunk', 15: 'vegetation', 9: 'road', 14: 'fence', 13: 'building', + 12: 'other-ground', 11: 'sidewalk', 10: 'parking', 3: 'motorcycle', 2: 'bicycle'} + file_num = 11 + for j in range(file_num): + X = [str(i) for i in range(20)] + Y = [] + fig = plt.figure() + with open('out' + str(j).zfill(2) + '.txt', 'r') as f: + for line in f.readlines(): + line = line.strip('\n') # 去掉列表中每一个元素的换行符 + Y.append(int(line)) + plt.bar(X, Y, 0.4, color="blue") + plt.xlabel("X-axis") + plt.ylabel("Y-axis") + plt.title('Sequence' + str(j).zfill(2)) + + # plt.show() + plt.savefig('barChart' + str(j).zfill(2) + '.jpg') + + +def statistc_class(): + learning_map = {0: 0, 1: 0, 10: 1, 11: 2, 13: 5, 15: 3, 16: 5, 18: 4, 20: 5, 30: 6, 31: 7, 32: 8, 40: 9, 44: 10, + 48: 11, 49: 12, 50: 13, 51: 14, 52: 0, 60: 9, 70: 15, 71: 16, 72: 17, 80: 18, 81: 19, 99: 0, 252: 1, + 253: 7, 254: 6, 255: 8, 256: 5, 257: 5, 258: 4, 259: 5} + + statistc_class_dic = {} + file_num = 11 + root_path = '/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences' + for i in range(20): + statistc_class_dic[i] = 0 + + for j in range(file_num): + folder_path = os.path.join(root_path, str(j).zfill(2), 'labels') + # folder_path = '/Users/yexinyi/Desktop/VE450.nosync/data/val_sub/dataset/sequences/08/labels' + for root, dirs, files in os.walk(folder_path): + for f in tqdm.tqdm(files): + # filepath='/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences/00/labels/003907.label' + filepath = os.path.join(root, f) + annotated_data = np.fromfile(filepath, dtype=np.uint32).reshape((-1, 1)) + annotated_data = annotated_data & 0xFFFF # delete high 16 digits binary + tmp = annotated_data.astype(np.uint8) + for i in range(len(tmp)): + if tmp[i][0] in learning_map.keys(): + if learning_map[tmp[i][0]] in statistc_class_dic.keys(): + statistc_class_dic[learning_map[tmp[i][0]]] += 1 + + with open('out_val' + str(j).zfill(2) + '.txt', 'w') as file: + for i in statistc_class_dic.values(): + file.write(str(i) + '\n') + + break + +def split_dataset(): + folder_path = '/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences/08/labels' + folder_path2 = '/Users/yexinyi/Desktop/VE450.nosync/data/merge/dataset/sequences/08/velodyne' + + file_list = random.sample(os.listdir(folder_path), 814) #0.2*4070 + for i in tqdm.tqdm(file_list): + source = os.path.join(folder_path, i) + target = os.path.join('/Users/yexinyi/Desktop/VE450.nosync/data/val_sub/dataset/sequences/08/labels', i) + source2 = os.path.join(folder_path2, i[:-5]+'bin') + target2 = os.path.join('/Users/yexinyi/Desktop/VE450.nosync/data/val_sub/dataset/sequences/08/velodyne', i[:-5]+'bin') + shutil.copy(source, target) + shutil.copy(source2, target2) + + + +if __name__ == '__main__': + statistc_class() + # plot() + # split_dataset()