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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,41 @@ | ||
| name: normal | ||
| ckpt_dir: "./data/ckpt/" | ||
| resume_path: "./data/ckpt/normal.ckpt" | ||
| results_path: "./results" | ||
|
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| dataset: | ||
| root: "./data/" | ||
| rotation_num: 36 | ||
| train_bsize: 1.0 | ||
| val_bsize: 1.0 | ||
| test_bsize: 1.0 | ||
| types: ["thuman2"] | ||
| scales: [100.0] | ||
|
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| net: | ||
| in_nml: (('image',3), ('T_normal_F',3), ('T_normal_B',3)) | ||
|
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| lr_N: 1e-4 | ||
| weight_decay: 0.0 | ||
| momentum: 0.0 | ||
| batch_size: 4 | ||
| num_threads: 4 | ||
| gpus: [0] | ||
| test_gpus: [0] | ||
|
|
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| fast_dev: 0 | ||
| resume: False | ||
| test_mode: False | ||
| num_sanity_val_steps: 1 | ||
|
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| momentum: 0.0 | ||
| optim: Adam | ||
|
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| # training (batch=4, set=agora, rot-6) | ||
| overfit: False | ||
| num_epoch: 20 | ||
| freq_show_train: 0.1 | ||
| freq_show_val: 0.01 | ||
| freq_plot: 0.01 | ||
| freq_eval: 0.1 | ||
| schedule: [18] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -15,9 +15,11 @@ | |
| # | ||
| # Contact: [email protected] | ||
|
|
||
| import random | ||
| import os.path as osp | ||
| import numpy as np | ||
| from PIL import Image | ||
| from termcolor import colored | ||
| import torchvision.transforms as transforms | ||
|
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|
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@@ -26,14 +28,13 @@ def __init__(self, cfg, split='train'): | |
|
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| self.split = split | ||
| self.root = cfg.root | ||
| self.bsize = cfg.batch_size | ||
| self.overfit = cfg.overfit | ||
|
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| self.opt = cfg.dataset | ||
| self.datasets = self.opt.types | ||
| self.input_size = self.opt.input_size | ||
| self.set_splits = self.opt.set_splits | ||
| self.scales = self.opt.scales | ||
| self.pifu = self.opt.pifu | ||
|
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| # input data types and dimensions | ||
| self.in_nml = [item[0] for item in cfg.net.in_nml] | ||
|
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@@ -44,19 +45,18 @@ def __init__(self, cfg, split='train'): | |
| if self.split != 'train': | ||
| self.rotations = range(0, 360, 120) | ||
| else: | ||
| self.rotations = np.arange(0, 360, 360 / | ||
| self.opt.rotation_num).astype(np.int) | ||
| self.rotations = np.arange( | ||
| 0, 360, 360//self.opt.rotation_num).astype(np.int) | ||
|
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| self.datasets_dict = {} | ||
|
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| for dataset_id, dataset in enumerate(self.datasets): | ||
| dataset_dir = osp.join(self.root, dataset, "smplx") | ||
|
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| dataset_dir = osp.join(self.root, dataset) | ||
|
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| self.datasets_dict[dataset] = { | ||
| "subjects": | ||
| np.loadtxt(osp.join(self.root, dataset, "all.txt"), dtype=str), | ||
| "path": | ||
| dataset_dir, | ||
| "scale": | ||
| self.scales[dataset_id] | ||
| "subjects": np.loadtxt(osp.join(dataset_dir, "all.txt"), dtype=str), | ||
| "scale": self.scales[dataset_id] | ||
| } | ||
|
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| self.subject_list = self.get_subject_list(split) | ||
|
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@@ -81,65 +81,35 @@ def get_subject_list(self, split): | |
|
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| for dataset in self.datasets: | ||
|
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| if self.pifu: | ||
| txt = osp.join(self.root, dataset, f'{split}_pifu.txt') | ||
| else: | ||
| txt = osp.join(self.root, dataset, f'{split}.txt') | ||
|
|
||
| if osp.exists(txt): | ||
| print(f"load from {txt}") | ||
| subject_list += sorted(np.loadtxt(txt, dtype=str).tolist()) | ||
|
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||
| if self.pifu: | ||
| miss_pifu = sorted( | ||
| np.loadtxt(osp.join(self.root, dataset, | ||
| "miss_pifu.txt"), | ||
| dtype=str).tolist()) | ||
| subject_list = [ | ||
| subject for subject in subject_list | ||
| if subject not in miss_pifu | ||
| ] | ||
| subject_list = [ | ||
| "renderpeople/" + subject for subject in subject_list | ||
| ] | ||
| split_txt = osp.join(self.root, dataset, f'{split}.txt') | ||
|
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||
| if osp.exists(split_txt): | ||
| print(f"load from {split_txt}") | ||
| subject_list += np.loadtxt(split_txt, dtype=str).tolist() | ||
| else: | ||
| train_txt = osp.join(self.root, dataset, 'train.txt') | ||
| val_txt = osp.join(self.root, dataset, 'val.txt') | ||
| test_txt = osp.join(self.root, dataset, 'test.txt') | ||
|
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||
| print( | ||
| f"generate lists of [train, val, test] \n {train_txt} \n {val_txt} \n {test_txt} \n" | ||
| ) | ||
|
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| split_txt = osp.join(self.root, dataset, f'{split}.txt') | ||
|
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| subjects = self.datasets_dict[dataset]['subjects'] | ||
| train_split = int(len(subjects) * self.set_splits[0]) | ||
| val_split = int( | ||
| len(subjects) * self.set_splits[1]) + train_split | ||
|
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| with open(train_txt, "w") as f: | ||
| f.write("\n".join(dataset + "/" + item | ||
| for item in subjects[:train_split])) | ||
| with open(val_txt, "w") as f: | ||
| f.write("\n".join( | ||
| dataset + "/" + item | ||
| for item in subjects[train_split:val_split])) | ||
| with open(test_txt, "w") as f: | ||
| f.write("\n".join(dataset + "/" + item | ||
| for item in subjects[val_split:])) | ||
|
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| subject_list += sorted( | ||
| np.loadtxt(split_txt, dtype=str).tolist()) | ||
|
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| bug_list = sorted( | ||
| np.loadtxt(osp.join(self.root, 'bug.txt'), dtype=str).tolist()) | ||
|
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| subject_list = [ | ||
| subject for subject in subject_list if (subject not in bug_list) | ||
| ] | ||
| full_txt = osp.join(self.root, dataset, 'all.txt') | ||
| print(f"split {full_txt} into train/val/test") | ||
|
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| full_lst = np.loadtxt(full_txt, dtype=str) | ||
| full_lst = [dataset+"/"+item for item in full_lst] | ||
| [train_lst, test_lst, val_lst] = np.split( | ||
| full_lst, [500, 500+5, ]) | ||
|
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| np.savetxt(full_txt.replace( | ||
| "all", "train"), train_lst, fmt="%s") | ||
| np.savetxt(full_txt.replace("all", "test"), test_lst, fmt="%s") | ||
| np.savetxt(full_txt.replace("all", "val"), val_lst, fmt="%s") | ||
|
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| print(f"load from {split_txt}") | ||
| subject_list += np.loadtxt(split_txt, dtype=str).tolist() | ||
|
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| if self.split != 'test': | ||
| subject_list += subject_list[:self.bsize - | ||
| len(subject_list) % self.bsize] | ||
| print(colored(f"total: {len(subject_list)}", "yellow")) | ||
| random.shuffle(subject_list) | ||
|
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| # subject_list = ["thuman2/0008"] | ||
| return subject_list | ||
|
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| def __len__(self): | ||
|
|
@@ -155,46 +125,38 @@ def __getitem__(self, index): | |
| mid = index // len(self.rotations) | ||
|
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| rotation = self.rotations[rid] | ||
|
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| # choose specific test sets | ||
| subject = self.subject_list[mid] | ||
|
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| subject_render = "/".join( | ||
| [subject.split("/")[0] + "_12views", | ||
| subject.split("/")[1]]) | ||
| subject = self.subject_list[mid].split("/")[1] | ||
| dataset = self.subject_list[mid].split("/")[0] | ||
| render_folder = "/".join([dataset + | ||
| f"_{self.opt.rotation_num}views", subject]) | ||
|
|
||
| # setup paths | ||
| data_dict = { | ||
| 'dataset': | ||
| subject.split("/")[0], | ||
| 'subject': | ||
| subject, | ||
| 'rotation': | ||
| rotation, | ||
| 'image_path': | ||
| osp.join(self.root, subject_render, 'render', | ||
| f'{rotation:03d}.png') | ||
| 'dataset': dataset, | ||
| 'subject': subject, | ||
| 'rotation': rotation, | ||
| 'scale': self.datasets_dict[dataset]["scale"], | ||
| 'image_path': osp.join(self.root, render_folder, 'render', f'{rotation:03d}.png') | ||
| } | ||
|
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||
| # image/normal/depth loader | ||
| for name, channel in zip(self.in_total, self.in_total_dim): | ||
|
|
||
| if name != 'image': | ||
| if f'{name}_path' not in data_dict.keys(): | ||
| data_dict.update({ | ||
| f'{name}_path': | ||
| osp.join(self.root, subject_render, name, | ||
| f'{rotation:03d}.png') | ||
| f'{name}_path': osp.join(self.root, render_folder, name, f'{rotation:03d}.png') | ||
| }) | ||
|
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||
| # tensor update | ||
| data_dict.update({ | ||
| name: | ||
| self.imagepath2tensor(data_dict[f'{name}_path'], | ||
| channel, | ||
| inv='depth_B' in name) | ||
| name: self.imagepath2tensor( | ||
| data_dict[f'{name}_path'], channel, inv=False) | ||
| }) | ||
|
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| path_keys = [ | ||
| key for key in data_dict.keys() if '_path' in key or '_dir' in key | ||
| ] | ||
|
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||
| for key in path_keys: | ||
| del data_dict[key] | ||
|
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||
|
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||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,21 @@ | ||
|
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| # -*- coding: utf-8 -*- | ||
|
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| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | ||
| # holder of all proprietary rights on this computer program. | ||
| # You can only use this computer program if you have closed | ||
| # a license agreement with MPG or you get the right to use the computer | ||
| # program from someone who is authorized to grant you that right. | ||
| # Any use of the computer program without a valid license is prohibited and | ||
| # liable to prosecution. | ||
| # | ||
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | ||
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | ||
| # for Intelligent Systems. All rights reserved. | ||
| # | ||
| # Contact: [email protected] | ||
|
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|
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| from lib.renderer.mesh import load_fit_body | ||
| from lib.dataset.hoppeMesh import HoppeMesh | ||
| from lib.dataset.body_model import TetraSMPLModel | ||
|
|
||
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