From 6de6e45d90d205d170c73bf3f8d6b2a07098113a Mon Sep 17 00:00:00 2001 From: Sandra Kara Date: Tue, 17 Sep 2024 10:52:55 +0200 Subject: [PATCH] initial release --- DIOD.png | Bin 0 -> 5760275 bytes DIOD_DINOv2/datasetKITTIEval_dinov2.py | 61 +++ DIOD_DINOv2/datasetKITTI_dinov2.py | 89 ++++ DIOD_DINOv2/datasetPDEval_dinov2.py | 98 ++++ DIOD_DINOv2/datasetPD_dinov2.py | 118 +++++ DIOD_DINOv2/evalKITTI_ARI_dinov2.py | 83 ++++ DIOD_DINOv2/evalKITTI_F1_score_dinov2.py | 277 +++++++++++ DIOD_DINOv2/evalPD_ARI_dinov2.py | 92 ++++ DIOD_DINOv2/evalPD_F1_score_dinov2.py | 266 +++++++++++ DIOD_DINOv2/models/ConvGRU.py | 232 +++++++++ DIOD_DINOv2/models/model_bg_dinov2.py | 304 ++++++++++++ DIOD_DINOv2/plotPD_dinov2.py | 84 ++++ DIOD_DINOv2/trainKITTI_ts_dinov2.py | 546 +++++++++++++++++++++ DIOD_DINOv2/trainPD_ts_dino.py | 576 +++++++++++++++++++++++ DIOD_DINOv2/transforms.py | 142 ++++++ DIOD_DINOv2/utils.py | 61 +++ DIOD_env.yml | 165 +++++++ LICENSE | 518 ++++++++++++++++++++ PlotKITTI.py | 132 ++++++ PlotPD.py | 92 ++++ README.md | 108 +++++ create_mp4.py | 117 +++++ datasetKITTI.py | 88 ++++ datasetKITTIEval.py | 62 +++ datasetPD.py | 127 +++++ datasetPDEval.py | 97 ++++ evalKITTI_ARI.py | 82 ++++ evalPD_ARI.py | 89 ++++ evalPD_F1_score.py | 269 +++++++++++ eval_KITTI_F1_score.py | 275 +++++++++++ models/.gitkeep | 0 models/ConvGRU.py | 232 +++++++++ models/model_bg.py | 299 ++++++++++++ models/resnet.py | 391 +++++++++++++++ trainKITTI_ts.py | 538 +++++++++++++++++++++ trainPD_ts.py | 569 ++++++++++++++++++++++ transforms.py | 125 +++++ utils.py | 61 +++ 38 files changed, 7465 insertions(+) create mode 100644 DIOD.png create mode 100644 DIOD_DINOv2/datasetKITTIEval_dinov2.py create mode 100644 DIOD_DINOv2/datasetKITTI_dinov2.py create mode 100644 DIOD_DINOv2/datasetPDEval_dinov2.py create mode 100644 DIOD_DINOv2/datasetPD_dinov2.py create mode 100644 DIOD_DINOv2/evalKITTI_ARI_dinov2.py create mode 100644 DIOD_DINOv2/evalKITTI_F1_score_dinov2.py create mode 100644 DIOD_DINOv2/evalPD_ARI_dinov2.py create mode 100644 DIOD_DINOv2/evalPD_F1_score_dinov2.py create mode 100644 DIOD_DINOv2/models/ConvGRU.py create mode 100644 DIOD_DINOv2/models/model_bg_dinov2.py create mode 100644 DIOD_DINOv2/plotPD_dinov2.py create mode 100644 DIOD_DINOv2/trainKITTI_ts_dinov2.py create mode 100644 DIOD_DINOv2/trainPD_ts_dino.py create mode 100644 DIOD_DINOv2/transforms.py create mode 100644 DIOD_DINOv2/utils.py create mode 100644 DIOD_env.yml create mode 100644 LICENSE create mode 100644 PlotKITTI.py create mode 100644 PlotPD.py create mode 100644 README.md create mode 100644 create_mp4.py create mode 100644 datasetKITTI.py create mode 100644 datasetKITTIEval.py create mode 100644 datasetPD.py create mode 100644 datasetPDEval.py create mode 100644 evalKITTI_ARI.py create mode 100644 evalPD_ARI.py create mode 100644 evalPD_F1_score.py create mode 100644 eval_KITTI_F1_score.py create mode 100644 models/.gitkeep create mode 100644 models/ConvGRU.py create mode 100644 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zmZw@?4T&*yQYInks3Q<_`(EkAJeBR+?r&~MI!d4DM$rjo57lmJ+-AzGhD$gyU%vZk zbFEnw+biV`HQ_B`uuP_ehUu}IgP1izbRwmMYX~PXM%$6TfJyINO!SwhgYt68IYA7Ea|MouS#d6B@9{ePP%bBr zod<18Q!chh)yMUrkjUBl>E_EJ^mEZc;nhr!S4o=obeYY}Igf@m#7?yhkgkY}i~EqF z?y=LGKkX>`p;{R1=5G4?;N1FBO-m2^JcBUOG3jT$x_)Pc-Ex^8V<9#%57)V?bQ@mI zC^oHE3{>(zMrj(^?{fG04ax&VQX9`v&9pIr>1oCvTuG?;N(+kSiGZHIS$(JGwe0Ut zMbzR^1@5LcGp{rZB~}0{B7eg4MgM3BwbJ+3XT3(e z^T(sHn-`AfS90yz_VuTFhTPhFIB(;i=7<1|!C9#!#6m!PSrQ^OW&z^6# 500: + mapping[values[i]] = mapping_count + mapping_count += 1 + cur_mapping = [] + for i in range(len(values)): + value = values[i] + if value not in mapping: + cur_mapping.append(0) + else: + cur_mapping.append(mapping[value]) + cur_mapping = np.array(cur_mapping) + _h, _w = mask.shape + mask = cur_mapping[indices].reshape((_h, _w)) + + mask = torch.Tensor(mask).long() + image = torch.Tensor(image).float() + image = image / 255.0 + image = image.permute(2,0,1) + image = self.img_transform(image) + ims.append(image) + masks.append(mask) + ims = torch.stack(ims) + masks = torch.stack(masks) + sample = {'image': ims, 'mask':masks} + return sample + + + def __len__(self): + return len(self.real_files) diff --git a/DIOD_DINOv2/datasetPD_dinov2.py b/DIOD_DINOv2/datasetPD_dinov2.py new file mode 100644 index 0000000..c8fc764 --- /dev/null +++ b/DIOD_DINOv2/datasetPD_dinov2.py @@ -0,0 +1,118 @@ +import os +import random +import torch +from typing import Callable +from torchvision import transforms +from torch.utils.data import Dataset +import cv2 +import math +import numpy as np + +banned_scenes = ['scene_000100','scene_000002','scene_000008','scene_000012','scene_000018','scene_000029', +'scene_000038','scene_000040','scene_000043','scene_000044','scene_000049','scene_000050','scene_000053','scene_000063', +'scene_000079','scene_000090','scene_000094','scene_000100','scene_000103','scene_000106','scene_000111','scene_000112', +'scene_000124','scene_000125','scene_000127','scene_000148','scene_000159','scene_000166','scene_000169', +'scene_000170','scene_000171','scene_000187', 'scene_000191','scene_000200','scene_000202','scene_000217', +'scene_000218','scene_000225','scene_000229','scene_000232','scene_000236','scene_000237','scene_000245', +'scene_000249' +] + +class PDDataset(Dataset): + def __init__(self, split='train', root = None, supervision = 'moving', transform: Callable = None, + apply_img_transform: bool = True): + super(PDDataset, self).__init__() + self.root_dir = root + self.files = os.listdir(self.root_dir) + self.files.sort() + if split == 'train': + self.files = self.files[1:] + elif split == 'eval': + self.files = self.files[0:1] + else: + self.files = self.files + self.annotation = None + if supervision == 'moving': + self.annotation = 'moving_masks' + elif supervision == 'all': + self.annotation = 'ari_masks' + elif supervision == 'est': + self.annotation = 'est_masks' + else: + raise ValueError("Need to choose from moving masks, all masks, or estimated masks. Or revise the code for customized setting.") + self.real_files = [] + self.mask_files = [] + for f in self.files: + if f in banned_scenes: + continue + for i in [1,5,6,7,8,9]: + if os.path.exists(os.path.join(self.root_dir,f+'/rgb/camera_0{}'.format(i))): + self.real_files.append(f+'/rgb/camera_0{}'.format(i)) + self.mask_files.append(f+'/{}/camera_0{}'.format(self.annotation, i)) + self.img_transform = transforms.Compose([ + transforms.Resize((490, 980)), # dinov2 + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))]) + self.apply_image_transform = apply_img_transform + self.transform = transform + + + def __getitem__(self, index): + path = self.real_files[index] + mask_path = self.mask_files[index] + all_images = os.listdir(os.path.join(self.root_dir,path)) + all_images.sort() + rand_id = random.randint(0,190) + real_idx = [rand_id + j for j in range(5)] + ims = [] + masks = [] + for idd in real_idx: + mapping = {0:0} + mapping_count = 1 + image = cv2.imread(os.path.join(os.path.join(self.root_dir, path),all_images[idd])) + mask = cv2.imread(os.path.join(os.path.join(self.root_dir, mask_path),all_images[idd]),-1) + downsampling_ratio = 0.5 + crop = 128 + width = int(math.ceil(image.shape[1] * downsampling_ratio)) + height = int(math.ceil(image.shape[0] * downsampling_ratio)) + dim = (width, height) + image = cv2.resize(image, dim, interpolation = cv2.INTER_LINEAR) + image = image[crop:, :, :] + mask = cv2.resize(mask, dim, interpolation = cv2.INTER_NEAREST) + mask = mask[crop:,:] + + values, indices, counts = np.unique(mask, return_inverse=True, return_counts=True) + for i in range(len(values)): + if values[i] not in mapping: + if counts[i] > 50: + mapping[values[i]] = mapping_count + mapping_count += 1 + cur_mapping = [] + for value in values: + if value not in mapping: + cur_mapping.append(0) + else: + cur_mapping.append(mapping[value]) + cur_mapping = np.array(cur_mapping) + _h, _w = mask.shape + mask = cur_mapping[indices].reshape((_h, _w)) + + mask = torch.Tensor(mask).long() + image = torch.Tensor(image).float() + image = image / 255.0 + image = image.permute(2,0,1) + + ims.append(image) + masks.append(mask) + ims = torch.stack(ims) + masks = torch.stack(masks) + sample = {'image': ims, 'mask':masks} + + if self.transform is not None: + sample = self.transform(sample) + elif self.apply_image_transform: + sample["image"] = self.img_transform(sample["image"]) + + return sample + + + def __len__(self): + return len(self.real_files) diff --git a/DIOD_DINOv2/evalKITTI_ARI_dinov2.py b/DIOD_DINOv2/evalKITTI_ARI_dinov2.py new file mode 100644 index 0000000..869632d --- /dev/null +++ b/DIOD_DINOv2/evalKITTI_ARI_dinov2.py @@ -0,0 +1,83 @@ +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetKITTIEval_dinov2 import KITTIDataset +from utils import adjusted_rand_index as ARI + +from models.model_bg_dinov2 import * + +parser = argparse.ArgumentParser() + +parser.add_argument +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODKITTI_100_dinov2.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_test', type = str, help = 'path of KITTI test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') + +def main(): + opt = parser.parse_args() + resolution = (378, 1260) + model_path = opt.ckpt_path + data_path = opt.test_path + + + test_set = KITTIDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, 3).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=8, + shuffle=True, num_workers=4, drop_last=False) + + ARIs, allARIs = [], [] + + for sample in tqdm(test_dataloader): + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ , _= model(image) + masks = masks.detach().cpu() + + for i in range(8): + gt_msk = mask_gt[i] + pred_msk = masks[i] + gt_msk = gt_msk.view(1,-1) + pred_msk = pred_msk.view(1,opt.num_slots,-1).permute(1,0,2) + + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + + _, n_cat = gt_msk.shape + if n_cat <= 2: + continue + + gt_msk = gt_msk[:,1:] + + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + + allARIs.append(all_ari) + ARIs.append(ari) + + del image, masks, mask_gt + + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final all-ARI:',sum(allARIs) / len(allARIs)) + +if __name__ == '__main__': + main() diff --git a/DIOD_DINOv2/evalKITTI_F1_score_dinov2.py b/DIOD_DINOv2/evalKITTI_F1_score_dinov2.py new file mode 100644 index 0000000..648ad2b --- /dev/null +++ b/DIOD_DINOv2/evalKITTI_F1_score_dinov2.py @@ -0,0 +1,277 @@ +import numpy as np +import torch +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetKITTIEval_dinov2 import KITTIDataset +from utils import adjusted_rand_index as ARI +import matplotlib.pyplot as plt +from models.model_bg_dinov2 import * + +EPS = 1e-10 + + +parser = argparse.ArgumentParser() + +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODKITTI_100_dinov2.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_test', type = str, help = 'path of KITTI test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--is_bg_handled', default=True, type=bool, help='is_bg_handled') + + +resolution = (378, 1260) + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + + +def calculate_iou(mask1, set_masks): + # Initialize a list to store the IoU values + iou_scores = [] + + # Convert masks to NumPy arrays + mask1 = np.array(mask1, dtype=bool) + + for mask2 in set_masks: + mask2 = np.array(mask2, dtype=bool) + + # Calculate intersection and union + intersection = np.logical_and(mask1, mask2) + union = np.logical_or(mask1, mask2) + + # Compute IoU + iou = np.sum(intersection) / np.sum(union) + iou_scores.append(iou) + + return iou_scores + +def voc_eval(gt_masks, + pred_scores, pred_masks, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + + + nb_images = len(pred_masks) #added + image_ids = [] + class_recs = {} + nb_gt = 0 + + for im in range(nb_images): + image_ids += [im]*len(pred_masks[im]) + class_recs[im] = [False] * len(gt_masks[im]) + nb_gt += len(gt_masks[im]) + + + + # flatten preds and scores + pred_scores_flat = np.array([item for sublist in pred_scores for item in sublist]) + + + pred_masks_flat = np.stack([item for sublist in pred_masks for item in sublist]) + + + + # sort by confidence + sorted_ind = np.argsort(-pred_scores_flat) + pred_masks_flat = pred_masks_flat[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + mask = pred_masks_flat[d] + ovmax = -np.inf + MASKSGT = gt_masks[image_ids[d]] + + + # compute overlaps + overlaps = calculate_iou(mask, MASKSGT) + + # case of no gt in one frame + if not len(overlaps): + ovmax = 0 + else: + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + + if ovmax > ovthresh: + if not R[jmax]: + tp[d] = 1. + R[jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + + # # check F1 score + # print('check_FP = ', fp[-1]) + # print('check_TP = ', tp[-1]) + # print('check_FN = ', nb_gt - tp[-1]) + # print('check_F1_score', 2*tp[-1]/(2*tp[-1] + fp[-1] + nb_gt - tp[-1])) + + + + rec = tp / float(nb_gt) + + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + +def compute_f1(precision, recall): + if precision + recall == 0: + return 0.0 + return 2 * (precision * recall) / (precision + recall) + + +def main(): + opt = parser.parse_args() + model_path = opt.ckpt_path + data_path = opt.test_path + test_set = KITTIDataset(split = 'test', root = data_path) + + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=8, + shuffle=False, num_workers=4, drop_last=False) + + + active_slots, scores, all_gt = [], [], [] + + + nb_samples = 0 + for sample in tqdm(test_dataloader): + + # evaluate our method with background modeling (within last slot) + if opt.is_bg_handled: + nb_samples += 1 + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ ,_= model(image) + masks = masks.detach().cpu() + + for i in range(8): + + gt_msk = F.one_hot(mask_gt[i,0,:,:]) + + pred_msk = F.one_hot(masks[i,0,:,:,:].argmax(dim=0, keepdim=False)) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[i,0,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + # evaluate other baselines w/o background modeling + else: + nb_samples += 1 + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ = model(image) + masks = masks.detach().cpu() + + for i in range(8): + + gt_msk = F.one_hot(mask_gt[i,0,:,:]) + + pred_msk = F.one_hot(masks[i,0,:,:,:].argmax(dim=0, keepdim=False), num_classes=opt.num_slots) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[i,0,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + del image, mask_gt, masks + + + rec, prec, _ = voc_eval(all_gt, scores, active_slots, 0.5) + f1_score = compute_f1(prec[-1], rec[-1]) + print(f"F1 Score: {f1_score}") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/DIOD_DINOv2/evalPD_ARI_dinov2.py b/DIOD_DINOv2/evalPD_ARI_dinov2.py new file mode 100644 index 0000000..14ae76d --- /dev/null +++ b/DIOD_DINOv2/evalPD_ARI_dinov2.py @@ -0,0 +1,92 @@ +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetPDEval_dinov2 import PDDataset +from utils import adjusted_rand_index as ARI +import matplotlib.pyplot as plt + + +from models.model_bg_dinov2 import * + +parser = argparse.ArgumentParser() + +parser.add_argument +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODPD_500_dinov2.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/test_video', type = str, help = 'path of PD test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') + + +resolution = (490, 980) + +def main(): + opt = parser.parse_args() + model_path = opt.ckpt_path + data_path = opt.test_path + test_set = PDDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, 3).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + ARIs, all_ARIs = [], [] + + for idx, sample in enumerate(tqdm(test_dataloader)): + image = sample['image'].to(device) + mask_gt = sample['mask'] + + """ + for i in range (5): + plt.imsave('./infer/epoch200_moving/eval_pd_mask{}.png'.format(i), mask_gt[0, i,:,:].unsqueeze(2).detach().cpu().numpy()) + plt.imsave('./infer/epoch200_moving/eval_pd_image{}.png'.format(i), image[0, i,:,:,:].permute(1,2,0).detach().cpu().numpy()*0.5+0.5) + """ + + mask_gts = F.interpolate(mask_gt.float(), (123, 245)).long() + + for i in range(40): + _, masks, _, _ = model(image[:,i*5:i*5+5,:,:,:]) + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + gt_msk = mask_gt[0] + pred_msk = masks[0] + gt_msk = gt_msk.view(5,-1) + pred_msk = pred_msk.view(5,opt.num_slots,-1).permute(1,0,2) + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + _,n_cat = gt_msk.shape + if n_cat <= 2: + continue + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + gt_msk = gt_msk[:,1:] + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + print('ari', ari) + # if not ari == ari: # one image in the val_set is causing instability in computing ari. Discarded + if not ari == ari: + print('discarded') + continue + + all_ARIs.append(all_ari) + ARIs.append(ari) + del image, mask_gt, masks + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final allARI:', sum(all_ARIs) / len(all_ARIs)) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/DIOD_DINOv2/evalPD_F1_score_dinov2.py b/DIOD_DINOv2/evalPD_F1_score_dinov2.py new file mode 100644 index 0000000..32063b8 --- /dev/null +++ b/DIOD_DINOv2/evalPD_F1_score_dinov2.py @@ -0,0 +1,266 @@ +import numpy as np +import torch +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetPDEval_dinov2 import PDDataset +from utils import adjusted_rand_index as ARI +import matplotlib.pyplot as plt +from models.model_bg_dinov2 import * + +EPS = 1e-10 + + +parser = argparse.ArgumentParser() + +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODPD_500_dinov2.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/test_video', type = str, help = 'path of PD test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--is_bg_handled', default=True, type=bool, help='is_bg_handled') + + +resolution = (490, 980) + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + + +def calculate_iou(mask1, set_masks): + # Initialize a list to store the IoU values + iou_scores = [] + + # Convert masks to NumPy arrays + mask1 = np.array(mask1, dtype=bool) + + for mask2 in set_masks: + mask2 = np.array(mask2, dtype=bool) + + # Calculate intersection and union + intersection = np.logical_and(mask1, mask2) + union = np.logical_or(mask1, mask2) + + # Compute IoU + iou = np.sum(intersection) / np.sum(union) + iou_scores.append(iou) + + return iou_scores + +def voc_eval(gt_masks, + pred_scores, pred_masks, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + + + nb_images = len(pred_masks) #added + image_ids = [] + class_recs = {} + nb_gt = 0 + + for im in range(nb_images): + image_ids += [im]*len(pred_masks[im]) + class_recs[im] = [False] * len(gt_masks[im]) + nb_gt += len(gt_masks[im]) + + + # flatten preds and scores + pred_scores_flat = np.array([item for sublist in pred_scores for item in sublist]) + + pred_masks_flat = np.stack([item for sublist in pred_masks for item in sublist]) + + # sort by confidence + sorted_ind = np.argsort(-pred_scores_flat) + pred_masks_flat = pred_masks_flat[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + mask = pred_masks_flat[d] + ovmax = -np.inf + MASKSGT = gt_masks[image_ids[d]] + + + # compute overlaps + overlaps = calculate_iou(mask, MASKSGT) + + # case of no gt in one frame + if not len(overlaps): + ovmax = 0 + else: + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + + if ovmax > ovthresh: + if not R[jmax]: + tp[d] = 1. + R[jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + + + rec = tp / float(nb_gt) + + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + +def compute_f1(precision, recall): + if precision + recall == 0: + return 0.0 + return 2 * (precision * recall) / (precision + recall) + + +def main(): + opt = parser.parse_args() + model_path = opt.ckpt_path + data_path = opt.test_path + test_set = PDDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + + active_slots, scores, all_gt = [], [], [] + + + nb_samples = 0 + for sample in tqdm(test_dataloader): + nb_samples += 1 + image = sample['image'].to(device) + + mask_gt = sample['mask'] + + mask_gts = F.interpolate(mask_gt.float(), (123, 245)).long() + + # evaluate our method with background modeling (within last slot) + if opt.is_bg_handled: + for i in range(40): + _, masks, _ , _= model(image[:,i*5:i*5+5,:,:,:]) + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + + masks = masks.detach().cpu() + + for fm in range(5): + + gt_msk = F.one_hot(mask_gt[0,fm,:,:]) + + pred_msk = F.one_hot(masks[0,fm,:,:,:].argmax(dim=0, keepdim=False)) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[0,fm,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + + # evaluate other baselines w/o background modeling + else: + for i in range(40): + _, masks, _ = model(image[:,i*5:i*5+5,:,:,:]) + + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + + masks = masks.detach().cpu() + + for fm in range(5): + + gt_msk = F.one_hot(mask_gt[0,fm,:,:]) + + pred_msk = F.one_hot(masks[0,fm,:,:,:].argmax(dim=0, keepdim=False), num_classes=opt.num_slots) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[0,fm,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + del image, mask_gt, masks + + + rec, prec, _ = voc_eval(all_gt, scores, active_slots, 0.5) + f1_score = compute_f1(prec[-1], rec[-1]) + print(f"F1 Score: {f1_score}") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/DIOD_DINOv2/models/ConvGRU.py b/DIOD_DINOv2/models/ConvGRU.py new file mode 100644 index 0000000..e6a8552 --- /dev/null +++ b/DIOD_DINOv2/models/ConvGRU.py @@ -0,0 +1,232 @@ +import os +import torch +from torch import nn +from torch.autograd import Variable + + +class ConvGRUCell(nn.Module): + def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias, dtype): + """ + Initialize the ConvLSTM cell + :param input_size: (int, int) + Height and width of input tensor as (height, width). + :param input_dim: int + Number of channels of input tensor. + :param hidden_dim: int + Number of channels of hidden state. + :param kernel_size: (int, int) + Size of the convolutional kernel. + :param bias: bool + Whether or not to add the bias. + :param dtype: torch.cuda.FloatTensor or torch.FloatTensor + Whether or not to use cuda. + """ + super(ConvGRUCell, self).__init__() + self.height, self.width = input_size + self.padding = kernel_size[0] // 2, kernel_size[1] // 2 + self.hidden_dim = hidden_dim + self.bias = bias + self.dtype = dtype + + self.conv_gates = nn.Conv2d(in_channels=input_dim + hidden_dim, + out_channels=2*self.hidden_dim, # for update_gate,reset_gate respectively + kernel_size=kernel_size, + padding=self.padding, + bias=self.bias) + + self.conv_can = nn.Conv2d(in_channels=input_dim+hidden_dim, + out_channels=self.hidden_dim, # for candidate neural memory + kernel_size=kernel_size, + padding=self.padding, + bias=self.bias) + + def init_hidden(self, batch_size): + return (Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)).type(self.dtype)) + + def forward(self, input_tensor, h_cur): + """ + :param self: + :param input_tensor: (b, c, h, w) + input is actually the target_model + :param h_cur: (b, c_hidden, h, w) + current hidden and cell states respectively + :return: h_next, + next hidden state + """ + combined = torch.cat([input_tensor, h_cur], dim=1) + combined_conv = self.conv_gates(combined) + + gamma, beta = torch.split(combined_conv, self.hidden_dim, dim=1) + reset_gate = torch.sigmoid(gamma) + update_gate = torch.sigmoid(beta) + + combined = torch.cat([input_tensor, reset_gate*h_cur], dim=1) + cc_cnm = self.conv_can(combined) + cnm = torch.tanh(cc_cnm) + + h_next = (1 - update_gate) * h_cur + update_gate * cnm + return h_next + + +class ConvGRU(nn.Module): + def __init__(self, input_size, input_dim, hidden_dim, kernel_size, num_layers, + dtype, batch_first=False, bias=True, return_all_layers=False): + """ + :param input_size: (int, int) + Height and width of input tensor as (height, width). + :param input_dim: int e.g. 256 + Number of channels of input tensor. + :param hidden_dim: int e.g. 1024 + Number of channels of hidden state. + :param kernel_size: (int, int) + Size of the convolutional kernel. + :param num_layers: int + Number of ConvLSTM layers + :param dtype: torch.cuda.FloatTensor or torch.FloatTensor + Whether or not to use cuda. + :param alexnet_path: str + pretrained alexnet parameters + :param batch_first: bool + if the first position of array is batch or not + :param bias: bool + Whether or not to add the bias. + :param return_all_layers: bool + if return hidden and cell states for all layers + """ + super(ConvGRU, self).__init__() + + # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers + kernel_size = self._extend_for_multilayer(kernel_size, num_layers) + hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers) + if not len(kernel_size) == len(hidden_dim) == num_layers: + raise ValueError('Inconsistent list length.') + + self.height, self.width = input_size + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.kernel_size = kernel_size + self.dtype = dtype + self.num_layers = num_layers + self.batch_first = batch_first + self.bias = bias + self.return_all_layers = return_all_layers + + cell_list = [] + for i in range(0, self.num_layers): + cur_input_dim = input_dim if i == 0 else hidden_dim[i - 1] + cell_list.append(ConvGRUCell(input_size=(self.height, self.width), + input_dim=cur_input_dim, + hidden_dim=self.hidden_dim[i], + kernel_size=self.kernel_size[i], + bias=self.bias, + dtype=self.dtype)) + + # convert python list to pytorch module + self.cell_list = nn.ModuleList(cell_list) + + def infer(self, input_tensor, hidden_state=None): + """ + :param input_tensor: (b, t, c, h, w) or (t,b,c,h,w) depends on if batch first or not + extracted features from alexnet + :param hidden_state: + :return: layer_output_list, last_state_list + """ + if not self.batch_first: + # (t, b, c, h, w) -> (b, t, c, h, w) + input_tensor = input_tensor.permute(1, 0, 2, 3, 4) + + # Implement stateful ConvLSTM + if hidden_state is None: + hidden_state = self._init_hidden(batch_size=input_tensor.size(0)) + + layer_output_list = [] + last_state_list = [] + hidden_state_out = [] + + seq_len = input_tensor.size(1) + cur_layer_input = input_tensor + + for layer_idx in range(self.num_layers): + h = hidden_state[layer_idx] + output_inner = [] + for t in range(seq_len): + # input current hidden and cell state then compute the next hidden and cell state through ConvLSTMCell forward function + h = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], # (b,t,c,h,w) + h_cur=h) + output_inner.append(h) + hidden_state_out.append(h) + + layer_output = torch.stack(output_inner, dim=1) + cur_layer_input = layer_output + + layer_output_list.append(layer_output) + last_state_list.append([h]) + + if not self.return_all_layers: + layer_output_list = layer_output_list[-1:] + last_state_list = last_state_list[-1:] + + return layer_output_list, last_state_list, hidden_state_out + + def forward(self, input_tensor, hidden_state=None): + """ + :param input_tensor: (b, t, c, h, w) or (t,b,c,h,w) depends on if batch first or not + extracted features from alexnet + :param hidden_state: + :return: layer_output_list, last_state_list + """ + if not self.batch_first: + # (t, b, c, h, w) -> (b, t, c, h, w) + input_tensor = input_tensor.permute(1, 0, 2, 3, 4) + + # Implement stateful ConvLSTM + if hidden_state is not None: + raise NotImplementedError() + else: + hidden_state = self._init_hidden(batch_size=input_tensor.size(0)) + + layer_output_list = [] + last_state_list = [] + + seq_len = input_tensor.size(1) + cur_layer_input = input_tensor + + for layer_idx in range(self.num_layers): + h = hidden_state[layer_idx] + output_inner = [] + for t in range(seq_len): + # input current hidden and cell state then compute the next hidden and cell state through ConvLSTMCell forward function + h = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], # (b,t,c,h,w) + h_cur=h) + output_inner.append(h) + + layer_output = torch.stack(output_inner, dim=1) + cur_layer_input = layer_output + + layer_output_list.append(layer_output) + last_state_list.append([h]) + + if not self.return_all_layers: + layer_output_list = layer_output_list[-1:] + last_state_list = last_state_list[-1:] + + return layer_output_list, last_state_list + + def _init_hidden(self, batch_size): + init_states = [] + for i in range(self.num_layers): + init_states.append(self.cell_list[i].init_hidden(batch_size)) + return init_states + + @staticmethod + def _check_kernel_size_consistency(kernel_size): + if not (isinstance(kernel_size, tuple) or + (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))): + raise ValueError('`kernel_size` must be tuple or list of tuples') + + @staticmethod + def _extend_for_multilayer(param, num_layers): + if not isinstance(param, list): + param = [param] * num_layers + return param + diff --git a/DIOD_DINOv2/models/model_bg_dinov2.py b/DIOD_DINOv2/models/model_bg_dinov2.py new file mode 100644 index 0000000..c45d4e0 --- /dev/null +++ b/DIOD_DINOv2/models/model_bg_dinov2.py @@ -0,0 +1,304 @@ +import numpy as np +from torch import nn +import torch +import torch.nn.functional as F + +from models.ConvGRU import * +from math import ceil + + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +''' +Slot Attention module +differences from the original repo: +1. learnable slot initializtaion +2. pad for the first frame +Inputs --> [batch_size, number_of_frame, sequence_length, hid_dim] +outputs --> slots[batch_size, number_of_frame, num_of_slots, hid_dim]; attn_masks [batch_size, number_of_frame, num_of_slots, sequence_length] +''' +class SlotAttention(nn.Module): + def __init__(self, num_slots, dim, eps = 1e-8, hidden_dim = 128): + super().__init__() + self.dim = dim + self.num_slots = num_slots + self.eps = eps + self.scale = dim ** -0.5 + + self.slots_mu = nn.Parameter(torch.randn(1, 1, dim)).to(device) + self.slots_sigma = nn.Parameter(abs(torch.randn(1, 1, dim))).to(device) + # self.slots_sigma = self.slots_sigma.absolute() + + self.to_q = nn.Linear(dim, dim) + self.to_k = nn.Linear(dim, dim) + self.to_v = nn.Linear(dim, dim) + + hidden_dim = max(dim, hidden_dim) + + self.fc1 = nn.Linear(dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, dim) + self.gru = nn.GRUCell(dim, dim) + + self.norm_input = nn.LayerNorm(dim) + self.norm_slots = nn.LayerNorm(dim) + self.norm_pre_ff = nn.LayerNorm(dim) + + mu = self.slots_mu.expand(1, self.num_slots, -1) + sigma = self.slots_sigma.expand(1, self.num_slots, -1) + slots = torch.normal(mu, sigma) + + slots = slots.contiguous() + self.register_buffer("slots", slots) + + def get_attention(self, slots, inputs): + slots_prev = slots + b, n, d = inputs.shape + inputs = self.norm_input(inputs) + k, v = self.to_k(inputs), self.to_v(inputs) + slots = self.norm_slots(slots) + q = self.to_q(slots) + + dots = torch.einsum('bid,bjd->bij', q, k) * self.scale + attn_ori = dots.softmax(dim=1) + self.eps + attn = attn_ori / attn_ori.sum(dim=-1, keepdim=True) + + updates = torch.einsum('bjd,bij->bid', v, attn) + + slots = self.gru( + updates.reshape(-1, d), + slots_prev.reshape(-1, d) + ) + + slots = slots.reshape(b, -1, d) + slots = slots + self.fc2(F.relu(self.fc1(self.norm_pre_ff(slots)))) + + attn_ori_fg = 1-attn_ori[:,-1,:] + attn_ori_fg = attn_ori_fg.unsqueeze(1) + + return slots, attn_ori, attn_ori_fg + + def forward(self, inputs, num_slots = None): + b, nf, n, d = inputs.shape + slots_out = [] + attns = [] + attns_ori_fg = [] + slots = self.slots.expand(b,-1,-1) + # pre-attention for the first frame + slots, _, _ = self.get_attention(slots, inputs[:,0,:,:]) + for f in range(nf): + cur_slots, cur_attn, attn_ori_fg = self.get_attention(slots,inputs[:,f,:,:]) + slots_out.append(cur_slots) + attns.append(cur_attn) + attns_ori_fg.append(attn_ori_fg) + + slots = cur_slots + slots_out = torch.stack([slot for slot in slots_out]) + slots_out = slots_out.permute(1,0,2,3) + attns = torch.stack([attn for attn in attns]) + attns = attns.permute(1,0,2,3) + + attns_ori_fg = torch.stack([attn for attn in attns_ori_fg]) + attns_ori_fg = attns_ori_fg.permute(1,0,2,3) + return slots_out, attns, attns_ori_fg + + def infer(self, inputs, num_slots = None, slot_state = None): + b, nf, n, d = inputs.shape + slots_out = [] + attns = [] + slots = self.slots.expand(b,-1,-1) + if slot_state is None: + slots, _ = self.get_attention(slots, inputs[:,0,:,:]) + else: + slots = slot_state + for f in range(nf): + cur_slots, cur_attn = self.get_attention(slots,inputs[:,f,:,:]) + slots_out.append(cur_slots) + attns.append(cur_attn) + slots = cur_slots + slots_out = torch.stack([slot for slot in slots_out]) + slots_out = slots_out.permute(1,0,2,3) + attns = torch.stack([attn for attn in attns]) + attns = attns.permute(1,0,2,3) + return slots_out, attns, slots + + +def build_grid(resolution): + ranges = [torch.linspace(0.0, 1.0, steps=res) for res in resolution] + grid = torch.meshgrid(*ranges) + grid = torch.stack(grid, dim=-1) + grid = torch.reshape(grid, [resolution[0], resolution[1], -1]) + grid = grid.unsqueeze(0) + return torch.cat([grid, 1.0 - grid], dim=-1).to(device) + + +"""Adds soft positional embedding with learnable projection.""" +class SoftPositionEmbed(nn.Module): + def __init__(self, hidden_size, resolution): + """Builds the soft position embedding layer. + Args: + hidden_size: Size of input feature dimension. + resolution: Tuple of integers specifying width and height of grid. + """ + super().__init__() + self.embedding = nn.Linear(4, hidden_size, bias=True) + self.register_buffer("grid", build_grid(resolution)) + + def forward(self, inputs): + grid = self.embedding(self.grid) + return inputs + grid +''' +encoder +input: image [bs, num_frames, n_channels, H, W] +outputs: features [bs*num_frames, ceil(H*W // 16),hid_dim], 16 is the (downsample ratio)**2 +''' +class Encoder(nn.Module): + def __init__(self, resolution, hid_dim, dresolution): + super().__init__() + self.resolution = resolution + self.hid_dim = hid_dim + self.dresolution = dresolution + + self.dinov2_backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') + self.n_last_blocks = 4 + + self.convGRU = ConvGRU(input_size=(self.dresolution[0],self.dresolution[1]),#(ceil(resolution[0]/4), ceil(resolution[1]/4)), + input_dim=384*4, + hidden_dim=hid_dim, + kernel_size=(3,3), + num_layers=2, + dtype = torch.cuda.FloatTensor, + batch_first=True, + bias = True, + return_all_layers = False) + self.encoder_pos = SoftPositionEmbed(hid_dim, (self.dresolution[0],self.dresolution[1])) + + def forward(self, x): + bs, n_frames, N, H, W = x.shape + x = x.view(-1, N, H,W) + x = self.dinov2_backbone.get_intermediate_layers(x, self.n_last_blocks, return_class_token=False ) + x = torch.cat((x[0], x[1], x[2], x[3]), dim=2) # concatenated_features from last 4 layers + + # (35,70) are the spatial dims after passing bigger image into ViT-S14 + x = x.permute(0,2,1) + x = x.view(bs*n_frames, x.shape[1], ceil(H/14), ceil(W/14)) # (35,70) + x = F.interpolate(x, (self.dresolution[0],self.dresolution[1])) + + x = x.view(bs, n_frames, x.shape[1], self.dresolution[0], self.dresolution[1]) + x,_ = self.convGRU(x) + x = x[0] + + x = x.view(bs*n_frames, -1, x.shape[3], x.shape[4]) + x = F.relu(x) + x = x.permute(0,2,3,1) + x = self.encoder_pos(x) + x = torch.flatten(x, 1, 2) + return x + + def infer(self, x, h = None): + bs, n_frames, N, H, W = x.shape + x = x.view(-1, N, H,W) + x = self.resnet(x) + x = x.view(bs, n_frames, x.shape[1], x.shape[2],x.shape[3]) + x,_,h_out = self.convGRU.infer(x,h) + x = x[0] + x = x.view(bs*n_frames, -1, x.shape[3], x.shape[4]) + x = F.relu(x) + x = x.permute(0,2,3,1) + x = self.encoder_pos(x) + x = torch.flatten(x, 1, 2) + return x, h_out + +class Decoder(nn.Module): + def __init__(self, hid_dim, resolution, output_channel): + super().__init__() + self.conv1 = nn.ConvTranspose2d(hid_dim, hid_dim, 4, stride=(2, 2), padding=1) + self.conv2 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv3 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv4 = nn.ConvTranspose2d(hid_dim, hid_dim, 4, stride=(2, 2), padding=1) + self.conv5 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv6 = nn.ConvTranspose2d(hid_dim, output_channel, 3, stride=(1, 1), padding=1) + self.resolution = resolution + self.output_channel = output_channel + + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.relu(x) + x = self.conv3(x) + x = F.relu(x) + x = self.conv4(x) + x = F.relu(x) + x = self.conv5(x) + x = F.relu(x) + x = self.conv6(x) + x = x[:,:,:self.resolution[0], :self.resolution[1]] + return x + + +"""Slot Attention-based auto-encoder for object discovery.""" +class SlotAttentionAutoEncoder(nn.Module): + def __init__(self, resolution, num_slots, hid_dim, output_channel = 3): + """Builds the Slot Attention-based auto-encoder. + Args: + resolution: Tuple of integers specifying width and height of input image. + num_slots: Number of slots in Slot Attention. + hid_dim: dimension for ConvGRU and slot attention + dresolution: downsampled resolution --> resnet 18 downsampled with a factor of 4 + """ + super().__init__() + self.hid_dim = hid_dim + self.resolution = resolution + self.dresolution = (ceil(resolution[0]/4), ceil(resolution[1]/4)) + self.num_slots = num_slots + self.output_channel = output_channel + + self.encoder_cnn = Encoder(self.resolution, self.hid_dim, self.dresolution) + self.decoder = Decoder(self.hid_dim, self.resolution, self.output_channel) + + self.fc1 = nn.Linear(hid_dim, hid_dim) + self.fc2 = nn.Linear(hid_dim, hid_dim) + + self.slot_attention = SlotAttention( + num_slots=self.num_slots, + dim=hid_dim, + eps = 1e-8, + hidden_dim = 128) + self.LN = nn.LayerNorm([self.dresolution[0] * self.dresolution[1], hid_dim]) + + def forward(self, image): + # `image` has shape: [batch_size, num_channels, width, height]. + bs, n_frames, dim,him,wim = image.shape + x = self.encoder_cnn(image) + x = self.LN(x) + x = self.fc1(x) + x = F.relu(x) + x = self.fc2(x) + # x shape: [bs * n_frames, width*height // 16, hiden_dim]. + _, H, W = x.shape + x = x.view(bs, n_frames, H, W) + + # Slot Attention module. + slots_ori, attn_masks, attn_fg = self.slot_attention(x) + + # reshape and broadcaast attention masks + attn_masks = attn_masks.reshape(bs*n_frames,self.num_slots, -1) + attn_masks = attn_masks.view(attn_masks.shape[0],attn_masks.shape[1],self.dresolution[0], self.dresolution[1]) + attn_masks = attn_masks.unsqueeze(-1) + + + attn_fg = attn_fg.reshape(bs*n_frames,1, -1) + attn_fg = attn_fg.view(attn_fg.shape[0],attn_fg.shape[1],self.dresolution[0], self.dresolution[1]) + + slots = slots_ori.reshape(bs*n_frames, self.num_slots, -1) + slots = slots.unsqueeze(2).unsqueeze(3) + slots_combine = slots * attn_masks + slots_combine = slots_combine.sum(dim = 1) + slots_combine = slots_combine.permute(0,3,1,2) + + recons = self.decoder(slots_combine) + + + return recons, attn_masks.view(bs,n_frames, self.num_slots, attn_masks.shape[2], attn_masks.shape[3]),\ + attn_fg.view(bs,n_frames, 1, attn_masks.shape[2], attn_masks.shape[3]),slots_combine + diff --git a/DIOD_DINOv2/plotPD_dinov2.py b/DIOD_DINOv2/plotPD_dinov2.py new file mode 100644 index 0000000..fb89ed7 --- /dev/null +++ b/DIOD_DINOv2/plotPD_dinov2.py @@ -0,0 +1,84 @@ +from datasetPDEval_dinov2 import PDDataset +from models.model_bg_dinov2 import * +import torch.nn as nn + +import matplotlib.pyplot as plt +from matplotlib.patches import Polygon +import cv2 + +resolution = (480, 968) + +model_path = '/home/users/skara/check_release/checkpoints/DIODPD_500_dinov2.ckpt' +data_path = '/home/data/skara/test_video' +test_set = PDDataset(split = 'test', root = data_path) + +model = SlotAttentionAutoEncoder(resolution, 45, 64, 3).to(device) +model = nn.DataParallel(model) +model.load_state_dict(torch.load(model_path)['model_state_dict']) + +print('model load finished!') + +for param in model.module.parameters(): + param.requires_grad = False + +sample = test_set[0] +images = sample['image'] + + +cmap = plt.get_cmap('rainbow') +colors = [cmap(ii) for ii in np.linspace(0, 1, 45)] + +image = images[:5] + +image = image.to(device) +image = image.unsqueeze(0) +recon_combined, masks,_, slots= model(image) +masks = masks.detach() +index_mask = masks.argmax(dim = 2) +index_mask = F.one_hot(index_mask,num_classes = 45) +index_mask = index_mask.permute(0,1,4,2,3) +masks = masks * index_mask +cur_image = F.interpolate(image, (3,120,242)) + +masks = masks[0] +cur_image = cur_image[0] +for j in range(5): + image_j = cur_image[j].permute(1,2,0).cpu().numpy() + image_j = image_j * 0.5 + 0.5 + masks_j = masks[j] + tk = 44 + masks_j = masks_j.cpu().numpy() + image_j = image_j[:,:,-1::-1] + + fig = plt.figure(frameon=False) + ax = plt.Axes(fig, [0., 0., 1., 1.]) + ax.axis('off') + fig.add_axes(ax) + ax.imshow(image_j, alpha = 1) + for seg in range(tk): + # fig = plt.figure(frameon=False) + # ax = plt.Axes(fig, [0., 0., 1., 1.]) + # ax.axis('off') + # fig.add_axes(ax) + # ax.imshow(image_j, alpha = 1) + msk = masks_j[seg] + threshold = 0 + + e = (msk > threshold).astype('uint8') + contour, hier = cv2.findContours( + e.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + cmax = None + for c in contour: + if cmax is None: + cmax = c + if len(c) > len(cmax): + cmax = c + if cmax is not None: + polygon = Polygon( + cmax.reshape((-1, 2)), + fill=True, facecolor=colors[seg], + edgecolor='r', linewidth=0.0, + alpha=0.5) + ax.add_patch(polygon) + fig.savefig('./infer/DIODPD_500_dinov2_{}.png'.format(j)) + plt.close(fig) diff --git a/DIOD_DINOv2/trainKITTI_ts_dinov2.py b/DIOD_DINOv2/trainKITTI_ts_dinov2.py new file mode 100644 index 0000000..9112f22 --- /dev/null +++ b/DIOD_DINOv2/trainKITTI_ts_dinov2.py @@ -0,0 +1,546 @@ +from __future__ import annotations + +from collections import OrderedDict +import os +import argparse +from tkinter import NONE +from tqdm import tqdm +import time +import datetime +import torch.optim as optim +import torch.nn as nn +import random +import matplotlib.pyplot as plt +import scipy.optimize +import torch.nn.functional as F +import numpy as np +import torch + +from datasetKITTI_dinov2 import KITTIDataset +from datasetKITTIEval_dinov2 import KITTIDataset as KITTIDatasetEval + +from models.model_bg_dinov2 import SlotAttentionAutoEncoder +from transforms import get_normalize_transform, get_ssl_train_online_transform, get_crop_transform +from utils import adjusted_rand_index as ARI +from utils import temporal_loss as t_loss + +from torch import Tensor +from torch.nn.utils import clip_grad_norm_ +from torchvision.transforms import InterpolationMode +from torch.nn.parameter import Parameter + +import math +from scipy.ndimage import label + +# Set the random seed for consistant comparisons/ablation studies and reproducibility +# No tuning was performed over multiple seeds +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) + +# Set CUDA deterministic flags +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + +torch.autograd.set_detect_anomaly(True) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +parser = argparse.ArgumentParser() + +parser.add_argument +# basic configurations +parser.add_argument('--model_dir', default='/home/data/skara/tmp/', type=str, help='where to save models' ) +parser.add_argument('--sample_dir', default = './samples/', type = str, help = 'where to save the plots') +parser.add_argument('--exp_name', default='exp_name', type=str, help='experiment name, used for model saving/plotting ect' ) +parser.add_argument('--num_workers', default=0, type=int, help='number of workers for loading data') +parser.add_argument('--seed', default=0, type=int, help='random seed') +parser.add_argument('--data_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_train/', type = str, help = 'path of PD dataset') +parser.add_argument('--supervision', default = 'est', choices=['moving', 'all','est'], help = 'type of supervision, currently available: moving and all') +# model parameters +parser.add_argument('--batch_size', default=8, type=int) +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--learning_rate', default=0.0005, type=float) +parser.add_argument('--warmup_steps', default=3000, type=int, help='Number of warmup steps for the learning rate.') +parser.add_argument('--decay_rate', default=0.5, type=float, help='Rate for the learning rate decay.') +parser.add_argument('--decay_steps', default=50000, type=int, help='Number of steps for the learning rate decay.') +parser.add_argument('--num_epochs', default=200, type=int, help='number of workers for loading data') +parser.add_argument('--weight_mask', default = 1.0, type = float, help = 'weight for the mask loss') +parser.add_argument('--weight_NLL', default = 1.0, type = float, help = 'weight for the NLL loss') +parser.add_argument('--weight_reg', default = 0.3, type = float, help = 'weight for the regularization term') +parser.add_argument('--confid_th', default = 0.9, type = float, help = 'confidence threshold used to filter pseudo-labels during distillation') + +# Data augmentations +parser.add_argument('--crop-size', default=[], type = int, nargs="*", help = "crop's size") +parser.add_argument('--crop-scale', default=[0.75, 1.0], type = int, nargs="+", help = "crop's size") +parser.add_argument('--crop-ratio', default=[2.0, 2.0], type = int, nargs="+", help = "crop's ratio") +parser.add_argument('--crop-interpolation', default="bilinear", type = str, help = "crop's interpolation") +parser.add_argument('--crop-interpolation-mask', default="nearest", type = str, help = "crop's interpolation mask") +parser.add_argument('--p-crop', default=0.4, type = float, help = "crop's probability") +parser.add_argument('--p-flip', default=0.4, type = float, help = "flip's probability") +parser.add_argument('--brightness', default=0.4, type = float, help = "color jitter's brightness") +parser.add_argument('--contrast', default=0.4, type = float, help = "color jitter's contrast") +parser.add_argument('--saturation', default=0.4, type = float, help = "color jitter's saturation") +parser.add_argument('--hue', default=0.1, type = float, help = "color jitter's hue") +parser.add_argument('--p-contrastive', default=0.8, type = float, help = "color jitter's probability") +parser.add_argument('--p-grayscale', default=0.2, type = float, help = "grayscale's probability") +parser.add_argument('--kernel-size-blur', default=11, type = int, help = "blur's kernel size") +parser.add_argument('--sigma-blur', default=[0.1, 2.0], type = float, nargs="+", help = "blur's sigma") +parser.add_argument('--p-blur', default=0.0, type = float, help = "blur's probability") +parser.add_argument('--threshold-solarize', default=0.5, type = float, help = "solarize's threshold") +parser.add_argument('--p-solarize', default=0.2, type = float, help = "solarize's probability") +parser.add_argument('--mean', default=[0.485, 0.456, 0.406], type = float, nargs="+", help = "normalize's mean") +parser.add_argument('--std', default=[0.229, 0.224, 0.225], type = float, nargs="+", help = "normalize's std") +# Burn_in +parser.add_argument('--burn_in_ckpt', default='DIODPD_500_dinov2.ckpt', type=str, help='ckpt from burn_in to load when starting distillation') +parser.add_argument('--burn_in_exp', default='checkpoints', type=str, help='burn_in experiment name' ) + +# burn_in or distillation? +parser.add_argument('--start_teacher', default=0, type = int, help = "teacher starting epoch") + + +def load_model(model, state_dict): + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + continue + if isinstance(param, Parameter): + # backwards compatibility for serialized parameters + param = param.data + if own_state[name].data.shape!=param.shape: + print(name) + continue + own_state[name].copy_(param) + + + +def computeWBCE(opt, masks, mask_gt, scores): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): continue # corresponding object non-available + fg_portion = torch.where(mask_gt[i,j,id_gt[idx],:,:]!=0)[0].size()[0]/(95*315) + + tmp_BCE += ( -(2-fg_portion)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + nb_objects += 1 + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + return loss_mask + + +def computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=None, device = None, teacher = 0 ): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + fg_gt_after_filtering = torch.zeros(mask_fg_t[:,:,0,:,:].shape).to(device) + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): + continue # corresponding object non-available + + # using fg_teacher as a confidence map on pseudo_labels + weight = mask_fg_t[i,j,0,:,:][mask_gt[i,j,id_gt[idx],:,:] == 1].mean() + if not teacher: + if weight>=0.9: + nb_objects += 1 + fg_gt_after_filtering[i,j,:,:] += mask_gt[i,j,id_gt[idx],:,:] + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + else: + if weight>=0.9: + nb_objects += 1 + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + + if not teacher: + return loss_mask, fg_gt_after_filtering + return loss_mask + + +@torch.no_grad() +def _update_teacher_model(model, teacher_model, keep_rate=0.996): + + student_model_dict = model.state_dict() + + new_teacher_dict = OrderedDict() + for key, value in teacher_model.state_dict().items(): + if key in student_model_dict.keys(): + new_teacher_dict[key] = ( + student_model_dict[key] * + (1 - keep_rate) + value * keep_rate + ) + else: + raise Exception("{} is not found in student model".format(key)) + + teacher_model.load_state_dict(new_teacher_dict) + + +def main(): + opt = parser.parse_args() + + resolution = (378, 1260) + + + if not os.path.exists(opt.model_dir): + os.mkdir(opt.model_dir) + if not os.path.exists(opt.sample_dir): + os.mkdir(opt.sample_dir) + if not os.path.exists(os.path.join(opt.model_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.model_dir, opt.exp_name)) + if not os.path.exists(os.path.join(opt.sample_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.sample_dir, opt.exp_name)) + + data_path = opt.data_path + train_transform = get_crop_transform( + resolution, # original size + opt.crop_size if opt.crop_size != [] else None, + opt.crop_scale, + opt.crop_ratio, + InterpolationMode(opt.crop_interpolation), + InterpolationMode(opt.crop_interpolation_mask), + opt.p_crop, + opt.p_flip + ) + print("train transform", train_transform) + + train_set = KITTIDataset(split = 'train', root = data_path, resolution = resolution, transform=train_transform, apply_img_transform=True) + test_set = KITTIDataset(split = 'eval', root = data_path, resolution = resolution, transform = None, apply_img_transform=True) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3) + for param in model.encoder_cnn.dinov2_backbone.parameters(): + param.requires_grad = False + + model = model.to(device) + model = nn.DataParallel(model) + student_train_transform = get_ssl_train_online_transform( + opt.brightness, + opt.contrast, + opt.saturation, + opt.hue, + opt.p_contrastive, + opt.p_grayscale, + opt.kernel_size_blur, + opt.sigma_blur, + opt.p_blur, + opt.threshold_solarize, + opt.p_solarize, + opt.mean, + opt.std + ).to(device) + print("student train transform", student_train_transform) + teacher_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("teacher train transform", teacher_transform) + val_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("val transform", val_transform) + + teacher = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + teacher = nn.DataParallel(teacher) + + + criterion = nn.MSELoss() + + params = [{'params': model.parameters()}] + + train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=opt.batch_size, + shuffle=True, num_workers=opt.num_workers, drop_last=True) + + optimizer = optim.Adam(params, lr=opt.learning_rate) + + start = time.time() + step = 0 + + + print('Model build finished!') + for epoch in range(opt.num_epochs): + + if epoch == opt.start_teacher: + print('____________________burn_in_ckpt', opt.burn_in_ckpt) + print('____________________burn_in_exp', opt.burn_in_exp) + print('____________________supervision', opt.supervision) + print('____________________regularization', opt.weight_reg) + + model_path = os.path.join(opt.model_dir, opt.burn_in_exp, opt.burn_in_ckpt) + state_dict = torch.load(model_path)['model_state_dict'] + load_model(model,state_dict) + for param in model.module.encoder_cnn.dinov2_backbone.parameters(): + param.requires_grad = False + load_model(teacher,state_dict) + + + model.train() + + total_loss = 0 + M_loss = 0 + N_loss = 0 + LR_loss = 0 + + M_loss_t = 0 + N_loss_t = 0 + for sample in tqdm(train_dataloader): + step += 1 + + if step < opt.warmup_steps: + learning_rate = opt.learning_rate * (step / opt.warmup_steps) + else: + learning_rate = opt.learning_rate + + learning_rate = learning_rate * (opt.decay_rate ** ( + step / opt.decay_steps)) + + optimizer.param_groups[0]['lr'] = learning_rate + + image = sample['image'].to(device, non_blocking=True) + mask_gt = sample['mask'].to(device, non_blocking=True) + student_image = student_train_transform(image) + teacher_image = teacher_transform(image) + + mask_gt = F.interpolate(mask_gt.float(), (95, 315)).long().to(device) + + if epoch >= opt.start_teacher: + with torch.no_grad(): + _, masks_t, mask_fg_t, _ = teacher(teacher_image) + masks_t = masks_t * 0.999 + 1e-8 + masks_t_quant = masks_t.argmax(2).cpu().numpy() + masks_t_quant = np.where(masks_t_quant==44, 0, masks_t_quant) + + + # get connex regions + for i in range(opt.batch_size): + for j in range(5): + activated_slots = list(np.unique(masks_t_quant[i,j,:,:])) + + + empty_slots = [x for x in range(45) if x not in activated_slots] + for s in activated_slots: + tmp_bin = np.where(masks_t_quant[i,j,:,:] == s, 1, 0) + # Label each connected component + labeled_mask, num_features = label(tmp_bin) + label_counts = np.bincount(labeled_mask.ravel()) + if num_features > 1: + # starts from 2 to discard background and 1st connected region + for ft in range(2, num_features + 1): + if label_counts[ft] > 10 and mask_fg_t[i,j,0,:,:][labeled_mask == ft].mean() > 0.9: + + try: # if no empty slot available, stop adding objects + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, empty_slots[0], masks_t_quant[i,j,:,:]) + empty_slots.remove(empty_slots[0]) + except: + # print('more teacher predictions than student slots') + continue + else: + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, 0, masks_t_quant[i,j,:,:]) + + + + _h, _w = masks_t_quant[0,0,:,:].shape + + masks_t_bin_np = np.zeros((opt.batch_size, 5,_h, _w)) + + # binarize the teacher predictions + for i in range(opt.batch_size): + for j in range(5): + values, indices, counts = np.unique(masks_t_quant[i,j,:,:], return_inverse=True, return_counts=True) + to_eliminate = counts <= 10 + mapping = np.arange(len(values)) + mapping[to_eliminate] = 0 + masks_t_bin_np[i,j,:,:] = mapping[indices].reshape((_h, _w)) + masks_t_bin = torch.Tensor(masks_t_bin_np).long() + n_objects_t = masks_t_bin.max() + masks_t_bin = F.one_hot(masks_t_bin, n_objects_t+1) + masks_t_bin = masks_t_bin[:,:,:,:,1:] + masks_t_bin = masks_t_bin.permute(0,1,4,2,3).float() + masks_t_bin_gpu = masks_t_bin.to(device) + masks_t_bin_np = masks_t_bin.flatten(3,4) + masks_t_bin_np = masks_t_bin_np.detach().cpu().numpy() + + + recon_combined, masks, mask_fg, slots = model(student_image) + recon_combined = recon_combined.view(opt.batch_size,5,3,resolution[0],resolution[1]) + + # reconstruction loss + loss = criterion(recon_combined, student_image) + # mask loss + + loss_mask = 0.0 + loss_mask_t = 0.0 + + NLL_loss = 0.0 + L1_reg = 0.0 + + mask_detach = masks.detach().flatten(3,4) + mask_detach = mask_detach * 0.999 + 1e-8 + mask_detach = mask_detach.cpu().numpy() + n_objects = mask_gt.max() + mask_gt = F.one_hot(mask_gt, n_objects+1) + mask_gt = mask_gt[:,:,:,:,1:] + mask_gt = mask_gt.permute(0,1,4,2,3).float() + mask_gt_np = mask_gt.flatten(3,4) + mask_gt_np = mask_gt_np.detach().cpu().numpy() + + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), mask_gt_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - mask_gt_np[i,j]).T) + scores[i,j] += cross_entropy_cur + + + # weighted BCE + if epoch >= opt.start_teacher: + loss_mask, filtered_fg_gt = computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=mask_fg_t, device=device) + else: + loss_mask = computeWBCE(opt, masks, mask_gt, scores) + + + + # NLL (reg on whole image) + mask_fg = mask_fg * 0.999 + 1e-8 + fg_gt = mask_gt.sum(axis = 2) + + if epoch >= opt.start_teacher: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * filtered_fg_gt).mean() + else: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * fg_gt).mean() + L1_reg = mask_fg[:,:,0,:,:].mean() + + + # teacherBCE + if epoch >= opt.start_teacher: + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects_t)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), masks_t_bin_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - masks_t_bin_np[i,j]).T) + scores[i,j] += cross_entropy_cur + loss_mask_t = computeWBCE_TS(opt, masks, masks_t_bin_gpu, scores, mask_fg_t, device=device, teacher = True) + + + if epoch >= opt.start_teacher: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + loss_mask_t + else: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + + optimizer.zero_grad() + whole_loss.backward() + clip_grad_norm_(model.parameters(),1.0) + + optimizer.step() + + # update teacher + if epoch >= opt.start_teacher: + _update_teacher_model(model, teacher, keep_rate=0.996) + + total_loss += loss.item() + try: + M_loss += loss_mask.item()*opt.weight_mask + except: + M_loss += loss_mask*opt.weight_mask + N_loss += NLL_loss.item()*opt.weight_NLL + LR_loss += L1_reg.item()*opt.weight_reg + + # teacher + if epoch >= opt.start_teacher: + try: + M_loss_t += loss_mask_t.item() + except: + M_loss_t += loss_mask_t + + del recon_combined, masks, mask_fg, image, student_image, teacher_image, loss, whole_loss, mask_gt, loss_mask, NLL_loss, L1_reg, loss_mask_t + # break + + + total_loss /= len(train_dataloader) + M_loss /= len(train_dataloader) + N_loss /= len(train_dataloader) + LR_loss /= len(train_dataloader) + + M_loss_t /= len(train_dataloader) + N_loss_t /= len(train_dataloader) + + print ("Epoch: {}, Loss: {}, Loss_mask: {}, NLL_loss: {}, L1_reg: {}, Loss_mask_t: {}, Time: {}".format(epoch, total_loss,M_loss, + N_loss, LR_loss, M_loss_t, datetime.timedelta(seconds=time.time() - start))) + + if not epoch % 10: + sample: dict[str, Tensor] = test_set[0] + image: Tensor = sample['image'] + image = image.unsqueeze(0) + + recon_combined, masks, mask_fg, slots = model(image) + + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = opt.num_slots) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + + image = image[0] + image = F.interpolate(image, (95,315)) + masks = masks[0] + + recon_combined = recon_combined.detach() + masks = masks.detach() + + fig, ax = plt.subplots(math.ceil((opt.num_slots+2) / 10), 10, figsize=(45, 5 * math.ceil((opt.num_slots +2)/ 10))) + for i in range(1): + image_i = image[i] + recon_combined_i = recon_combined[i] + masks_i = masks[i].cpu().numpy() + image_i = image_i.permute(1,2,0).cpu().numpy() + image_i = image_i * 0.5 + 0.5 + recon_combined_i = recon_combined_i.permute(1,2,0) + recon_combined_i = recon_combined_i.cpu().numpy() + recon_combined_i = recon_combined_i * 0.5 + 0.5 + ax[i,0].imshow(image_i) + ax[i,0].set_title('Image-f{}'.format(i)) + ax[i,1].imshow(recon_combined_i) + ax[i,1].set_title('Recon.') + for j in range(opt.num_slots): + ax[(j+2)//10,(j + 2)%10].imshow(image_i) + ax[(j+2)//10,(j + 2)%10].imshow(masks_i[j], cmap = 'viridis', alpha = 0.6) + ax[(j+2)//10,(j + 2)%10].set_title('Slot %s' % str(j + 1)) + for j in range(math.ceil((opt.num_slots+2) / 10) * 10): + ax[(j)//10,(j)%10].grid(False) + ax[(j)//10,(j)%10].axis('off') + eval_name = os.path.join(opt.sample_dir,opt.exp_name,'epoch_{}_slot.png'.format(epoch)) + fig.savefig(eval_name) + plt.close(fig) + plt.imsave(eval_name + '_fg.png', mask_fg.detach().cpu().numpy()[0,0,0,:,:]) + + + del masks, mask_fg, recon_combined, image, slots + + if not epoch % 10: + torch.save({ + 'model_state_dict': model.state_dict(), + }, os.path.join(opt.model_dir, opt.exp_name, 'epoch_{}.ckpt'.format(epoch)) + ) + + +if __name__ == '__main__': + main() diff --git a/DIOD_DINOv2/trainPD_ts_dino.py b/DIOD_DINOv2/trainPD_ts_dino.py new file mode 100644 index 0000000..7b7564c --- /dev/null +++ b/DIOD_DINOv2/trainPD_ts_dino.py @@ -0,0 +1,576 @@ +from __future__ import annotations + +from collections import OrderedDict +import os +import argparse +from tkinter import NONE +from tqdm import tqdm +import time +import datetime +import torch.optim as optim +import torch.nn as nn +import random +import matplotlib.pyplot as plt +import scipy.optimize +import torch.nn.functional as F +import numpy as np +import torch + +from datasetPD_dinov2 import PDDataset +from datasetPDEval_dinov2 import PDDataset as PDDatasetEval + +from models.model_bg_dinov2 import SlotAttentionAutoEncoder +from transforms import get_normalize_transform, get_ssl_train_online_transform, get_crop_transform +from utils import adjusted_rand_index as ARI + +from torch import Tensor +from torch.nn.utils import clip_grad_norm_ +from torchvision.transforms import InterpolationMode + +import math +from scipy.ndimage import label + +# Set the random seed for consistant comparisons/ablation studies and reproducibility +# No tuning was performed over multiple seeds +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) + +# Set CUDA deterministic flags +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + +torch.autograd.set_detect_anomaly(True) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +parser = argparse.ArgumentParser() + +parser.add_argument +# basic configurations +parser.add_argument('--model_dir', default='/home/data/skara/tmp/', type=str, help='where to save models' ) +parser.add_argument('--sample_dir', default = './samples/', type = str, help = 'where to save the plots') +parser.add_argument('--exp_name', default='exp_name', type=str, help='experiment name, used for model saving/plotting ect' ) +parser.add_argument('--num_workers', default=12, type=int, help='number of workers for loading data') +parser.add_argument('--seed', default=0, type=int, help='random seed') +parser.add_argument('--data_path', default = '/home/data/skara/TRI_PD/TRI_PD_train', type = str, help = 'path of PD dataset') +parser.add_argument('--supervision', default = 'est', choices=['moving', 'all','est'], help = 'type of supervision, currently available: moving and all') +# model parameters +parser.add_argument('--batch_size', default=8, type=int) +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--learning_rate', default=0.0005, type=float) +parser.add_argument('--warmup_steps', default=3000, type=int, help='Number of warmup steps for the learning rate.') +parser.add_argument('--decay_rate', default=0.5, type=float, help='Rate for the learning rate decay.') +parser.add_argument('--decay_steps', default=50000, type=int, help='Number of steps for the learning rate decay.') +parser.add_argument('--num_epochs', default=500, type=int, help='number of workers for loading data') +parser.add_argument('--weight_mask', default = 1.0, type = float, help = 'weight for the mask loss') +parser.add_argument('--weight_NLL', default = 1.0, type = float, help = 'weight for the NLL loss') +parser.add_argument('--weight_reg', default = 0.3, type = float, help = 'weight for the regularization term') +parser.add_argument('--confid_th', default = 0.9, type = float, help = 'confidence threshold used to filter pseudo-labels during distillation') +# Data augmentations +parser.add_argument('--crop-size', default=[], type = int, nargs="*", help = "crop's size") +parser.add_argument('--crop-scale', default=[0.75, 1.0], type = int, nargs="+", help = "crop's size") +parser.add_argument('--crop-ratio', default=[2.0, 2.0], type = int, nargs="+", help = "crop's ratio") +parser.add_argument('--crop-interpolation', default="bilinear", type = str, help = "crop's interpolation") +parser.add_argument('--crop-interpolation-mask', default="nearest", type = str, help = "crop's interpolation mask") +parser.add_argument('--p-crop', default=0.4, type = float, help = "crop's probability") +parser.add_argument('--p-flip', default=0.4, type = float, help = "flip's probability") +parser.add_argument('--brightness', default=0.4, type = float, help = "color jitter's brightness") +parser.add_argument('--contrast', default=0.4, type = float, help = "color jitter's contrast") +parser.add_argument('--saturation', default=0.4, type = float, help = "color jitter's saturation") +parser.add_argument('--hue', default=0.1, type = float, help = "color jitter's hue") +parser.add_argument('--p-contrastive', default=0.8, type = float, help = "color jitter's probability") +parser.add_argument('--p-grayscale', default=0.2, type = float, help = "grayscale's probability") +parser.add_argument('--kernel-size-blur', default=11, type = int, help = "blur's kernel size") +parser.add_argument('--sigma-blur', default=[0.1, 2.0], type = float, nargs="+", help = "blur's sigma") +parser.add_argument('--p-blur', default=0.0, type = float, help = "blur's probability") +parser.add_argument('--threshold-solarize', default=0.5, type = float, help = "solarize's threshold") +parser.add_argument('--p-solarize', default=0.2, type = float, help = "solarize's probability") +parser.add_argument('--mean', default=[0.485, 0.456, 0.406], type = float, nargs="+", help = "normalize's mean") +parser.add_argument('--std', default=[0.229, 0.224, 0.225], type = float, nargs="+", help = "normalize's std") +# Burn_in +parser.add_argument('--burn_in_ckpt', default='DIODPD_burn_in_400_dinov2.ckpt', type=str, help='ckpt from burn_in to load when starting distillation') +parser.add_argument('--burn_in_exp', default='checkpoints', type=str, help='burn_in experiment name' ) + +# burn_in or distillation? +parser.add_argument('--start_teacher', default=0, type = int, help = "teacher starting epoch") + +def computeWBCE(opt, masks, mask_gt, scores): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): continue # corresponding object non-available + fg_portion = torch.where(mask_gt[i,j,id_gt[idx],:,:]!=0)[0].size()[0]/(123*245) + + tmp_BCE += ( -(2-fg_portion)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + nb_objects += 1 + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + return loss_mask + + +def computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=None, device = None, teacher = 0 ): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + fg_gt_after_filtering = torch.zeros(mask_fg_t[:,:,0,:,:].shape).to(device) + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): + continue # corresponding object non-available + + # using fg_teacher as a confidence map on pseudo_labels + weight = mask_fg_t[i,j,0,:,:][mask_gt[i,j,id_gt[idx],:,:] == 1].mean() + if not teacher: + if weight>=opt.confid_th: + nb_objects += 1 + fg_gt_after_filtering[i,j,:,:] += mask_gt[i,j,id_gt[idx],:,:] + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + else: + if weight>=opt.confid_th: + nb_objects += 1 + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + + if not teacher: + return loss_mask, fg_gt_after_filtering + return loss_mask + + +@torch.no_grad() +def _update_teacher_model(model, teacher_model, keep_rate=0.996): + + student_model_dict = model.state_dict() + + new_teacher_dict = OrderedDict() + for key, value in teacher_model.state_dict().items(): + if key in student_model_dict.keys(): + new_teacher_dict[key] = ( + student_model_dict[key] * + (1 - keep_rate) + value * keep_rate + ) + else: + raise Exception("{} is not found in student model".format(key)) + + teacher_model.load_state_dict(new_teacher_dict) + + +def main(): + opt = parser.parse_args() + + resolution = (490, 980) #dino_v2 + + if not os.path.exists(opt.model_dir): + os.mkdir(opt.model_dir) + if not os.path.exists(opt.sample_dir): + os.mkdir(opt.sample_dir) + if not os.path.exists(os.path.join(opt.model_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.model_dir, opt.exp_name)) + if not os.path.exists(os.path.join(opt.sample_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.sample_dir, opt.exp_name)) + + data_path = opt.data_path + train_transform = get_crop_transform( + resolution, # original size + opt.crop_size if opt.crop_size != [] else None, + opt.crop_scale, + opt.crop_ratio, + InterpolationMode(opt.crop_interpolation), + InterpolationMode(opt.crop_interpolation_mask), + opt.p_crop, + opt.p_flip + ) + print("train transform", train_transform) + train_set = PDDataset(split = 'train', root = data_path, supervision = opt.supervision, transform=train_transform, apply_img_transform=True) + test_set = PDDatasetEval(split = 'eval', root = data_path) + + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3) + for param in model.encoder_cnn.dinov2_backbone.parameters(): + param.requires_grad = False + + model = model.to(device) + model = nn.DataParallel(model) + + student_train_transform = get_ssl_train_online_transform( + opt.brightness, + opt.contrast, + opt.saturation, + opt.hue, + opt.p_contrastive, + opt.p_grayscale, + opt.kernel_size_blur, + opt.sigma_blur, + opt.p_blur, + opt.threshold_solarize, + opt.p_solarize, + opt.mean, + opt.std + ).to(device) + print("student train transform", student_train_transform) + teacher_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("teacher train transform", teacher_transform) + val_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("val transform", val_transform) + + teacher = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + + teacher = nn.DataParallel(teacher) + + + criterion = nn.MSELoss() + + params = [{'params': model.parameters()}] + + train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=opt.batch_size, + shuffle=True, num_workers=opt.num_workers, drop_last=True) + + optimizer = optim.Adam(params, lr=opt.learning_rate) + + start = time.time() + step = 0 + + + print('Model build finished!') + for epoch in range(opt.num_epochs): + + if epoch == opt.start_teacher: + print('____________________burn_in_ckpt', opt.burn_in_ckpt) + print('____________________supervision', opt.supervision) + print('____________________regularization', opt.weight_reg) + + model_path = os.path.join(opt.model_dir, opt.burn_in_exp, opt.burn_in_ckpt) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + for param in model.module.encoder_cnn.dinov2_backbone.parameters(): + param.requires_grad = False + teacher.load_state_dict(torch.load(model_path)['model_state_dict']) + + model.train() + + total_loss = 0 + M_loss = 0 + N_loss = 0 + LR_loss = 0 + + M_loss_t = 0 + N_loss_t = 0 + for sample in tqdm(train_dataloader): + step += 1 + + if step < opt.warmup_steps: + learning_rate = opt.learning_rate * (step / opt.warmup_steps) + else: + learning_rate = opt.learning_rate + + learning_rate = learning_rate * (opt.decay_rate ** ( + step / opt.decay_steps)) + + optimizer.param_groups[0]['lr'] = learning_rate + + image = sample['image'].to(device, non_blocking=True) + mask_gt = sample['mask'].to(device, non_blocking=True) + student_image = student_train_transform(image) + teacher_image = teacher_transform(image) + + mask_gt = F.interpolate(mask_gt.float(), (123, 245)).long().to(device) + + if epoch >= opt.start_teacher: + with torch.no_grad(): + _, masks_t, mask_fg_t, _ = teacher(teacher_image) + masks_t = masks_t * 0.999 + 1e-8 + masks_t_quant = masks_t.argmax(2).cpu().numpy() + masks_t_quant = np.where(masks_t_quant==44, 0, masks_t_quant) + + + # get connex regions + for i in range(opt.batch_size): + for j in range(5): + activated_slots = list(np.unique(masks_t_quant[i,j,:,:])) + + + empty_slots = [x for x in range(45) if x not in activated_slots] + for s in activated_slots: + tmp_bin = np.where(masks_t_quant[i,j,:,:] == s, 1, 0) + # Label each connected component + labeled_mask, num_features = label(tmp_bin) + label_counts = np.bincount(labeled_mask.ravel()) + if num_features > 1: + # starts from 2 to discard background and 1st connected region + for ft in range(2, num_features + 1): + if label_counts[ft] > 10 and mask_fg_t[i,j,0,:,:][labeled_mask == ft].mean() > opt.confid_th: + + try: # if no empty slot available, stop adding objects + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, empty_slots[0], masks_t_quant[i,j,:,:]) + empty_slots.remove(empty_slots[0]) + except: + # print('more teacher predictions than student slots') + continue + else: + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, 0, masks_t_quant[i,j,:,:]) + + + + _h, _w = masks_t_quant[0,0,:,:].shape + + masks_t_bin_np = np.zeros((opt.batch_size, 5,_h, _w)) + + # binarize the teacher predictions + for i in range(opt.batch_size): + for j in range(5): + values, indices, counts = np.unique(masks_t_quant[i,j,:,:], return_inverse=True, return_counts=True) + to_eliminate = counts <= 10 + mapping = np.arange(len(values)) + mapping[to_eliminate] = 0 + masks_t_bin_np[i,j,:,:] = mapping[indices].reshape((_h, _w)) + masks_t_bin = torch.Tensor(masks_t_bin_np).long() + n_objects_t = masks_t_bin.max() + masks_t_bin = F.one_hot(masks_t_bin, n_objects_t+1) + masks_t_bin = masks_t_bin[:,:,:,:,1:] + masks_t_bin = masks_t_bin.permute(0,1,4,2,3).float() + masks_t_bin_gpu = masks_t_bin.to(device) + masks_t_bin_np = masks_t_bin.flatten(3,4) + masks_t_bin_np = masks_t_bin_np.detach().cpu().numpy() + + + recon_combined, masks, mask_fg, slots = model(student_image) + recon_combined = recon_combined.view(opt.batch_size,5,3,resolution[0],resolution[1]) + + # reconstruction loss + loss = criterion(recon_combined, student_image) + + # mask loss + loss_mask = 0.0 + loss_mask_t = 0.0 + + NLL_loss = 0.0 + L1_reg = 0.0 + + mask_detach = masks.detach().flatten(3,4) + mask_detach = mask_detach * 0.999 + 1e-8 + mask_detach = mask_detach.cpu().numpy() + n_objects = mask_gt.max() + mask_gt = F.one_hot(mask_gt, n_objects+1) + mask_gt = mask_gt[:,:,:,:,1:] + mask_gt = mask_gt.permute(0,1,4,2,3).float() + mask_gt_np = mask_gt.flatten(3,4) + mask_gt_np = mask_gt_np.detach().cpu().numpy() + + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), mask_gt_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - mask_gt_np[i,j]).T) + scores[i,j] += cross_entropy_cur + + + # weighted BCE + if epoch >= opt.start_teacher: + loss_mask, filtered_fg_gt = computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=mask_fg_t, device=device) + else: + try: + loss_mask = computeWBCE(opt, masks, mask_gt, scores) + except: + print(scores) + + + + # NLL (reg on whole image) + mask_fg = mask_fg * 0.999 + 1e-8 + fg_gt = mask_gt.sum(axis = 2) + + if epoch >= opt.start_teacher: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * filtered_fg_gt).mean() + else: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * fg_gt).mean() + L1_reg = mask_fg[:,:,0,:,:].mean() + + + # teacherBCE + if epoch >= opt.start_teacher: + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects_t)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), masks_t_bin_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - masks_t_bin_np[i,j]).T) + scores[i,j] += cross_entropy_cur + loss_mask_t = computeWBCE_TS(opt, masks, masks_t_bin_gpu, scores, mask_fg_t, device=device, teacher = True) + + + if epoch >= opt.start_teacher: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + loss_mask_t + else: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + + optimizer.zero_grad() + whole_loss.backward() + clip_grad_norm_(model.parameters(),1.0) + + optimizer.step() + + # undate teacher + if epoch >= opt.start_teacher: + _update_teacher_model(model, teacher, keep_rate=0.996) + + total_loss += loss.item() + try: + M_loss += loss_mask.item()*opt.weight_mask + except: + M_loss += loss_mask*opt.weight_mask + N_loss += NLL_loss.item()*opt.weight_NLL + LR_loss += L1_reg.item()*opt.weight_reg + + # teacher + if epoch >= opt.start_teacher: + try: + M_loss_t += loss_mask_t.item() + except: + M_loss_t += loss_mask_t + + del recon_combined, masks, mask_fg, image, student_image, teacher_image, loss, whole_loss, mask_gt, loss_mask, NLL_loss, L1_reg, loss_mask_t + # break + + + total_loss /= len(train_dataloader) + M_loss /= len(train_dataloader) + N_loss /= len(train_dataloader) + LR_loss /= len(train_dataloader) + + M_loss_t /= len(train_dataloader) + N_loss_t /= len(train_dataloader) + + print ("Epoch: {}, Loss: {}, Loss_mask: {}, NLL_loss: {}, L1_reg: {}, Loss_mask_t: {}, Time: {}".format(epoch, total_loss,M_loss, + N_loss, LR_loss, M_loss_t, datetime.timedelta(seconds=time.time() - start))) + + if not epoch % 10: + + rand_id = random.randint(0,190) + sample: dict[str, Tensor] = test_set[0] + image: Tensor = sample['image'][rand_id:rand_id+5,:,:,:].to(device, non_blocking=True) + image = image.unsqueeze(0) + + recon_combined, masks, mask_fg, slots = model(image) + + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = opt.num_slots) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + + image = image[0] + image = F.interpolate(image, (123,245)) + masks = masks[0] + + recon_combined = recon_combined.detach() + masks = masks.detach() + + fig, ax = plt.subplots(math.ceil((opt.num_slots+2) / 10), 10, figsize=(45, 5 * math.ceil((opt.num_slots +2)/ 10))) + for i in range(1): + image_i = image[i] + recon_combined_i = recon_combined[i] + masks_i = masks[i].cpu().numpy() + image_i = image_i.permute(1,2,0).cpu().numpy() + image_i = image_i * 0.5 + 0.5 + recon_combined_i = recon_combined_i.permute(1,2,0) + recon_combined_i = recon_combined_i.cpu().numpy() + recon_combined_i = recon_combined_i * 0.5 + 0.5 + ax[i,0].imshow(image_i) + ax[i,0].set_title('Image-f{}'.format(i)) + ax[i,1].imshow(recon_combined_i) + ax[i,1].set_title('Recon.') + for j in range(opt.num_slots): + ax[(j+2)//10,(j + 2)%10].imshow(image_i) + ax[(j+2)//10,(j + 2)%10].imshow(masks_i[j], cmap = 'viridis', alpha = 0.6) + ax[(j+2)//10,(j + 2)%10].set_title('Slot %s' % str(j + 1)) + for j in range(math.ceil((opt.num_slots+2) / 10) * 10): + ax[(j)//10,(j)%10].grid(False) + ax[(j)//10,(j)%10].axis('off') + eval_name = os.path.join(opt.sample_dir,opt.exp_name,'epoch_{}_slot.png'.format(epoch)) + fig.savefig(eval_name) + plt.close(fig) + plt.imsave(eval_name + '_fg.png', mask_fg.detach().cpu().numpy()[0,0,0,:,:]) + + + del masks, mask_fg, recon_combined, image, slots + + if not epoch % 10: + torch.save({ + 'model_state_dict': model.state_dict(), + }, os.path.join(opt.model_dir, opt.exp_name, 'epoch_{}.ckpt'.format(epoch)) + ) + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + with torch.no_grad(): + ARIs, all_ARIs = [], [] + for idx, sample in enumerate(tqdm(test_dataloader)): + + image = sample['image'].to(device) + mask_gt = sample['mask'] + + mask_gts = F.interpolate(mask_gt.float(), (123, 245)).long() + + for i in range(40): + _, masks, _, _= model(image[:,i*5:i*5+5,:,:,:]) + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + gt_msk = mask_gt[0] + pred_msk = masks[0] + gt_msk = gt_msk.view(5,-1) + pred_msk = pred_msk.view(5,opt.num_slots,-1).permute(1,0,2) + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + _,n_cat = gt_msk.shape + if n_cat <= 2: + continue + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + gt_msk = gt_msk[:,1:] + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + + # if not ari == ari: # one image in the val_set is causing instability in computing ari. Discarded + if not ari == ari: + print('discarded') + continue + + all_ARIs.append(all_ari) + ARIs.append(ari) + del image, mask_gt, masks + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final allARI:', sum(all_ARIs) / len(all_ARIs)) + + +if __name__ == '__main__': + main() diff --git a/DIOD_DINOv2/transforms.py b/DIOD_DINOv2/transforms.py new file mode 100644 index 0000000..6dbfb99 --- /dev/null +++ b/DIOD_DINOv2/transforms.py @@ -0,0 +1,142 @@ +from __future__ import annotations + +from typing import Callable +import torch +from torch import Tensor, nn +from torchaug.batch_transforms import ( + BatchVideoWrapper, + BatchRandomColorJitter, + BatchRandomGaussianBlur, + BatchRandomGrayScale, + BatchRandomSolarize +) +from torchaug.transforms import VideoNormalize +from torchvision.transforms import Compose, InterpolationMode, RandomResizedCrop, RandomHorizontalFlip, Resize +import torchvision.transforms.functional as F +from torchvision.transforms.functional import InterpolationMode + + +class RandomHorizontalFlipSample(RandomHorizontalFlip): + def __init__(self, p: float) -> None: + super().__init__(p) + + def forward(self, sample: dict[str: Tensor]) -> dict[str: Tensor]: + if torch.rand(1) < self.p: + sample["image"] = F.hflip(sample["image"]) + sample["mask"] = F.hflip(sample["mask"]) + sample["flipped"] = torch.Tensor([True]) + else: + sample["flipped"] = torch.Tensor([False]) + return sample + + +class RandomResizedCropSample(RandomResizedCrop): + def __init__(self, size, scale=..., ratio=..., interpolation=InterpolationMode.BILINEAR, antialias=True, interpolation_mask=InterpolationMode.NEAREST, p:float =0.): + if size is None: + size = 4 + none_size = True + else: + none_size = False + + super().__init__(size, scale, ratio, interpolation, antialias) + + if none_size: + self.size = None + self.p=p + self.interpolation_mask = interpolation_mask + + def forward(self, sample: dict[str: Tensor]) -> dict[str: Tensor]: + if torch.rand(1) < self.p: + if self.size is None: + size: list[int] = sample["image"].shape[-2:] + else: + size = self.size + i, j, h, w = self.get_params(sample["image"], self.scale, self.ratio) + sample["image"] = F.resized_crop(sample["image"], i, j, h, w, size, self.interpolation, antialias=self.antialias) + sample["mask"] = F.resized_crop(sample["mask"], i, j, h, w, size, self.interpolation_mask, antialias=self.antialias) + sample["cropped"] = torch.Tensor([True]) + else: + sample["cropped"] = torch.Tensor([False]) + return sample + + def __repr__(self) -> str: + interpolate_str = self.interpolation.value + interpolate_mask_str = self.interpolation_mask.value + format_string = self.__class__.__name__ + f"(size={self.size}" + format_string += f", scale={tuple(round(s, 4) for s in self.scale)}" + format_string += f", ratio={tuple(round(r, 4) for r in self.ratio)}" + format_string += f", interpolation={interpolate_str}" + format_string += f", interpolation_mask={interpolate_mask_str}" + format_string += f", antialias={self.antialias}" + format_string += f", p={self.p})" + return format_string + + + +class ResizeSample(Resize): + def __init__(self, size: int | list[int] | None = None, interpolation=F.InterpolationMode.BILINEAR) -> None: + super().__init__(size, interpolation) + + def forward(self, sample: dict[str: torch.Tensor]) -> dict[str: torch.Tensor]: + # Resize image + sample["image"] = F.resize(sample["image"], self.size, self.interpolation) + sample["mask"] = F.resize(sample["mask"], self.size, self.interpolation) + + return sample + + + + +def get_crop_transform( + org_size: list[int] = (480, 968), + size: int | list[int] | None = None, + scale: list[float] = (0.08, 1.0), + ratio: list[float] = (3.0 / 4.0, 4.0 / 3.0), + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + interpolation_mask: InterpolationMode = InterpolationMode.NEAREST, + p_crop: float = 0., + p_flip: float = 0.5, +) -> Callable: + return Compose( + [ + RandomResizedCropSample(size, scale, ratio, interpolation, True, interpolation_mask, p_crop), + ResizeSample(org_size), + RandomHorizontalFlipSample(p_flip) + ] + ) + + +def get_ssl_train_online_transform( + brightness: float = 0.4, + contrast: float = 0.4, + saturation: float = 0.4, + hue: float = 0.1, + p_contrastive: float = 0.8, + p_grayscale: float = 0.2, + kernel_size_blur: int = 23, + sigma_blur: list[int] = [0.1, 2.], + p_blur: float = 0.5, + threshold_solarize: float = 0.5, + p_solarize: float = 0.2, + mean: list[int] = [0.485, 0.456, 0.406], + std: list[int] = [0.229, 0.224, 0.225] +) -> nn.Module: + return BatchVideoWrapper( + [ + BatchRandomColorJitter(brightness, contrast, saturation, hue, p_contrastive), + BatchRandomGrayScale(p_grayscale), + BatchRandomGaussianBlur(kernel_size_blur, sigma_blur, p_blur), + BatchRandomSolarize(threshold_solarize, p_solarize), + VideoNormalize(mean, std, video_format="TCHW") + ], + inplace=False, + same_on_frames=True, + video_format="TCHW", + ) + + +def get_normalize_transform( + mean: list[int] = [0.485, 0.456, 0.406], + std: list[int] = [0.229, 0.224, 0.225] +) -> nn.Module: + return VideoNormalize(mean, std, inplace=False, video_format="TCHW") diff --git a/DIOD_DINOv2/utils.py b/DIOD_DINOv2/utils.py new file mode 100644 index 0000000..3719408 --- /dev/null +++ b/DIOD_DINOv2/utils.py @@ -0,0 +1,61 @@ +import torch +import torch.nn.functional as F + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +def temporal_loss(slots): + bs, nf, ns, ds = slots.shape + slot0 = slots[:,:-1,:,:].reshape(bs*(nf-1),ns,ds) + slot1 = slots[:,1:,:,:].reshape(bs*(nf-1),ns,ds) + slot1 = slot1.permute(0,2,1) + scores = torch.einsum('bij,bjk->bik', slot0, slot1) + scores = scores.softmax(dim=1) + 0.0001 + gt = torch.eye(ns).unsqueeze(0).repeat(bs*(nf-1),1,1).to(device) + return torch.mean((gt-scores)**2) + +def adjusted_rand_index(true_mask, pred_mask): + _, n_points, n_true_groups = true_mask.shape + n_pred_groups = pred_mask.shape[-1] + assert not (n_points <= n_true_groups and n_points <= n_pred_groups), ("adjusted_rand_index requires n_groups < n_points. We don't handle the special cases that can occur when you have one cluster per datapoint.") + + true_group_ids = torch.argmax(true_mask, -1) + pred_group_ids = torch.argmax(pred_mask, -1) + true_mask_oh = true_mask.to(torch.float32) + pred_mask_oh = F.one_hot(pred_group_ids, n_pred_groups).to(torch.float32) + + n_points = torch.sum(true_mask_oh, dim=[1, 2]).to(torch.float32) + + nij = torch.einsum('bji,bjk->bki', pred_mask_oh, true_mask_oh) + a = torch.sum(nij, dim=1) + b = torch.sum(nij, dim=2) + + rindex = torch.sum(nij * (nij - 1), dim=[1, 2]) + aindex = torch.sum(a * (a - 1), dim=1) + bindex = torch.sum(b * (b - 1), dim=1) + expected_rindex = aindex * bindex / (n_points*(n_points-1)) + max_rindex = (aindex + bindex) / 2 + ari = (rindex - expected_rindex) / (max_rindex - expected_rindex+0.000000000001) + + _all_equal = lambda values: torch.all(torch.eq(values, values[..., :1]), dim=-1) + both_single_cluster = torch.logical_and(_all_equal(true_group_ids), _all_equal(pred_group_ids)) + + return torch.where(both_single_cluster, torch.ones_like(ari), ari) + + +def build_grid(resolution): + ranges = [torch.linspace(0.0, 1.0, steps=res) for res in resolution] + grid = torch.meshgrid(*ranges) + grid = torch.stack(grid, dim=-1) + grid = torch.reshape(grid, [resolution[0], resolution[1], -1]) + grid = grid.unsqueeze(0) + return torch.cat([grid, 1.0 - grid], dim=-1) + + +def first(x): + return next(iter(x)) + + +def only(x): + materialized_x = list(x) + assert len(materialized_x) == 1 + return materialized_x[0] diff --git a/DIOD_env.yml b/DIOD_env.yml new file mode 100644 index 0000000..b217280 --- /dev/null +++ b/DIOD_env.yml @@ -0,0 +1,165 @@ +name: DIODENV +channels: + - pytorch + - nvidia + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - blas=1.0=mkl + - brotli=1.0.9=h5eee18b_8 + - brotli-bin=1.0.9=h5eee18b_8 + - brotli-python=1.0.9=py39h6a678d5_8 + - bzip2=1.0.8=h5eee18b_6 + - ca-certificates=2024.7.2=h06a4308_0 + - certifi=2024.8.30=py39h06a4308_0 + - charset-normalizer=3.3.2=pyhd3eb1b0_0 + - contourpy=1.2.0=py39hdb19cb5_0 + - cuda-cudart=11.7.99=0 + - cuda-cupti=11.7.101=0 + - cuda-libraries=11.7.1=0 + - cuda-nvrtc=11.7.99=0 + - cuda-nvtx=11.7.91=0 + - cuda-runtime=11.7.1=0 + - cuda-version=12.4=hbda6634_3 + - cycler=0.11.0=pyhd3eb1b0_0 + - cyrus-sasl=2.1.28=h52b45da_1 + - dbus=1.13.18=hb2f20db_0 + - expat=2.6.3=h6a678d5_0 + - ffmpeg=4.3=hf484d3e_0 + - filelock=3.13.1=py39h06a4308_0 + - fontconfig=2.14.1=h55d465d_3 + - fonttools=4.51.0=py39h5eee18b_0 + - freetype=2.12.1=h4a9f257_0 + - glib=2.78.4=h6a678d5_0 + - glib-tools=2.78.4=h6a678d5_0 + - gmp=6.2.1=h295c915_3 + - gmpy2=2.1.2=py39heeb90bb_0 + - gnutls=3.6.15=he1e5248_0 + - gst-plugins-base=1.14.1=h6a678d5_1 + - gstreamer=1.14.1=h5eee18b_1 + - icu=73.1=h6a678d5_0 + - idna=3.7=py39h06a4308_0 + - importlib_resources=6.4.0=py39h06a4308_0 + - intel-openmp=2023.1.0=hdb19cb5_46306 + - jinja2=3.1.4=py39h06a4308_0 + - jpeg=9e=h5eee18b_3 + - kiwisolver=1.4.4=py39h6a678d5_0 + - krb5=1.20.1=h143b758_1 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=3.0=h295c915_0 + - libbrotlicommon=1.0.9=h5eee18b_8 + - libbrotlidec=1.0.9=h5eee18b_8 + - libbrotlienc=1.0.9=h5eee18b_8 + - libclang=14.0.6=default_hc6dbbc7_1 + - libclang13=14.0.6=default_he11475f_1 + - libcublas=11.10.3.66=0 + - libcufft=10.7.2.124=h4fbf590_0 + - libcufile=1.9.1.3=h99ab3db_1 + - libcups=2.4.2=h2d74bed_1 + - libcurand=10.3.5.147=h99ab3db_1 + - libcusolver=11.4.0.1=0 + - libcusparse=11.7.4.91=0 + - libdeflate=1.17=h5eee18b_1 + - libedit=3.1.20230828=h5eee18b_0 + - libffi=3.4.4=h6a678d5_1 + - libgcc-ng=11.2.0=h1234567_1 + - libgfortran-ng=11.2.0=h00389a5_1 + - libgfortran5=11.2.0=h1234567_1 + - libglib=2.78.4=hdc74915_0 + - libgomp=11.2.0=h1234567_1 + - libiconv=1.16=h5eee18b_3 + - libidn2=2.3.4=h5eee18b_0 + - libllvm14=14.0.6=hdb19cb5_3 + - libnpp=11.7.4.75=0 + - libnvjpeg=11.8.0.2=0 + - libpng=1.6.39=h5eee18b_0 + - libpq=12.17=hdbd6064_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libtasn1=4.19.0=h5eee18b_0 + - libtiff=4.5.1=h6a678d5_0 + - libunistring=0.9.10=h27cfd23_0 + - libuuid=1.41.5=h5eee18b_0 + - libwebp-base=1.3.2=h5eee18b_0 + - libxcb=1.15=h7f8727e_0 + - libxkbcommon=1.0.1=h097e994_2 + - libxml2=2.13.1=hfdd30dd_2 + - lz4-c=1.9.4=h6a678d5_1 + - markupsafe=2.1.3=py39h5eee18b_0 + - matplotlib=3.7.1=py39h06a4308_1 + - matplotlib-base=3.7.1=py39h417a72b_1 + - mkl=2023.1.0=h213fc3f_46344 + - mkl-service=2.4.0=py39h5eee18b_1 + - mkl_fft=1.3.10=py39h5eee18b_0 + - mkl_random=1.2.7=py39h1128e8f_0 + - mpc=1.1.0=h10f8cd9_1 + - mpfr=4.0.2=hb69a4c5_1 + - mpmath=1.3.0=py39h06a4308_0 + - mysql=5.7.24=h721c034_2 + - ncurses=6.4=h6a678d5_0 + - nettle=3.7.3=hbbd107a_1 + - networkx=3.2.1=py39h06a4308_0 + - openh264=2.1.1=h4ff587b_0 + - openjpeg=2.5.2=he7f1fd0_0 + - openssl=3.0.15=h5eee18b_0 + - packaging=24.1=py39h06a4308_0 + - pcre2=10.42=hebb0a14_1 + - pillow=10.4.0=py39h5eee18b_0 + - pip=24.2=py39h06a4308_0 + - platformdirs=3.10.0=py39h06a4308_0 + - ply=3.11=py39h06a4308_0 + - pooch=1.7.0=py39h06a4308_0 + - pyparsing=3.1.2=py39h06a4308_0 + - pyqt=5.15.10=py39h6a678d5_0 + - pyqt5-sip=12.13.0=py39h5eee18b_0 + - pysocks=1.7.1=py39h06a4308_0 + - python=3.9.19=h955ad1f_1 + - python-dateutil=2.9.0post0=py39h06a4308_2 + - pytorch=2.0.0=py3.9_cuda11.7_cudnn8.5.0_0 + - pytorch-cuda=11.7=h778d358_5 + - pytorch-mutex=1.0=cuda + - qt-main=5.15.2=h53bd1ea_10 + - readline=8.2=h5eee18b_0 + - requests=2.32.3=py39h06a4308_0 + - scipy=1.10.1=py39hf6e8229_1 + - setuptools=72.1.0=py39h06a4308_0 + - sip=6.7.12=py39h6a678d5_0 + - six=1.16.0=pyhd3eb1b0_1 + - sqlite=3.45.3=h5eee18b_0 + - sympy=1.13.2=py39h06a4308_0 + - tbb=2021.8.0=hdb19cb5_0 + - tk=8.6.14=h39e8969_0 + - tomli=2.0.1=py39h06a4308_0 + - torchtriton=2.0.0=py39 + - torchvision==0.15.0 + - tornado=6.4.1=py39h5eee18b_0 + - typing_extensions=4.11.0=py39h06a4308_0 + - tzdata=2024a=h04d1e81_0 + - unicodedata2=15.1.0=py39h5eee18b_0 + - urllib3=2.2.2=py39h06a4308_0 + - wheel=0.44.0=py39h06a4308_0 + - xz=5.4.6=h5eee18b_1 + - zipp=3.17.0=py39h06a4308_0 + - zlib=1.2.13=h5eee18b_1 + - zstd=1.5.5=hc292b87_2 + - pip: + - fsspec==2024.9.0 + - numpy==1.23.5 + - nvidia-cublas-cu12==12.1.3.1 + - nvidia-cuda-cupti-cu12==12.1.105 + - nvidia-cuda-nvrtc-cu12==12.1.105 + - nvidia-cuda-runtime-cu12==12.1.105 + - nvidia-cudnn-cu12==9.1.0.70 + - nvidia-cufft-cu12==11.0.2.54 + - nvidia-curand-cu12==10.3.2.106 + - nvidia-cusolver-cu12==11.4.5.107 + - nvidia-cusparse-cu12==12.1.0.106 + - nvidia-nccl-cu12==2.20.5 + - nvidia-nvjitlink-cu12==12.6.68 + - nvidia-nvtx-cu12==12.1.105 + - opencv-python==4.7.0.72 + - torchaug==0.3.2 + - tqdm==4.66.5 + - triton==3.0.0 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..94d3e5a --- /dev/null +++ b/LICENSE @@ -0,0 +1,518 @@ + + CeCILL FREE SOFTWARE LICENSE AGREEMENT + +Version 2.1 dated 2013-06-21 + + + Notice + +This Agreement is a Free Software license agreement that is the result +of discussions between its authors in order to ensure compliance with +the two main principles guiding its drafting: + + * firstly, compliance with the principles governing the distribution + of Free Software: access to source code, broad rights granted to users, + * secondly, the election of a governing law, French law, with which it + is conformant, both as regards the law of torts and intellectual + property law, and the protection that it offers to both authors and + holders of the economic rights over software. + +The authors of the CeCILL (for Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre]) +license are: + +Commissariat à l'énergie atomique et aux énergies alternatives - 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No warranty is given as regards the existence of +prior rights over the name of the Software or as regards the existence +of a trademark. + + + Article 10 - TERMINATION + +10.1 In the event of a breach by the Licensee of its obligations +hereunder, the Licensor may automatically terminate this Agreement +thirty (30) days after notice has been sent to the Licensee and has +remained ineffective. + +10.2 A Licensee whose Agreement is terminated shall no longer be +authorized to use, modify or distribute the Software. However, any +licenses that it may have granted prior to termination of the Agreement +shall remain valid subject to their having been granted in compliance +with the terms and conditions hereof. + + + Article 11 - MISCELLANEOUS + + + 11.1 EXCUSABLE EVENTS + +Neither Party shall be liable for any or all delay, or failure to +perform the Agreement, that may be attributable to an event of force +majeure, an act of God or an outside cause, such as defective +functioning or interruptions of the electricity or telecommunications +networks, network paralysis following a virus attack, intervention by +government authorities, natural disasters, water damage, earthquakes, +fire, explosions, strikes and labor unrest, war, etc. + +11.2 Any failure by either Party, on one or more occasions, to invoke +one or more of the provisions hereof, shall under no circumstances be +interpreted as being a waiver by the interested Party of its right to +invoke said provision(s) subsequently. + +11.3 The Agreement cancels and replaces any or all previous agreements, +whether written or oral, between the Parties and having the same +purpose, and constitutes the entirety of the agreement between said +Parties concerning said purpose. No supplement or modification to the +terms and conditions hereof shall be effective as between the Parties +unless it is made in writing and signed by their duly authorized +representatives. + +11.4 In the event that one or more of the provisions hereof were to +conflict with a current or future applicable act or legislative text, +said act or legislative text shall prevail, and the Parties shall make +the necessary amendments so as to comply with said act or legislative +text. All other provisions shall remain effective. Similarly, invalidity +of a provision of the Agreement, for any reason whatsoever, shall not +cause the Agreement as a whole to be invalid. + + + 11.5 LANGUAGE + +The Agreement is drafted in both French and English and both versions +are deemed authentic. + + + Article 12 - NEW VERSIONS OF THE AGREEMENT + +12.1 Any person is authorized to duplicate and distribute copies of this +Agreement. + +12.2 So as to ensure coherence, the wording of this Agreement is +protected and may only be modified by the authors of the License, who +reserve the right to periodically publish updates or new versions of the +Agreement, each with a separate number. These subsequent versions may +address new issues encountered by Free Software. + +12.3 Any Software distributed under a given version of the Agreement may +only be subsequently distributed under the same version of the Agreement +or a subsequent version, subject to the provisions of Article 5.3.4 +<#compatibility>. + + + Article 13 - GOVERNING LAW AND JURISDICTION + +13.1 The Agreement is governed by French law. The Parties agree to +endeavor to seek an amicable solution to any disagreements or disputes +that may arise during the performance of the Agreement. + +13.2 Failing an amicable solution within two (2) months as from their +occurrence, and unless emergency proceedings are necessary, the +disagreements or disputes shall be referred to the Paris Courts having +jurisdiction, by the more diligent Party. diff --git a/PlotKITTI.py b/PlotKITTI.py new file mode 100644 index 0000000..8926781 --- /dev/null +++ b/PlotKITTI.py @@ -0,0 +1,132 @@ +import os +import argparse +from tqdm import tqdm +import time +import datetime +import torch.optim as optim +import torch.nn as nn +import random +import matplotlib.pyplot as plt +import scipy.optimize +import torch.nn.functional as F +import numpy as np + +from datasetKITTIEval import KITTIDataset +from models.model_bg import SlotAttentionAutoEncoder + +import math + +import cv2 +from matplotlib.patches import Polygon +import torch +from torch.nn.parameter import Parameter + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +resolution = (368, 1248) + + +def load_model(model, state_dict): + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + continue + if isinstance(param, Parameter): + # backwards compatibility for serialized parameters + param = param.data + if own_state[name].data.shape!=param.shape: + print(name) + continue + own_state[name].copy_(param) + +evalname = 'infer/DIOD_KITTI_100' + +if not os.path.exists(evalname): + os.mkdir(evalname) + + +model_path = '/home/users/skara/check_release/checkpoints/DIODKITTI_100.ckpt' +data_path = '/home/data/skara/KITTI_DOM/KITTI_DOM_test' +test_set = KITTIDataset(split = 'test', root = data_path) + + +model = SlotAttentionAutoEncoder(resolution, 45, 64, 3).to(device) +model = nn.DataParallel(model) +model.load_state_dict(torch.load(model_path)['model_state_dict']) + + +cmap = plt.get_cmap('rainbow') +tk = 44 +colors = [cmap(i) for i in np.linspace(0, 1, tk)] +colors_rain = [cmap(i) for i in np.linspace(0, 1, 128)] +colors_rain = np.array(colors_rain) +np.random.seed(7) +np.random.shuffle(colors_rain) +np.random.shuffle(colors) + + +print('model load finished!') + +for param in model.module.parameters(): + param.requires_grad = False + +for k in range(len(test_set)): + print(k) + sample = test_set[k] + image = sample['image'].to(device) + image = image.unsqueeze(0) + image = image.unsqueeze(0) + image = image.repeat(1,5,1,1,1) + + recon_combined, masks,_, slots = model(image) + + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = 45) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + + image = F.interpolate(image, (3,92,312)) + masks = masks.detach() + masks = masks[0] + image = image[0] + + for j in range(1): + image_j = image[j].permute(1,2,0).cpu().numpy() + image_j = image_j * 0.5 + 0.5 + + masks_j = masks[j] + + masks_j = masks_j.cpu().numpy() + + fig = plt.figure(frameon=False) + ax = plt.Axes(fig, [0., 0., 1., 1.]) + ax.axis('off') + fig.add_axes(ax) + image_j = image_j[:,:,-1::-1] + ax.imshow(image_j, alpha = 1) + + for seg in range(tk): + msk = masks_j[seg] + threshold = 0 + + e = (msk > threshold).astype('uint8') + contour, hier = cv2.findContours( + e.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + cmax = None + for c in contour: + if cmax is None: + cmax = c + if len(c) > len(cmax): + cmax = c + + if cmax is not None: + polygon = Polygon( + cmax.reshape((-1, 2)), + fill=True, facecolor=colors[seg], + edgecolor='r', linewidth=0.0, + alpha=0.65) + ax.add_patch(polygon) + fig.savefig('{}/scene-{}-frame-{}.png'.format(evalname,k,j)) + plt.close(fig) + + \ No newline at end of file diff --git a/PlotPD.py b/PlotPD.py new file mode 100644 index 0000000..262bda3 --- /dev/null +++ b/PlotPD.py @@ -0,0 +1,92 @@ +from datasetPDEval import PDDataset +from models.model_bg import * +import torch.nn as nn + +import matplotlib.pyplot as plt +from matplotlib.patches import Polygon +import cv2 + +resolution = (480, 968) + + +model_path = '/home/users/skara/check_release/checkpoints/DIODPD_500.ckpt' +data_path = '/home/data/skara/test_video' +test_set = PDDataset(split = 'test', root = data_path) + +# Define output directory +output = './infer' +os.makedirs(output, exist_ok=True) + +model = SlotAttentionAutoEncoder(resolution, 45, 64, 3).to(device) + +model = nn.DataParallel(model) +model.load_state_dict(torch.load(model_path)['model_state_dict']) +print('model load finished!') + +for param in model.module.parameters(): + param.requires_grad = False + +# plot on some scenes +for nb_scene in [0,1,2]: + sample = test_set[nb_scene] + images = sample['image'] + + + cmap = plt.get_cmap('rainbow') + colors = [cmap(ii) for ii in np.linspace(0, 1, 45)] + + image = images[0:5] + + image = image.to(device) + image = image.unsqueeze(0) + recon_combined, masks,_, slots= model(image) + masks = masks.detach() + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = 45) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + cur_image = F.interpolate(image, (3,120,242)) + + masks = masks[0] + cur_image = cur_image[0] + for j in range(5): + image_j = cur_image[j].permute(1,2,0).cpu().numpy() + image_j = image_j * 0.5 + 0.5 + masks_j = masks[j] + tk = 44 + + masks_j = masks_j.cpu().numpy() + image_j = image_j[:,:,-1::-1] + + fig = plt.figure(frameon=False) + ax = plt.Axes(fig, [0., 0., 1., 1.]) + ax.axis('off') + fig.add_axes(ax) + ax.imshow(image_j, alpha = 1) + for seg in range(tk): + # fig = plt.figure(frameon=False) + # ax = plt.Axes(fig, [0., 0., 1., 1.]) + # ax.axis('off') + # fig.add_axes(ax) + # ax.imshow(image_j, alpha = 1) + msk = masks_j[seg] + threshold = 0 + + e = (msk > threshold).astype('uint8') + contour, hier = cv2.findContours( + e.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + cmax = None + for c in contour: + if cmax is None: + cmax = c + if len(c) > len(cmax): + cmax = c + if cmax is not None: + polygon = Polygon( + cmax.reshape((-1, 2)), + fill=True, facecolor=colors[seg], + edgecolor='w', linewidth=1.0, + alpha=0.5) + ax.add_patch(polygon) + fig.savefig(output + '/DIODPD_500_{}-{}.png'.format(nb_scene, j)) + plt.close(fig) diff --git a/README.md b/README.md new file mode 100644 index 0000000..cd04bf8 --- /dev/null +++ b/README.md @@ -0,0 +1,108 @@ +# Self Distillation Meets Object Discovery + +[Paper](https://openaccess.thecvf.com/content/CVPR2024/html/Kara_DIOD_Self-Distillation_Meets_Object_Discovery_CVPR_2024_paper.html) | [Video](https://www.youtube.com/watch?v=2uC4jSAt6-U) + +This repository is the official implementation of [**DIOD: Self-Distillation Meets Object Discovery**](https://openaccess.thecvf.com/content/CVPR2024/html/Kara_DIOD_Self-Distillation_Meets_Object_Discovery_CVPR_2024_paper.html), published at CVPR 2024. + +![Method Scheme](DIOD.png) + +## Set Up + +Use the following command to create the environment from the DIOD_env.yml file: + +```bash +conda env create -f DIOD_env.yml +``` + +## Datasets + +To train/evaluate DIOD, please download the required datasets along with the pseudo-labels shared by [DOM](https://github.com/zpbao/Discovery_Obj_Move): + +- [TRI-PD](https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_Discovering_Objects_That_Can_Move_CVPR_2022_paper.pdf) +- [KITTI-train](https://www.cvlibs.net/datasets/kitti/raw_data.php), and [KITTI-test](https://www.cvlibs.net/datasets/kitti/eval_instance_seg.php?benchmark=instanceSeg2015) + +## Training + +**On TRI-PD:** + +- For burn-in phase, run: + + ```bash + # set start_teacher > num_epochs not to run distillation, example: + python trainPD_ts.py --num_epochs 500\ + --start_teacher 501 + ``` + +- For teacher-student training, run: + + ```bash + python trainPD_ts.py --start_teacher 0\ + --burn_in_exp 'your_burn_in_experiment_directory'\ + --burn_in_ckpt 'ckpt_name' + ``` + +- Our used configuration is set as default values. +- We provide [here](https://drive.google.com/drive/u/0/folders/1OJMQeH4gJu9D1aF9lQ9azs7tvkXW_SZJ) our model checkpoint at the end of burn in. It can be used to directly run distillation. Example: + ```bash + python trainPD_ts.py --start_teacher 0\ + --burn_in_exp 'checkpoints'\ + --burn_in_ckpt 'DIODPD_burn_in_400.ckpt' + ``` + +**On KITTI:** + +Models trained on KITTI are initialized from TRI-PD experiment, so for KITTI directly run: + + ```bash + python trainKITTI_ts.py --start_teacher 0\ + --burn_in_exp 'TRI-PD_experiment_ckpt_directory'\ + --burn_in_ckpt 'ckpt_name' + ``` +- The used configuration is set as default values. +- Example using our checkpoint **DIODPD_500.ckpt** provided [here](https://drive.google.com/drive/u/0/folders/1OJMQeH4gJu9D1aF9lQ9azs7tvkXW_SZJ): +```bash +python trainKITTI_ts.py --start_teacher 0\ +--burn_in_exp 'checkpoints'\ +--burn_in_ckpt 'DIODPD_500.ckpt' +``` + +## Evaluation +- We provide [here](https://drive.google.com/drive/u/0/folders/1OJMQeH4gJu9D1aF9lQ9azs7tvkXW_SZJ) the checkpoints for our trained models. + +For fg-ARI and all-ARI, run: + +```bash +python evalPD_ARI.py # for TRI-PD +python evalKITTI_ARI.py # for KITTI +``` + +For F1 score, run: +```bash +python evalPD_F1_score.py # for TRI-PD +python evalKITTI_F1_score.py # for KITTI +``` + + +**DIOD_DINOv2 is the version of DIOD that uses a ViT-S14 backbone pre-trained with DINOv2. Inside this directory, you can use the same commands provided above to run this version.** + +## Acknowledgements + +This code is built upon the [DOM](https://github.com/zpbao/Discovery_Obj_Move) codebase, and uses pre-trained models from [DINOv2](https://github.com/facebookresearch/dinov2). We thank the authors for their great work and for sharing their code/pre-trained models. + +## Citation + +```bibtex +@inproceedings{kara2024diod, + title={DIOD: Self-Distillation Meets Object Discovery}, + author={Kara, Sandra and Ammar, Hejer and Denize, Julien and Chabot, Florian and Pham, Quoc-Cuong}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={3975--3985}, + year={2024} +} +``` + +## License + +This project is under the CeCILL license 2.1. + + diff --git a/create_mp4.py b/create_mp4.py new file mode 100644 index 0000000..5c09fa7 --- /dev/null +++ b/create_mp4.py @@ -0,0 +1,117 @@ +from io import BytesIO +from moviepy.editor import ImageSequenceClip +from PIL import ImageFont, ImageDraw, Image +import numpy as np +import os +import re + +def atoi(text): + return int(text) if text.isdigit() else text + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + ''' + return [ atoi(c) for c in re.split(r'(\d+)', text) ] + +def load_frames_from_directory(directory, input = False): + frames = [] + frames_paths = os.listdir(directory) + # print(sorted(frames_paths)) + + frames_paths.sort(key=natural_keys) + print(frames_paths) + for f in frames_paths: + frame_path = os.path.join(directory, f) # Assuming the frames are named as 'frame0.png', 'frame1.png', etc. + frame = Image.open(frame_path) + if input: + frame = frame.resize((640, 320), Image.ANTIALIAS) + frames.append(frame) + return frames + +frames1 = load_frames_from_directory('/home/sandra/Documents/diod-main/demo_cvpr_supp_mat/Input', 0) +frames2 = load_frames_from_directory('/home/sandra/Documents/diod-main/demo_cvpr_supp_mat/DOM') +frames3 = load_frames_from_directory('/home/sandra/Documents/diod-main/demo_cvpr_supp_mat/BMOD') +frames4 = load_frames_from_directory('/home/sandra/Documents/diod-main/demo_cvpr_supp_mat/DIOD') + + +# Define caption texts for each frame set +captions = ["Input sequence", "Bao et al. [1]", "BMOD [15]", "DIOD (ours)"] + +# Define the size of the caption area +caption_height = 30 + +# Define a font for the caption text with the new size +try: + # Use a nice font + font = ImageFont.truetype("LiberationSans-Regular.ttf", size=30) +except IOError: + # Fallback to the default PIL font if it's not found + font = ImageFont.load_default() + +# Define margins +horizontal_margin = 10 # Space between images horizontally +vertical_margin = 10 # Space between images vertically + +# Determine the size of the combined image +frame_width = max(frame.width for frame in frames1) +frame_height = max(frame.height for frame in frames1) + +# The width of the canvas should be the width of three frames plus two horizontal margins +canvas_width = 3 * frame_width + 2 * horizontal_margin + +# The height of the canvas should be twice the frame height plus a vertical margin and caption heights +canvas_height = 2 * frame_height + vertical_margin + 2 * caption_height + +# Create a list to hold the combined frames +combined_clips = [] +frame_arrays = [] + +for i in range(len(frames1)): + # Create a new blank image with a white background + new_im = Image.new('RGB', (canvas_width, canvas_height), (255, 255, 255)) + + # Paste the single frame on the top row, centered + new_im.paste(frames1[i], (canvas_width // 2 - frame_width // 2, 0)) + + # Paste the three frames on the second row + new_im.paste(frames2[i], (0, frame_height + vertical_margin)) + new_im.paste(frames3[i], (frame_width + horizontal_margin, frame_height + vertical_margin)) + new_im.paste(frames4[i], (2 * frame_width + 2 * horizontal_margin, frame_height + vertical_margin)) + + # Initialize ImageDraw to add text + draw = ImageDraw.Draw(new_im) + + y_offset = 100 + + # Draw the caption for the single frame on the first row, centered + draw.text( + (canvas_width // 2 - draw.textsize(captions[0], font=font)[0] // 2, frame_height - y_offset + 30), + captions[0], + font=font, + fill="black" + ) + + # Draw the captions for the three frames on the second row + draw.text((frame_width // 2 - draw.textsize(captions[1], font=font)[0] // 2, canvas_height - caption_height- y_offset), captions[1], font=font, fill="black") + draw.text((3 * frame_width // 2 + horizontal_margin - draw.textsize(captions[2], font=font)[0] // 2, canvas_height - caption_height - y_offset), captions[2], font=font, fill="black") + draw.text((5 * frame_width // 2 + 2 * horizontal_margin - draw.textsize(captions[3], font=font)[0] // 2, canvas_height - caption_height - y_offset), captions[3], font=font, fill="black") + + # Save the combined image to a file-like object + combined_image_io = BytesIO() + new_im.save(combined_image_io, format='PNG') + combined_image_io.seek(0) + + # Convert BytesIO back to a PIL image + pil_image = Image.open(combined_image_io) + + # Convert PIL image to numpy array and append to the list + frame_arrays.append(np.array(pil_image)) + +# Use moviepy to create a clip from this combined image +clip = ImageSequenceClip(frame_arrays, fps=5) # fps=1 if we want each image to show for 1 second + +# Write the result to a file +output_path = '/home/sandra/Documents/diod-main/demo_cvpr_supp_mat/combined_animation3.mp4' +clip.write_videofile(output_path, codec='libx264') \ No newline at end of file diff --git a/datasetKITTI.py b/datasetKITTI.py new file mode 100644 index 0000000..d6b2e85 --- /dev/null +++ b/datasetKITTI.py @@ -0,0 +1,88 @@ +import os +import random +from typing import Callable +import torch +from torchvision import transforms +from torch.utils.data import Dataset + +import random +import cv2 + + +class KITTIDataset(Dataset): + def __init__( + self, + split='train', + root = None, + resolution = (1248,368), + transform: Callable = None, + apply_img_transform: bool = True + ): + super(KITTIDataset, self).__init__() + self.resolution = (resolution[1],resolution[0]) + self.dresolution = (resolution[1]//4, resolution[0]//4) + self.root_dir = root + self.files = os.listdir(self.root_dir) + self.files.sort() + self.files = self.files[:151] + if split == 'train': + self.files = self.files[5:] + else: + self.files = self.files[0:5] + self.real_files = [] + self.mask_files = [] + self.flow_files = [] + self.depth_files = [] + for f in self.files: + for i in ['image_02','image_03']: + if os.path.exists(os.path.join(self.root_dir,f+'/{}/'.format(i))): + self.real_files.append(f+'/{}/data'.format(i)) + self.mask_files.append(f+'/{}/raft_seg'.format(i)) + self.img_transform = transforms.Compose([ + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + self.apply_image_transform = apply_img_transform + self.transform = transform + + def __getitem__(self, index): + path = self.real_files[index] + mask_path = self.mask_files[index] + + all_images = os.listdir(os.path.join(self.root_dir,path)) + all_images.sort() + N = len(all_images) + # print('N=', N, path) + rand_id = random.randint(0,N-10) + # rand_id = 0 + real_idx = [rand_id + j for j in range(5)] + ims = [] + masks = [] + for idd in real_idx: + image = cv2.imread(os.path.join(os.path.join(self.root_dir, path),all_images[idd])) + mask = cv2.imread(os.path.join(os.path.join(self.root_dir, mask_path),all_images[idd]),-1) + + image = cv2.resize(image, self.resolution, interpolation = cv2.INTER_LINEAR) + # mask = cv2.resize(mask, self.dresolution, interpolation = cv2.INTER_NEAREST) + mask = cv2.resize(mask, self.resolution, interpolation = cv2.INTER_NEAREST) + + mask = torch.Tensor(mask) + image = torch.Tensor(image) + # image = self.img_transform(image) + ims.append(image) + masks.append(mask) + + masks = torch.stack(masks).long() + ims = torch.stack(ims).float() + ims /= 255.0 + ims = ims.permute(0, 3, 1, 2) + sample = {'image': ims, 'mask':masks} + + if self.transform is not None: + sample = self.transform(sample) + elif self.apply_image_transform: + sample["image"] = self.img_transform(sample["image"]) + + return sample + + + def __len__(self): + return len(self.real_files) \ No newline at end of file diff --git a/datasetKITTIEval.py b/datasetKITTIEval.py new file mode 100644 index 0000000..fa2dcc6 --- /dev/null +++ b/datasetKITTIEval.py @@ -0,0 +1,62 @@ +import os +import random +import json +import numpy as np +from PIL import Image +import torch +from torchvision import transforms +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.dataloader import default_collate +import torch.nn.functional as F + +from torchvision.io import read_video +import random +import cv2 +import math + +resolution = (1248,368) + +dresolution = (312,92) + + +class KITTIDataset(Dataset): + def __init__(self, split='train', root = None): + super(KITTIDataset, self).__init__() + self.resolution = resolution + self.root_dir = root + + self.rgb_dir = os.path.join(self.root_dir,'rgb') + self.instance_dir = os.path.join(self.root_dir,'instance') + + + self.files = os.listdir(self.rgb_dir) + self.files.sort() + + if split == 'eval': + self.files = self.files[0:5] + + self.img_transform = transforms.Compose([ + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + + + def __getitem__(self, index): + path = self.files[index] + image = cv2.imread(os.path.join(self.rgb_dir,path)) + mask = cv2.imread(os.path.join(self.instance_dir,path),-1) + + image = cv2.resize(image, resolution, interpolation = cv2.INTER_LINEAR) + mask = cv2.resize(mask, dresolution, interpolation = cv2.INTER_NEAREST) + + mask = torch.Tensor(mask).long() + image = torch.Tensor(image).float() + + image = image / 255.0 + image = image.permute(2,0,1) + image = self.img_transform(image) + + sample = {'image': image, 'mask':mask} + return sample + + + def __len__(self): + return len(self.files) diff --git a/datasetPD.py b/datasetPD.py new file mode 100644 index 0000000..6ba733a --- /dev/null +++ b/datasetPD.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +import os +import random +from typing import Callable +import torch +from torch.utils.data import Dataset +from torchvision import transforms +import cv2 +import math +import numpy as np + +banned_scenes = ['scene_000100','scene_000002','scene_000008','scene_000012','scene_000018','scene_000029', +'scene_000038','scene_000040','scene_000043','scene_000044','scene_000049','scene_000050','scene_000053','scene_000063', +'scene_000079','scene_000090','scene_000094','scene_000100','scene_000103','scene_000106','scene_000111','scene_000112', +'scene_000124','scene_000125','scene_000127','scene_000148','scene_000159','scene_000166','scene_000169', +'scene_000170','scene_000171','scene_000187', 'scene_000191','scene_000200','scene_000202','scene_000217', +'scene_000218','scene_000225','scene_000229','scene_000232','scene_000236','scene_000237','scene_000245', +'scene_000249' +] + +class PDDataset(Dataset): + def __init__( + self, + split='train', + root = None, + supervision = 'moving', + transform: Callable = None, + apply_img_transform: bool = True + ): + super(PDDataset, self).__init__() + self.root_dir = root + self.files = os.listdir(self.root_dir) + self.files.sort() + if split == 'train': + self.files = self.files[1:] + elif split == 'eval': + self.files = self.files[0:1] + else: + self.files = self.files + self.annotation = None + if supervision == 'moving': + self.annotation = 'moving_masks' + elif supervision == 'all': + self.annotation = 'ari_masks' + elif supervision == 'est': + self.annotation = 'est_masks' + else: + raise ValueError("Need to choose from moving masks, all masks, or estimated masks. Or revise the code for customized setting.") + self.real_files = [] + self.mask_files = [] + for f in self.files: + if f in banned_scenes: + continue + for i in [1,5,6,7,8,9]: + if os.path.exists(os.path.join(self.root_dir,f+'/rgb/camera_0{}'.format(i))): + self.real_files.append(f+'/rgb/camera_0{}'.format(i)) + self.mask_files.append(f+'/{}/camera_0{}'.format(self.annotation, i)) + + self.img_transform = transforms.Compose([ + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + self.apply_image_transform = apply_img_transform + self.transform = transform + + def __getitem__(self, index): + path = self.real_files[index] + mask_path = self.mask_files[index] + all_images = os.listdir(os.path.join(self.root_dir,path)) + all_images.sort() + rand_id = random.randint(0,190) + real_idx = [rand_id + j for j in range(5)] + ims = [None for _ in range(len(real_idx))] + masks = [None for _ in range(len(real_idx))] + for idx, idd in enumerate(real_idx): + mapping = {0:0} + mapping_count = 1 + image = cv2.imread(os.path.join(os.path.join(self.root_dir, path),all_images[idd])) + mask = cv2.imread(os.path.join(os.path.join(self.root_dir, mask_path),all_images[idd]),-1) + downsampling_ratio = 0.5 + crop = 128 + width = int(math.ceil(image.shape[1] * downsampling_ratio)) + height = int(math.ceil(image.shape[0] * downsampling_ratio)) + dim = (width, height) + image = cv2.resize(image, dim, interpolation = cv2.INTER_LINEAR) + image = image[crop:, :, :] + mask = cv2.resize(mask, dim, interpolation = cv2.INTER_NEAREST) + mask = mask[crop:,:] + + values, indices, counts = np.unique(mask, return_inverse=True, return_counts=True) + for i in range(len(values)): + if values[i] not in mapping: + if counts[i] > 50: + mapping[values[i]] = mapping_count + mapping_count += 1 + cur_mapping = [] + for value in values: + if value not in mapping: + cur_mapping.append(0) + else: + cur_mapping.append(mapping[value]) + cur_mapping = np.array(cur_mapping) + _h, _w = mask.shape + mask = cur_mapping[indices].reshape((_h, _w)) + + mask = torch.Tensor(mask).long() + image = torch.Tensor(image).float() + ims[idx] = image + masks[idx] = mask + + ims = torch.stack(ims) + masks = torch.stack(masks) + + ims /= 255.0 + ims = ims.permute(0, 3, 1, 2) + + sample = {'image': ims, 'mask': masks} + + if self.transform is not None: + sample = self.transform(sample) + elif self.apply_image_transform: + sample["image"] = self.img_transform(sample["image"]) + + return sample + + + def __len__(self): + return len(self.real_files) diff --git a/datasetPDEval.py b/datasetPDEval.py new file mode 100644 index 0000000..71f599b --- /dev/null +++ b/datasetPDEval.py @@ -0,0 +1,97 @@ +import os +import random +import json +import numpy as np +from PIL import Image +import torch +from torchvision import transforms +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.dataloader import default_collate +import torch.nn.functional as F + +from torchvision.io import read_video +import random +import cv2 +import math + +class PDDataset(Dataset): + def __init__(self, split='train', root = None): + super(PDDataset, self).__init__() + + self.root_dir = root + self.files = os.listdir(self.root_dir) + self.files.sort() + + if split == 'eval': + self.files = self.files[0:1] + elif split == 'test': + self.files = self.files + + self.real_files = [] + self.mask_files = [] + for f in self.files: + for i in [1,5,6,7,8,9]: + if os.path.exists(os.path.join(self.root_dir,f+'/rgb/camera_0{}'.format(i))): + self.real_files.append(f+'/rgb/camera_0{}'.format(i)) + self.mask_files.append(f+'/ari_masks/camera_0{}'.format(i)) + self.img_transform = transforms.Compose([ + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + + def __getitem__(self, index): + path = self.real_files[index] + mask_path = self.mask_files[index] + + all_images = os.listdir(os.path.join(self.root_dir,path)) + all_images.sort() + rand_id = 0 + real_idx = [rand_id + 1*j for j in range(200)] + ims = [] + masks = [] + mapping = {0:0} + mapping_count = 1 + for idd in real_idx: + image = cv2.imread(os.path.join(os.path.join(self.root_dir, path),all_images[idd])) + mask = cv2.imread(os.path.join(os.path.join(self.root_dir, mask_path),all_images[idd]),-1) + + downsampling_ratio = 0.5 + crop = 128 + width = int(math.ceil(image.shape[1] * downsampling_ratio)) + height = int(math.ceil(image.shape[0] * downsampling_ratio)) + dim = (width, height) + image = cv2.resize(image, dim, interpolation = cv2.INTER_LINEAR) + image = image[crop:, :, :] + mask = cv2.resize(mask, dim, interpolation = cv2.INTER_NEAREST) + mask = mask[crop:,:] + + values, indices, counts = np.unique(mask, return_inverse=True, return_counts=True) + for i in range(len(values)): + if values[i] not in mapping: + if counts[i] > 500: + mapping[values[i]] = mapping_count + mapping_count += 1 + cur_mapping = [] + for i in range(len(values)): + value = values[i] + if value not in mapping: + cur_mapping.append(0) + else: + cur_mapping.append(mapping[value]) + cur_mapping = np.array(cur_mapping) + _h, _w = mask.shape + mask = cur_mapping[indices].reshape((_h, _w)) + + mask = torch.Tensor(mask).long() + image = torch.Tensor(image).float() + image = image / 255.0 + image = image.permute(2,0,1) + image = self.img_transform(image) + ims.append(image) + masks.append(mask) + ims = torch.stack(ims) + masks = torch.stack(masks) + sample = {'image': ims, 'mask':masks} + return sample + + + def __len__(self): + return len(self.real_files) diff --git a/evalKITTI_ARI.py b/evalKITTI_ARI.py new file mode 100644 index 0000000..31689f3 --- /dev/null +++ b/evalKITTI_ARI.py @@ -0,0 +1,82 @@ +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetKITTIEval import KITTIDataset +from utils import adjusted_rand_index as ARI + +from models.model_bg import * + +parser = argparse.ArgumentParser() + +parser.add_argument + +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODKITTI_100.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_test', type = str, help = 'path of KITTI test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') + +def main(): + opt = parser.parse_args() + resolution = (368, 1248) + model_path = opt.ckpt_path + data_path = opt.test_path + + + test_set = KITTIDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, 3).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=8, + shuffle=True, num_workers=4, drop_last=False) + + ARIs, allARIs = [], [] + + for sample in tqdm(test_dataloader): + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ ,_= model(image) + masks = masks.detach().cpu() + + for i in range(8): + gt_msk = mask_gt[i] + pred_msk = masks[i] + gt_msk = gt_msk.view(1,-1) + pred_msk = pred_msk.view(1,opt.num_slots,-1).permute(1,0,2) + + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + + _, n_cat = gt_msk.shape + if n_cat <= 2: + continue + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + gt_msk = gt_msk[:,1:] + + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + print('ari', ari, 'all-ari', all_ari ) + + allARIs.append(all_ari) + ARIs.append(ari) + del image, masks, mask_gt + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final all-ARI:',sum(allARIs) / len(allARIs)) + +if __name__ == '__main__': + main() diff --git a/evalPD_ARI.py b/evalPD_ARI.py new file mode 100644 index 0000000..82b68c7 --- /dev/null +++ b/evalPD_ARI.py @@ -0,0 +1,89 @@ +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetPDEval import PDDataset +from utils import adjusted_rand_index as ARI +import matplotlib.pyplot as plt + + +from models.model_bg import * + +parser = argparse.ArgumentParser() + +parser.add_argument + + +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODPD_500.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/test_video', type = str, help = 'path of PD test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') + +resolution = (480, 968) + +def main(): + opt = parser.parse_args() + model_path = opt.ckpt_path + data_path = opt.test_path + test_set = PDDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, 3).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + ARIs, all_ARIs = [], [] + + for idx, sample in enumerate(tqdm(test_dataloader)): + + image = sample['image'].to(device) + mask_gt = sample['mask'] + + + mask_gts = F.interpolate(mask_gt.float(), (120, 242)).long() + + for i in range(40): + _, masks, _, _= model(image[:,i*5:i*5+5,:,:,:]) + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + gt_msk = mask_gt[0] + pred_msk = masks[0] + gt_msk = gt_msk.view(5,-1) + pred_msk = pred_msk.view(5,opt.num_slots,-1).permute(1,0,2) + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + _,n_cat = gt_msk.shape + if n_cat <= 2: + continue + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + gt_msk = gt_msk[:,1:] + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + + # one image in the val_set is causing instability in computing ari. Discarded + if not ari == ari: + print('discarded') + continue + + all_ARIs.append(all_ari) + ARIs.append(ari) + del image, mask_gt, masks + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final allARI:', sum(all_ARIs) / len(all_ARIs)) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/evalPD_F1_score.py b/evalPD_F1_score.py new file mode 100644 index 0000000..74203af --- /dev/null +++ b/evalPD_F1_score.py @@ -0,0 +1,269 @@ +import numpy as np +import torch +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetPDEval import PDDataset +from utils import adjusted_rand_index as ARI +import matplotlib.pyplot as plt +from models.model_bg import * + +EPS = 1e-10 + + +parser = argparse.ArgumentParser() +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODPD_500.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/test_video', type = str, help = 'path of PD test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--is_bg_handled', default=True, type=bool, help='is_bg_handled') + + +resolution = (480, 968) + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + + +def calculate_iou(mask1, set_masks): + # Initialize a list to store the IoU values + iou_scores = [] + + # Convert masks to NumPy arrays + mask1 = np.array(mask1, dtype=bool) + + for mask2 in set_masks: + mask2 = np.array(mask2, dtype=bool) + + # Calculate intersection and union + intersection = np.logical_and(mask1, mask2) + union = np.logical_or(mask1, mask2) + + # Compute IoU + iou = np.sum(intersection) / np.sum(union) + iou_scores.append(iou) + + return iou_scores + +def voc_eval(gt_masks, + pred_scores, pred_masks, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + + + nb_images = len(pred_masks) #added + image_ids = [] + class_recs = {} + nb_gt = 0 + + for im in range(nb_images): + image_ids += [im]*len(pred_masks[im]) + class_recs[im] = [False] * len(gt_masks[im]) + nb_gt += len(gt_masks[im]) + + + + # flatten preds and scores + pred_scores_flat = np.array([item for sublist in pred_scores for item in sublist]) + + pred_masks_flat = np.stack([item for sublist in pred_masks for item in sublist]) + + + # sort by confidence + sorted_ind = np.argsort(-pred_scores_flat) + pred_masks_flat = pred_masks_flat[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + mask = pred_masks_flat[d] + ovmax = -np.inf + MASKSGT = gt_masks[image_ids[d]] + + + # compute overlaps + overlaps = calculate_iou(mask, MASKSGT) + + # case of no gt in one frame + if not len(overlaps): + ovmax = 0 + else: + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + + if ovmax > ovthresh: + if not R[jmax]: + tp[d] = 1. + R[jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + + rec = tp / float(nb_gt) + + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + +def compute_f1(precision, recall): + if precision + recall == 0: + return 0.0 + return 2 * (precision * recall) / (precision + recall) + + +def main(): + opt = parser.parse_args() + print('evaluating', opt.ckpt_path) + model_path = opt.ckpt_path + data_path = opt.test_path + + test_set = PDDataset(split = 'test', root = data_path) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + + active_slots, scores, all_gt = [], [], [] + + + nb_samples = 0 + for sample in tqdm(test_dataloader): + nb_samples += 1 + image = sample['image'].to(device) + mask_gt = sample['mask'] + + + mask_gts = F.interpolate(mask_gt.float(), (120, 242)).long() + + # evaluate our method with background modeling (within last slot) + if opt.is_bg_handled: + + for i in range(40): + _, masks, _ , _= model(image[:,i*5:i*5+5,:,:,:]) + + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + + for fm in range(5): + + gt_msk = F.one_hot(mask_gt[0,fm,:,:]) + + pred_msk = F.one_hot(masks[0,fm,:,:,:].argmax(dim=0, keepdim=False)) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[0,fm,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + + # evaluate other baselines w/o background modeling + else: + for i in range(40): + _, masks, _ = model(image[:,i*5:i*5+5,:,:,:]) + + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + for fm in range(5): + + gt_msk = F.one_hot(mask_gt[0,fm,:,:]) + + pred_msk = F.one_hot(masks[0,fm,:,:,:].argmax(dim=0, keepdim=False), num_classes=opt.num_slots) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[0,fm,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + del image, mask_gt, masks + + + rec, prec, _ = voc_eval(all_gt, scores, active_slots, 0.5) + f1_score = compute_f1(prec[-1], rec[-1]) + print(f"F1 Score: {f1_score}") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/eval_KITTI_F1_score.py b/eval_KITTI_F1_score.py new file mode 100644 index 0000000..70480ed --- /dev/null +++ b/eval_KITTI_F1_score.py @@ -0,0 +1,275 @@ +import numpy as np +import torch +import os +import argparse +from tqdm import tqdm +import torch.nn as nn +import torch.nn.functional as F +from datasetKITTIEval import KITTIDataset +from utils import adjusted_rand_index as ARI +from models.model_bg import * + +EPS = 1e-10 + + +parser = argparse.ArgumentParser() +parser.add_argument('--ckpt_path', default='/home/users/skara/check_release/checkpoints/DIODKITTI_100.ckpt', type=str, help='pre-trained model path' ) +parser.add_argument('--test_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_test', type = str, help = 'path of KITTI test set') +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--is_bg_handled', default=True, type=bool, help='is_bg_handled') + + +resolution = (368, 1248) + + + +def voc_ap(rec, prec, use_07_metric=False): + """Compute VOC AP given precision and recall. If use_07_metric is true, uses + the VOC 07 11-point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + + +def calculate_iou(mask1, set_masks): + # Initialize a list to store the IoU values + iou_scores = [] + + # Convert masks to NumPy arrays + mask1 = np.array(mask1, dtype=bool) + + for mask2 in set_masks: + mask2 = np.array(mask2, dtype=bool) + + # Calculate intersection and union + intersection = np.logical_and(mask1, mask2) + union = np.logical_or(mask1, mask2) + + # Compute IoU + iou = np.sum(intersection) / np.sum(union) + iou_scores.append(iou) + + return iou_scores + +def voc_eval(gt_masks, + pred_scores, pred_masks, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + + Top level function that does the PASCAL VOC evaluation. + + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + + + nb_images = len(pred_masks) #added + image_ids = [] + class_recs = {} + nb_gt = 0 + + for im in range(nb_images): + image_ids += [im]*len(pred_masks[im]) + class_recs[im] = [False] * len(gt_masks[im]) + nb_gt += len(gt_masks[im]) + + + + # flatten preds and scores + pred_scores_flat = np.array([item for sublist in pred_scores for item in sublist]) + + + pred_masks_flat = np.stack([item for sublist in pred_masks for item in sublist]) + + + + # sort by confidence + sorted_ind = np.argsort(-pred_scores_flat) + pred_masks_flat = pred_masks_flat[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + mask = pred_masks_flat[d] + ovmax = -np.inf + MASKSGT = gt_masks[image_ids[d]] + + + # compute overlaps + overlaps = calculate_iou(mask, MASKSGT) + + # case of no gt in one frame + if not len(overlaps): + ovmax = 0 + else: + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + + if ovmax > ovthresh: + if not R[jmax]: + tp[d] = 1. + R[jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + + # # check F1 score + # print('check_FP = ', fp[-1]) + # print('check_TP = ', tp[-1]) + # print('check_FN = ', nb_gt - tp[-1]) + # print('check_F1_score', 2*tp[-1]/(2*tp[-1] + fp[-1] + nb_gt - tp[-1])) + + + + rec = tp / float(nb_gt) + + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + +def compute_f1(precision, recall): + if precision + recall == 0: + return 0.0 + return 2 * (precision * recall) / (precision + recall) + + +def main(): + opt = parser.parse_args() + model_path = opt.ckpt_path + data_path = opt.test_path + test_set = KITTIDataset(split = 'test', root = data_path) + + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim).to(device) + model = nn.DataParallel(model) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + + print('model load finished!') + + for param in model.module.parameters(): + param.requires_grad = False + + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=8, + shuffle=False, num_workers=4, drop_last=False) + + + active_slots, scores, all_gt = [], [], [] + + + nb_samples = 0 + for sample in tqdm(test_dataloader): + # evaluate our method with background modeling (within last slot) + if opt.is_bg_handled: + nb_samples += 1 + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ ,_= model(image) + masks = masks.detach().cpu() + + for i in range(8): + + gt_msk = F.one_hot(mask_gt[i,0,:,:]) + + pred_msk = F.one_hot(masks[i,0,:,:,:].argmax(dim=0, keepdim=False)) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[i,0,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots-1) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + # evaluate other baselines w/o background modeling + else: + nb_samples += 1 + image = sample['image'].to(device) + image = image.unsqueeze(1) + mask_gt = sample['mask'] + mask_gt = mask_gt.unsqueeze(1) + + _, masks, _ = model(image) + masks = masks.detach().cpu() + + for i in range(8): + + gt_msk = F.one_hot(mask_gt[i,0,:,:]) + + pred_msk = F.one_hot(masks[i,0,:,:,:].argmax(dim=0, keepdim=False), num_classes=opt.num_slots) + + active_slots.append([pred_msk[:,:,s] for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + scores.append([masks[i,0,s,:,:][pred_msk[:,:,s] == 1].mean() for s in range(opt.num_slots) if pred_msk[:,:,s].max().item()>0]) + + all_gt.append([gt_msk[:,:,s] for s in range(1, gt_msk.shape[2]) if gt_msk[:,:,s].max().item()>0]) + + del image, mask_gt, masks + + + rec, prec, _ = voc_eval(all_gt, scores, active_slots, 0.5) + f1_score = compute_f1(prec[-1], rec[-1]) + print(f"F1 Score: {f1_score}") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/models/ConvGRU.py b/models/ConvGRU.py new file mode 100644 index 0000000..e6a8552 --- /dev/null +++ b/models/ConvGRU.py @@ -0,0 +1,232 @@ +import os +import torch +from torch import nn +from torch.autograd import Variable + + +class ConvGRUCell(nn.Module): + def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias, dtype): + """ + Initialize the ConvLSTM cell + :param input_size: (int, int) + Height and width of input tensor as (height, width). + :param input_dim: int + Number of channels of input tensor. + :param hidden_dim: int + Number of channels of hidden state. + :param kernel_size: (int, int) + Size of the convolutional kernel. + :param bias: bool + Whether or not to add the bias. + :param dtype: torch.cuda.FloatTensor or torch.FloatTensor + Whether or not to use cuda. + """ + super(ConvGRUCell, self).__init__() + self.height, self.width = input_size + self.padding = kernel_size[0] // 2, kernel_size[1] // 2 + self.hidden_dim = hidden_dim + self.bias = bias + self.dtype = dtype + + self.conv_gates = nn.Conv2d(in_channels=input_dim + hidden_dim, + out_channels=2*self.hidden_dim, # for update_gate,reset_gate respectively + kernel_size=kernel_size, + padding=self.padding, + bias=self.bias) + + self.conv_can = nn.Conv2d(in_channels=input_dim+hidden_dim, + out_channels=self.hidden_dim, # for candidate neural memory + kernel_size=kernel_size, + padding=self.padding, + bias=self.bias) + + def init_hidden(self, batch_size): + return (Variable(torch.zeros(batch_size, self.hidden_dim, self.height, self.width)).type(self.dtype)) + + def forward(self, input_tensor, h_cur): + """ + :param self: + :param input_tensor: (b, c, h, w) + input is actually the target_model + :param h_cur: (b, c_hidden, h, w) + current hidden and cell states respectively + :return: h_next, + next hidden state + """ + combined = torch.cat([input_tensor, h_cur], dim=1) + combined_conv = self.conv_gates(combined) + + gamma, beta = torch.split(combined_conv, self.hidden_dim, dim=1) + reset_gate = torch.sigmoid(gamma) + update_gate = torch.sigmoid(beta) + + combined = torch.cat([input_tensor, reset_gate*h_cur], dim=1) + cc_cnm = self.conv_can(combined) + cnm = torch.tanh(cc_cnm) + + h_next = (1 - update_gate) * h_cur + update_gate * cnm + return h_next + + +class ConvGRU(nn.Module): + def __init__(self, input_size, input_dim, hidden_dim, kernel_size, num_layers, + dtype, batch_first=False, bias=True, return_all_layers=False): + """ + :param input_size: (int, int) + Height and width of input tensor as (height, width). + :param input_dim: int e.g. 256 + Number of channels of input tensor. + :param hidden_dim: int e.g. 1024 + Number of channels of hidden state. + :param kernel_size: (int, int) + Size of the convolutional kernel. + :param num_layers: int + Number of ConvLSTM layers + :param dtype: torch.cuda.FloatTensor or torch.FloatTensor + Whether or not to use cuda. + :param alexnet_path: str + pretrained alexnet parameters + :param batch_first: bool + if the first position of array is batch or not + :param bias: bool + Whether or not to add the bias. + :param return_all_layers: bool + if return hidden and cell states for all layers + """ + super(ConvGRU, self).__init__() + + # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers + kernel_size = self._extend_for_multilayer(kernel_size, num_layers) + hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers) + if not len(kernel_size) == len(hidden_dim) == num_layers: + raise ValueError('Inconsistent list length.') + + self.height, self.width = input_size + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.kernel_size = kernel_size + self.dtype = dtype + self.num_layers = num_layers + self.batch_first = batch_first + self.bias = bias + self.return_all_layers = return_all_layers + + cell_list = [] + for i in range(0, self.num_layers): + cur_input_dim = input_dim if i == 0 else hidden_dim[i - 1] + cell_list.append(ConvGRUCell(input_size=(self.height, self.width), + input_dim=cur_input_dim, + hidden_dim=self.hidden_dim[i], + kernel_size=self.kernel_size[i], + bias=self.bias, + dtype=self.dtype)) + + # convert python list to pytorch module + self.cell_list = nn.ModuleList(cell_list) + + def infer(self, input_tensor, hidden_state=None): + """ + :param input_tensor: (b, t, c, h, w) or (t,b,c,h,w) depends on if batch first or not + extracted features from alexnet + :param hidden_state: + :return: layer_output_list, last_state_list + """ + if not self.batch_first: + # (t, b, c, h, w) -> (b, t, c, h, w) + input_tensor = input_tensor.permute(1, 0, 2, 3, 4) + + # Implement stateful ConvLSTM + if hidden_state is None: + hidden_state = self._init_hidden(batch_size=input_tensor.size(0)) + + layer_output_list = [] + last_state_list = [] + hidden_state_out = [] + + seq_len = input_tensor.size(1) + cur_layer_input = input_tensor + + for layer_idx in range(self.num_layers): + h = hidden_state[layer_idx] + output_inner = [] + for t in range(seq_len): + # input current hidden and cell state then compute the next hidden and cell state through ConvLSTMCell forward function + h = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], # (b,t,c,h,w) + h_cur=h) + output_inner.append(h) + hidden_state_out.append(h) + + layer_output = torch.stack(output_inner, dim=1) + cur_layer_input = layer_output + + layer_output_list.append(layer_output) + last_state_list.append([h]) + + if not self.return_all_layers: + layer_output_list = layer_output_list[-1:] + last_state_list = last_state_list[-1:] + + return layer_output_list, last_state_list, hidden_state_out + + def forward(self, input_tensor, hidden_state=None): + """ + :param input_tensor: (b, t, c, h, w) or (t,b,c,h,w) depends on if batch first or not + extracted features from alexnet + :param hidden_state: + :return: layer_output_list, last_state_list + """ + if not self.batch_first: + # (t, b, c, h, w) -> (b, t, c, h, w) + input_tensor = input_tensor.permute(1, 0, 2, 3, 4) + + # Implement stateful ConvLSTM + if hidden_state is not None: + raise NotImplementedError() + else: + hidden_state = self._init_hidden(batch_size=input_tensor.size(0)) + + layer_output_list = [] + last_state_list = [] + + seq_len = input_tensor.size(1) + cur_layer_input = input_tensor + + for layer_idx in range(self.num_layers): + h = hidden_state[layer_idx] + output_inner = [] + for t in range(seq_len): + # input current hidden and cell state then compute the next hidden and cell state through ConvLSTMCell forward function + h = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], # (b,t,c,h,w) + h_cur=h) + output_inner.append(h) + + layer_output = torch.stack(output_inner, dim=1) + cur_layer_input = layer_output + + layer_output_list.append(layer_output) + last_state_list.append([h]) + + if not self.return_all_layers: + layer_output_list = layer_output_list[-1:] + last_state_list = last_state_list[-1:] + + return layer_output_list, last_state_list + + def _init_hidden(self, batch_size): + init_states = [] + for i in range(self.num_layers): + init_states.append(self.cell_list[i].init_hidden(batch_size)) + return init_states + + @staticmethod + def _check_kernel_size_consistency(kernel_size): + if not (isinstance(kernel_size, tuple) or + (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))): + raise ValueError('`kernel_size` must be tuple or list of tuples') + + @staticmethod + def _extend_for_multilayer(param, num_layers): + if not isinstance(param, list): + param = [param] * num_layers + return param + diff --git a/models/model_bg.py b/models/model_bg.py new file mode 100644 index 0000000..48326b7 --- /dev/null +++ b/models/model_bg.py @@ -0,0 +1,299 @@ +import numpy as np +from torch import nn +import torch +import torch.nn.functional as F + +import models.resnet as resnet +from models.ConvGRU import * +from math import ceil + + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +''' +Slot Attention module +differences from the original repo: +1. learnable slot initializtaion +2. pad for the first frame +Inputs --> [batch_size, number_of_frame, sequence_length, hid_dim] +outputs --> slots[batch_size, number_of_frame, num_of_slots, hid_dim]; attn_masks [batch_size, number_of_frame, num_of_slots, sequence_length] +''' +class SlotAttention(nn.Module): + def __init__(self, num_slots, dim, eps = 1e-8, hidden_dim = 128): + super().__init__() + self.dim = dim + self.num_slots = num_slots + self.eps = eps + self.scale = dim ** -0.5 + + # self.slots_mu = nn.Parameter(abs(torch.randn(1, 1, dim))).to(device) # put abs or not + self.slots_mu = nn.Parameter(torch.randn(1, 1, dim)).to(device) + self.slots_sigma = nn.Parameter(abs(torch.randn(1, 1, dim))).to(device) + # self.slots_sigma = self.slots_sigma.absolute() + + self.to_q = nn.Linear(dim, dim) + self.to_k = nn.Linear(dim, dim) + self.to_v = nn.Linear(dim, dim) + + hidden_dim = max(dim, hidden_dim) + + self.fc1 = nn.Linear(dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, dim) + self.gru = nn.GRUCell(dim, dim) + + self.norm_input = nn.LayerNorm(dim) + self.norm_slots = nn.LayerNorm(dim) + self.norm_pre_ff = nn.LayerNorm(dim) + + mu = self.slots_mu.expand(1, self.num_slots, -1) + sigma = self.slots_sigma.expand(1, self.num_slots, -1) + slots = torch.normal(mu, sigma) + + slots = slots.contiguous() + self.register_buffer("slots", slots) + + def get_attention(self, slots, inputs): + slots_prev = slots + b, n, d = inputs.shape + inputs = self.norm_input(inputs) + k, v = self.to_k(inputs), self.to_v(inputs) + slots = self.norm_slots(slots) + q = self.to_q(slots) + + dots = torch.einsum('bid,bjd->bij', q, k) * self.scale + attn_ori = dots.softmax(dim=1) + self.eps + attn = attn_ori / attn_ori.sum(dim=-1, keepdim=True) + + updates = torch.einsum('bjd,bij->bid', v, attn) + + slots = self.gru( + updates.reshape(-1, d), + slots_prev.reshape(-1, d) + ) + + slots = slots.reshape(b, -1, d) + slots = slots + self.fc2(F.relu(self.fc1(self.norm_pre_ff(slots)))) + + attn_ori_fg = 1-attn_ori[:,-1,:] + attn_ori_fg = attn_ori_fg.unsqueeze(1) + + return slots, attn_ori, attn_ori_fg + + def forward(self, inputs, num_slots = None): + b, nf, n, d = inputs.shape + slots_out = [] + attns = [] + attns_ori_fg = [] + slots = self.slots.expand(b,-1,-1) + # pre-attention for the first frame + slots, _, _ = self.get_attention(slots, inputs[:,0,:,:]) + for f in range(nf): + cur_slots, cur_attn, attn_ori_fg = self.get_attention(slots,inputs[:,f,:,:]) + slots_out.append(cur_slots) + attns.append(cur_attn) + attns_ori_fg.append(attn_ori_fg) + + slots = cur_slots + slots_out = torch.stack([slot for slot in slots_out]) + slots_out = slots_out.permute(1,0,2,3) + attns = torch.stack([attn for attn in attns]) + attns = attns.permute(1,0,2,3) + + attns_ori_fg = torch.stack([attn for attn in attns_ori_fg]) + attns_ori_fg = attns_ori_fg.permute(1,0,2,3) + return slots_out, attns, attns_ori_fg + + def infer(self, inputs, num_slots = None, slot_state = None): + b, nf, n, d = inputs.shape + slots_out = [] + attns = [] + slots = self.slots.expand(b,-1,-1) + if slot_state is None: + slots, _ = self.get_attention(slots, inputs[:,0,:,:]) + else: + slots = slot_state + for f in range(nf): + cur_slots, cur_attn = self.get_attention(slots,inputs[:,f,:,:]) + slots_out.append(cur_slots) + attns.append(cur_attn) + slots = cur_slots + slots_out = torch.stack([slot for slot in slots_out]) + slots_out = slots_out.permute(1,0,2,3) + attns = torch.stack([attn for attn in attns]) + attns = attns.permute(1,0,2,3) + return slots_out, attns, slots + + +def build_grid(resolution): + ranges = [torch.linspace(0.0, 1.0, steps=res) for res in resolution] + grid = torch.meshgrid(*ranges) + grid = torch.stack(grid, dim=-1) + grid = torch.reshape(grid, [resolution[0], resolution[1], -1]) + grid = grid.unsqueeze(0) + return torch.cat([grid, 1.0 - grid], dim=-1).to(device) + + +"""Adds soft positional embedding with learnable projection.""" +class SoftPositionEmbed(nn.Module): + def __init__(self, hidden_size, resolution): + """Builds the soft position embedding layer. + Args: + hidden_size: Size of input feature dimension. + resolution: Tuple of integers specifying width and height of grid. + """ + super().__init__() + self.embedding = nn.Linear(4, hidden_size, bias=True) + self.register_buffer("grid", build_grid(resolution)) + + def forward(self, inputs): + grid = self.embedding(self.grid) + return inputs + grid +''' +encoder +input: image [bs, num_frames, n_channels, H, W] +outputs: features [bs*num_frames, ceil(H*W // 16),hid_dim], 16 is the (downsample ratio)**2 +''' +class Encoder(nn.Module): + def __init__(self, resolution, hid_dim): + super().__init__() + self.resolution = resolution + self.hid_dim = hid_dim + self.resnet = resnet.resnet18(pretrained=False) + self.convGRU = ConvGRU(input_size=(ceil(resolution[0]/4), ceil(resolution[1]/4)), + input_dim=512, + hidden_dim=hid_dim, + kernel_size=(3,3), + num_layers=2, + dtype = torch.cuda.FloatTensor, + batch_first=True, + bias = True, + return_all_layers = False) + self.encoder_pos = SoftPositionEmbed(hid_dim, (ceil(resolution[0]/4), ceil(resolution[1]/4))) + + def forward(self, x): + bs, n_frames, N, H, W = x.shape + x = x.view(-1, N, H,W) + x = self.resnet(x) + x = x.view(bs, n_frames, x.shape[1], x.shape[2],x.shape[3]) + x,_ = self.convGRU(x) + x = x[0] + x = x.view(bs*n_frames, -1, x.shape[3], x.shape[4]) + x = F.relu(x) + x = x.permute(0,2,3,1) + x = self.encoder_pos(x) + x = torch.flatten(x, 1, 2) + return x + + def infer(self, x, h = None): + bs, n_frames, N, H, W = x.shape + x = x.view(-1, N, H,W) + x = self.resnet(x) + x = x.view(bs, n_frames, x.shape[1], x.shape[2],x.shape[3]) + x,_,h_out = self.convGRU.infer(x,h) + x = x[0] + x = x.view(bs*n_frames, -1, x.shape[3], x.shape[4]) + x = F.relu(x) + x = x.permute(0,2,3,1) + x = self.encoder_pos(x) + x = torch.flatten(x, 1, 2) + return x, h_out + +class Decoder(nn.Module): + def __init__(self, hid_dim, resolution, output_channel): + super().__init__() + self.conv1 = nn.ConvTranspose2d(hid_dim, hid_dim, 4, stride=(2, 2), padding=1) + self.conv2 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv3 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv4 = nn.ConvTranspose2d(hid_dim, hid_dim, 4, stride=(2, 2), padding=1) + self.conv5 = nn.ConvTranspose2d(hid_dim, hid_dim, 5, stride=(1, 1), padding=2) + self.conv6 = nn.ConvTranspose2d(hid_dim, output_channel, 3, stride=(1, 1), padding=1) + self.resolution = resolution + self.output_channel = output_channel + + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.relu(x) + x = self.conv3(x) + x = F.relu(x) + x = self.conv4(x) + x = F.relu(x) + x = self.conv5(x) + x = F.relu(x) + x = self.conv6(x) + x = x[:,:,:self.resolution[0], :self.resolution[1]] + return x + + +"""Slot Attention-based auto-encoder for object discovery.""" +class SlotAttentionAutoEncoder(nn.Module): + def __init__(self, resolution, num_slots, hid_dim, output_channel = 3): + """Builds the Slot Attention-based auto-encoder. + Args: + resolution: Tuple of integers specifying width and height of input image. + num_slots: Number of slots in Slot Attention. + hid_dim: dimension for ConvGRU and slot attention + dresolution: downsampled resolution --> resnet 18 downsampled with a factor of 4 + """ + super().__init__() + self.hid_dim = hid_dim + self.resolution = resolution + self.dresolution = (ceil(resolution[0]/4), ceil(resolution[1]/4)) + self.num_slots = num_slots + self.output_channel = output_channel + + self.encoder_cnn = Encoder(self.resolution, self.hid_dim) + self.decoder = Decoder(self.hid_dim, self.resolution, self.output_channel) + + self.fc1 = nn.Linear(hid_dim, hid_dim) + self.fc2 = nn.Linear(hid_dim, hid_dim) + + self.slot_attention = SlotAttention( + num_slots=self.num_slots, + dim=hid_dim, + eps = 1e-8, + hidden_dim = 128) + self.LN = nn.LayerNorm([self.dresolution[0] * self.dresolution[1], hid_dim]) + + def forward(self, image): + # `image` has shape: [batch_size, num_channels, width, height]. + bs, n_frames, dim,him,wim = image.shape + x = self.encoder_cnn(image) + x = self.LN(x) + x = self.fc1(x) + x = F.relu(x) + x = self.fc2(x) + # x shape: [bs * n_frames, width*height // 16, hiden_dim]. + _, H, W = x.shape + x = x.view(bs, n_frames, H, W) + + # Slot Attention module. + slots_ori, attn_masks, attn_fg = self.slot_attention(x) + # reshape and broadcaast attention masks + attn_masks = attn_masks.reshape(bs*n_frames,self.num_slots, -1) + attn_masks = attn_masks.view(attn_masks.shape[0],attn_masks.shape[1],self.dresolution[0], self.dresolution[1]) + attn_masks = attn_masks.unsqueeze(-1) + + # attn_masks = attn_masks.reshape(bs*n_frames,self.num_slots, -1) + # attn_masks = attn_masks.view(attn_masks.shape[0],attn_masks.shape[1],self.dresolution[0], self.dresolution[1]) + # attn_masks = attn_masks.unsqueeze(-1) + + attn_fg = attn_fg.reshape(bs*n_frames,1, -1) + attn_fg = attn_fg.view(attn_fg.shape[0],attn_fg.shape[1],self.dresolution[0], self.dresolution[1]) + + slots = slots_ori.reshape(bs*n_frames, self.num_slots, -1) + slots = slots.unsqueeze(2).unsqueeze(3) + slots_combine = slots * attn_masks + slots_combine = slots_combine.sum(dim = 1) + slots_combine = slots_combine.permute(0,3,1,2) + + # `slots_combine` has shape: [batch_size*num_slots, width_init, height_init, slot_size]. + recons = self.decoder(slots_combine) + + # recons = recons.view(bs, n_frames, 3, self.resolution[0],self.resolution[1]) + # `recons` has shape: [bs, n_frames, 3, H//4, W // 4] + + return recons, attn_masks.view(bs,n_frames, self.num_slots, attn_masks.shape[2], attn_masks.shape[3]),\ + attn_fg.view(bs,n_frames, 1, attn_masks.shape[2], attn_masks.shape[3]),slots_combine + diff --git a/models/resnet.py b/models/resnet.py new file mode 100644 index 0000000..08d3de0 --- /dev/null +++ b/models/resnet.py @@ -0,0 +1,391 @@ +import torch +from torch import Tensor +import torch.nn as nn +try: + from torch.hub import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url +from typing import Type, Any, Callable, Union, List, Optional + + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2'] + + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + + +def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion: int = 1 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion: int = 4 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x: Tensor) -> Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + + def __init__( + self, + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + num_classes: int = 1000, + zero_init_residual: bool = False, + groups: int = 1, + width_per_group: int = 64, + replace_stride_with_dilation: Optional[List[bool]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=1, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=1, + dilate=replace_stride_with_dilation[2]) + # self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + # self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] + + def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, + stride: int = 1, dilate: bool = False) -> nn.Sequential: + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + # x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + # x = self.avgpool(x) + # x = torch.flatten(x, 1) + # x = self.fc(x) + + return x + + def forward(self, x: Tensor) -> Tensor: + return self._forward_impl(x) + + +def _resnet( + arch: str, + block: Type[Union[BasicBlock, Bottleneck]], + layers: List[int], + pretrained: bool, + progress: bool, + **kwargs: Any +) -> ResNet: + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + return model + +def resnet10(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [1, 1, 1, 1], pretrained, progress, + **kwargs) + +def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + + +def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_. + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_. + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + diff --git a/trainKITTI_ts.py b/trainKITTI_ts.py new file mode 100644 index 0000000..3e149dc --- /dev/null +++ b/trainKITTI_ts.py @@ -0,0 +1,538 @@ +from __future__ import annotations + +from collections import OrderedDict +import os +import argparse +from tkinter import NONE +from tqdm import tqdm +import time +import datetime +import torch.optim as optim +import torch.nn as nn +import random +import matplotlib.pyplot as plt +import scipy.optimize +import torch.nn.functional as F +import numpy as np +import torch + +from datasetKITTI import KITTIDataset +from datasetKITTIEval import KITTIDataset as KITTIDatasetEval + +from models.model_bg import SlotAttentionAutoEncoder +from transforms import get_normalize_transform, get_ssl_train_online_transform, get_crop_transform +from utils import adjusted_rand_index as ARI +from utils import temporal_loss as t_loss + +from torch import Tensor +from torch.nn.utils import clip_grad_norm_ +from torchvision.transforms import InterpolationMode +from torch.nn.parameter import Parameter + +import math +from scipy.ndimage import label + +# Set the random seed for consistant comparisons/ablation studies and reproducibility +# No tuning was performed over multiple seeds +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) + +# Set CUDA deterministic flags +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + +torch.autograd.set_detect_anomaly(True) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +parser = argparse.ArgumentParser() + +parser.add_argument +# basic configurations +parser.add_argument('--model_dir', default='/home/data/skara/tmp/', type=str, help='where to save models' ) +parser.add_argument('--sample_dir', default = './samples/', type = str, help = 'where to save the plots') +parser.add_argument('--exp_name', default='check_release_train_KITTI_100', type=str, help='experiment name, used for model saving/plotting ect' ) +parser.add_argument('--num_workers', default=12, type=int, help='number of workers for loading data') +parser.add_argument('--seed', default=0, type=int, help='random seed') +parser.add_argument('--data_path', default = '/home/data/skara/KITTI_DOM/KITTI_DOM_train/', type = str, help = 'path of PD dataset') +parser.add_argument('--supervision', default = 'est', choices=['moving', 'all','est'], help = 'type of supervision, currently available: moving and all') +# model parameters +parser.add_argument('--batch_size', default=8, type=int) +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--learning_rate', default=0.0005, type=float) +parser.add_argument('--warmup_steps', default=3000, type=int, help='Number of warmup steps for the learning rate.') +parser.add_argument('--decay_rate', default=0.5, type=float, help='Rate for the learning rate decay.') +parser.add_argument('--decay_steps', default=50000, type=int, help='Number of steps for the learning rate decay.') +parser.add_argument('--num_epochs', default=101, type=int, help='number of workers for loading data') +parser.add_argument('--weight_mask', default = 1.0, type = float, help = 'weight for the mask loss') +parser.add_argument('--weight_NLL', default = 1.0, type = float, help = 'weight for the NLL loss') +parser.add_argument('--weight_reg', default = 0.3, type = float, help = 'weight for the regularization term') +parser.add_argument('--confid_th', default = 0.9, type = float, help = 'confidence threshold used to filter pseudo-labels during distillation') +# Data augmentations +parser.add_argument('--crop-size', default=[], type = int, nargs="*", help = "crop's size") +parser.add_argument('--crop-scale', default=[0.75, 1.0], type = int, nargs="+", help = "crop's size") +parser.add_argument('--crop-ratio', default=[2.0, 2.0], type = int, nargs="+", help = "crop's ratio") +parser.add_argument('--crop-interpolation', default="bilinear", type = str, help = "crop's interpolation") +parser.add_argument('--crop-interpolation-mask', default="nearest", type = str, help = "crop's interpolation mask") +parser.add_argument('--p-crop', default=0.4, type = float, help = "crop's probability") +parser.add_argument('--p-flip', default=0.4, type = float, help = "flip's probability") +parser.add_argument('--brightness', default=0.4, type = float, help = "color jitter's brightness") +parser.add_argument('--contrast', default=0.4, type = float, help = "color jitter's contrast") +parser.add_argument('--saturation', default=0.4, type = float, help = "color jitter's saturation") +parser.add_argument('--hue', default=0.1, type = float, help = "color jitter's hue") +parser.add_argument('--p-contrastive', default=0.8, type = float, help = "color jitter's probability") +parser.add_argument('--p-grayscale', default=0.2, type = float, help = "grayscale's probability") +parser.add_argument('--kernel-size-blur', default=11, type = int, help = "blur's kernel size") +parser.add_argument('--sigma-blur', default=[0.1, 2.0], type = float, nargs="+", help = "blur's sigma") +parser.add_argument('--p-blur', default=0.0, type = float, help = "blur's probability") +parser.add_argument('--threshold-solarize', default=0.5, type = float, help = "solarize's threshold") +parser.add_argument('--p-solarize', default=0.2, type = float, help = "solarize's probability") +parser.add_argument('--mean', default=[0.5, 0.5, 0.5], type = float, nargs="+", help = "normalize's mean") +parser.add_argument('--std', default=[0.5, 0.5, 0.5], type = float, nargs="+", help = "normalize's std") +# Burn_in +parser.add_argument('--burn_in_ckpt', default='DIODPD_500.ckpt', type=str, help='ckpt from burn_in to load when starting distillation') +parser.add_argument('--burn_in_exp', default='checkpoints', type=str, help='burn_in experiment name' ) + +# burn_in or distillation? +parser.add_argument('--start_teacher', default=0, type = int, help = "teacher starting epoch") + + +def load_model(model, state_dict): + own_state = model.state_dict() + for name, param in state_dict.items(): + if name not in own_state: + continue + if isinstance(param, Parameter): + # backwards compatibility for serialized parameters + param = param.data + if own_state[name].data.shape!=param.shape: + print(name) + continue + own_state[name].copy_(param) + + + +def computeWBCE(opt, masks, mask_gt, scores): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): continue # corresponding object non-available + fg_portion = torch.where(mask_gt[i,j,id_gt[idx],:,:]!=0)[0].size()[0]/(92*312) + + tmp_BCE += ( -(2-fg_portion)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + nb_objects += 1 + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + return loss_mask + + +def computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=None, device = None, teacher = 0 ): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + fg_gt_after_filtering = torch.zeros(mask_fg_t[:,:,0,:,:].shape).to(device) + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): + continue # corresponding object non-available + + # using fg_teacher as a confidence map on pseudo_labels + weight = mask_fg_t[i,j,0,:,:][mask_gt[i,j,id_gt[idx],:,:] == 1].mean() + if not teacher: + if weight>=opt.confid_th: + nb_objects += 1 + fg_gt_after_filtering[i,j,:,:] += mask_gt[i,j,id_gt[idx],:,:] + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + else: + if weight>=opt.confid_th: + nb_objects += 1 + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + + if not teacher: + return loss_mask, fg_gt_after_filtering + return loss_mask + + +@torch.no_grad() +def _update_teacher_model(model, teacher_model, keep_rate=0.996): + + student_model_dict = model.state_dict() + + new_teacher_dict = OrderedDict() + for key, value in teacher_model.state_dict().items(): + if key in student_model_dict.keys(): + new_teacher_dict[key] = ( + student_model_dict[key] * + (1 - keep_rate) + value * keep_rate + ) + else: + raise Exception("{} is not found in student model".format(key)) + + teacher_model.load_state_dict(new_teacher_dict) + + +def main(): + opt = parser.parse_args() + + resolution = (368, 1248) + + if not os.path.exists(opt.model_dir): + os.mkdir(opt.model_dir) + if not os.path.exists(opt.sample_dir): + os.mkdir(opt.sample_dir) + if not os.path.exists(os.path.join(opt.model_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.model_dir, opt.exp_name)) + if not os.path.exists(os.path.join(opt.sample_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.sample_dir, opt.exp_name)) + + data_path = opt.data_path + train_transform = get_crop_transform( + opt.crop_size if opt.crop_size != [] else None, + opt.crop_scale, + opt.crop_ratio, + InterpolationMode(opt.crop_interpolation), + InterpolationMode(opt.crop_interpolation_mask), + opt.p_crop, + opt.p_flip + ) + print("train transform", train_transform) + + + train_set = KITTIDataset(split = 'train', root = data_path, resolution = resolution, transform=train_transform, apply_img_transform=True) + test_set = KITTIDataset(split = 'eval', root = data_path, resolution = resolution, transform = None, apply_img_transform=True) + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + model = nn.DataParallel(model) + student_train_transform = get_ssl_train_online_transform( + opt.brightness, + opt.contrast, + opt.saturation, + opt.hue, + opt.p_contrastive, + opt.p_grayscale, + opt.kernel_size_blur, + opt.sigma_blur, + opt.p_blur, + opt.threshold_solarize, + opt.p_solarize, + opt.mean, + opt.std + ).to(device) + print("student train transform", student_train_transform) + teacher_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("teacher train transform", teacher_transform) + val_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("val transform", val_transform) + + teacher = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + teacher = nn.DataParallel(teacher) + + + criterion = nn.MSELoss() + + params = [{'params': model.parameters()}] + + train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=opt.batch_size, + shuffle=True, num_workers=opt.num_workers, drop_last=True) + + optimizer = optim.Adam(params, lr=opt.learning_rate) + + start = time.time() + step = 0 + + + print('Model build finished!') + for epoch in range(opt.num_epochs): + + if epoch == opt.start_teacher: + print('____________________burn_in_ckpt', opt.burn_in_ckpt) + print('____________________burn_in_exp', opt.burn_in_exp) + print('____________________supervision', opt.supervision) + print('____________________regularization', opt.weight_reg) + + model_path = os.path.join(opt.model_dir, opt.burn_in_exp, opt.burn_in_ckpt) + state_dict = torch.load(model_path)['model_state_dict'] + load_model(model,state_dict) + load_model(teacher,state_dict) + + + model.train() + + total_loss = 0 + M_loss = 0 + N_loss = 0 + LR_loss = 0 + + M_loss_t = 0 + N_loss_t = 0 + for sample in tqdm(train_dataloader): + step += 1 + + if step < opt.warmup_steps: + learning_rate = opt.learning_rate * (step / opt.warmup_steps) + else: + learning_rate = opt.learning_rate + + learning_rate = learning_rate * (opt.decay_rate ** ( + step / opt.decay_steps)) + + optimizer.param_groups[0]['lr'] = learning_rate + + image = sample['image'].to(device, non_blocking=True) + mask_gt = sample['mask'].to(device, non_blocking=True) + student_image = student_train_transform(image) + teacher_image = teacher_transform(image) + + mask_gt = F.interpolate(mask_gt.float(), (92, 312)).long().to(device) + + if epoch >= opt.start_teacher: + with torch.no_grad(): + _, masks_t, mask_fg_t, _ = teacher(teacher_image) + masks_t = masks_t * 0.999 + 1e-8 + masks_t_quant = masks_t.argmax(2).cpu().numpy() + masks_t_quant = np.where(masks_t_quant==44, 0, masks_t_quant) + + + # get connex regions + for i in range(opt.batch_size): + for j in range(5): + activated_slots = list(np.unique(masks_t_quant[i,j,:,:])) + + + empty_slots = [x for x in range(45) if x not in activated_slots] + for s in activated_slots: + tmp_bin = np.where(masks_t_quant[i,j,:,:] == s, 1, 0) + # Label each connected component + labeled_mask, num_features = label(tmp_bin) + label_counts = np.bincount(labeled_mask.ravel()) + if num_features > 1: + # starts from 2 to discard background and 1st connected region + for ft in range(2, num_features + 1): # starts from 1 as 0 is background + if label_counts[ft] > 10 and mask_fg_t[i,j,0,:,:][labeled_mask == ft].mean() > opt.confid_th: + + try: # if no empty slot available, stop adding objects + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, empty_slots[0], masks_t_quant[i,j,:,:]) + empty_slots.remove(empty_slots[0]) + except: + # print('more teacher predictions than student slots') + continue + else: + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, 0, masks_t_quant[i,j,:,:]) + + + + _h, _w = masks_t_quant[0,0,:,:].shape + + masks_t_bin_np = np.zeros((opt.batch_size, 5,_h, _w)) + + # binarize the teacher predictions + for i in range(opt.batch_size): + for j in range(5): + values, indices, counts = np.unique(masks_t_quant[i,j,:,:], return_inverse=True, return_counts=True) + to_eliminate = counts <= 10 + mapping = np.arange(len(values)) + mapping[to_eliminate] = 0 + masks_t_bin_np[i,j,:,:] = mapping[indices].reshape((_h, _w)) + masks_t_bin = torch.Tensor(masks_t_bin_np).long() + n_objects_t = masks_t_bin.max() + masks_t_bin = F.one_hot(masks_t_bin, n_objects_t+1) + masks_t_bin = masks_t_bin[:,:,:,:,1:] + masks_t_bin = masks_t_bin.permute(0,1,4,2,3).float() + masks_t_bin_gpu = masks_t_bin.to(device) + masks_t_bin_np = masks_t_bin.flatten(3,4) + masks_t_bin_np = masks_t_bin_np.detach().cpu().numpy() + + + recon_combined, masks, mask_fg, slots = model(student_image) + recon_combined = recon_combined.view(opt.batch_size,5,3,resolution[0],resolution[1]) + + # reconstruction loss + loss = criterion(recon_combined, student_image) + + # mask loss + loss_mask = 0.0 + loss_mask_t = 0.0 + + NLL_loss = 0.0 + L1_reg = 0.0 + + mask_detach = masks.detach().flatten(3,4) + mask_detach = mask_detach * 0.999 + 1e-8 + mask_detach = mask_detach.cpu().numpy() + n_objects = mask_gt.max() + mask_gt = F.one_hot(mask_gt, n_objects+1) + mask_gt = mask_gt[:,:,:,:,1:] + mask_gt = mask_gt.permute(0,1,4,2,3).float() + mask_gt_np = mask_gt.flatten(3,4) + mask_gt_np = mask_gt_np.detach().cpu().numpy() + + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), mask_gt_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - mask_gt_np[i,j]).T) + scores[i,j] += cross_entropy_cur + + + # weighted BCE + if epoch >= opt.start_teacher: + loss_mask, filtered_fg_gt = computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=mask_fg_t, device=device) + else: + loss_mask = computeWBCE(opt, masks, mask_gt, scores) + + + + # NLL (reg on whole image) + mask_fg = mask_fg * 0.999 + 1e-8 + fg_gt = mask_gt.sum(axis = 2) + + if epoch >= opt.start_teacher: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * filtered_fg_gt).mean() + else: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * fg_gt).mean() + L1_reg = mask_fg[:,:,0,:,:].mean() + + + # teacherBCE + if epoch >= opt.start_teacher: + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects_t)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), masks_t_bin_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - masks_t_bin_np[i,j]).T) + scores[i,j] += cross_entropy_cur + loss_mask_t = computeWBCE_TS(opt, masks, masks_t_bin_gpu, scores, mask_fg_t, device=device, teacher = True) + + + if epoch >= opt.start_teacher: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + loss_mask_t + else: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + + optimizer.zero_grad() + whole_loss.backward() + clip_grad_norm_(model.parameters(),1.0) + + optimizer.step() + + # update teacher + if epoch >= opt.start_teacher: + _update_teacher_model(model, teacher, keep_rate=0.996) + + total_loss += loss.item() + try: + M_loss += loss_mask.item()*opt.weight_mask + except: + M_loss += loss_mask*opt.weight_mask + N_loss += NLL_loss.item()*opt.weight_NLL + LR_loss += L1_reg.item()*opt.weight_reg + + # teacher + if epoch >= opt.start_teacher: + try: + M_loss_t += loss_mask_t.item() + except: + M_loss_t += loss_mask_t + + del recon_combined, masks, mask_fg, image, student_image, teacher_image, loss, whole_loss, mask_gt, loss_mask, NLL_loss, L1_reg, loss_mask_t + # break + + + total_loss /= len(train_dataloader) + M_loss /= len(train_dataloader) + N_loss /= len(train_dataloader) + LR_loss /= len(train_dataloader) + + M_loss_t /= len(train_dataloader) + N_loss_t /= len(train_dataloader) + + print ("Epoch: {}, Loss: {}, Loss_mask: {}, NLL_loss: {}, L1_reg: {}, Loss_mask_t: {}, Time: {}".format(epoch, total_loss,M_loss, + N_loss, LR_loss, M_loss_t, datetime.timedelta(seconds=time.time() - start))) + + if not epoch % 10: + sample: dict[str, Tensor] = test_set[0] + image: Tensor = sample['image'] + image = image.unsqueeze(0) + + recon_combined, masks, mask_fg, slots = model(image) + + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = opt.num_slots) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + + image = image[0] + image = F.interpolate(image, (92,312)) + masks = masks[0] + + recon_combined = recon_combined.detach() + masks = masks.detach() + + fig, ax = plt.subplots(math.ceil((opt.num_slots+2) / 10), 10, figsize=(45, 5 * math.ceil((opt.num_slots +2)/ 10))) + for i in range(1): + image_i = image[i] + recon_combined_i = recon_combined[i] + masks_i = masks[i].cpu().numpy() + image_i = image_i.permute(1,2,0).cpu().numpy() + image_i = image_i * 0.5 + 0.5 + recon_combined_i = recon_combined_i.permute(1,2,0) + recon_combined_i = recon_combined_i.cpu().numpy() + recon_combined_i = recon_combined_i * 0.5 + 0.5 + ax[i,0].imshow(image_i) + ax[i,0].set_title('Image-f{}'.format(i)) + ax[i,1].imshow(recon_combined_i) + ax[i,1].set_title('Recon.') + for j in range(opt.num_slots): + ax[(j+2)//10,(j + 2)%10].imshow(image_i) + ax[(j+2)//10,(j + 2)%10].imshow(masks_i[j], cmap = 'viridis', alpha = 0.6) + ax[(j+2)//10,(j + 2)%10].set_title('Slot %s' % str(j + 1)) + for j in range(math.ceil((opt.num_slots+2) / 10) * 10): + ax[(j)//10,(j)%10].grid(False) + ax[(j)//10,(j)%10].axis('off') + eval_name = os.path.join(opt.sample_dir,opt.exp_name,'epoch_{}_slot.png'.format(epoch)) + fig.savefig(eval_name) + plt.close(fig) + plt.imsave(eval_name + '_fg.png', mask_fg.detach().cpu().numpy()[0,0,0,:,:]) + + + del masks, mask_fg, recon_combined, image, slots + + if not epoch % 10: + torch.save({ + 'model_state_dict': model.state_dict(), + }, os.path.join(opt.model_dir, opt.exp_name, 'epoch_{}.ckpt'.format(epoch)) + ) + + +if __name__ == '__main__': + main() diff --git a/trainPD_ts.py b/trainPD_ts.py new file mode 100644 index 0000000..ffc09d7 --- /dev/null +++ b/trainPD_ts.py @@ -0,0 +1,569 @@ +from __future__ import annotations + +from collections import OrderedDict +import os +import argparse +from tkinter import NONE +from tqdm import tqdm +import time +import datetime +import torch.optim as optim +import torch.nn as nn +import random +import matplotlib.pyplot as plt +import scipy.optimize +import torch.nn.functional as F +import numpy as np +import torch + +from datasetPD import PDDataset +from datasetPDEval import PDDataset as PDDatasetEval + +from models.model_bg import SlotAttentionAutoEncoder +from transforms import get_normalize_transform, get_ssl_train_online_transform, get_crop_transform +from utils import adjusted_rand_index as ARI +from utils import temporal_loss as t_loss + +from torch import Tensor +from torch.nn.utils import clip_grad_norm_ +from torchvision.transforms import InterpolationMode + +import math +from scipy.ndimage import label + +# Set the random seed for consistant comparisons/ablation studies and reproducibility +# No tuning was performed over multiple seeds +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) + +# Set CUDA deterministic flags +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + +torch.autograd.set_detect_anomaly(True) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +parser = argparse.ArgumentParser() + +parser.add_argument +# basic configurations +parser.add_argument('--model_dir', default='/home/data/skara/tmp/', type=str, help='where to save models' ) +parser.add_argument('--sample_dir', default = './samples/', type = str, help = 'where to save the plots') +# parser.add_argument('--exp_name', default='check_release_train_DIODPD_500_se42', type=str, help='experiment name, used for model saving/plotting ect' ) +parser.add_argument('--exp_name', default='check_release_train_DIODPD_500_DIODENV', type=str, help='experiment name, used for model saving/plotting ect' ) + +parser.add_argument('--num_workers', default=12, type=int, help='number of workers for loading data') +parser.add_argument('--seed', default=0, type=int, help='random seed') +parser.add_argument('--data_path', default = '/home/data/skara/TRI_PD/TRI_PD_train', type = str, help = 'path of PD dataset') +parser.add_argument('--supervision', default = 'est', choices=['moving', 'all','est'], help = 'type of supervision, currently available: moving and all') +# model parameters +parser.add_argument('--batch_size', default=8, type=int) +parser.add_argument('--num_slots', default=45, type=int, help='Number of slots in Slot Attention.') +parser.add_argument('--hid_dim', default=64, type=int, help='hidden dimension size') +parser.add_argument('--learning_rate', default=0.0005, type=float) +parser.add_argument('--warmup_steps', default=3000, type=int, help='Number of warmup steps for the learning rate.') +parser.add_argument('--decay_rate', default=0.5, type=float, help='Rate for the learning rate decay.') +parser.add_argument('--decay_steps', default=50000, type=int, help='Number of steps for the learning rate decay.') +parser.add_argument('--num_epochs', default=500, type=int, help='number of workers for loading data') +parser.add_argument('--weight_mask', default = 1.0, type = float, help = 'weight for the mask loss') +parser.add_argument('--weight_NLL', default = 1.0, type = float, help = 'weight for the NLL loss') +parser.add_argument('--weight_reg', default = 0.3, type = float, help = 'weight for the regularization term') +parser.add_argument('--confid_th', default = 0.9, type = float, help = 'confidence threshold used to filter pseudo-labels during distillation') +# Data augmentations +parser.add_argument('--crop-size', default=[], type = int, nargs="*", help = "crop's size") +parser.add_argument('--crop-scale', default=[0.75, 1.0], type = int, nargs="+", help = "crop's size") +parser.add_argument('--crop-ratio', default=[2.0, 2.0], type = int, nargs="+", help = "crop's ratio") +parser.add_argument('--crop-interpolation', default="bilinear", type = str, help = "crop's interpolation") +parser.add_argument('--crop-interpolation-mask', default="nearest", type = str, help = "crop's interpolation mask") +parser.add_argument('--p-crop', default=0.4, type = float, help = "crop's probability") +parser.add_argument('--p-flip', default=0.4, type = float, help = "flip's probability") +parser.add_argument('--brightness', default=0.4, type = float, help = "color jitter's brightness") +parser.add_argument('--contrast', default=0.4, type = float, help = "color jitter's contrast") +parser.add_argument('--saturation', default=0.4, type = float, help = "color jitter's saturation") +parser.add_argument('--hue', default=0.1, type = float, help = "color jitter's hue") +parser.add_argument('--p-contrastive', default=0.8, type = float, help = "color jitter's probability") +parser.add_argument('--p-grayscale', default=0.2, type = float, help = "grayscale's probability") +parser.add_argument('--kernel-size-blur', default=11, type = int, help = "blur's kernel size") +parser.add_argument('--sigma-blur', default=[0.1, 2.0], type = float, nargs="+", help = "blur's sigma") +parser.add_argument('--p-blur', default=0.0, type = float, help = "blur's probability") +parser.add_argument('--threshold-solarize', default=0.5, type = float, help = "solarize's threshold") +parser.add_argument('--p-solarize', default=0.2, type = float, help = "solarize's probability") +parser.add_argument('--mean', default=[0.5, 0.5, 0.5], type = float, nargs="+", help = "normalize's mean") +parser.add_argument('--std', default=[0.5, 0.5, 0.5], type = float, nargs="+", help = "normalize's std") +# Burn_in +parser.add_argument('--burn_in_ckpt', default='DIODPD_burn_in_400.ckpt', type=str, help='ckpt from burn_in to load when starting distillation') +parser.add_argument('--burn_in_exp', default='checkpoints', type=str, help='burn_in experiment name' ) + +# burn_in or distillation? +parser.add_argument('--start_teacher', default=0, type = int, help = "teacher starting epoch") + +def computeWBCE(opt, masks, mask_gt, scores): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): continue # corresponding object non-available + fg_portion = torch.where(mask_gt[i,j,id_gt[idx],:,:]!=0)[0].size()[0]/(120*242) + + tmp_BCE += ( -(2-fg_portion)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + nb_objects += 1 + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + return loss_mask + + +def computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=None, device = None, teacher = 0 ): + scores = scores[:,:,:-1,:] # to discard last slot (reserved for Bg) + masks = masks * 0.999 + 1e-8 + loss_mask = 0 + fg_gt_after_filtering = torch.zeros(mask_fg_t[:,:,0,:,:].shape).to(device) + for i in range(opt.batch_size): + for j in range(5): + matches = scipy.optimize.linear_sum_assignment(-scores[i,j]) + id_slot, id_gt = matches + tmp_BCE, nb_objects = 0, 0 + for idx, s in enumerate(id_slot): + mask_2_weight = mask_gt[i,j,id_gt[idx],:,:] + if not mask_2_weight.max(): + continue # corresponding object non-available + + # using fg_teacher as a confidence map on pseudo_labels + weight = mask_fg_t[i,j,0,:,:][mask_gt[i,j,id_gt[idx],:,:] == 1].mean() + if not teacher: + if weight>=opt.confid_th: + nb_objects += 1 + fg_gt_after_filtering[i,j,:,:] += mask_gt[i,j,id_gt[idx],:,:] + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + else: + if weight>=opt.confid_th: + nb_objects += 1 + tmp_BCE += ( -(1+weight)*torch.log(masks[i,j,s,:,:]) * mask_gt[i,j,id_gt[idx],:,:] - (1- mask_gt[i,j,id_gt[idx],:,:]) * torch.log(1-masks[i,j,s,:,:])).mean() + + if nb_objects == 0: + continue + tmp_BCE /= nb_objects + loss_mask += tmp_BCE + + loss_mask /= opt.batch_size + + if not teacher: + return loss_mask, fg_gt_after_filtering + return loss_mask + + +@torch.no_grad() +def _update_teacher_model(model, teacher_model, keep_rate=0.996): + + student_model_dict = model.state_dict() + + new_teacher_dict = OrderedDict() + for key, value in teacher_model.state_dict().items(): + if key in student_model_dict.keys(): + new_teacher_dict[key] = ( + student_model_dict[key] * + (1 - keep_rate) + value * keep_rate + ) + else: + raise Exception("{} is not found in student model".format(key)) + + teacher_model.load_state_dict(new_teacher_dict) + + +def main(): + opt = parser.parse_args() + + resolution = (480, 968) + + + if not os.path.exists(opt.model_dir): + os.mkdir(opt.model_dir) + if not os.path.exists(opt.sample_dir): + os.mkdir(opt.sample_dir) + if not os.path.exists(os.path.join(opt.model_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.model_dir, opt.exp_name)) + if not os.path.exists(os.path.join(opt.sample_dir, opt.exp_name)): + os.mkdir(os.path.join(opt.sample_dir, opt.exp_name)) + + data_path = opt.data_path + train_transform = get_crop_transform( + opt.crop_size if opt.crop_size != [] else None, + opt.crop_scale, + opt.crop_ratio, + InterpolationMode(opt.crop_interpolation), + InterpolationMode(opt.crop_interpolation_mask), + opt.p_crop, + opt.p_flip + ) + print("train transform", train_transform) + train_set = PDDataset(split = 'train', root = data_path, supervision = opt.supervision, transform=train_transform, apply_img_transform=True) + test_set = PDDatasetEval(split = 'eval', root = data_path) + + + model = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + model = nn.DataParallel(model) + student_train_transform = get_ssl_train_online_transform( + opt.brightness, + opt.contrast, + opt.saturation, + opt.hue, + opt.p_contrastive, + opt.p_grayscale, + opt.kernel_size_blur, + opt.sigma_blur, + opt.p_blur, + opt.threshold_solarize, + opt.p_solarize, + opt.mean, + opt.std + ).to(device) + print("student train transform", student_train_transform) + teacher_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("teacher train transform", teacher_transform) + val_transform = get_normalize_transform( + opt.mean, + opt.std + ).to(device) + print("val transform", val_transform) + + teacher = SlotAttentionAutoEncoder(resolution, opt.num_slots, opt.hid_dim, output_channel = 3).to(device) + teacher = nn.DataParallel(teacher) + + + criterion = nn.MSELoss() + + params = [{'params': model.parameters()}] + + train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=opt.batch_size, + shuffle=True, num_workers=opt.num_workers, drop_last=True) + + optimizer = optim.Adam(params, lr=opt.learning_rate) + + start = time.time() + step = 0 + + + print('Model build finished!') + for epoch in range(opt.num_epochs): + + if epoch == opt.start_teacher: + print('____________________burn_in_ckpt', opt.burn_in_ckpt) + print('____________________burn_in_exp', opt.burn_in_exp) + print('____________________supervision', opt.supervision) + print('____________________regularization', opt.weight_reg) + + model_path = os.path.join(opt.model_dir, opt.burn_in_exp, opt.burn_in_ckpt) + model.load_state_dict(torch.load(model_path)['model_state_dict']) + teacher.load_state_dict(torch.load(model_path)['model_state_dict']) + + model.train() + + total_loss = 0 + M_loss = 0 + N_loss = 0 + LR_loss = 0 + + M_loss_t = 0 + N_loss_t = 0 + for sample in tqdm(train_dataloader): + step += 1 + + if step < opt.warmup_steps: + learning_rate = opt.learning_rate * (step / opt.warmup_steps) + else: + learning_rate = opt.learning_rate + + learning_rate = learning_rate * (opt.decay_rate ** ( + step / opt.decay_steps)) + + optimizer.param_groups[0]['lr'] = learning_rate + + image = sample['image'].to(device, non_blocking=True) + mask_gt = sample['mask'].to(device, non_blocking=True) + student_image = student_train_transform(image) + teacher_image = teacher_transform(image) + + mask_gt = F.interpolate(mask_gt.float(), (120, 242)).long().to(device) + + if epoch >= opt.start_teacher: + with torch.no_grad(): + _, masks_t, mask_fg_t, _ = teacher(teacher_image) + masks_t = masks_t * 0.999 + 1e-8 + masks_t_quant = masks_t.argmax(2).cpu().numpy() + masks_t_quant = np.where(masks_t_quant==44, 0, masks_t_quant) + + + # get connex regions + for i in range(opt.batch_size): + for j in range(5): + activated_slots = list(np.unique(masks_t_quant[i,j,:,:])) + + + empty_slots = [x for x in range(45) if x not in activated_slots] + for s in activated_slots: + tmp_bin = np.where(masks_t_quant[i,j,:,:] == s, 1, 0) + # Label each connected component + labeled_mask, num_features = label(tmp_bin) + label_counts = np.bincount(labeled_mask.ravel()) + if num_features > 1: + # starts from 2 to discard background and 1st connected region + for ft in range(2, num_features + 1): + if label_counts[ft] > 10 and mask_fg_t[i,j,0,:,:][labeled_mask == ft].mean() > opt.confid_th: + + try: # if no empty slot available, stop adding objects + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, empty_slots[0], masks_t_quant[i,j,:,:]) + empty_slots.remove(empty_slots[0]) + except: + print('more teacher predictions than student slots') + continue + else: + masks_t_quant[i,j,:,:] = np.where(labeled_mask == ft, 0, masks_t_quant[i,j,:,:]) + + + + _h, _w = masks_t_quant[0,0,:,:].shape + + masks_t_bin_np = np.zeros((opt.batch_size, 5,_h, _w)) + + # binarize the teacher predictions + for i in range(opt.batch_size): + for j in range(5): + values, indices, counts = np.unique(masks_t_quant[i,j,:,:], return_inverse=True, return_counts=True) + to_eliminate = counts <= 10 + mapping = np.arange(len(values)) + mapping[to_eliminate] = 0 + masks_t_bin_np[i,j,:,:] = mapping[indices].reshape((_h, _w)) + masks_t_bin = torch.Tensor(masks_t_bin_np).long() + n_objects_t = masks_t_bin.max() + masks_t_bin = F.one_hot(masks_t_bin, n_objects_t+1) + masks_t_bin = masks_t_bin[:,:,:,:,1:] + masks_t_bin = masks_t_bin.permute(0,1,4,2,3).float() + masks_t_bin_gpu = masks_t_bin.to(device) + masks_t_bin_np = masks_t_bin.flatten(3,4) + masks_t_bin_np = masks_t_bin_np.detach().cpu().numpy() + + + recon_combined, masks, mask_fg, slots = model(student_image) + recon_combined = recon_combined.view(opt.batch_size,5,3,resolution[0],resolution[1]) + + # reconstruction loss + loss = criterion(recon_combined, student_image) + + # mask loss + loss_mask = 0.0 + loss_mask_t = 0.0 + + NLL_loss = 0.0 + L1_reg = 0.0 + + mask_detach = masks.detach().flatten(3,4) + mask_detach = mask_detach * 0.999 + 1e-8 + mask_detach = mask_detach.cpu().numpy() + n_objects = mask_gt.max() + mask_gt = F.one_hot(mask_gt, n_objects+1) + mask_gt = mask_gt[:,:,:,:,1:] + mask_gt = mask_gt.permute(0,1,4,2,3).float() + mask_gt_np = mask_gt.flatten(3,4) + mask_gt_np = mask_gt_np.detach().cpu().numpy() + + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), mask_gt_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - mask_gt_np[i,j]).T) + scores[i,j] += cross_entropy_cur + + + # weighted BCE + if epoch >= opt.start_teacher: + loss_mask, filtered_fg_gt = computeWBCE_TS(opt, masks, mask_gt, scores, mask_fg_t=mask_fg_t, device=device) + else: + loss_mask = computeWBCE(opt, masks, mask_gt, scores) + + + + # NLL (reg on whole image) + mask_fg = mask_fg * 0.999 + 1e-8 + fg_gt = mask_gt.sum(axis = 2) + + if epoch >= opt.start_teacher: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * filtered_fg_gt).mean() + else: + NLL_loss = (- torch.log(mask_fg[:,:,0,:,:]) * fg_gt).mean() + L1_reg = mask_fg[:,:,0,:,:].mean() + + + # teacherBCE + if epoch >= opt.start_teacher: + scores = np.zeros((opt.batch_size, 5, opt.num_slots, n_objects_t)) + for i in range(opt.batch_size): + for j in range(5): + cross_entropy_cur = np.matmul(np.log( mask_detach[i,j]), masks_t_bin_np[i,j].T) + np.matmul(np.log(1 - mask_detach[i,j]), (1 - masks_t_bin_np[i,j]).T) + scores[i,j] += cross_entropy_cur + loss_mask_t = computeWBCE_TS(opt, masks, masks_t_bin_gpu, scores, mask_fg_t, device=device, teacher = True) + + + if epoch >= opt.start_teacher: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + loss_mask_t + else: + whole_loss = loss + opt.weight_mask*loss_mask + opt.weight_NLL*NLL_loss + opt.weight_reg*L1_reg + + optimizer.zero_grad() + whole_loss.backward() + clip_grad_norm_(model.parameters(),1.0) + + optimizer.step() + + # update teacher + if epoch >= opt.start_teacher: + _update_teacher_model(model, teacher, keep_rate=0.996) + + total_loss += loss.item() + try: + M_loss += loss_mask.item()*opt.weight_mask + except: + M_loss += loss_mask*opt.weight_mask + N_loss += NLL_loss.item()*opt.weight_NLL + LR_loss += L1_reg.item()*opt.weight_reg + + # teacher + if epoch >= opt.start_teacher: + try: + M_loss_t += loss_mask_t.item() + except: + M_loss_t += loss_mask_t + + del recon_combined, masks, mask_fg, image, student_image, teacher_image, loss, whole_loss, mask_gt, loss_mask, NLL_loss, L1_reg, loss_mask_t + # break + + + total_loss /= len(train_dataloader) + M_loss /= len(train_dataloader) + N_loss /= len(train_dataloader) + LR_loss /= len(train_dataloader) + + M_loss_t /= len(train_dataloader) + N_loss_t /= len(train_dataloader) + + print ("Epoch: {}, Loss: {}, Loss_mask: {}, NLL_loss: {}, L1_reg: {}, Loss_mask_t: {}, Time: {}".format(epoch, total_loss,M_loss, + N_loss, LR_loss, M_loss_t, datetime.timedelta(seconds=time.time() - start))) + + if not epoch % 10: + rand_id = random.randint(0,190) + sample: dict[str, Tensor] = test_set[0] + image: Tensor = sample['image'][rand_id:rand_id+5,:,:,:].to(device, non_blocking=True) + image = image.unsqueeze(0) + + recon_combined, masks, mask_fg, slots = model(image) + + index_mask = masks.argmax(dim = 2) + index_mask = F.one_hot(index_mask,num_classes = opt.num_slots) + index_mask = index_mask.permute(0,1,4,2,3) + masks = masks * index_mask + + image = image[0] + image = F.interpolate(image, (120,242)) + masks = masks[0] + + recon_combined = recon_combined.detach() + masks = masks.detach() + + fig, ax = plt.subplots(math.ceil((opt.num_slots+2) / 10), 10, figsize=(45, 5 * math.ceil((opt.num_slots +2)/ 10))) + for i in range(1): + image_i = image[i] + recon_combined_i = recon_combined[i] + masks_i = masks[i].cpu().numpy() + image_i = image_i.permute(1,2,0).cpu().numpy() + image_i = image_i * 0.5 + 0.5 + recon_combined_i = recon_combined_i.permute(1,2,0) + recon_combined_i = recon_combined_i.cpu().numpy() + recon_combined_i = recon_combined_i * 0.5 + 0.5 + ax[i,0].imshow(image_i) + ax[i,0].set_title('Image-f{}'.format(i)) + ax[i,1].imshow(recon_combined_i) + ax[i,1].set_title('Recon.') + for j in range(opt.num_slots): + ax[(j+2)//10,(j + 2)%10].imshow(image_i) + ax[(j+2)//10,(j + 2)%10].imshow(masks_i[j], cmap = 'viridis', alpha = 0.6) + ax[(j+2)//10,(j + 2)%10].set_title('Slot %s' % str(j + 1)) + for j in range(math.ceil((opt.num_slots+2) / 10) * 10): + ax[(j)//10,(j)%10].grid(False) + ax[(j)//10,(j)%10].axis('off') + eval_name = os.path.join(opt.sample_dir,opt.exp_name,'epoch_{}_slot.png'.format(epoch)) + fig.savefig(eval_name) + plt.close(fig) + plt.imsave(eval_name + '_fg.png', mask_fg.detach().cpu().numpy()[0,0,0,:,:]) + + + + del masks, mask_fg, recon_combined, image, slots + + if not epoch % 10: + torch.save({ + 'model_state_dict': model.state_dict(), + }, os.path.join(opt.model_dir, opt.exp_name, 'epoch_{}.ckpt'.format(epoch)) + ) + + test_dataloader = torch.utils.data.DataLoader(test_set, batch_size=1, + shuffle=False, num_workers=4, drop_last=False) + + with torch.no_grad(): + ARIs, all_ARIs = [], [] + for idx, sample in enumerate(tqdm(test_dataloader)): + + image = sample['image'].to(device) + mask_gt = sample['mask'] + + mask_gts = F.interpolate(mask_gt.float(), (120, 242)).long() + + for i in range(40): + _, masks, _, _= model(image[:,i*5:i*5+5,:,:,:]) + mask_gt = mask_gts[:,i*5:i*5+5,:,:] + masks = masks.detach().cpu() + + gt_msk = mask_gt[0] + pred_msk = masks[0] + gt_msk = gt_msk.view(5,-1) + pred_msk = pred_msk.view(5,opt.num_slots,-1).permute(1,0,2) + gt_msk = gt_msk.view(-1) + pred_msk = pred_msk.reshape(opt.num_slots,-1) + + + pred_msk = pred_msk.permute(1,0) + gt_msk = F.one_hot(gt_msk) + _,n_cat = gt_msk.shape + if n_cat <= 2: + continue + + all_ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + gt_msk = gt_msk[:,1:] + ari = ARI(gt_msk.unsqueeze(0), pred_msk.unsqueeze(0)) + + # if not ari == ari: # one image in the val_set is causing instability in computing ari. Discarded + if not ari == ari: + print('discarded') + continue + + all_ARIs.append(all_ari) + ARIs.append(ari) + del image, mask_gt, masks + print('final ARI:',sum(ARIs) / len(ARIs)) + print('final allARI:', sum(all_ARIs) / len(all_ARIs)) + + +if __name__ == '__main__': + main() diff --git a/transforms.py b/transforms.py new file mode 100644 index 0000000..0a93a8b --- /dev/null +++ b/transforms.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +from typing import Callable +import torch +from torch import Tensor, nn +from torchaug.batch_transforms import ( + BatchVideoWrapper, + BatchRandomColorJitter, + BatchRandomGaussianBlur, + BatchRandomGrayScale, + BatchRandomSolarize +) +from torchaug.transforms import VideoNormalize +from torchvision.transforms import Compose, InterpolationMode, RandomResizedCrop, RandomHorizontalFlip +import torchvision.transforms.functional as F +from torchvision.transforms.functional import InterpolationMode + + +class RandomHorizontalFlipSample(RandomHorizontalFlip): + def __init__(self, p: float) -> None: + super().__init__(p) + + def forward(self, sample: dict[str: Tensor]) -> dict[str: Tensor]: + if torch.rand(1) < self.p: + sample["image"] = F.hflip(sample["image"]) + sample["mask"] = F.hflip(sample["mask"]) + sample["flipped"] = torch.Tensor([True]) + else: + sample["flipped"] = torch.Tensor([False]) + return sample + + +class RandomResizedCropSample(RandomResizedCrop): + def __init__(self, size, scale=..., ratio=..., interpolation=InterpolationMode.BILINEAR, antialias=True, interpolation_mask=InterpolationMode.NEAREST, p:float =0.): + if size is None: + size = 4 + none_size = True + else: + none_size = False + + super().__init__(size, scale, ratio, interpolation, antialias) + + if none_size: + self.size = None + self.p=p + self.interpolation_mask = interpolation_mask + + def forward(self, sample: dict[str: Tensor]) -> dict[str: Tensor]: + if torch.rand(1) < self.p: + if self.size is None: + size: list[int] = sample["image"].shape[-2:] + else: + size = self.size + i, j, h, w = self.get_params(sample["image"], self.scale, self.ratio) + sample["image"] = F.resized_crop(sample["image"], i, j, h, w, size, self.interpolation, antialias=self.antialias) + sample["mask"] = F.resized_crop(sample["mask"], i, j, h, w, size, self.interpolation_mask, antialias=self.antialias) + sample["cropped"] = torch.Tensor([True]) + else: + sample["cropped"] = torch.Tensor([False]) + return sample + + def __repr__(self) -> str: + interpolate_str = self.interpolation.value + interpolate_mask_str = self.interpolation_mask.value + format_string = self.__class__.__name__ + f"(size={self.size}" + format_string += f", scale={tuple(round(s, 4) for s in self.scale)}" + format_string += f", ratio={tuple(round(r, 4) for r in self.ratio)}" + format_string += f", interpolation={interpolate_str}" + format_string += f", interpolation_mask={interpolate_mask_str}" + format_string += f", antialias={self.antialias}" + format_string += f", p={self.p})" + return format_string + + +def get_crop_transform( + size: int | list[int] | None = None, + scale: list[float] = (0.08, 1.0), + ratio: list[float] = (3.0 / 4.0, 4.0 / 3.0), + interpolation: InterpolationMode = InterpolationMode.BILINEAR, + interpolation_mask: InterpolationMode = InterpolationMode.NEAREST, + p_crop: float = 0., + p_flip: float = 0.5, +) -> Callable: + return Compose( + [ + RandomResizedCropSample(size, scale, ratio, interpolation, True, interpolation_mask, p_crop), + RandomHorizontalFlipSample(p_flip) + ] + ) + + +def get_ssl_train_online_transform( + brightness: float = 0.4, + contrast: float = 0.4, + saturation: float = 0.4, + hue: float = 0.1, + p_contrastive: float = 0.8, + p_grayscale: float = 0.2, + kernel_size_blur: int = 23, + sigma_blur: list[int] = [0.1, 2.], + p_blur: float = 0.5, + threshold_solarize: float = 0.5, + p_solarize: float = 0.2, + mean: list[int] = [0.485, 0.456, 0.406], + std: list[int] = [0.229, 0.224, 0.225] +) -> nn.Module: + return BatchVideoWrapper( + [ + BatchRandomColorJitter(brightness, contrast, saturation, hue, p_contrastive), + BatchRandomGrayScale(p_grayscale), + BatchRandomGaussianBlur(kernel_size_blur, sigma_blur, p_blur), + BatchRandomSolarize(threshold_solarize, p_solarize), + VideoNormalize(mean, std, video_format="TCHW") + ], + inplace=False, + same_on_frames=True, + video_format="TCHW", + ) + + +def get_normalize_transform( + mean: list[int] = [0.485, 0.456, 0.406], + std: list[int] = [0.229, 0.224, 0.225] +) -> nn.Module: + return VideoNormalize(mean, std, inplace=False, video_format="TCHW") diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..3719408 --- /dev/null +++ b/utils.py @@ -0,0 +1,61 @@ +import torch +import torch.nn.functional as F + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +def temporal_loss(slots): + bs, nf, ns, ds = slots.shape + slot0 = slots[:,:-1,:,:].reshape(bs*(nf-1),ns,ds) + slot1 = slots[:,1:,:,:].reshape(bs*(nf-1),ns,ds) + slot1 = slot1.permute(0,2,1) + scores = torch.einsum('bij,bjk->bik', slot0, slot1) + scores = scores.softmax(dim=1) + 0.0001 + gt = torch.eye(ns).unsqueeze(0).repeat(bs*(nf-1),1,1).to(device) + return torch.mean((gt-scores)**2) + +def adjusted_rand_index(true_mask, pred_mask): + _, n_points, n_true_groups = true_mask.shape + n_pred_groups = pred_mask.shape[-1] + assert not (n_points <= n_true_groups and n_points <= n_pred_groups), ("adjusted_rand_index requires n_groups < n_points. We don't handle the special cases that can occur when you have one cluster per datapoint.") + + true_group_ids = torch.argmax(true_mask, -1) + pred_group_ids = torch.argmax(pred_mask, -1) + true_mask_oh = true_mask.to(torch.float32) + pred_mask_oh = F.one_hot(pred_group_ids, n_pred_groups).to(torch.float32) + + n_points = torch.sum(true_mask_oh, dim=[1, 2]).to(torch.float32) + + nij = torch.einsum('bji,bjk->bki', pred_mask_oh, true_mask_oh) + a = torch.sum(nij, dim=1) + b = torch.sum(nij, dim=2) + + rindex = torch.sum(nij * (nij - 1), dim=[1, 2]) + aindex = torch.sum(a * (a - 1), dim=1) + bindex = torch.sum(b * (b - 1), dim=1) + expected_rindex = aindex * bindex / (n_points*(n_points-1)) + max_rindex = (aindex + bindex) / 2 + ari = (rindex - expected_rindex) / (max_rindex - expected_rindex+0.000000000001) + + _all_equal = lambda values: torch.all(torch.eq(values, values[..., :1]), dim=-1) + both_single_cluster = torch.logical_and(_all_equal(true_group_ids), _all_equal(pred_group_ids)) + + return torch.where(both_single_cluster, torch.ones_like(ari), ari) + + +def build_grid(resolution): + ranges = [torch.linspace(0.0, 1.0, steps=res) for res in resolution] + grid = torch.meshgrid(*ranges) + grid = torch.stack(grid, dim=-1) + grid = torch.reshape(grid, [resolution[0], resolution[1], -1]) + grid = grid.unsqueeze(0) + return torch.cat([grid, 1.0 - grid], dim=-1) + + +def first(x): + return next(iter(x)) + + +def only(x): + materialized_x = list(x) + assert len(materialized_x) == 1 + return materialized_x[0]

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