From 69e5ad36601be1ffceac1ceebe97dcff1b3a0f6d Mon Sep 17 00:00:00 2001 From: Wei Sun Date: Sun, 13 Oct 2019 12:55:03 -0400 Subject: [PATCH] init commit --- .gitignore | 27 ++ INSTALL.md | 26 ++ README.md | 42 ++ data/__init__.py | 0 data/cocostuff_loader.py | 551 +++++++++++++++++++++++ data/vg.py | 249 ++++++++++ figures/add_obj.png | Bin 0 -> 524173 bytes figures/mask.png | Bin 0 -> 382674 bytes figures/network_structure.png | Bin 0 -> 164274 bytes figures/style_morph.png | Bin 0 -> 411885 bytes figures/various_outs.png | Bin 0 -> 867068 bytes model/__init__.py | 0 model/csrc/ROIAlign.h | 46 ++ model/csrc/ROIPool.h | 48 ++ model/csrc/cpu/ROIAlign_cpu.cpp | 257 +++++++++++ model/csrc/cpu/vision.h | 11 + model/csrc/cuda/ROIAlign_cuda.cu | 346 ++++++++++++++ model/csrc/cuda/ROIPool_cuda.cu | 202 +++++++++ model/csrc/cuda/vision.h | 84 ++++ model/csrc/vision.cpp | 11 + model/mask_regression.py | 56 +++ model/norm_module.py | 190 ++++++++ model/rcnn_discriminator.py | 233 ++++++++++ model/resnet_generator.py | 189 ++++++++ model/roi_layers/__init__.py | 8 + model/roi_layers/roi_align.py | 67 +++ model/roi_layers/roi_pool.py | 63 +++ model/sync_batchnorm/__init__.py | 12 + model/sync_batchnorm/batchnorm.py | 315 +++++++++++++ model/sync_batchnorm/batchnorm_reimpl.py | 74 +++ model/sync_batchnorm/comm.py | 137 ++++++ model/sync_batchnorm/replicate.py | 94 ++++ model/sync_batchnorm/unittest.py | 29 ++ requirements.txt | 8 + scripts/download_coco.sh | 29 ++ scripts/download_vg.sh | 34 ++ scripts/preprocess_vg.py | 511 +++++++++++++++++++++ setup.py | 67 +++ test.py | 81 ++++ train.py | 163 +++++++ utils/__init__.py | 0 utils/bilinear.py | 210 +++++++++ utils/logger.py | 27 ++ utils/util.py | 43 ++ 44 files changed, 4540 insertions(+) create mode 100644 .gitignore create mode 100644 INSTALL.md create mode 100644 README.md create mode 100644 data/__init__.py create mode 100644 data/cocostuff_loader.py create mode 100644 data/vg.py create mode 100644 figures/add_obj.png create mode 100644 figures/mask.png create mode 100644 figures/network_structure.png create mode 100644 figures/style_morph.png create mode 100644 figures/various_outs.png create mode 100644 model/__init__.py create mode 100644 model/csrc/ROIAlign.h create mode 100644 model/csrc/ROIPool.h create mode 100644 model/csrc/cpu/ROIAlign_cpu.cpp create mode 100644 model/csrc/cpu/vision.h create mode 100644 model/csrc/cuda/ROIAlign_cuda.cu create mode 100644 model/csrc/cuda/ROIPool_cuda.cu create mode 100644 model/csrc/cuda/vision.h create mode 100644 model/csrc/vision.cpp create mode 100644 model/mask_regression.py create mode 100644 model/norm_module.py create mode 100644 model/rcnn_discriminator.py create mode 100644 model/resnet_generator.py create mode 100644 model/roi_layers/__init__.py create mode 100644 model/roi_layers/roi_align.py create mode 100644 model/roi_layers/roi_pool.py create mode 100644 model/sync_batchnorm/__init__.py create mode 100644 model/sync_batchnorm/batchnorm.py create mode 100644 model/sync_batchnorm/batchnorm_reimpl.py create mode 100644 model/sync_batchnorm/comm.py create mode 100644 model/sync_batchnorm/replicate.py create mode 100644 model/sync_batchnorm/unittest.py create mode 100644 requirements.txt create mode 100644 scripts/download_coco.sh create mode 100644 scripts/download_vg.sh create mode 100644 scripts/preprocess_vg.py create mode 100644 setup.py create mode 100644 test.py create mode 100644 train.py create mode 100644 utils/__init__.py create mode 100644 utils/bilinear.py create mode 100644 utils/logger.py create mode 100644 utils/util.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c170b98 --- /dev/null +++ b/.gitignore @@ -0,0 +1,27 @@ + +.vscode/ + +*.pyc +*pynb* + +datasets/ + +outputs/ + +samples/ + +eval/ + +pretrained_model/ + +eval/model/ + +build/ + +roi_layers.egg-info/ + +*.so + +.ftpconfig + +.ftpignore diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000..7a34c9e --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,26 @@ +## Installation + +#### Requiements +* python3 +* pytorch 1.0 +* numpy +* matplotlib +* opencv + +``` +pip install -r requiements.txt + +# setup for roi_layers +python setup.py build develop +``` + +#### Data Preparation +Download COCO dataset to `datasets/coco` +``` +bash scripts/download_coco.sh +``` +Download VG dataset to `datasets/vg` +``` +bash scripts/download_vg.sh +python scripts/preprocess_vg.py +``` diff --git a/README.md b/README.md new file mode 100644 index 0000000..6c72034 --- /dev/null +++ b/README.md @@ -0,0 +1,42 @@ +# LostGANs: Image Synthesis From Reconfigurable Layout and Style +This is implementation of our ICCV19 paper [**Image Synthesis From Reconfigurable Layout and Style**](https://arxiv.org/abs/1908.07500) + +## Network Structure +![network_structure](./figures/network_structure.png) + +## Installation +Check [INSTALL.md](INSTALL.md) for installation instructions. +#### 1. Download pretrained model +Download pretrained models to `pretrained_model/` +* Pretrained model on [COCO](https://drive.google.com/open?id=1WO6fLZqJeTUnmJTmieUopKLj9KGBhGd6) +* Pretrained model on [VG](https://drive.google.com/open?id=1A_gP_WwZWonlXJhwcdBDgHuVaGSiVjMO) + +#### 2. Train models +``` +python train.py --dataset coco --out_path outputs/ +``` + +#### 3. Run pretrained model +``` +python test.py --dataset coco --model_path pretrained_model/G_coco.pth --sample_path samples/coco/ +``` + + +## Results +###### Multiple samples generated from same layout +![various_out](./figures/various_outs.png) +###### Generation results by adding new objects or change spatial position of object +![add_obj](./figures/add_obj.png) +###### Linear interpolation of instance style +![style_morph](./figures/style_morph.png) +###### Synthesized images and learned masks for given layout +![mask](./figures/mask.png) + +## Contact +Please feel free to report issues and any related problems to Wei Sun (wsun12 at ncsu dot edu) and Tianfu Wu (tianfu_wu at ncsu dot edu). + + +## Reference +* Synchronized-BatchNorm-PyTorch: [https://github.com/vacancy/Synchronized-BatchNorm-PyTorch](https://github.com/vacancy/Synchronized-BatchNorm-PyTorch) +* Image Generation from Scene Graphs: [https://github.com/google/sg2im](https://github.com/google/sg2im) +* Faster R-CNN and Mask R-CNN in PyTorch 1.0: [https://github.com/facebookresearch/maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/cocostuff_loader.py b/data/cocostuff_loader.py new file mode 100644 index 0000000..22e5049 --- /dev/null +++ b/data/cocostuff_loader.py @@ -0,0 +1,551 @@ +import json, os, random, math +from collections import defaultdict + +import torch +from torch.utils.data import Dataset +import torchvision.transforms as T + +import numpy as np +import PIL +from skimage.transform import resize as imresize +import pycocotools.mask as mask_utils +from random import shuffle + + +class CocoSceneGraphDataset(Dataset): + def __init__(self, image_dir, instances_json, stuff_json=None, + stuff_only=True, image_size=(64, 64), mask_size=16, + normalize_images=True, max_samples=None, + include_relationships=True, min_object_size=0.02, + min_objects_per_image=3, max_objects_per_image=8, left_right_flip=False, + include_other=False, instance_whitelist=None, stuff_whitelist=None): + """ + A PyTorch Dataset for loading Coco and Coco-Stuff annotations and converting + them to scene graphs on the fly. + + Inputs: + - image_dir: Path to a directory where images are held + - instances_json: Path to a JSON file giving COCO annotations + - stuff_json: (optional) Path to a JSON file giving COCO-Stuff annotations + - stuff_only: (optional, default True) If True then only iterate over + images which appear in stuff_json; if False then iterate over all images + in instances_json. + - image_size: Size (H, W) at which to load images. Default (64, 64). + - mask_size: Size M for object segmentation masks; default 16. + - normalize_image: If True then normalize images by subtracting ImageNet + mean pixel and dividing by ImageNet std pixel. + - max_samples: If None use all images. Other wise only use images in the + range [0, max_samples). Default None. + - include_relationships: If True then include spatial relationships; if + False then only include the trivial __in_image__ relationship. + - min_object_size: Ignore objects whose bounding box takes up less than + this fraction of the image. + - min_objects_per_image: Ignore images which have fewer than this many + object annotations. + - max_objects_per_image: Ignore images which have more than this many + object annotations. + - include_other: If True, include COCO-Stuff annotations which have category + "other". Default is False, because I found that these were really noisy + and pretty much impossible for the system to model. + - instance_whitelist: None means use all instance categories. Otherwise a + list giving a whitelist of instance category names to use. + - stuff_whitelist: None means use all stuff categories. Otherwise a list + giving a whitelist of stuff category names to use. + """ + super(Dataset, self).__init__() + + if stuff_only and stuff_json is None: + print('WARNING: Got stuff_only=True but stuff_json=None.') + print('Falling back to stuff_only=False.') + + self.image_dir = image_dir + self.mask_size = mask_size + self.max_samples = max_samples + self.max_objects_per_image = max_objects_per_image + self.normalize_images = normalize_images + self.include_relationships = include_relationships + self.left_right_flip = left_right_flip + self.set_image_size(image_size) + + with open(instances_json, 'r') as f: + instances_data = json.load(f) + + stuff_data = None + if stuff_json is not None and stuff_json != '': + with open(stuff_json, 'r') as f: + stuff_data = json.load(f) + + self.image_ids = [] + self.image_id_to_filename = {} + self.image_id_to_size = {} + for image_data in instances_data['images']: + image_id = image_data['id'] + filename = image_data['file_name'] + width = image_data['width'] + height = image_data['height'] + self.image_ids.append(image_id) + self.image_id_to_filename[image_id] = filename + self.image_id_to_size[image_id] = (width, height) + + self.vocab = { + 'object_name_to_idx': {}, + 'pred_name_to_idx': {}, + } + object_idx_to_name = {} + all_instance_categories = [] + for category_data in instances_data['categories']: + category_id = category_data['id'] + category_name = category_data['name'] + all_instance_categories.append(category_name) + object_idx_to_name[category_id] = category_name + self.vocab['object_name_to_idx'][category_name] = category_id + all_stuff_categories = [] + if stuff_data: + for category_data in stuff_data['categories']: + category_name = category_data['name'] + category_id = category_data['id'] + all_stuff_categories.append(category_name) + object_idx_to_name[category_id] = category_name + self.vocab['object_name_to_idx'][category_name] = category_id + + if instance_whitelist is None: + instance_whitelist = all_instance_categories + if stuff_whitelist is None: + stuff_whitelist = all_stuff_categories + category_whitelist = set(instance_whitelist) | set(stuff_whitelist) + + # Add object data from instances + self.image_id_to_objects = defaultdict(list) + for object_data in instances_data['annotations']: + image_id = object_data['image_id'] + _, _, w, h = object_data['bbox'] + W, H = self.image_id_to_size[image_id] + box_area = (w * h) / (W * H) + # box_area = object_data['area'] / (W * H) + box_ok = box_area > min_object_size + object_name = object_idx_to_name[object_data['category_id']] + category_ok = object_name in category_whitelist + other_ok = object_name != 'other' or include_other + if box_ok and category_ok and other_ok and (object_data['iscrowd'] != 1): + self.image_id_to_objects[image_id].append(object_data) + + # Add object data from stuff + if stuff_data: + image_ids_with_stuff = set() + for object_data in stuff_data['annotations']: + image_id = object_data['image_id'] + image_ids_with_stuff.add(image_id) + _, _, w, h = object_data['bbox'] + W, H = self.image_id_to_size[image_id] + box_area = (w * h) / (W * H) + # box_area = object_data['area'] / (W * H) + box_ok = box_area > min_object_size + object_name = object_idx_to_name[object_data['category_id']] + category_ok = object_name in category_whitelist + other_ok = object_name != 'other' or include_other + if box_ok and category_ok and other_ok and (object_data['iscrowd'] != 1): + self.image_id_to_objects[image_id].append(object_data) + + if stuff_only: + new_image_ids = [] + for image_id in self.image_ids: + if image_id in image_ids_with_stuff: + new_image_ids.append(image_id) + self.image_ids = new_image_ids + + all_image_ids = set(self.image_id_to_filename.keys()) + image_ids_to_remove = all_image_ids - image_ids_with_stuff + for image_id in image_ids_to_remove: + self.image_id_to_filename.pop(image_id, None) + self.image_id_to_size.pop(image_id, None) + self.image_id_to_objects.pop(image_id, None) + + # COCO category labels start at 1, so use 0 for __image__ + self.vocab['object_name_to_idx']['__image__'] = 0 + + # Build object_idx_to_name + name_to_idx = self.vocab['object_name_to_idx'] + assert len(name_to_idx) == len(set(name_to_idx.values())) + max_object_idx = max(name_to_idx.values()) + idx_to_name = ['NONE'] * (1 + max_object_idx) + for name, idx in self.vocab['object_name_to_idx'].items(): + idx_to_name[idx] = name + self.vocab['object_idx_to_name'] = idx_to_name + + # Prune images that have too few or too many objects + new_image_ids = [] + total_objs = 0 + for image_id in self.image_ids: + num_objs = len(self.image_id_to_objects[image_id]) + total_objs += num_objs + if min_objects_per_image <= num_objs <= max_objects_per_image: + new_image_ids.append(image_id) + self.image_ids = new_image_ids + + self.vocab['pred_idx_to_name'] = [ + '__in_image__', + 'left of', + 'right of', + 'above', + 'below', + 'inside', + 'surrounding', + ] + self.vocab['pred_name_to_idx'] = {} + for idx, name in enumerate(self.vocab['pred_idx_to_name']): + self.vocab['pred_name_to_idx'][name] = idx + + def set_image_size(self, image_size): + print('called set_image_size', image_size) + transform = [Resize(image_size), T.ToTensor()] + if self.normalize_images: + transform.append(imagenet_preprocess()) + self.transform = T.Compose(transform) + self.image_size = image_size + + def total_objects(self): + total_objs = 0 + for i, image_id in enumerate(self.image_ids): + if self.max_samples and i >= self.max_samples: + break + num_objs = len(self.image_id_to_objects[image_id]) + total_objs += num_objs + return total_objs + + def __len__(self): + if self.max_samples is None: + if self.left_right_flip: + return len(self.image_ids)*2 + return len(self.image_ids) + return min(len(self.image_ids), self.max_samples) + + def __getitem__(self, index): + """ + Get the pixels of an image, and a random synthetic scene graph for that + image constructed on-the-fly from its COCO object annotations. We assume + that the image will have height H, width W, C channels; there will be O + object annotations, each of which will have both a bounding box and a + segmentation mask of shape (M, M). There will be T triples in the scene + graph. + + Returns a tuple of: + - image: FloatTensor of shape (C, H, W) + - objs: LongTensor of shape (O,) + - boxes: FloatTensor of shape (O, 4) giving boxes for objects in + (x0, y0, x1, y1) format, in a [0, 1] coordinate system + - masks: LongTensor of shape (O, M, M) giving segmentation masks for + objects, where 0 is background and 1 is object. + - triples: LongTensor of shape (T, 3) where triples[t] = [i, p, j] + means that (objs[i], p, objs[j]) is a triple. + """ + flip = False + if index >= len(self.image_ids): + index = index - len(self.image_ids) + flip = True + image_id = self.image_ids[index] + + filename = self.image_id_to_filename[image_id] + image_path = os.path.join(self.image_dir, filename) + with open(image_path, 'rb') as f: + with PIL.Image.open(f) as image: + if flip: + image = PIL.ImageOps.mirror(image) + WW, HH = image.size + image = self.transform(image.convert('RGB')) + + objs, boxes, masks = [], [], [] + # obj_masks = [] + for object_data in self.image_id_to_objects[image_id]: + objs.append(object_data['category_id']) + x, y, w, h = object_data['bbox'] + x0 = x / WW + y0 = y / HH + x1 = (w) / WW + y1 = (h) / HH + if flip: + x0 = 1 - (x0 + x1) + boxes.append(np.array([x0, y0, x1, y1])) + + # # This will give a numpy array of shape (HH, WW) + # mask = seg_to_mask(object_data['segmentation'], WW, HH) + # if flip: + # mask = mask[:, ::-1] + # x = WW - x - w + # # Crop the mask according to the bounding box, being careful to + # # ensure that we don't crop a zero-area region + # # mx0, mx1 = int(round(x)), int(round(x + w)) + # # my0, my1 = int(round(y)), int(round(y + h)) + # # mx1 = max(mx0 + 1, mx1) + # # my1 = max(my0 + 1, my1) + # # obj_mask = mask[my0:my1, mx0:mx1] + # # obj_mask = imresize(255.0 * obj_mask, (self.mask_size, self.mask_size), + # # mode='constant') + # # obj_mask = torch.from_numpy((obj_mask > 128).astype(np.int64)) + # # obj_masks.append(obj_mask) + # + # mask = imresize(255.0 * mask, (self.image_size[0], self.image_size[1]), + # mode='constant') + # mask = torch.from_numpy((mask > 128).astype(np.int64)) + # masks.append(mask) + + # shuffle(objs) + # shuffle(boxes) + # shuffle(masks) + # Add dummy __image__ object + # objs.append(184) + # boxes.append(torch.FloatTensor([0, 0, 1, 1])) + # masks.append(torch.ones(self.mask_size, self.mask_size).long()) + + # add 0 for number of objects + for _ in range(len(objs), self.max_objects_per_image): + objs.append(self.vocab['object_name_to_idx']['__image__']) + boxes.append(np.array([-0.6, -0.6, 0.5, 0.5])) + # masks.append(torch.zeros((self.image_size[0], self.image_size[1])).long()) + # obj_masks.append(torch.zeros((self.mask_size, self.mask_size)).long()) + + objs = torch.LongTensor(objs) + boxes = np.vstack(boxes) + # masks = torch.stack(masks, dim=0) + # obj_masks = torch.stack(obj_masks, dim=0) + # b_map = self.get_bbox_map_p(boxes) + + # box_areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + # Compute centers of all objects + """ + obj_centers = [] + _, MH, MW = masks.size() + for i, obj_idx in enumerate(objs): + x0, y0, x1, y1 = boxes[i] + mask = (masks[i] == 1) + xs = torch.linspace(x0, x1, MW).view(1, MW).expand(MH, MW) + ys = torch.linspace(y0, y1, MH).view(MH, 1).expand(MH, MW) + if mask.sum() == 0: + mean_x = 0.5 * (x0 + x1) + mean_y = 0.5 * (y0 + y1) + else: + mean_x = xs[mask].mean() + mean_y = ys[mask].mean() + obj_centers.append([mean_x, mean_y]) + obj_centers = torch.FloatTensor(obj_centers) + + # Add triples + + triples = [] + num_objs = objs.size(0) + __image__ = self.vocab['object_name_to_idx']['__image__'] + real_objs = [] + if num_objs > 1: + real_objs = (objs != __image__).nonzero().squeeze(1) + for cur in real_objs: + choices = [obj for obj in real_objs if obj != cur] + if len(choices) == 0 or not self.include_relationships: + break + other = random.choice(choices) + if random.random() > 0.5: + s, o = cur, other + else: + s, o = other, cur + + # Check for inside / surrounding + sx0, sy0, sx1, sy1 = boxes[s] + ox0, oy0, ox1, oy1 = boxes[o] + d = obj_centers[s] - obj_centers[o] + theta = math.atan2(d[1], d[0]) + + if sx0 < ox0 and sx1 > ox1 and sy0 < oy0 and sy1 > oy1: + p = 'surrounding' + elif sx0 > ox0 and sx1 < ox1 and sy0 > oy0 and sy1 < oy1: + p = 'inside' + elif theta >= 3 * math.pi / 4 or theta <= -3 * math.pi / 4: + p = 'left of' + elif -3 * math.pi / 4 <= theta < -math.pi / 4: + p = 'above' + elif -math.pi / 4 <= theta < math.pi / 4: + p = 'right of' + elif math.pi / 4 <= theta < 3 * math.pi / 4: + p = 'below' + p = self.vocab['pred_name_to_idx'][p] + triples.append([s, p, o]) + + # Add __in_image__ triples + O = objs.size(0) + in_image = self.vocab['pred_name_to_idx']['__in_image__'] + for i in range(O - 1): + triples.append([i, in_image, O - 1]) + """ + + # triples = torch.LongTensor(triples) + return image, objs, boxes## , b_map #, None # obj_masks #, obj_masks # , b_map # masks # , triples + + def get_bbox_map_p(self, bbox): + mapping = np.zeros((len(bbox), self.image_size[0], self.image_size[0])) + for idx in range(self.max_objects_per_image): + if min(bbox[idx]) < 0: + continue + line_space = np.linspace(0, self.image_size[0]-1, num=self.image_size[0]) + xv, yv = np.meshgrid(line_space, line_space) + mapping[idx][(xv < int((bbox[idx][0] + bbox[idx][2]) * self.image_size[0])) * (xv > int(bbox[idx][0] * self.image_size[0])) * + (yv < int((bbox[idx][1] + bbox[idx][3]) * self.image_size[0])) * (yv > int(bbox[idx][1] * self.image_size[0]))] = 1 + return mapping + + +def seg_to_mask(seg, width=1.0, height=1.0): + """ + Tiny utility for decoding segmentation masks using the pycocotools API. + """ + if type(seg) == list: + rles = mask_utils.frPyObjects(seg, height, width) + rle = mask_utils.merge(rles) + elif type(seg['counts']) == list: + rle = mask_utils.frPyObjects(seg, height, width) + else: + rle = seg + return mask_utils.decode(rle) + + +def coco_collate_fn(batch): + """ + Collate function to be used when wrapping CocoSceneGraphDataset in a + DataLoader. Returns a tuple of the following: + + - imgs: FloatTensor of shape (N, C, H, W) + - objs: LongTensor of shape (O,) giving object categories + - boxes: FloatTensor of shape (O, 4) + - masks: FloatTensor of shape (O, M, M) + - triples: LongTensor of shape (T, 3) giving triples + - obj_to_img: LongTensor of shape (O,) mapping objects to images + - triple_to_img: LongTensor of shape (T,) mapping triples to images + """ + all_imgs, all_objs, all_boxes, all_masks, all_triples = [], [], [], [], [] + all_obj_to_img, all_triple_to_img = [], [] + obj_offset = 0 + for i, (img, objs, boxes, masks, triples) in enumerate(batch): + all_imgs.append(img[None]) + if objs.dim() == 0 or triples.dim() == 0: + continue + O, T = objs.size(0), triples.size(0) + all_objs.append(objs) + all_boxes.append(boxes) + all_masks.append(masks) + triples = triples.clone() + triples[:, 0] += obj_offset + triples[:, 2] += obj_offset + all_triples.append(triples) + + all_obj_to_img.append(torch.LongTensor(O).fill_(i)) + all_triple_to_img.append(torch.LongTensor(T).fill_(i)) + obj_offset += O + + all_imgs = torch.cat(all_imgs) + all_objs = torch.cat(all_objs) + all_boxes = torch.cat(all_boxes) + all_masks = torch.cat(all_masks) + all_triples = torch.cat(all_triples) + all_obj_to_img = torch.cat(all_obj_to_img) + all_triple_to_img = torch.cat(all_triple_to_img) + + out = (all_imgs, all_objs, all_boxes, all_masks, all_triples, + all_obj_to_img, all_triple_to_img) + return out + + +# IMAGENET_MEAN = [0.485, 0.456, 0.406] +# IMAGENET_STD = [0.229, 0.224, 0.225] +IMAGENET_MEAN = [0.5, 0.5, 0.5] +IMAGENET_STD = [0.5, 0.5, 0.5] + +INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN] +INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD] + + +def imagenet_preprocess(): + return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD) + + +def rescale(x): + lo, hi = x.min(), x.max() + return x.sub(lo).div(hi - lo) + + +def imagenet_deprocess(rescale_image=True): + transforms = [ + T.Normalize(mean=[0, 0, 0], std=INV_IMAGENET_STD), + T.Normalize(mean=INV_IMAGENET_MEAN, std=[1.0, 1.0, 1.0]), + ] + if rescale_image: + transforms.append(rescale) + return T.Compose(transforms) + + +def imagenet_deprocess_batch(imgs, rescale=True): + """ + Input: + - imgs: FloatTensor of shape (N, C, H, W) giving preprocessed images + + Output: + - imgs_de: ByteTensor of shape (N, C, H, W) giving deprocessed images + in the range [0, 255] + """ + if isinstance(imgs, torch.autograd.Variable): + imgs = imgs.data + imgs = imgs.cpu().clone() + deprocess_fn = imagenet_deprocess(rescale_image=rescale) + imgs_de = [] + for i in range(imgs.size(0)): + img_de = deprocess_fn(imgs[i])[None] + img_de = img_de.mul(255).clamp(0, 255).byte() + imgs_de.append(img_de) + imgs_de = torch.cat(imgs_de, dim=0) + return imgs_de + + +class Resize(object): + def __init__(self, size, interp=PIL.Image.BILINEAR): + if isinstance(size, tuple): + H, W = size + self.size = (W, H) + else: + self.size = (size, size) + self.interp = interp + + def __call__(self, img): + return img.resize(self.size, self.interp) + + +def unpack_var(v): + if isinstance(v, torch.autograd.Variable): + return v.data + return v + + +def split_graph_batch(triples, obj_data, obj_to_img, triple_to_img): + triples = unpack_var(triples) + obj_data = [unpack_var(o) for o in obj_data] + obj_to_img = unpack_var(obj_to_img) + triple_to_img = unpack_var(triple_to_img) + + triples_out = [] + obj_data_out = [[] for _ in obj_data] + obj_offset = 0 + N = obj_to_img.max() + 1 + for i in range(N): + o_idxs = (obj_to_img == i).nonzero().view(-1) + t_idxs = (triple_to_img == i).nonzero().view(-1) + + cur_triples = triples[t_idxs].clone() + cur_triples[:, 0] -= obj_offset + cur_triples[:, 2] -= obj_offset + triples_out.append(cur_triples) + + for j, o_data in enumerate(obj_data): + cur_o_data = None + if o_data is not None: + cur_o_data = o_data[o_idxs] + obj_data_out[j].append(cur_o_data) + + obj_offset += o_idxs.size(0) + + return triples_out, obj_data_out + + diff --git a/data/vg.py b/data/vg.py new file mode 100644 index 0000000..9cd856c --- /dev/null +++ b/data/vg.py @@ -0,0 +1,249 @@ +#!/usr/bin/python +# +# Copyright 2018 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import random +from collections import defaultdict + +import torch +from torch.utils.data import Dataset +import torchvision.transforms as T +from skimage.transform import resize as imresize + +import numpy as np +import h5py +import PIL + + +class VgSceneGraphDataset(Dataset): + def __init__(self, vocab, h5_path, image_dir, image_size=(256, 256), + normalize_images=True, max_objects=10, max_samples=None, + include_relationships=True, use_orphaned_objects=True, + left_right_flip=False): + super(VgSceneGraphDataset, self).__init__() + + self.image_dir = image_dir + self.image_size = image_size + self.vocab = vocab + self.num_objects = len(vocab['object_idx_to_name']) + self.use_orphaned_objects = use_orphaned_objects + self.max_objects = max_objects + self.max_samples = max_samples + self.left_right_flip = left_right_flip + self.include_relationships = include_relationships + + transform = [Resize(image_size), T.ToTensor()] + if normalize_images: + transform.append(imagenet_preprocess()) + self.transform = T.Compose(transform) + + self.data = {} + with h5py.File(h5_path, 'r') as f: + for k, v in f.items(): + if k == 'image_paths': + self.image_paths = list(v) + else: + self.data[k] = torch.IntTensor(np.asarray(v)) + + def __len__(self): + num = self.data['object_names'].size(0) + if self.max_samples is not None: + return min(self.max_samples, num) + if self.left_right_flip: + return num * 2 + return num + + def __getitem__(self, index): + """ + Returns a tuple of: + - image: FloatTensor of shape (C, H, W) + - objs: LongTensor of shape (O,) + - boxes: FloatTensor of shape (O, 4) giving boxes for objects in + (x0, y0, x1, y1) format, in a [0, 1] coordinate system. + - triples: LongTensor of shape (T, 3) where triples[t] = [i, p, j] + means that (objs[i], p, objs[j]) is a triple. + """ + flip = False + if index >= self.data['object_names'].size(0): + index = index - self.data['object_names'].size(0) + flip = True + + img_path = os.path.join(self.image_dir, self.image_paths[index]) + + with open(img_path, 'rb') as f: + with PIL.Image.open(f) as image: + if flip: + image = PIL.ImageOps.mirror(image) + WW, HH = image.size + image = self.transform(image.convert('RGB')) + + H, W = self.image_size + + # Figure out which objects appear in relationships and which don't + obj_idxs_with_rels = set() + obj_idxs_without_rels = set(range(self.data['objects_per_image'][index].item())) + for r_idx in range(self.data['relationships_per_image'][index]): + s = self.data['relationship_subjects'][index, r_idx].item() + o = self.data['relationship_objects'][index, r_idx].item() + obj_idxs_with_rels.add(s) + obj_idxs_with_rels.add(o) + obj_idxs_without_rels.discard(s) + obj_idxs_without_rels.discard(o) + + obj_idxs = list(obj_idxs_with_rels) + obj_idxs_without_rels = list(obj_idxs_without_rels) + if len(obj_idxs) > self.max_objects - 1: + obj_idxs = random.sample(obj_idxs, self.max_objects) + if len(obj_idxs) < self.max_objects - 1 and self.use_orphaned_objects: + num_to_add = self.max_objects - 1 - len(obj_idxs) + num_to_add = min(num_to_add, len(obj_idxs_without_rels)) + obj_idxs += random.sample(obj_idxs_without_rels, num_to_add) + O = len(obj_idxs) + 1 + + objs = torch.LongTensor(self.max_objects+1).fill_(-1) + + boxes = torch.FloatTensor([[0, 0, 1, 1]]).repeat(self.max_objects+1, 1) + obj_idx_mapping = {} + for i, obj_idx in enumerate(obj_idxs): + objs[i] = self.data['object_names'][index, obj_idx].item() + x, y, w, h = self.data['object_boxes'][index, obj_idx].tolist() + x0 = float(x) / WW + y0 = float(y) / HH + x1 = float(w) / WW + y1 = float(h) / HH + if flip: + x0 = 1 - (x0 + x1) + boxes[i] = torch.FloatTensor([x0, y0, x1, y1]) + obj_idx_mapping[obj_idx] = i + + # The last object will be the special __image__ object + objs[O - 1] = self.vocab['object_name_to_idx']['__image__'] + + for i in range(O, self.max_objects+1): + # objs.append(self.vocab['object_name_to_idx']['__image__']) + # boxes.append(np.array([-0.6, -0.6, 0.5, 0.5])) + objs[i] = self.vocab['object_name_to_idx']['__image__'] + boxes[i] = torch.FloatTensor([-0.6, -0.6, 0.5, 0.5]) + + # triples = [] + # for r_idx in range(self.data['relationships_per_image'][index].item()): + # if not self.include_relationships: + # break + # s = self.data['relationship_subjects'][index, r_idx].item() + # p = self.data['relationship_predicates'][index, r_idx].item() + # o = self.data['relationship_objects'][index, r_idx].item() + # s = obj_idx_mapping.get(s, None) + # o = obj_idx_mapping.get(o, None) + # if s is not None and o is not None: + # triples.append([s, p, o]) + # + # # Add dummy __in_image__ relationships for all objects + # in_image = self.vocab['pred_name_to_idx']['__in_image__'] + # for i in range(O - 1): + # triples.append([i, in_image, O - 1]) + # + # triples = torch.LongTensor(triples) + return image, objs, boxes #, triples + + +class Resize(object): + def __init__(self, size, interp=PIL.Image.BILINEAR): + if isinstance(size, tuple): + H, W = size + self.size = (W, H) + else: + self.size = (size, size) + self.interp = interp + + def __call__(self, img): + return img.resize(self.size, self.interp) + + +IMAGENET_MEAN = [0.5, 0.5, 0.5] +IMAGENET_STD = [0.5, 0.5, 0.5] + +INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN] +INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD] + + +def imagenet_preprocess(): + return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD) + + +def vg_collate_fn(batch): + """ + Collate function to be used when wrapping a VgSceneGraphDataset in a + DataLoader. Returns a tuple of the following: + + - imgs: FloatTensor of shape (N, C, H, W) + - objs: LongTensor of shape (O,) giving categories for all objects + - boxes: FloatTensor of shape (O, 4) giving boxes for all objects + - triples: FloatTensor of shape (T, 3) giving all triples, where + triples[t] = [i, p, j] means that [objs[i], p, objs[j]] is a triple + - obj_to_img: LongTensor of shape (O,) mapping objects to images; + obj_to_img[i] = n means that objs[i] belongs to imgs[n] + - triple_to_img: LongTensor of shape (T,) mapping triples to images; + triple_to_img[t] = n means that triples[t] belongs to imgs[n]. + """ + # batch is a list, and each element is (image, objs, boxes, triples) + all_imgs, all_objs, all_boxes, all_triples = [], [], [], [] + all_obj_to_img, all_triple_to_img = [], [] + obj_offset = 0 + for i, (img, objs, boxes, triples) in enumerate(batch): + all_imgs.append(img[None]) + O, T = objs.size(0), triples.size(0) + all_objs.append(objs) + all_boxes.append(boxes) + triples = triples.clone() + triples[:, 0] += obj_offset + triples[:, 2] += obj_offset + all_triples.append(triples) + + all_obj_to_img.append(torch.LongTensor(O).fill_(i)) + all_triple_to_img.append(torch.LongTensor(T).fill_(i)) + obj_offset += O + + all_imgs = torch.cat(all_imgs) + all_objs = torch.cat(all_objs) + all_boxes = torch.cat(all_boxes) + all_triples = torch.cat(all_triples) + all_obj_to_img = torch.cat(all_obj_to_img) + all_triple_to_img = torch.cat(all_triple_to_img) + + out = (all_imgs, all_objs, all_boxes, all_triples, + all_obj_to_img, all_triple_to_img) + return out + + +def vg_uncollate_fn(batch): + """ + Inverse operation to the above. + """ + imgs, objs, boxes, triples, obj_to_img, triple_to_img = batch + out = [] + obj_offset = 0 + for i in range(imgs.size(0)): + cur_img = imgs[i] + o_idxs = (obj_to_img == i).nonzero().view(-1) + t_idxs = (triple_to_img == i).nonzero().view(-1) + cur_objs = objs[o_idxs] + cur_boxes = boxes[o_idxs] + cur_triples = 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