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import os
import cv2
import copy
import random
import json
import contextlib
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
from . import transforms as T
from pycocotools.coco import COCO
from PIL import Image
class ReactionDataset(Dataset):
def __init__(self, args, tokenizer, data_file=None, image_files=None, split='train', debug=False):
super().__init__()
self.args = args
self.tokenizer = tokenizer
if data_file:
data_file = os.path.join(args.data_path, data_file)
with open(data_file) as f:
self.data = json.load(f)['images']
if split == 'train' and args.num_train_example is not None:
self.data = self.data[:args.num_train_example]
if split != 'train':
with open(os.devnull, 'w') as devnull:
with contextlib.redirect_stdout(devnull):
self.coco = COCO(data_file)
self.name = os.path.basename(data_file).split('.')[0]
if image_files:
self.data = [{'file_name': file} for file in image_files]
self.image_path = args.image_path
self.split = split
self.format = args.format
self.is_train = (split == 'train')
self.transform = make_transforms(split, args.augment, debug)
# self.reaction_transform = T.RandomReactionCrop()
def __len__(self):
return len(self.data)
@property
def pad_id(self):
return self.tokenizer[self.format].PAD_ID
def generate_sample(self, image, target):
ref = {}
# coordinates are normalized after transform
image, target = self.transform(image, target)
ref['scale'] = target['scale']
if self.is_train:
args = self.args
if self.format == 'reaction':
max_len = self.tokenizer['reaction'].max_len
label, label_out = self.tokenizer['reaction'].data_to_sequence(
target, rand_order=args.rand_order, shuffle_bbox=args.shuffle_bbox, add_noise=args.add_noise,
mix_noise=args.mix_noise)
ref['reaction'] = torch.LongTensor(label[:max_len])
ref['reaction_out'] = torch.LongTensor(label_out[:max_len])
if self.format == 'bbox':
max_len = self.tokenizer['bbox'].max_len
label, label_out = self.tokenizer['bbox'].data_to_sequence(
target, rand_order=args.rand_order, add_noise=args.add_noise)
ref['bbox'] = torch.LongTensor(label[:max_len])
ref['bbox_out'] = torch.LongTensor(label_out[:max_len])
return image, ref
def __getitem__(self, idx):
image, target = self.load_and_prepare(idx)
if self.is_train and self.args.composite_augment:
cnt = 0
while idx % 2 == random.randrange(2) and cnt < 5:
# Augment with probability 0.5
n = len(self)
idx2 = (idx + random.randrange(n)) % n
image2, target2 = self.load_and_prepare(idx2)
# if 'reaction' in self.formats:
# image, target = self.reaction_transform(image, target)
# image2, target2 = self.reaction_transform(image2, target2)
image, target = self.concat(image, target, image2, target2)
cnt += 1
if self.is_train and self.args.augment:
image1, ref1 = self.generate_sample(image, target)
image2, ref2 = self.generate_sample(image, target)
return [[idx, image1, ref1], [idx, image2, ref2]]
else:
image, ref = self.generate_sample(image, target)
ref['file_name'] = self.data[idx]['file_name']
return [[idx, image, ref]]
def load_and_prepare(self, idx):
target = self.data[idx]
path = os.path.join(self.image_path, target['file_name'])
if not os.path.exists(path):
print(path, "doesn't exists.", flush=True)
image = Image.open(path).convert("RGB")
if self.is_train:
image, target = self.prepare(image, target)
return image, target
def prepare(self, image, target):
w, h = target['width'], target['height']
image_id = target["id"]
image_id = torch.tensor([image_id])
anno = target["bboxes"]
boxes = [obj['bbox'] for obj in anno]
# guard against no boxes via resizing
boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2].clamp_(min=0, max=w)
boxes[:, 1::2].clamp_(min=0, max=h)
classes = [obj["category_id"] for obj in anno]
classes = torch.tensor(classes, dtype=torch.int64)
target = copy.deepcopy(target)
target["boxes"] = boxes
target["labels"] = classes
target["image_id"] = image_id
# for conversion to coco api
area = torch.tensor([obj["bbox"][2] * obj['bbox'][3] for obj in anno])
target["area"] = area
target["orig_size"] = torch.as_tensor([int(w), int(h)])
target["size"] = torch.as_tensor([int(w), int(h)])
return image, target
def concat(self, image1, target1, image2, target2):
color = (255, 255, 255)
if random.random() < 1:
# Vertically concat two images
w = max(image1.width, image2.width)
h = image1.height + image2.height
if image1.width > image2.width:
x1, y1 = 0, 0
x2, y2 = random.randint(0, image1.width - image2.width), image1.height
else:
x1, y1 = random.randint(0, image2.width - image1.width), 0
x2, y2 = 0, image1.height
else:
# Horizontally concat two images
w = image1.width + image2.width
h = max(image1.height, image2.height)
if image1.height > image2.height:
x1, y1 = 0, 0
x2, y2 = image1.width, random.randint(0, image1.height - image2.height)
else:
x1, y1 = 0, random.randint(0, image2.height - image1.height)
x2, y2 = image1.width, 0
image = Image.new('RGB', (w, h), color)
image.paste(image1, (x1, y1))
image.paste(image2, (x2, y2))
target = {
"image_id": target1["image_id"],
"orig_size": torch.as_tensor([int(w), int(h)]),
"size": torch.as_tensor([int(w), int(h)])
}
target1["boxes"][:, 0::2] += x1
target1["boxes"][:, 1::2] += y1
target2["boxes"][:, 0::2] += x2
target2["boxes"][:, 1::2] += y2
for key in ["boxes", "labels", "area"]:
target[key] = torch.cat([target1[key], target2[key]], dim=0)
if "reactions" in target1:
target["reactions"] = [r for r in target1["reactions"]]
nbox = len(target1["boxes"])
for r in target2["reactions"]:
newr = {}
for key, seq in r.items():
newr[key] = [x + nbox for x in seq]
target["reactions"].append(newr)
return image, target
def make_transforms(image_set, augment=False, debug=False):
normalize = T.Compose([
# T.Resize((1333, 1333)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], debug)
])
if image_set == 'train' and augment:
return T.Compose([
T.RandomRotate(),
T.RandomHorizontalFlip(),
T.LargeScaleJitter(output_size=1333, aug_scale_min=0.3, aug_scale_max=2.0),
T.RandomDistortion(0.5, 0.5, 0.5, 0.5),
normalize])
else:
return T.Compose([
T.LargeScaleJitter(output_size=1333, aug_scale_min=1.0, aug_scale_max=1.0),
normalize])
def pad_images(imgs):
# B, C, H, W
max_shape = [0, 0]
for img in imgs:
for i in range(len(max_shape)):
max_shape[i] = max(max_shape[i], img.shape[-1-i])
stack = []
for img in imgs:
pad = []
for i in range(len(max_shape)):
pad = pad + [0, max_shape[i] - img.shape[-1-i]]
stack.append(F.pad(img, pad, value=0))
return torch.stack(stack)
def get_collate_fn(pad_id):
def rxn_collate(batch):
ids = []
imgs = []
batch = [ex for seq in batch for ex in seq]
keys = list(batch[0][2].keys())
seq_formats = [key for key in keys if key in ['bbox', 'bbox_out', 'reaction', 'reaction_out']]
refs = {key: [[], []] for key in seq_formats}
for ex in batch:
ids.append(ex[0])
imgs.append(ex[1])
ref = ex[2]
for key in seq_formats:
refs[key][0].append(ref[key])
refs[key][1].append(torch.LongTensor([len(ref[key])]))
# Sequence
for key in keys:
if key in seq_formats:
refs[key][0] = pad_sequence(refs[key][0], batch_first=True, padding_value=pad_id)
refs[key][1] = torch.stack(refs[key][1]).reshape(-1, 1)
else:
refs[key] = [ex[2][key] for ex in batch]
return ids, pad_images(imgs), refs
return rxn_collate
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