Spaces:
Running
on
Zero
Running
on
Zero
| import torch | |
| import torchvision | |
| import numpy as np | |
| from torchtools.transforms import SmartCrop | |
| import math | |
| class Bucketeer(): | |
| def __init__(self, dataloader, density=256*256, factor=8, ratios=[1/1, 1/2, 3/4, 3/5, 4/5, 6/9, 9/16], reverse_list=True, randomize_p=0.3, randomize_q=0.2, crop_mode='random', p_random_ratio=0.0, interpolate_nearest=False): | |
| assert crop_mode in ['center', 'random', 'smart'] | |
| self.crop_mode = crop_mode | |
| self.ratios = ratios | |
| if reverse_list: | |
| for r in list(ratios): | |
| if 1/r not in self.ratios: | |
| self.ratios.append(1/r) | |
| self.sizes = {} | |
| for dd in density: | |
| self.sizes[dd]= [(int(((dd/r)**0.5//factor)*factor), int(((dd*r)**0.5//factor)*factor)) for r in ratios] | |
| self.batch_size = dataloader.batch_size | |
| self.iterator = iter(dataloader) | |
| all_sizes = [] | |
| for k, vs in self.sizes.items(): | |
| all_sizes += vs | |
| self.buckets = {s: [] for s in all_sizes} | |
| self.smartcrop = SmartCrop(int(density**0.5), randomize_p, randomize_q) if self.crop_mode=='smart' else None | |
| self.p_random_ratio = p_random_ratio | |
| self.interpolate_nearest = interpolate_nearest | |
| def get_available_batch(self): | |
| for b in self.buckets: | |
| if len(self.buckets[b]) >= self.batch_size: | |
| batch = self.buckets[b][:self.batch_size] | |
| self.buckets[b] = self.buckets[b][self.batch_size:] | |
| return batch | |
| return None | |
| def get_closest_size(self, x): | |
| w, h = x.size(-1), x.size(-2) | |
| best_size_idx = np.argmin([abs(w/h-r) for r in self.ratios]) | |
| find_dict = {dd : abs(w*h - self.sizes[dd][best_size_idx][0]*self.sizes[dd][best_size_idx][1]) for dd, vv in self.sizes.items()} | |
| min_ = find_dict[list(find_dict.keys())[0]] | |
| find_size = self.sizes[list(find_dict.keys())[0]][best_size_idx] | |
| for dd, val in find_dict.items(): | |
| if val < min_: | |
| min_ = val | |
| find_size = self.sizes[dd][best_size_idx] | |
| return find_size | |
| def get_resize_size(self, orig_size, tgt_size): | |
| if (tgt_size[1]/tgt_size[0] - 1) * (orig_size[1]/orig_size[0] - 1) >= 0: | |
| alt_min = int(math.ceil(max(tgt_size)*min(orig_size)/max(orig_size))) | |
| resize_size = max(alt_min, min(tgt_size)) | |
| else: | |
| alt_max = int(math.ceil(min(tgt_size)*max(orig_size)/min(orig_size))) | |
| resize_size = max(alt_max, max(tgt_size)) | |
| return resize_size | |
| def __next__(self): | |
| batch = self.get_available_batch() | |
| while batch is None: | |
| elements = next(self.iterator) | |
| for dct in elements: | |
| img = dct['images'] | |
| size = self.get_closest_size(img) | |
| resize_size = self.get_resize_size(img.shape[-2:], size) | |
| if self.interpolate_nearest: | |
| img = torchvision.transforms.functional.resize(img, resize_size, interpolation=torchvision.transforms.InterpolationMode.NEAREST) | |
| else: | |
| img = torchvision.transforms.functional.resize(img, resize_size, interpolation=torchvision.transforms.InterpolationMode.BILINEAR, antialias=True) | |
| if self.crop_mode == 'center': | |
| img = torchvision.transforms.functional.center_crop(img, size) | |
| elif self.crop_mode == 'random': | |
| img = torchvision.transforms.RandomCrop(size)(img) | |
| elif self.crop_mode == 'smart': | |
| self.smartcrop.output_size = size | |
| img = self.smartcrop(img) | |
| self.buckets[size].append({**{'images': img}, **{k:dct[k] for k in dct if k != 'images'}}) | |
| batch = self.get_available_batch() | |
| out = {k:[batch[i][k] for i in range(len(batch))] for k in batch[0]} | |
| return {k: torch.stack(o, dim=0) if isinstance(o[0], torch.Tensor) else o for k, o in out.items()} | |