243 lines
9.7 KiB
Python
243 lines
9.7 KiB
Python
import os
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import pickle
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from collections import defaultdict
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from pathlib import Path
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import lmdb
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision.datasets import ImageFolder
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from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS
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from torchvision.transforms import functional as F
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from tqdm import tqdm
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from .registry import DATASET
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from .transform import transform_pipeline
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from .util import dlib_landmark
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def default_transform_way(transform, sample):
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return [transform(sample[0]), *sample[1:]]
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class LMDBDataset(Dataset):
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def __init__(self, lmdb_path, pipeline=None, transform_way=default_transform_way, map_size=2 ** 40, readonly=True,
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**lmdb_kwargs):
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self.path = lmdb_path
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self.db = lmdb.open(lmdb_path, subdir=os.path.isdir(lmdb_path), map_size=map_size, readonly=readonly,
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lock=False, **lmdb_kwargs)
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with self.db.begin(write=False) as txn:
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self._len = pickle.loads(txn.get(b"$$len$$"))
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self.done_pipeline = pickle.loads(txn.get(b"$$done_pipeline$$"))
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if pipeline is None:
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self.not_done_pipeline = []
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else:
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self.not_done_pipeline = self._remain_pipeline(pipeline)
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self.transform = transform_pipeline(self.not_done_pipeline)
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self.transform_way = transform_way
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essential_attr = pickle.loads(txn.get(b"$$essential_attr$$"))
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for ea in essential_attr:
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setattr(self, ea, pickle.loads(txn.get(f"${ea}$".encode(encoding="utf-8"))))
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def _remain_pipeline(self, pipeline):
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for i, dp in enumerate(self.done_pipeline):
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if pipeline[i] != dp:
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raise ValueError(
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f"pipeline {self.done_pipeline} saved in this lmdb database is not match with pipeline:{pipeline}")
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return pipeline[len(self.done_pipeline):]
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def __repr__(self):
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return f"LMDBDataset: {self.path}\nlength: {len(self)}\n{self.transform}"
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def __len__(self):
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return self._len
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def __getitem__(self, idx):
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with self.db.begin(write=False) as txn:
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sample = pickle.loads(txn.get("{}".format(idx).encode()))
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sample = self.transform_way(self.transform, sample)
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return sample
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@staticmethod
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def lmdbify(dataset, done_pipeline, lmdb_path):
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env = lmdb.open(lmdb_path, map_size=2 ** 40, subdir=os.path.isdir(lmdb_path))
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with env.begin(write=True) as txn:
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for i in tqdm(range(len(dataset)), ncols=0):
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txn.put("{}".format(i).encode(), pickle.dumps(dataset[i]))
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txn.put(b"$$len$$", pickle.dumps(len(dataset)))
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txn.put(b"$$done_pipeline$$", pickle.dumps(done_pipeline))
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essential_attr = getattr(dataset, "essential_attr", list())
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txn.put(b"$$essential_attr$$", pickle.dumps(essential_attr))
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for ea in essential_attr:
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txn.put(f"${ea}$".encode(encoding="utf-8"), pickle.dumps(getattr(dataset, ea)))
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@DATASET.register_module()
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class ImprovedImageFolder(ImageFolder):
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def __init__(self, root, pipeline):
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super().__init__(root, transform_pipeline(pipeline), loader=lambda x: x)
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self.classes_list = defaultdict(list)
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self.essential_attr = ["classes_list"]
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for i in range(len(self)):
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self.classes_list[self.samples[i][-1]].append(i)
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assert len(self.classes_list) == len(self.classes)
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class EpisodicDataset(Dataset):
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def __init__(self, origin_dataset, num_class, num_query, num_support, num_episodes):
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self.origin = origin_dataset
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self.num_class = num_class
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assert self.num_class < len(self.origin.classes_list)
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self.num_query = num_query # K
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self.num_support = num_support # K
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self.num_episodes = num_episodes
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def _fetch_list_data(self, id_list):
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return [self.origin[i][0] for i in id_list]
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def __len__(self):
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return self.num_episodes
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def __getitem__(self, _):
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random_classes = torch.randperm(len(self.origin.classes_list))[:self.num_class].tolist()
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support_set = []
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query_set = []
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target_set = []
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for tag, c in enumerate(random_classes):
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image_list = self.origin.classes_list[c]
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if len(image_list) >= self.num_query + self.num_support:
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# have enough images belong to this class
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idx_list = torch.randperm(len(image_list))[:self.num_query + self.num_support].tolist()
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else:
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idx_list = torch.randint(high=len(image_list), size=(self.num_query + self.num_support,)).tolist()
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support = self._fetch_list_data(map(image_list.__getitem__, idx_list[:self.num_support]))
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query = self._fetch_list_data(map(image_list.__getitem__, idx_list[self.num_support:]))
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support_set.extend(support)
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query_set.extend(query)
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target_set.extend([tag] * self.num_query)
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return {
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"support": torch.stack(support_set),
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"query": torch.stack(query_set),
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"target": torch.tensor(target_set)
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}
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def __repr__(self):
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return f"<EpisodicDataset NE{self.num_episodes} NC{self.num_class} NS{self.num_support} NQ{self.num_query}>"
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@DATASET.register_module()
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class SingleFolderDataset(Dataset):
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def __init__(self, root, pipeline, with_path=False):
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assert os.path.isdir(root)
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self.root = root
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samples = []
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for r, _, fns in sorted(os.walk(self.root, followlinks=True)):
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for fn in sorted(fns):
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path = os.path.join(r, fn)
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if has_file_allowed_extension(path, IMG_EXTENSIONS):
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samples.append(path)
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self.samples = samples
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self.pipeline = transform_pipeline(pipeline)
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self.with_path = with_path
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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if not self.with_path:
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return self.pipeline(self.samples[idx])
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else:
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return self.pipeline(self.samples[idx]), self.samples[idx]
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def __repr__(self):
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return f"<SingleFolderDataset root={self.root} len={len(self)}>"
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@DATASET.register_module()
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class GenerationUnpairedDataset(Dataset):
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def __init__(self, root_a, root_b, random_pair, pipeline, with_path=False):
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self.A = SingleFolderDataset(root_a, pipeline, with_path)
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self.B = SingleFolderDataset(root_b, pipeline, with_path)
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self.random_pair = random_pair
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def __getitem__(self, idx):
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a_idx = idx % len(self.A)
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b_idx = idx % len(self.B) if not self.random_pair else torch.randint(len(self.B), (1,)).item()
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return dict(a=self.A[a_idx], b=self.B[b_idx])
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def __len__(self):
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return max(len(self.A), len(self.B))
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def __repr__(self):
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return f"<GenerationUnpairedDataset:\n\tA: {self.A}\n\tB: {self.B}>\nPipeline:\n{self.A.pipeline}"
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def normalize_tensor(tensor):
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tensor = tensor.float()
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tensor -= tensor.min()
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tensor /= tensor.max()
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return tensor
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@DATASET.register_module()
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class GenerationUnpairedDatasetWithEdge(Dataset):
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def __init__(self, root_a, root_b, random_pair, pipeline, edge_type, edges_path, landmarks_path, size=(256, 256),
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with_path=False):
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assert edge_type in ["hed", "canny", "landmark_hed", "landmark_canny"]
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self.edge_type = edge_type
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self.size = size
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self.edges_path = Path(edges_path)
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self.landmarks_path = Path(landmarks_path)
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assert self.edges_path.exists()
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assert self.landmarks_path.exists()
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self.A = SingleFolderDataset(root_a, pipeline, with_path=True)
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self.B = SingleFolderDataset(root_b, pipeline, with_path=True)
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self.random_pair = random_pair
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self.with_path = with_path
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def get_edge(self, origin_path):
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op = Path(origin_path)
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if self.edge_type.startswith("landmark_"):
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edge_type = self.edge_type.lstrip("landmark_")
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use_landmark = True
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else:
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edge_type = self.edge_type
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use_landmark = False
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edge_path = self.edges_path / f"{op.parent.name}/{op.stem}.{edge_type}.png"
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origin_edge = F.to_tensor(Image.open(edge_path).resize(self.size, Image.BILINEAR))
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if not use_landmark:
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return origin_edge
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else:
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landmark_path = self.landmarks_path / f"{op.parent.name}/{op.stem}.txt"
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key_points, part_labels, part_edge = dlib_landmark.read_keypoints(landmark_path, size=self.size)
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dist_tensor = normalize_tensor(torch.from_numpy(dlib_landmark.dist_tensor(key_points, size=self.size)))
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part_labels = normalize_tensor(torch.from_numpy(part_labels))
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part_edge = torch.from_numpy(part_edge).unsqueeze(0).float()
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# edges = origin_edge * (part_labels.sum(0) == 0) # remove edges within face
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# edges = part_edge + edges
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return torch.cat([origin_edge, part_edge, dist_tensor, part_labels])
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def __getitem__(self, idx):
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a_idx = idx % len(self.A)
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b_idx = idx % len(self.B) if not self.random_pair else torch.randint(len(self.B), (1,)).item()
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if self.with_path:
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output = {"a": self.A[a_idx], "b": self.B[b_idx]}
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output["edge_a"] = output["a"][1]
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return output
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output = dict()
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output["a"], path_a = self.A[a_idx]
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output["b"], path_b = self.B[b_idx]
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output["edge_a"] = self.get_edge(path_a)
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return output
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def __len__(self):
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return max(len(self.A), len(self.B))
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def __repr__(self):
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return f"<GenerationUnpairedDatasetWithEdge:\n\tA: {self.A}\n\tB: {self.B}>\nPipeline:\n{self.A.pipeline}"
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