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dsprites.py
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dsprites.py
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import os
import numpy as np
from sklearn.utils.extmath import cartesian
from torch.utils.data import DataLoader, Dataset
from urllib import request
import torch
class SequenceDataset(Dataset):
def __init__(self, seq_len=None, **kwargs):
super().__init__(**kwargs)
self.seq_len = seq_len
self.seq_transform_idxs = None # set in child class
self.index_manager = None # set in child class
self.factor_sizes = None # set in child class
self.data = None # set in child class
def __len__(self):
return len(self.data)
def __getitem__(self, id):
index = torch.randint(len(self.seq_transform_idxs), (1,))
transform_idx = self.seq_transform_idxs[index]
img_seq = []
feat_seq = []
for idx in id:
x0 = self.data[idx]
x0_feat = self.index_manager.index_to_features(idx)
xt_feat = x0_feat.copy()
xt_feat1 = x0_feat.copy()
img_x_seq = [x0]
feat_x_seq = [x0_feat]
step = torch.randint(0,self.seq_len-1, (1,))
#for t in range(self.seq_len):
#print(step % self.factor_sizes[transform_idx])
xt_feat[transform_idx] = (x0_feat[transform_idx] + (step.cpu().numpy())) % self.factor_sizes[transform_idx]
xt_idx = self.index_manager.features_to_index(xt_feat)
xt = self.data[xt_idx]
img_x_seq.append(xt.copy())
feat_x_seq.append(xt_feat.copy())
xt_feat1[transform_idx] = (x0_feat[transform_idx] + (step.cpu().numpy()+1)) % self.factor_sizes[transform_idx]
xt_idx1 = self.index_manager.features_to_index(xt_feat1)
xt1 = self.data[xt_idx1]
img_x_seq.append(xt1.copy())
feat_x_seq.append(xt_feat1.copy())
img_seq.append(img_x_seq)
feat_seq.append(feat_x_seq)
img_seq = torch.tensor(img_seq).unsqueeze(1)
feat_seq = torch.tensor(feat_seq)
return img_seq, feat_seq, index, step
class IndexManger(object):
"""Index mapping from features to positions of state space atoms."""
def __init__(self, factor_sizes):
"""Index to latent (= features) space and vice versa.
Args:
factor_sizes: List of integers with the number of distinct values for each
of the factors.
"""
self.factor_sizes = np.array(factor_sizes)
self.num_total = np.prod(self.factor_sizes)
self.factor_bases = self.num_total / np.cumprod(self.factor_sizes)
self.index_to_feat = cartesian([np.array(list(range(i))) for i in self.factor_sizes])
def features_to_index(self, features):
"""Returns the indices in the input space for given factor configurations.
Args:
features: Numpy matrix where each row contains a different factor
configuration for which the indices in the input space should be
returned.
"""
#assert np.all((0 <= features) & (features <= self.factor_sizes))
index = np.array(np.dot(features, self.factor_bases), dtype=np.int64)
#assert np.all((0 <= index) & (index < self.num_total))
return index
def index_to_features(self, index):
#assert np.all((0 <= index) & (index < self.num_total))
features = self.index_to_feat[index]
#assert np.all((0 <= features) & (features <= self.factor_sizes))
return features
class DSpritesDataset(SequenceDataset):
"""
A PyTorch wrapper for the dSprites dataset by
Matthey et al. 2017. The dataset provides a 2D scene
with a sprite under different transformations:
# dim, type, #values avail.-range
* 0, color | 1 | 1-1
* 1, shape | 3 | 1-3
* 2, scale | 6 | 0.5-1.
* 3, orientation | 40 | 0-2pi
* 4, x-position | 32 | 0-1
* 5, y-position | 32 | 0-1
for details see https://github.com/deepmind/dsprites-dataset
"""
def __init__(self, dir='/home/akeller/repo/TECA/tvae/datasets/dsprites/',
seq_transforms=['orientation'],
avail_transforms=None,
max_transform_len=18,
**kwargs):
super().__init__(**kwargs)
self.url = "https://github.com/deepmind/dsprites-dataset/raw/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz"
self.path = dir
self.path_npz = os.path.join(self.path, 'dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz')
print("Loading DSprites...")
try:
full_data = np.load(self.path_npz, encoding="latin1", allow_pickle=True)
except FileNotFoundError:
os.makedirs(self.path, exist_ok=True)
print(f'downloading dataset ... saving to {self.path_npz}')
request.urlretrieve(self.url, self.path_npz)
full_data = np.load(self.path_npz, encoding="latin1", allow_pickle=True)
self.data = full_data['imgs'].squeeze().astype(np.float32)
self.latents_values = full_data['latents_values']
self.latents_classes = full_data['latents_classes']
self.metadata = full_data['metadata'][()]
original_factor_sizes = [3, 6, 40, 32, 32]
speeds = [1, 1, 4, 4, 4]
start_idx = [0, 1, 0, 0, 0]
data_by_factor = self.data.reshape((*original_factor_sizes,) + (64, 64))
# Subselect Avail Transforms
if avail_transforms is None:
avail_transforms = seq_transforms
print(f"Removing all transforms but {avail_transforms}")
avail_transform_idxs = [self.metadata['latents_names'][1:].index(x) for x in avail_transforms]
data_indexer = [-1 if i not in avail_transform_idxs else np.s_[start_idx[i]:max_transform_len:speeds[i]] for i
in range(len(original_factor_sizes))]
self.data = data_by_factor[data_indexer]
self.factor_sizes = [1 if i not in avail_transform_idxs else self.data.shape[i] for i in
range(len(original_factor_sizes))]
self.data = self.data.reshape(-1, 64, 64)
# Ignore color index
self.seq_transforms = seq_transforms
self.seq_transform_idxs = [self.metadata['latents_names'][1:].index(x) for x in self.seq_transforms]
self.index_manager = IndexManger(self.factor_sizes)
print("Done")
def __len__(self):
return len(self.data)
def get_dataloader(dir='./',
seq_transforms=['posX', 'posY'],
avail_transforms=None,
seq_len=9,
max_transform_len=18,
batch_size=20):
train_data = DSpritesDataset(dir=dir, seq_transforms=seq_transforms,
avail_transforms=avail_transforms,
max_transform_len=max_transform_len,
seq_len=seq_len)
sampler = torch.utils.data.sampler.BatchSampler(
torch.utils.data.sampler.RandomSampler(train_data),
batch_size=batch_size,
drop_last=True)
return DataLoader(
train_data,
sampler=sampler,
generator = torch.Generator(device='cuda'),
)
if __name__ == "__main__":
data_loader = get_dataloader()
for x, f in data_loader:
print(x, f)
break