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data.py
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data.py
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import copy
import h5py
import torch
import warnings
import numpy as np
from time import time
from torch_geometric.data import Data as PyGData
from torch_geometric.data import Batch as PyGBatch
from torch_geometric.nn.pool.consecutive import consecutive_cluster
import src
from src.data.cluster import Cluster, ClusterBatch
from src.utils import tensor_idx, is_dense, has_duplicates, \
isolated_nodes, knn_2, save_tensor, load_tensor, save_dense_to_csr, \
load_csr_to_dense, to_trimmed, to_float_rgb, to_byte_rgb
__all__ = ['Data', 'Batch']
class Data(PyGData):
"""Inherit from torch_geometric.Data with extensions tailored to our
specific needs.
"""
_NOT_INDEXABLE = ['_csr_', '_cluster_', 'edge_index', 'edge_attr']
def __init__(self, **kwargs):
super().__init__(**kwargs)
if src.is_debug_enabled():
self.debug()
@property
def pos(self):
return self['pos'] if 'pos' in self._store else None
@property
def rgb(self):
return self['rgb'] if 'rgb' in self._store else None
@property
def pred(self):
return self['pred'] if 'pred' in self._store else None
@property
def neighbor_index(self):
return self['neighbor_index'] if 'neighbor_index' in self._store \
else None
@property
def sub(self):
"""Cluster object indicating subpoint indices for each point."""
return self['sub'] if 'sub' in self._store else None
@property
def super_index(self):
"""Index of the superpoint each point belongs to."""
return self['super_index'] if 'super_index' in self._store else None
@property
def v_edge_attr(self):
"""Vertical edge features."""
return self['v_edge_attr'] if 'v_edge_attr' in self._store else None
def norm_index(self, mode='graph'):
"""Index to be used for LayerNorm.
:param mode: str
Normalization mode. 'graph' will normalize per graph (ie per
cloud, ie per batch). 'node' will normalize per node (ie per
point). 'segment' will normalize per segment (ie per
cluster)
"""
if getattr(self, 'batch', None) is not None:
batch = self.batch
else:
batch = torch.zeros(
self.num_nodes, device=self.device, dtype=torch.long)
if self.super_index is not None:
super_index = self.super_index
else:
super_index = torch.zeros(
self.num_nodes, device=self.device, dtype=torch.long)
if mode == 'graph':
return batch
elif mode == 'node':
return torch.arange(self.num_nodes, device=self.device)
elif mode == 'segment':
num_batches = batch.max() + 1
return super_index * num_batches + batch
else:
raise NotImplementedError(f"Unkown mode='{mode}'")
@property
def is_super(self):
"""Whether the points are superpoints for a denser sub-graph."""
return self.sub is not None
@property
def is_sub(self):
"""Whether the points belong to a coarser super-graph."""
return self.super_index is not None
@property
def has_neighbors(self):
"""Whether the points have neighbors."""
return self.neighbor_index is not None and self.neighbor_index.shape[1] > 0
@property
def has_edges(self):
"""Whether the points have edges."""
return self.edge_index is not None and self.edge_index.shape[1] > 0
@property
def has_edge_attr(self):
"""Whether the edges have features in `edge_attr`."""
return self.edge_attr is not None and self.edge_attr.shape[0] > 0
@property
def edge_keys(self):
"""All keys starting with `edge_`, apart from `edge_index` and
`edge_attr`.
"""
return [
k for k in self.keys
if k.startswith('edge_') and k not in ['edge_index', 'edge_attr']]
def raise_if_edge_keys(self):
"""This is a TEMPORARY, HACKY method to be called wherever
edge_keys may cause an issue.
"""
if len(self.edge_keys) > 0:
raise NotImplementedError(
"Edge keys are not fully supported yet, please consider "
"stacking all your `edge_` attributes in `edge_attr` for the "
"time being")
@property
def v_edge_keys(self):
"""All keys starting with `v_edge_`."""
return [k for k in self.keys if k.startswith('v_edge_')]
@property
def num_edges(self):
"""Overwrite the torch_geometric initial definition, which
somehow returns incorrect results, like:
data.num_edges != data.edge_index.shape[1]
"""
return self.edge_index.shape[1] if self.has_edges else 0
@property
def num_points(self):
return self.num_nodes
@property
def num_super(self):
return self.super_index.max() + 1 if self.is_sub else 0
@property
def num_sub(self):
return self.sub.points.max() + 1 if self.is_super else 0
def detach(self):
"""Extend `torch_geometric.Data.detach` to handle Cluster
attributes.
"""
self = super().detach()
if self.is_super:
self.sub = self.sub.detach()
return self
def to(self, device, **kwargs):
"""Extend `torch_geometric.Data.to` to handle Cluster attributes.
"""
self = super().to(device, **kwargs)
if self.is_super:
self.sub = self.sub.to(device, **kwargs)
return self
def cpu(self, **kwargs):
"""Move the NAG with all Data in it to CPU."""
return self.to('cpu', **kwargs)
def cuda(self, **kwargs):
"""Move the NAG with all Data in it to CUDA."""
return self.to('cuda', **kwargs)
@property
def device(self):
"""Device of the first-encountered tensor in 'self'."""
for key, item in self:
if torch.is_tensor(item):
return item.device
return torch.tensor([]).device
def debug(self):
"""Sanity checks."""
if self.is_super:
assert isinstance(self.sub, Cluster), \
"Clusters must be expressed using a Cluster object"
assert self.y is None or self.y.dim() == 2, \
"Clusters must hold label histograms"
if self.is_sub:
if not is_dense(self.super_index):
print(
"WARNING: super_index indices are generally expected to be "
"dense (ie all indices in [0, super_index.max()] are used),"
" which is not the case here. This may be because you are "
"creating a Data object after applying a selection of "
"points without updating the cluster indices.")
if self.has_edges:
assert self.edge_index.max() < self.num_points
assert 0 <= self.edge_index.min()
def __inc__(self, key, value, *args, **kwargs):
"""Extend the PyG.Data.__inc__ behavior on '*index*' and
'*face*' attributes to our 'super_index'. This is needed for
maintaining clusters when batching Data objects together.
"""
return self.num_super if key in ['super_index'] \
else super().__inc__(key, value, *args, **kwargs)
def __cat_dim__(self, key, value, *args, **kwargs):
"""Extend the PyG.Data.__inc__ behavior on '*index*' and
'*face*' attributes to our 'neighbor_index'. This is needed for
maintaining neighbors when batching Data objects together.
"""
return 0 if key == 'neighbor_index' \
else super().__cat_dim__(key, value, *args, **kwargs)
def select(self, idx, update_sub=True, update_super=True):
"""Returns a new Data with updated clusters, which indexes
`self` using entries in `idx`. Supports torch and numpy fancy
indexing. `idx` must not contain duplicate entries, as this
would cause ambiguities in edges and super- and sub- indices.
This operations breaks neighborhoods, so if 'self.has_neighbors'
the output Data will not.
NB: if `self` belongs to a NAG, calling this function in
isolation may break compatibility with point and cluster indices
in the other hierarchy levels. If consistency matters, prefer
using NAG indexing instead.
:parameter
idx: int or 1D torch.LongTensor or numpy.NDArray
Data indices to select from 'self'. Must NOT contain
duplicates
update_sub: bool
If True, the point (ie subpoint) indices will also be
updated to maintain dense indices. The output will then
contain '(idx_sub, sub_super)' which can help apply these
changes to maintain consistency with lower hierarchy levels
of a NAG.
update_super: bool
If True, the cluster (ie superpoint) indices will also be
updated to maintain dense indices. The output will then
contain '(idx_super, super_sub)' which can help apply these
changes to maintain consistency with higher hierarchy levels
of a NAG.
:returns data, (idx_sub, sub_super), (idx_super, super_sub)
data: Data
indexed data
idx_sub: torch.LongTensor
to be used with 'Data.select()' on the sub-level
sub_super: torch.LongTensor
to replace 'Data.super_index' on the sub-level
idx_super: torch.LongTensor
to be used with 'Data.select()' on the super-level
super_sub: Cluster
to replace 'Data.sub' on the super-level
"""
device = self.device
# Convert idx to a torch.LongTensor
idx = tensor_idx(idx).to(device)
# Make sure idx contains no duplicate entries
if src.is_debug_enabled():
assert not has_duplicates(idx), \
"Duplicate indices are not supported. This would cause " \
"ambiguities in edges and super- and sub- indices."
# Output Data will not share memory with input Data.
# NB: it is generally not recommended to instantiate en empty
# Data like this, as it might cause issues when calling
# 'data.num_nodes' later on. Need to be careful when calling
# 'data.num_nodes' before having set any of the pointwise
# attributes (eg 'x', 'pos', 'rgb', 'y', etc)
data = self.__class__()
# If Data contains edges, we will want to update edge indices
# and attributes with respect to the new point order. Edge
# indices are updated here, so as to compute 'idx_edge', which
# will be used to select edge attributes
if self.has_edges:
# To update edge indices, create a 'reindex' tensor so that
# the desired output can be computed with simple indexation
# 'reindex[edge_index]'. This avoids using map() or
# numpy.vectorize alternatives.
reindex = torch.full(
(self.num_nodes,), -1, dtype=torch.int64, device=device)
reindex = reindex.scatter_(
0, idx, torch.arange(idx.shape[0], device=device))
edge_index = reindex[self.edge_index]
# Remove obsolete edges (ie those involving a '-1' index)
idx_edge = torch.where((edge_index != -1).all(dim=0))[0]
data.edge_index = edge_index[:, idx_edge]
# Selecting points may affect their order, if we need to
# preserve subpoint consistency, we need to update the
# 'Data.sub' of the current level and the 'Data.super_index'
# of the level below
out_sub = (None, None)
if self.is_super:
data.sub, out_sub = self.sub.select(idx, update_sub=update_sub)
# Selecting points may affect their order, if we need to
# preserve superpoint consistency, we need to update the
# 'Data.super_index' of the current level along with the
# 'Data.sub' of the level above
out_super = (None, None)
if self.is_sub:
data.super_index = self.super_index[idx]
if self.is_sub and update_super:
# Convert superpoint indices, in case some superpoints have
# disappeared. 'idx_super' is intended to be used with
# Data.select() on the level above
new_super_index, perm = consecutive_cluster(data.super_index)
idx_super = data.super_index[perm]
data.super_index = new_super_index
# Selecting the superpoints with 'idx_super' will not be
# enough to maintain consistency with the current points. We
# also need to update the super-level's 'Data.sub', which
# can be computed from 'super_index'
super_sub = Cluster(
data.super_index, torch.arange(idx.shape[0], device=device),
dense=True)
out_super = (idx_super, super_sub)
# Index data items depending on their type
warn_keys = ['neighbor_index', 'neighbor_distance']
skip_keys = ['edge_index', 'sub', 'super_index'] + warn_keys
for key, item in self:
# 'skip_keys' have already been dealt with earlier on, so we
# can skip them here
if key in warn_keys and src.is_debug_enabled():
print(
f"WARNING: Data.select does not support '{key}', this "
f"attribute will be absent from the output")
if key in skip_keys:
continue
is_tensor = torch.is_tensor(item)
is_node_size = item.shape[0] == self.num_nodes
is_edge_size = item.shape[0] == self.num_edges
# Slice tensor elements containing num_edges elements. Note
# we deal with edges first, to rule out the case where
# num_edges = num_nodes. This will deal with `edge_attr` but
# also any other attribute whose key starts with 'edge_' and
# whose first dimension size matches the number of edges in
# `edge_index`. An exception is made for attributes
# starting with 'v_edge': those are expected to be node
# attributes and must be treated as such
if is_tensor and is_node_size and key in self.v_edge_keys:
data[key] = item[idx]
elif self.has_edges and is_tensor and is_edge_size and \
key in ['edge_attr'] + self.edge_keys:
data[key] = item[idx_edge]
# Slice other tensor elements containing num_nodes elements
elif is_tensor and is_node_size:
data[key] = item[idx]
# Other Data attributes are simply copied
else:
data[key] = copy.deepcopy(item)
# Security just in case no node-level attribute was passed, Data
# will not be able to properly infer its number of nodes
if data.num_nodes != idx.shape[0]:
data.num_nodes = idx.shape[0]
return data, out_sub, out_super
def is_isolated(self):
"""If self.has_edges, returns a boolean tensor of size
self.num_nodes indicating which are absent from self.edge_index.
Will raise an error if self.has_edges is False.
"""
edge_index = self.edge_index if self.has_edges \
else torch.zeros(2, 0, dtype=torch.long, device=self.device)
return isolated_nodes(edge_index, num_nodes=self.num_nodes)
def connect_isolated(self, k=1):
"""Search for nodes with no edges in the graph and connect them
to their k nearest neighbors. Update self.edge_index and
self.edge_attr accordingly.
Will raise an error if self has no edges or no pos.
Returns self updated with the newly-created edges.
"""
assert self.pos is not None
# Make sure there is no edge_attr if there is no edge_index
if not self.has_edges:
self.edge_attr = None
self.raise_if_edge_keys()
# Search for isolated nodes and exit if no node is isolated
is_isolated = self.is_isolated()
is_out = torch.where(is_isolated)[0]
if not is_isolated.any():
return self
# Search the nearest nodes for isolated nodes, among all nodes
# NB: we remove the nodes themselves from their own neighborhood
high = self.pos.max(dim=0).values
low = self.pos.min(dim=0).values
r_max = (high - low).norm()
neighbors, distances = knn_2(
self.pos, self.pos[is_out], k + 1, r_max=r_max)
distances = distances[:, 1:]
neighbors = neighbors[:, 1:]
# Add new edges between the nodes
source = is_out.repeat_interleave(k)
target = neighbors.flatten()
edge_index_new = torch.vstack((source, target))
edge_index_old = self.edge_index
self.edge_index = torch.cat((edge_index_old, edge_index_new), dim=1)
# Exit here if there are no edge attributes
if self.edge_attr is None:
return self
# If the edges have attributes, we also create attributes for
# the new edges. There is no trivial way of doing so, the
# heuristic here simply attempts to linearly regress the edge
# weights based on the corresponding node distances.
# First, get existing edges attributes and associated distance
w = self.edge_attr
s = edge_index_old[0]
t = edge_index_old[1]
d = (self.pos[s] - self.pos[t]).norm(dim=1)
d_1 = torch.vstack((d, torch.ones_like(d))).T
# Least square on d_1.x = w (ie d.a + b = w)
# NB: CUDA may crash trying to solve this simple system, in
# which case we will fall back to CPU. Not ideal though
try:
a, b = torch.linalg.lstsq(d_1, w).solution
except:
if src.is_debug_enabled():
print(
'\nWarning: torch.linalg.lstsq failed, trying again '
'on CPU')
a, b = torch.linalg.lstsq(d_1.cpu(), w.cpu()).solution
a = a.to(self.device)
b = b.to(self.device)
# Heuristic: linear approximation of w by d
edge_attr_new = distances.flatten() * a + b
# Append to existing self.edge_attr
self.edge_attr = torch.cat((self.edge_attr, edge_attr_new))
return self
def to_trimmed(self, reduce='mean'):
"""Convert to 'trimmed' graph: same as coalescing with the
additional constraint that (i, j) and (j, i) edges are duplicates.
If edge attributes are passed, 'reduce' will indicate how to fuse
duplicate edges' attributes.
NB: returned edges are expressed with i<j by default.
"""
assert self.has_edges
self.raise_if_edge_keys()
if self.edge_attr is not None:
edge_index, edge_attr = to_trimmed(
self.edge_index, edge_attr=self.edge_attr, reduce=reduce)
else:
edge_index = to_trimmed(self.edge_index)
edge_attr = None
self.edge_index = edge_index
self.edge_attr = edge_attr
return self
def __eq__(self, other):
if not isinstance(other, self.__class__):
if src.is_debug_enabled():
print(f'{self.__class__.__name__}.__eq__: classes differ')
return False
if sorted(self.keys) != sorted(other.keys):
if src.is_debug_enabled():
print(f'{self.__class__.__name__}.__eq__: keys differ')
return False
for k, v in self.items():
if isinstance(v, torch.Tensor):
if not torch.equal(v, other[k]):
if src.is_debug_enabled():
print(f'{self.__class__.__name__}.__eq__: {k} differ')
return False
continue
if isinstance(v, np.ndarray):
if not np.array_equal(v, other[k]):
if src.is_debug_enabled():
print(f'{self.__class__.__name__}.__eq__: {k} differ')
return False
continue
if v != other[k]:
if src.is_debug_enabled():
print(f'{self.__class__.__name__}.__eq__: {k} differ')
return False
return True
def save(
self,
f,
y_to_csr=True,
pos_dtype=torch.float,
fp_dtype=torch.float):
"""Save Data to HDF5 file.
:param f: h5 file path of h5py.File or h5py.Group
:param y_to_csr: bool
Convert 'y' to CSR format before saving. Only applies if
'y' is a 2D histogram
:param pos_dtype: torch dtype
Data type to which 'pos' should be cast before saving. The
reason for this separate treatment of 'pos' is that global
coordinates may be too large and casting to 'fp_dtype' may
result in hurtful precision loss
:param fp_dtype: torch dtype
Data type to which floating point tensors should be cast
before saving
:return:
"""
if not isinstance(f, (h5py.File, h5py.Group)):
with h5py.File(f, 'w') as file:
self.save(
file,
y_to_csr=y_to_csr,
pos_dtype=pos_dtype,
fp_dtype=fp_dtype)
return
assert isinstance(f, (h5py.File, h5py.Group))
for k, val in self.items():
if k == 'pos':
save_tensor(val, f, k, fp_dtype=pos_dtype)
elif k == 'y' and val.dim() > 1 and y_to_csr:
sg = f.create_group(f"{f.name}/_csr_/{k}")
save_dense_to_csr(val, sg, fp_dtype=fp_dtype)
elif isinstance(val, Cluster):
sg = f.create_group(f"{f.name}/_cluster_/sub")
val.save(sg, fp_dtype=fp_dtype)
elif k in ['rgb', 'mean_rgb']:
if val.is_floating_point():
save_tensor((val * 255).byte(), f, k, fp_dtype=fp_dtype)
else:
save_tensor(val.byte(), f, k, fp_dtype=fp_dtype)
elif isinstance(val, torch.Tensor):
save_tensor(val, f, k, fp_dtype=fp_dtype)
else:
raise NotImplementedError(f'Unsupported type={type(val)}')
@staticmethod
def load(
f, idx=None, keys_idx=None, keys=None, update_sub=True,
verbose=False, rgb_to_float=False):
"""Read an HDF5 file and return its content as a dictionary.
:param f: h5 file path of h5py.File or h5py.Group
:param idx: int, list, numpy.ndarray, torch.Tensor
Used to select the elements in `keys_idx`. Supports fancy
indexing
:param keys_idx: List(str)
Keys on which the indexing should be applied
:param keys: List(str)
Keys should be loaded from the file, ignoring the rest
:param update_sub: bool
If True, the point (ie subpoint) indices will also be
updated to maintain dense indices. The output will then
contain '(idx_sub, sub_super)' which can help apply these
changes to maintain consistency with lower hierarchy levels
of a NAG.
:param verbose: bool
:param rgb_to_float: bool
If True and an integer 'rgb' or 'mean_rgb' attribute is
loaded, it will be cast to float
:return:
"""
if not isinstance(f, (h5py.File, h5py.Group)):
with h5py.File(f, 'r') as file:
out = Data.load(
file, idx=idx, keys_idx=keys_idx, keys=keys,
update_sub=update_sub, verbose=verbose,
rgb_to_float=rgb_to_float)
return out
idx = tensor_idx(idx)
if idx.shape[0] == 0:
keys_idx = []
elif keys_idx is None:
keys_idx = list(set(f.keys()) - set(Data._NOT_INDEXABLE))
if keys is None:
all_keys = list(f.keys())
for k in ['_csr_', '_cluster_']:
if k in all_keys:
all_keys.remove(k)
all_keys += list(f[k].keys())
keys = all_keys
d_dict = {}
csr_keys = []
cluster_keys = []
# Deal with special keys first, then read other keys if required
for k in f.keys():
start = time()
if k == '_csr_':
csr_keys = list(f[k].keys())
continue
if k == '_cluster_':
cluster_keys = list(f[k].keys())
continue
if k in keys_idx:
d_dict[k] = load_tensor(f[k], idx=idx)
elif k in keys:
d_dict[k] = load_tensor(f[k])
if verbose and k in d_dict.keys():
print(f'Data.load {k:<22}: {time() - start:0.5f}s')
# Update the 'keys_idx' with newly-found 'csr_keys' and
# 'cluster_keys'
if idx.shape[0] != 0:
keys_idx = list(set(keys_idx).union(set(csr_keys)))
keys_idx = list(set(keys_idx).union(set(cluster_keys)))
# Special key '_csr_' holds data saved in CSR format
for k in csr_keys:
start = time()
if k in keys_idx:
d_dict[k] = load_csr_to_dense(
f['_csr_'][k], idx=idx, verbose=verbose)
elif k in keys:
d_dict[k] = load_csr_to_dense(f['_csr_'][k], verbose=verbose)
if verbose and k in d_dict.keys():
print(f'Data.load {k:<22}: {time() - start:0.5f}s')
# Special key '_cluster_' holds Cluster data
for k in cluster_keys:
start = time()
if k in keys_idx:
d_dict[k] = Cluster.load(
f['_cluster_'][k], idx=idx, update_sub=update_sub,
verbose=verbose)[0]
elif k in keys:
d_dict[k] = Cluster.load(
f['_cluster_'][k], update_sub=update_sub,
verbose=verbose)[0]
if verbose and k in d_dict.keys():
print(f'Data.load {k:<22}: {time() - start:0.5f}s')
# In case RGB is among the keys and is in integer type, convert
# to float
for k in ['rgb', 'mean_rgb']:
if k in d_dict.keys():
d_dict[k] = to_float_rgb(d_dict[k]) if rgb_to_float \
else to_byte_rgb(d_dict[k])
return Data(**d_dict)
class Batch(PyGBatch):
"""Inherit from torch_geometric.Batch with extensions tailored to
our specific needs.
"""
@classmethod
def from_data_list(cls, data_list, follow_batch=None, exclude_keys=None):
"""Overwrite torch_geometric from_data_list to be able to handle
Cluster objects batching.
"""
# Local hack to avoid being overflowed with pesky warnings
# see: https://github.com/pyg-team/pytorch_geometric/issues/4848
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for d in data_list:
d.raise_if_edge_keys()
# Little trick to prevent Batch.from_data_list from crashing
# when some Data objects have edges while others don't
has = [
i for i, d in enumerate(data_list) if d.edge_index is not None]
has_not = [
i for i, d in enumerate(data_list) if d.edge_index is None]
if len(has) > 0 and len(has_not) > 0:
device = data_list[0].device
edge_index = torch.empty((2, 0), device=device).long()
if data_list[has[0]].edge_attr is not None:
dim = data_list[has[0]].edge_attr.shape[1]
edge_attr = torch.empty((0, dim), device=device).long()
else:
edge_attr = None
for i in has_not:
data_list[i].edge_index = edge_index
data_list[i].edge_attr = edge_attr
# PyG way of batching does not recognize some local classes such
# as Cluster and CSRData, so it will accumulate them in lists
batch = super().from_data_list(
data_list, follow_batch=follow_batch, exclude_keys=exclude_keys)
# Dirty trick: manually convert 'sub' to a proper ClusterBatch.
# Note we will need to do the same in `get_example` to avoid
# breaking PyG Batch mechanisms
if batch.is_super:
batch.sub = ClusterBatch.from_csr_list(batch.sub)
return batch
def get_example(self, idx):
"""Overwrite torch_geometric get_example to be able to handle
Cluster objects batching.
"""
if self.is_super:
sub_bckp = self.sub.clone()
self.sub = self.sub.to_csr_list()
data = super().get_example(idx)
if self.is_super:
self.sub = sub_bckp
return data