/
common_functions.py
473 lines (359 loc) · 13.3 KB
/
common_functions.py
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import collections
import glob
import logging
import os
import re
import numpy as np
import scipy.stats
import torch
NUMPY_RANDOM = np.random
class Identity(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x
def pos_inf(dtype):
return torch.finfo(dtype).max
def neg_inf(dtype):
return torch.finfo(dtype).min
def small_val(dtype):
return torch.finfo(dtype).tiny
def is_list_or_tuple(x):
return isinstance(x, (list, tuple))
def try_next_on_generator(gen, iterable):
try:
return gen, next(gen)
except StopIteration:
gen = iter(iterable)
return gen, next(gen)
def numpy_to_torch(v):
try:
return torch.from_numpy(v)
except TypeError:
return v
def to_numpy(v):
if is_list_or_tuple(v):
return np.stack([to_numpy(sub_v) for sub_v in v], axis=1)
try:
return v.cpu().numpy()
except AttributeError:
return v
def get_hierarchy_label(batch_labels, hierarchy_level):
if hierarchy_level == "all":
return batch_labels
if is_list_or_tuple(hierarchy_level):
max_hierarchy_level = max(hierarchy_level)
else:
max_hierarchy_level = hierarchy_level
if max_hierarchy_level > 0:
assert (batch_labels.ndim == 2) and batch_labels.shape[1] > max_hierarchy_level
if batch_labels.ndim == 2:
batch_labels = batch_labels[:, hierarchy_level]
return batch_labels
def map_labels(label_map, labels):
labels = to_numpy(labels)
if labels.ndim == 2:
for h in range(labels.shape[1]):
labels[:, h] = label_map(labels[:, h], h)
else:
labels = label_map(labels, 0)
return labels
def process_label(labels, hierarchy_level, label_map):
labels = map_labels(label_map, labels)
labels = get_hierarchy_label(labels, hierarchy_level)
labels = numpy_to_torch(labels)
return labels
def set_requires_grad(model, requires_grad):
for param in model.parameters():
param.requires_grad = requires_grad
def shift_indices_tuple(indices_tuple, batch_size):
"""
Shifts indices of positives and negatives of pairs or triplets by batch_size
if len(indices_tuple) != 3 or len(indices_tuple) != 4, it will return indices_tuple
Args:
indices_tuple is a tuple with torch.Tensor
batch_size is an int
Returns:
A tuple with shifted indices
"""
if len(indices_tuple) == 3:
indices_tuple = (indices_tuple[0],) + tuple(
[x + batch_size if len(x) > 0 else x for x in indices_tuple[1:]]
)
elif len(indices_tuple) == 4:
indices_tuple = tuple(
[
x + batch_size if len(x) > 0 and i % 2 == 1 else x
for i, x in enumerate(indices_tuple)
]
)
return indices_tuple
def safe_random_choice(input_data, size):
"""
Randomly samples without replacement from a sequence. It is "safe" because
if len(input_data) < size, it will randomly sample WITH replacement
Args:
input_data is a sequence, like a torch tensor, numpy array,
python list, tuple etc
size is the number of elements to randomly sample from input_data
Returns:
An array of size "size", randomly sampled from input_data
"""
replace = len(input_data) < size
return NUMPY_RANDOM.choice(input_data, size=size, replace=replace)
def longest_list(list_of_lists):
return max(list_of_lists, key=len)
def slice_by_n(input_array, n):
output = []
for i in range(n):
output.append(input_array[i::n])
return output
def unslice_by_n(input_tensors):
n = len(input_tensors)
rows, cols = input_tensors[0].size()
output = torch.zeros((rows * n, cols), device=input_tensors[0].device)
for i in range(n):
output[i::n] = input_tensors[i]
return output
def set_layers_to_eval(layer_name):
def set_to_eval(m):
classname = m.__class__.__name__
if classname.find(layer_name) != -1:
m.eval()
return set_to_eval
def get_train_dataloader(dataset, batch_size, sampler, num_workers, collate_fn):
return torch.utils.data.DataLoader(
dataset,
batch_size=int(batch_size),
sampler=sampler,
drop_last=True,
num_workers=num_workers,
collate_fn=collate_fn,
shuffle=sampler is None,
pin_memory=False,
)
def get_eval_dataloader(dataset, batch_size, num_workers, collate_fn):
return torch.utils.data.DataLoader(
dataset,
batch_size=int(batch_size),
drop_last=False,
num_workers=num_workers,
collate_fn=collate_fn,
shuffle=False,
pin_memory=False,
)
def try_torch_operation(torch_op, input_val):
return torch_op(input_val) if torch.is_tensor(input_val) else input_val
def get_labels_to_indices(labels):
"""
Creates labels_to_indices, which is a dictionary mapping each label
to a numpy array of indices that will be used to index into self.dataset
"""
if torch.is_tensor(labels):
labels = labels.cpu().numpy()
labels_to_indices = collections.defaultdict(list)
for i, label in enumerate(labels):
labels_to_indices[label].append(i)
for k, v in labels_to_indices.items():
labels_to_indices[k] = np.array(v, dtype=np.int)
return labels_to_indices
def make_label_to_rank_dict(label_set):
"""
Args:
label_set: type sequence, a set of integer labels
(no duplicates in the sequence)
Returns:
A dictionary mapping each label to its numeric rank in the original set
"""
ranked = scipy.stats.rankdata(label_set) - 1
return {k: v for k, v in zip(label_set, ranked)}
def get_label_map(labels):
# Returns a nested dictionary.
# First level of dictionary represents label hierarchy level.
# Second level is the label map for that hierarchy level
labels = np.array(labels)
if labels.ndim == 2:
label_map = {}
for hierarchy_level in range(labels.shape[1]):
label_map[hierarchy_level] = make_label_to_rank_dict(
list(set(labels[:, hierarchy_level]))
)
return label_map
return {0: make_label_to_rank_dict(list(set(labels)))}
class LabelMapper:
def __init__(self, set_min_label_to_zero=False, dataset_labels=None):
self.set_min_label_to_zero = set_min_label_to_zero
if dataset_labels is not None:
self.label_map = get_label_map(dataset_labels)
def map(self, labels, hierarchy_level):
if not self.set_min_label_to_zero:
return labels
else:
return np.array(
[self.label_map[hierarchy_level][x] for x in labels], dtype=np.int
)
def add_to_recordable_attributes(
input_obj, name=None, list_of_names=None, is_stat=False
):
if is_stat:
attr_name_list_name = "_record_these_stats"
else:
attr_name_list_name = "_record_these"
if not hasattr(input_obj, attr_name_list_name):
setattr(input_obj, attr_name_list_name, [])
attr_name_list = getattr(input_obj, attr_name_list_name)
if name is not None:
if name not in attr_name_list:
attr_name_list.append(name)
if not hasattr(input_obj, name):
setattr(input_obj, name, 0)
if list_of_names is not None and is_list_or_tuple(list_of_names):
for n in list_of_names:
add_to_recordable_attributes(input_obj, name=n, is_stat=is_stat)
def reset_stats(input_obj):
for attr_list in ["_record_these_stats"]:
for r in getattr(input_obj, attr_list, []):
setattr(input_obj, r, 0)
def list_of_recordable_attributes_list_names():
return ["_record_these", "_record_these_stats"]
def modelpath_creator(folder, basename, identifier, extension=".pth"):
if identifier is None:
return os.path.join(folder, basename + extension)
else:
return os.path.join(folder, "%s_%s%s" % (basename, str(identifier), extension))
def save_model(model, model_name, filepath):
if any(
isinstance(model, x)
for x in [torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel]
):
torch.save(model.module.state_dict(), filepath)
else:
torch.save(model.state_dict(), filepath)
def load_model(model_def, model_filename, device):
try:
model_def.load_state_dict(torch.load(model_filename, map_location=device))
except KeyError:
# original saved file with DataParallel
state_dict = torch.load(model_filename)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
model_def.load_state_dict(new_state_dict)
def operate_on_dict_of_models(
input_dict,
suffix,
folder,
operation,
logging_string="",
log_if_successful=False,
assert_success=False,
):
for k, v in input_dict.items():
model_path = modelpath_creator(folder, k, suffix)
try:
operation(k, v, model_path)
if log_if_successful:
logging.info("%s %s" % (logging_string, model_path))
except IOError:
logging.warning("Could not %s %s" % (logging_string, model_path))
if assert_success:
raise IOError
def save_dict_of_models(input_dict, suffix, folder, **kwargs):
def operation(k, v, model_path):
save_model(v, k, model_path)
operate_on_dict_of_models(input_dict, suffix, folder, operation, "SAVE", **kwargs)
def load_dict_of_models(input_dict, suffix, folder, device, **kwargs):
def operation(k, v, model_path):
load_model(v, model_path, device)
operate_on_dict_of_models(input_dict, suffix, folder, operation, "LOAD", **kwargs)
def delete_dict_of_models(input_dict, suffix, folder, **kwargs):
def operation(k, v, model_path):
if os.path.exists(model_path):
os.remove(model_path)
operate_on_dict_of_models(input_dict, suffix, folder, operation, "DELETE", **kwargs)
def regex_wrapper(x):
if isinstance(x, list):
return [re.compile(z) for z in x]
return re.compile(x)
def regex_replace(search, replace, contents):
return re.sub(search, replace, contents)
def latest_version(folder, string_to_glob="trunk_*.pth", best=False):
items = glob.glob(os.path.join(folder, string_to_glob))
if items == []:
return (0, None)
model_regex = (
regex_wrapper("best[0-9]+\.pth$") if best else regex_wrapper("[0-9]+\.pth$")
)
epoch_regex = regex_wrapper("[0-9]+\.pth$")
items = [x for x in items if model_regex.search(x)]
version = [int(epoch_regex.findall(x)[-1].split(".")[0]) for x in items]
resume_epoch = max(version)
suffix = "best%d" % resume_epoch if best else resume_epoch
return resume_epoch, suffix
def return_input(x):
return x
def angle_to_coord(angle):
x = np.cos(np.radians(angle))
y = np.sin(np.radians(angle))
return x, y
def assert_embeddings_and_labels_are_same_size(embeddings, labels):
assert embeddings.size(0) == labels.size(
0
), "Number of embeddings must equal number of labels"
def assert_distance_type(obj, distance_type=None, **kwargs):
if distance_type is not None:
if is_list_or_tuple(distance_type):
distance_type_str = ", ".join(x.__name__ for x in distance_type)
distance_type_str = "one of " + distance_type_str
else:
distance_type_str = distance_type.__name__
obj_name = obj.__class__.__name__
assert isinstance(
obj.distance, distance_type
), "{} requires the distance metric to be {}".format(
obj_name, distance_type_str
)
for k, v in kwargs.items():
assert getattr(obj.distance, k) == v, "{} requires distance.{} to be {}".format(
obj_name, k, v
)
def torch_arange_from_size(input, size_dim=0):
return torch.arange(input.size(size_dim), device=input.device)
class TorchInitWrapper:
def __init__(self, init_func, **kwargs):
self.init_func = init_func
self.kwargs = kwargs
def __call__(self, tensor):
self.init_func(tensor, **self.kwargs)
class EmbeddingDataset(torch.utils.data.Dataset):
def __init__(self, embeddings, labels):
self.embeddings = embeddings
self.labels = labels
def __len__(self):
return len(self.embeddings)
def __getitem__(self, idx):
return self.embeddings[idx], self.labels[idx]
def sqlite_obj_to_dict(sqlite_obj):
return {k: [row[k] for row in sqlite_obj] for k in sqlite_obj[0].keys()}
def torch_all_from_dim_to_end(x, dim):
return torch.all(x.view(*x.shape[:dim], -1), dim=-1)
def torch_standard_scaler(x):
mean = torch.mean(x, dim=0)
std = torch.std(x, dim=0)
return (x - mean) / std
def to_dtype(x, tensor=None, dtype=None):
dt = dtype if dtype is not None else tensor.dtype
if x.dtype != dt:
x = x.type(dt)
return x
def to_device(x, tensor=None, device=None, dtype=None):
dv = device if device is not None else tensor.device
if x.device != dv:
x = x.to(dv)
if dtype is not None:
x = to_dtype(x, dtype=dtype)
return x