|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""Utility functions for the project.""" |
|
|
|
from __future__ import print_function |
|
|
|
from collections import defaultdict |
|
from collections import deque |
|
from copy import deepcopy |
|
import datetime |
|
import errno |
|
import os |
|
import sys |
|
import time |
|
import numpy as np |
|
from PIL import Image |
|
import torch |
|
from torchvision import transforms |
|
import yaml |
|
|
|
|
|
from data.voc import CLASS2ID |
|
from data.voc import VOC_CLASSES |
|
|
|
|
|
_MB = 1024.0 * 1024.0 |
|
|
|
DINO_transform = transforms.Compose([ |
|
transforms.ToTensor(), |
|
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), |
|
]) |
|
|
|
|
|
class Config: |
|
|
|
def __init__(self, **kwargs): |
|
for key, value in kwargs.items(): |
|
if isinstance(value, dict): |
|
setattr(self, key, Config(**value)) |
|
else: |
|
setattr(self, key, value) |
|
|
|
|
|
def load_yaml(filename): |
|
with open(filename) as file: |
|
try: |
|
data = yaml.safe_load(file) |
|
return data |
|
except yaml.YAMLError as e: |
|
print(f"Error while loading YAML file: {e}") |
|
|
|
|
|
def normalize(x, dim=None, eps=1e-15): |
|
if dim is None: |
|
return (x - x.min()) / (x.max() - x.min()) |
|
|
|
numerator = x - x.min(axis=dim, keepdims=True)[0] |
|
denominator = ( |
|
x.max(axis=dim, keepdims=True)[0] |
|
- x.min(axis=dim, keepdims=True)[0] |
|
+ eps |
|
) |
|
return numerator / denominator |
|
|
|
|
|
class SmoothedValue(object): |
|
"""Track a series of values and provide access to smoothed values over a window or the global series average.""" |
|
|
|
def __init__(self, window_size=20, fmt=None): |
|
if fmt is None: |
|
fmt = "{median:.4f} ({global_avg:.4f})" |
|
self.deque = deque(maxlen=window_size) |
|
self.total = 0.0 |
|
self.count = 0 |
|
self.fmt = fmt |
|
|
|
def update(self, value, n=1): |
|
self.deque.append(value) |
|
self.count += n |
|
self.total += value * n |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@property |
|
def median(self): |
|
d = torch.tensor(list(self.deque)) |
|
return d.median().item() |
|
|
|
@property |
|
def avg(self): |
|
d = torch.tensor(list(self.deque), dtype=torch.float32) |
|
return d.mean().item() |
|
|
|
@property |
|
def global_avg(self): |
|
return self.total / self.count |
|
|
|
@property |
|
def max(self): |
|
return max(self.deque) |
|
|
|
@property |
|
def value(self): |
|
return self.deque[-1] |
|
|
|
def __str__(self): |
|
return self.fmt.format( |
|
median=self.median, |
|
avg=self.avg, |
|
global_avg=self.global_avg, |
|
max=self.max, |
|
value=self.value, |
|
) |
|
|
|
|
|
class MetricLogger(object): |
|
"""Log the metrics.""" |
|
|
|
def __init__(self, delimiter="\t"): |
|
self.meters = defaultdict(SmoothedValue) |
|
self.delimiter = delimiter |
|
|
|
def update(self, **kwargs): |
|
for k, v in kwargs.items(): |
|
if isinstance(v, torch.Tensor): |
|
v = v.item() |
|
assert isinstance(v, (float, int)) |
|
self.meters[k].update(v) |
|
|
|
def __getattr__(self, attr): |
|
if attr in self.meters: |
|
return self.meters[attr] |
|
if attr in self.__dict__: |
|
return self.__dict__[attr] |
|
raise AttributeError( |
|
"'{}' object has no attribute '{}'".format(type(self).__name__, attr) |
|
) |
|
|
|
def __str__(self): |
|
loss_str = [] |
|
for name, meter in self.meters.items(): |
|
loss_str.append("{}: {}".format(name, str(meter))) |
|
return self.delimiter.join(loss_str) |
|
|
|
def synchronize_between_processes(self): |
|
for meter in self.meters.values(): |
|
meter.synchronize_between_processes() |
|
|
|
def add_meter(self, name, meter): |
|
self.meters[name] = meter |
|
|
|
def log_every(self, iterable, print_freq, header=None): |
|
"""Log every `print_freq` times.""" |
|
i = 0 |
|
if not header: |
|
header = "" |
|
start_time = time.time() |
|
end = time.time() |
|
iter_time = SmoothedValue(fmt="{avg:.4f}") |
|
data_time = SmoothedValue(fmt="{avg:.4f}") |
|
space_fmt = ":" + str(len(str(len(iterable)))) + "d" |
|
log_msg = self.delimiter.join([ |
|
header, |
|
"[{0" + space_fmt + "}/{1}]", |
|
"eta: {eta}", |
|
"{meters}", |
|
"time: {time}", |
|
"data: {data}", |
|
"max mem: {memory:.0f}", |
|
]) |
|
for obj in iterable: |
|
data_time.update(time.time() - end) |
|
yield obj |
|
iter_time.update(time.time() - end) |
|
if i % print_freq == 0: |
|
eta_seconds = iter_time.global_avg * (len(iterable) - i) |
|
eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
|
print( |
|
log_msg.format( |
|
i, |
|
len(iterable), |
|
eta=eta_string, |
|
meters=str(self), |
|
time=str(iter_time), |
|
data=str(data_time), |
|
memory=torch.cuda.max_memory_allocated() / _MB, |
|
) |
|
) |
|
sys.stdout.flush() |
|
|
|
i += 1 |
|
end = time.time() |
|
total_time = time.time() - start_time |
|
total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
|
print("{} Total time: {}".format(header, total_time_str)) |
|
|
|
|
|
def mkdir(path): |
|
try: |
|
os.makedirs(path) |
|
except OSError as e: |
|
if e.errno != errno.EEXIST: |
|
raise |
|
|
|
|
|
def pad_to_square(im): |
|
"""Pad the images to square shape.""" |
|
im = deepcopy(im) |
|
width, height = im.size |
|
top_pad = (max(width, height) - height) // 2 |
|
bot_pad = max(width, height) - height - top_pad |
|
left_pad = (max(width, height) - width) // 2 |
|
right_pad = max(width, height) - width - left_pad |
|
|
|
if len(im.mode) == 3: |
|
color = (0, 0, 0) |
|
elif len(im.mode) == 1: |
|
color = 0 |
|
else: |
|
raise ValueError(f"Image mode not supported. Image has {im.mode} channels.") |
|
|
|
return add_margin(im, top_pad, right_pad, bot_pad, left_pad, color=color) |
|
|
|
|
|
def add_margin(pil_img, top, right, bottom, left, color=(0, 0, 0)): |
|
"""Ref: https://note.nkmk.me/en/python-pillow-add-margin-expand-canvas/.""" |
|
width, height = pil_img.size |
|
new_width = width + right + left |
|
new_height = height + top + bottom |
|
result = Image.new(pil_img.mode, (new_width, new_height), color) |
|
result.paste(pil_img, (left, top)) |
|
|
|
|
|
pad = [left, top, width, height] |
|
return result, pad |
|
|
|
|
|
def process_sentence(sentence, ds_name): |
|
"""Dataset specific sentence processing.""" |
|
if "refcoco" in ds_name: |
|
sentence = sentence[0].lower() |
|
|
|
sentence = sentence.replace('"', "") |
|
sentence = sentence.replace("/", "") |
|
if ds_name == "voc": |
|
if sentence in list(CLASS2ID.keys()): |
|
label_id = CLASS2ID[sentence] - 1 |
|
sentence = VOC_CLASSES[label_id] |
|
|
|
if not isinstance(sentence, str): |
|
sentence = sentence[0] |
|
return sentence |
|
|