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"""
DEIM: DETR with Improved Matching for Fast Convergence
Copyright (c) 2024 The DEIM Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from D-FINE (https://github.com/Peterande/D-FINE)
Copyright (c) 2024 D-FINE authors. All Rights Reserved.
"""
import torch
import torch.utils.data as data
import torch.nn.functional as F
from torch.utils.data import default_collate
import torchvision
import torchvision.transforms.v2 as VT
from torchvision.transforms.v2 import functional as VF, InterpolationMode
import random
from functools import partial
from ..core import register
torchvision.disable_beta_transforms_warning()
from copy import deepcopy
from PIL import Image, ImageDraw
import os
__all__ = [
'DataLoader',
'BaseCollateFunction',
'BatchImageCollateFunction',
'batch_image_collate_fn'
]
@register()
class DataLoader(data.DataLoader):
__inject__ = ['dataset', 'collate_fn']
def __repr__(self) -> str:
format_string = self.__class__.__name__ + "("
for n in ['dataset', 'batch_size', 'num_workers', 'drop_last', 'collate_fn']:
format_string += "\n"
format_string += " {0}: {1}".format(n, getattr(self, n))
format_string += "\n)"
return format_string
def set_epoch(self, epoch):
self._epoch = epoch
self.dataset.set_epoch(epoch)
self.collate_fn.set_epoch(epoch)
@property
def epoch(self):
return self._epoch if hasattr(self, '_epoch') else -1
@property
def shuffle(self):
return self._shuffle
@shuffle.setter
def shuffle(self, shuffle):
assert isinstance(shuffle, bool), 'shuffle must be a boolean'
self._shuffle = shuffle
@register()
def batch_image_collate_fn(items):
"""only batch image
"""
return torch.cat([x[0][None] for x in items], dim=0), [x[1] for x in items]
class BaseCollateFunction(object):
def set_epoch(self, epoch):
self._epoch = epoch
@property
def epoch(self):
return self._epoch if hasattr(self, '_epoch') else -1
def __call__(self, items):
raise NotImplementedError('')
def generate_scales(base_size, base_size_repeat):
scale_repeat = (base_size - int(base_size * 0.75 / 32) * 32) // 32
scales = [int(base_size * 1.2 / 32) * 32 + i * 32 for i in range(scale_repeat)]
scales += [base_size] * base_size_repeat
scales += [int(base_size * 1.6 / 32) * 32 - i * 32 for i in range(scale_repeat)]
return scales
@register()
class BatchImageCollateFunction(BaseCollateFunction):
def __init__(
self,
stop_epoch=None,
ema_restart_decay=0.9999,
base_size=640,
base_size_repeat=None,
mixup_prob=0.0,
mixup_epochs=[0, 0],
data_vis=False,
vis_save='./vis_dataset/'
) -> None:
super().__init__()
self.base_size = base_size
self.scales = generate_scales(base_size, base_size_repeat) if base_size_repeat is not None else None
self.stop_epoch = stop_epoch if stop_epoch is not None else 100000000
self.ema_restart_decay = ema_restart_decay
# FIXME Mixup
self.mixup_prob, self.mixup_epochs = mixup_prob, mixup_epochs
if self.mixup_prob > 0:
self.data_vis, self.vis_save = data_vis, vis_save
os.makedirs(self.vis_save, exist_ok=True) if self.data_vis else None
print(" ### Using MixUp with Prob@{} in {} epochs ### ".format(self.mixup_prob, self.mixup_epochs))
if stop_epoch is not None:
print(" ### Multi-scale Training until {} epochs ### ".format(self.stop_epoch))
print(" ### Multi-scales@ {} ### ".format(self.scales))
self.print_info_flag = True
# self.interpolation = interpolation
def apply_mixup(self, images, targets):
"""
Applies Mixup augmentation to the batch if conditions are met.
Args:
images (torch.Tensor): Batch of images.
targets (list[dict]): List of target dictionaries corresponding to images.
Returns:
tuple: Updated images and targets
"""
# Log when Mixup is permanently disabled
if self.epoch == self.mixup_epochs[-1] and self.print_info_flag:
print(f" ### Attention --- Mixup is closed after epoch@ {self.epoch} ###")
self.print_info_flag = False
# Apply Mixup if within specified epoch range and probability threshold
if random.random() < self.mixup_prob and self.mixup_epochs[0] <= self.epoch < self.mixup_epochs[-1]:
# Generate mixup ratio
beta = round(random.uniform(0.45, 0.55), 6)
# Mix images
images = images.roll(shifts=1, dims=0).mul_(1.0 - beta).add_(images.mul(beta))
# Prepare targets for Mixup
shifted_targets = targets[-1:] + targets[:-1]
updated_targets = deepcopy(targets)
for i in range(len(targets)):
# Combine boxes, labels, and areas from original and shifted targets
updated_targets[i]['boxes'] = torch.cat([targets[i]['boxes'], shifted_targets[i]['boxes']], dim=0)
updated_targets[i]['labels'] = torch.cat([targets[i]['labels'], shifted_targets[i]['labels']], dim=0)
updated_targets[i]['area'] = torch.cat([targets[i]['area'], shifted_targets[i]['area']], dim=0)
# Add mixup ratio to targets
updated_targets[i]['mixup'] = torch.tensor(
[beta] * len(targets[i]['labels']) + [1.0 - beta] * len(shifted_targets[i]['labels']),
dtype=torch.float32
)
targets = updated_targets
if self.data_vis:
for i in range(len(updated_targets)):
image_tensor = images[i]
image_tensor_uint8 = (image_tensor * 255).type(torch.uint8)
image_numpy = image_tensor_uint8.numpy().transpose((1, 2, 0))
pilImage = Image.fromarray(image_numpy)
draw = ImageDraw.Draw(pilImage)
print('mix_vis:', i, 'boxes.len=', len(updated_targets[i]['boxes']))
for box in updated_targets[i]['boxes']:
draw.rectangle([int(box[0]*640 - (box[2]*640)/2), int(box[1]*640 - (box[3]*640)/2),
int(box[0]*640 + (box[2]*640)/2), int(box[1]*640 + (box[3]*640)/2)], outline=(255,255,0))
pilImage.save(self.vis_save + str(i) + "_"+ str(len(updated_targets[i]['boxes'])) +'_out.jpg')
return images, targets
def __call__(self, items):
images = torch.cat([x[0][None] for x in items], dim=0)
targets = [x[1] for x in items]
# Mixup
images, targets = self.apply_mixup(images, targets)
if self.scales is not None and self.epoch < self.stop_epoch:
# sz = random.choice(self.scales)
# sz = [sz] if isinstance(sz, int) else list(sz)
# VF.resize(inpt, sz, interpolation=self.interpolation)
sz = random.choice(self.scales)
images = F.interpolate(images, size=sz)
if 'masks' in targets[0]:
for tg in targets:
tg['masks'] = F.interpolate(tg['masks'], size=sz, mode='nearest')
raise NotImplementedError('')
return images, targets
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