mirrors
/
fast-reid
mirror of https://github.com/JDAI-CV/fast-reid.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

chagne arch 

1. change dataset show to trainset show and testset show seperately
2. add cls layer to easily plug in circle loss and arcface
pull/43/head
liaoxingyu 2020-04-19 12:54:01 +08:00
parent be9faa5605
commit 9684500a57
34 changed files with 691 additions and 149 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
fastreid/config/defaults.py
View File

@ -45,18 +45,20 @@ _C.MODEL.BACKBONE.PRETRAIN_PATH = ''
# ---------------------------------------------------------------------------- #
_C.MODEL.HEADS = CN()
_C.MODEL.HEADS.NAME = "BNneckHead"
_C.MODEL.HEADS.POOL_LAYER = 'avgpool'
_C.MODEL.HEADS.NUM_CLASSES = 751
# Reduction dimension
_C.MODEL.HEADS.REDUCTION_DIM = 512
# Pooling layer type
_C.MODEL.HEADS.POOL_LAYER = 'avgpool'
# Arcface head
_C.MODEL.HEADS.ARCFACE = CN()
_C.MODEL.HEADS.ARCFACE.MARGIN = 0.5
_C.MODEL.HEADS.ARCFACE.SCALE = 30.0
# Classification layer type
_C.MODEL.HEADS.CLS_LAYER = 'linear' # 'arcface' or 'circle'
# Margin and Scale for margin-based classification layer
_C.MODEL.HEADS.MARGIN = 0.15
_C.MODEL.HEADS.SCALE = 128
# Circle Loss
_C.MODEL.HEADS.CIRCLE = CN()
_C.MODEL.HEADS.CIRCLE.MARGIN = 0.15
_C.MODEL.HEADS.CIRCLE.SCALE = 128.0
# ---------------------------------------------------------------------------- #
# REID LOSSES options
@ -69,7 +71,7 @@ _C.MODEL.LOSSES.CE = CN()
# if epsilon == 0, it means no label smooth regularization,
# if epsilon == -1, it means adaptive label smooth regularization
_C.MODEL.LOSSES.CE.EPSILON = 0.0
_C.MODEL.LOSSES.CE.ALPHA = 0.2
_C.MODEL.LOSSES.CE.ALPHA = 0.3
_C.MODEL.LOSSES.CE.SCALE = 1.0
# Triplet Loss options
@ -86,8 +88,7 @@ _C.MODEL.LOSSES.FL.ALPHA = 0.25
_C.MODEL.LOSSES.FL.GAMMA = 2
_C.MODEL.LOSSES.FL.SCALE = 1.0
# Path (possibly with schema like catalog:// or detectron2://) to a checkpoint file
# to be loaded to the model. You can find available models in the model zoo.
# Path to a checkpoint file to be loaded to the model. You can find available models in the model zoo.
_C.MODEL.WEIGHTS = ""
# Values to be used for image normalization

4
fastreid/data/build.py
View File

@ -23,7 +23,7 @@ def build_reid_train_loader(cfg):
for d in cfg.DATASETS.NAMES:
logger.info('prepare training set {}'.format(d))
dataset = DATASET_REGISTRY.get(d)()
dataset.show_summary()
dataset.show_train()
train_items.extend(dataset.train)
train_set = CommDataset(train_items, train_transforms, relabel=True)
@ -53,7 +53,7 @@ def build_reid_test_loader(cfg, dataset_name):
logger = logging.getLogger(__name__)
logger.info('prepare test set {}'.format(dataset_name))
dataset = DATASET_REGISTRY.get(dataset_name)()
dataset.show_summary()
dataset.show_test()
test_items = dataset.query + dataset.gallery
test_set = CommDataset(test_items, test_transforms, relabel=False)

15
fastreid/data/datasets/bases.py
View File

@ -197,17 +197,24 @@ class ImageDataset(Dataset):
def __init__(self, train, query, gallery, **kwargs):
super(ImageDataset, self).__init__(train, query, gallery, **kwargs)
def show_summary(self):
def show_train(self):
logger = logging.getLogger(__name__)
num_train_pids, num_train_cams = self.parse_data(self.train)
num_query_pids, num_query_cams = self.parse_data(self.query)
num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
logger.info('=> Loaded {}'.format(self.__class__.__name__))
logger.info(' ----------------------------------------')
logger.info(' subset | # ids | # images | # cameras')
logger.info(' ----------------------------------------')
logger.info(' train | {:5d} | {:8d} | {:9d}'.format(num_train_pids, len(self.train), num_train_cams))
logger.info(' ----------------------------------------')
def show_test(self):
logger = logging.getLogger(__name__)
num_query_pids, num_query_cams = self.parse_data(self.query)
num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)
logger.info('=> Loaded {}'.format(self.__class__.__name__))
logger.info(' ----------------------------------------')
logger.info(' subset | # ids | # images | # cameras')
logger.info(' ----------------------------------------')
logger.info(' query | {:5d} | {:8d} | {:9d}'.format(num_query_pids, len(self.query), num_query_cams))
logger.info(' gallery | {:5d} | {:8d} | {:9d}'.format(num_gallery_pids, len(self.gallery), num_gallery_cams))
logger.info(' ----------------------------------------')

238
fastreid/data/transforms/autoaugment.py 100644
View File

@ -0,0 +1,238 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
# ref: https://github.com/DeepVoltaire/AutoAugment/blob/master/autoaugment.py
# fix some color augmentation methods for adaptation to reid task
from PIL import Image, ImageEnhance, ImageOps
import numpy as np
import random
__all__ = ['ImageNetPolicy', 'CIFAR10Policy', 'SVHNPolicy']
class ImageNetPolicy(object):
""" Randomly choose one of the best 24 Sub-policies on ImageNet.
Example:
>>> policy = ImageNetPolicy()
>>> transformed = policy(image)
Example as a PyTorch Transform:
>>> transform=transforms.Compose([
>>> transforms.Resize(256),
>>> ImageNetPolicy(),
>>> transforms.ToTensor()])
"""
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
# SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
# SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
# SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
# SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
# SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
# SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
# SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
# SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
# SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
# SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
# SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
# SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
# SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
# SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
# SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
# SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
# SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def __repr__(self):
return "AutoAugment ImageNet Policy"
class CIFAR10Policy(object):
""" Randomly choose one of the best 25 Sub-policies on CIFAR10.
Example:
>>> policy = CIFAR10Policy()
>>> transformed = policy(image)
Example as a PyTorch Transform:
>>> transform=transforms.Compose([
>>> transforms.Resize(256),
>>> CIFAR10Policy(),
>>> transforms.ToTensor()])
"""
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor),
SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def __repr__(self):
return "AutoAugment CIFAR10 Policy"
class SVHNPolicy(object):
""" Randomly choose one of the best 25 Sub-policies on SVHN.
Example:
>>> policy = SVHNPolicy()
>>> transformed = policy(image)
Example as a PyTorch Transform:
>>> transform=transforms.Compose([
>>> transforms.Resize(256),
>>> SVHNPolicy(),
>>> transforms.ToTensor()])
"""
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def __repr__(self):
return "AutoAugment SVHN Policy"
class SubPolicy(object):
def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
ranges = {
"shearX": np.linspace(0, 0.3, 10),
"shearY": np.linspace(0, 0.3, 10),
"translateX": np.linspace(0, 150 / 331, 10),
"translateY": np.linspace(0, 150 / 331, 10),
"rotate": np.linspace(0, 30, 10),
"color": np.linspace(0.0, 0.9, 10),
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
"solarize": np.linspace(256, 0, 10),
"contrast": np.linspace(0.0, 0.9, 10),
"sharpness": np.linspace(0.0, 0.9, 10),
"brightness": np.linspace(0.0, 0.9, 10),
"autocontrast": [0] * 10,
"equalize": [0] * 10,
"invert": [0] * 10
}
# from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
def rotate_with_fill(img, magnitude):
rot = img.convert("RGBA").rotate(magnitude)
return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
func = {
"shearX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"shearY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"translateX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
fillcolor=fillcolor),
"translateY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
fillcolor=fillcolor),
"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
"equalize": lambda img, magnitude: ImageOps.equalize(img),
"invert": lambda img, magnitude: ImageOps.invert(img)
}
self.p1 = p1
self.operation1 = func[operation1]
self.magnitude1 = ranges[operation1][magnitude_idx1]
self.p2 = p2
self.operation2 = func[operation2]
self.magnitude2 = ranges[operation2][magnitude_idx2]
def __call__(self, img):
if random.random() < self.p1: img = self.operation1(img, self.magnitude1)
if random.random() < self.p2: img = self.operation2(img, self.magnitude2)
return img

2
fastreid/modeling/heads/__init__.py
View File

@ -9,5 +9,3 @@ from .build import REID_HEADS_REGISTRY, build_reid_heads
# import all the meta_arch, so they will be registered
from .linear_head import LinearHead
from .bnneck_head import BNneckHead
from .arcface_head import ArcfaceHead
from .circle_head import CircleHead

23
fastreid/modeling/heads/bnneck_head.py
View File

@ -4,12 +4,9 @@
@contact: sherlockliao01@gmail.com
"""
from torch import nn
from .build import REID_HEADS_REGISTRY
from .linear_head import LinearHead
from ..model_utils import weights_init_classifier, weights_init_kaiming
from ...layers import NoBiasBatchNorm1d, Flatten
from ..layers import *
from ..model_utils import weights_init_kaiming
@REID_HEADS_REGISTRY.register()
@ -25,8 +22,14 @@ class BNneckHead(nn.Module):
self.bnneck = NoBiasBatchNorm1d(in_feat)
self.bnneck.apply(weights_init_kaiming)
if cfg.MODEL.HEADS.CLS_LAYER == 'linear':
self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
elif cfg.MODEL.HEADS.CLS_LAYER == 'arcface':
self.classifier = Arcface(cfg, in_feat)
elif cfg.MODEL.HEADS.CLS_LAYER == 'circle':
self.classifier = Circle(cfg, in_feat)
else:
self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
self.classifier.apply(weights_init_classifier)
def forward(self, features, targets=None):
"""
@ -34,12 +37,12 @@ class BNneckHead(nn.Module):
"""
global_feat = self.pool_layer(features)
bn_feat = self.bnneck(global_feat)
# evaluation
if not self.training:
return bn_feat
# training
try:
pred_class_logits = self.classifier(bn_feat)
except TypeError:
pred_class_logits = self.classifier(bn_feat, targets)
return pred_class_logits, global_feat
@classmethod
def losses(cls, cfg, pred_class_logits, global_features, gt_classes, prefix='') -> dict:
return LinearHead.losses(cfg, pred_class_logits, global_features, gt_classes, prefix)

30
fastreid/modeling/heads/linear_head.py
View File

@ -4,12 +4,8 @@
@contact: sherlockliao01@gmail.com
"""
from torch import nn
from .build import REID_HEADS_REGISTRY
from .. import losses as Loss
from ..model_utils import weights_init_classifier
from ...layers import Flatten
from ..layers import *
@REID_HEADS_REGISTRY.register()
@ -24,8 +20,14 @@ class LinearHead(nn.Module):
Flatten()
)
if cfg.MODEL.HEADS.CLS_LAYER == 'linear':
self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
elif cfg.MODEL.HEADS.CLS_LAYER == 'arcface':
self.classifier = Arcface(cfg, in_feat)
elif cfg.MODEL.HEADS.CLS_LAYER == 'circle':
self.classifier = Circle(cfg, in_feat)
else:
self.classifier = nn.Linear(in_feat, self._num_classes, bias=False)
self.classifier.apply(weights_init_classifier)
def forward(self, features, targets=None):
"""
@ -35,18 +37,8 @@ class LinearHead(nn.Module):
if not self.training:
return global_feat
# training
try:
pred_class_logits = self.classifier(global_feat)
except TypeError:
pred_class_logits = self.classifier(global_feat, targets)
return pred_class_logits, global_feat
@classmethod
def losses(cls, cfg, pred_class_logits, global_features, gt_classes, prefix='') -> dict:
loss_dict = {}
for loss_name in cfg.MODEL.LOSSES.NAME:
loss = getattr(Loss, loss_name)(cfg)(pred_class_logits, global_features, gt_classes)
loss_dict.update(loss)
# rename
name_loss_dict = {}
for name in loss_dict.keys():
name_loss_dict[prefix + name] = loss_dict[name]
del loss_dict
return name_loss_dict

58
fastreid/modeling/heads/reduction_head.py 100644
View File

@ -0,0 +1,58 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
from .build import REID_HEADS_REGISTRY
from ..layers import *
from ..model_utils import weights_init_kaiming
@REID_HEADS_REGISTRY.register()
class ReductionHead(nn.Module):
def __init__(self, cfg, in_feat, pool_layer=nn.AdaptiveAvgPool2d(1)):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
reduction_dim = cfg.MODEL.HEADS.REDUCTION_DIM
self.pool_layer = nn.Sequential(
pool_layer,
Flatten()
)
self.bottleneck = nn.Sequential(
nn.Linear(in_feat, reduction_dim, bias=False),
NoBiasBatchNorm1d(reduction_dim),
nn.LeakyReLU(0.1),
nn.Dropout(0.5),
)
self.bnneck = NoBiasBatchNorm1d(reduction_dim)
self.bottleneck.apply(weights_init_kaiming)
self.bnneck.apply(weights_init_kaiming)
if cfg.MODEL.HEADS.CLS_LAYER == 'linear':
self.classifier = nn.Linear(reduction_dim, self._num_classes, bias=False)
elif cfg.MODEL.HEADS.CLS_LAYER == 'arcface':
self.classifier = Arcface(cfg, reduction_dim)
elif cfg.MODEL.HEADS.CLS_LAYER == 'circle':
self.classifier = Circle(cfg, reduction_dim)
else:
self.classifier = nn.Linear(reduction_dim, self._num_classes, bias=False)
def forward(self, features, targets=None):
"""
See :class:`ReIDHeads.forward`.
"""
global_feat = self.pool_layer(features)
global_feat = self.bottleneck(global_feat)
bn_feat = self.bnneck(global_feat)
if not self.training:
return bn_feat
# training
try:
pred_class_logits = self.classifier(bn_feat)
except TypeError:
pred_class_logits = self.classifier(bn_feat, targets)
return pred_class_logits, global_feat

2
fastreid/layers/__init__.py → fastreid/modeling/layers/__init__.py
View File

@ -12,6 +12,8 @@ from .context_block import ContextBlock
from .frn import FRN, TLU
from .mish import Mish
from .gem_pool import GeneralizedMeanPoolingP
from .arcface import Arcface
from .circle import Circle
class Flatten(nn.Module):

46
fastreid/modeling/heads/circle_head.py → fastreid/modeling/layers/adacos.py
View File

@ -7,18 +7,14 @@
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from .build import REID_HEADS_REGISTRY
from .linear_head import LinearHead
from ..layers import *
from ..model_utils import weights_init_kaiming
from ...layers import NoBiasBatchNorm1d, Flatten
@REID_HEADS_REGISTRY.register()
class CircleHead(nn.Module):
class AdaCos(nn.Module):
def __init__(self, cfg, in_feat, pool_layer=nn.AdaptiveAvgPool2d(1)):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
@ -27,20 +23,19 @@ class CircleHead(nn.Module):
pool_layer,
Flatten()
)
# bnneck
self.bnneck = NoBiasBatchNorm1d(in_feat)
self.bnneck.apply(weights_init_kaiming)
# classifier
self._s = cfg.MODEL.HEADS.CIRCLE.SCALE
self._m = cfg.MODEL.HEADS.CIRCLE.MARGIN
self._s = math.sqrt(2) * math.log(self._num_classes - 1)
self._m = 0.50
self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
nn.init.xavier_uniform_(self.weight)
def forward(self, features, targets=None):
global_feat = self.pool_layer(features)
@ -48,22 +43,23 @@ class CircleHead(nn.Module):
if not self.training:
return bn_feat
sim_mat = F.linear(F.normalize(bn_feat), F.normalize(self.weight))
alpha_p = F.relu(-sim_mat.detach() + 1 + self._m)
alpha_n = F.relu(sim_mat.detach() + self._m)
delta_p = 1 - self._m
delta_n = self._m
s_p = self._s * alpha_p * (sim_mat - delta_p)
s_n = self._s * alpha_n * (sim_mat - delta_n)
one_hot = torch.zeros(sim_mat.size()).to(targets.device)
# normalize features
x = F.normalize(bn_feat)
# normalize weights
weight = F.normalize(self.weight)
# dot product
logits = F.linear(x, weight)
# feature re-scale
theta = torch.acos(torch.clamp(logits, -1.0 + 1e-7, 1.0 - 1e-7))
one_hot = torch.zeros_like(logits)
one_hot.scatter_(1, targets.view(-1, 1).long(), 1)
with torch.no_grad():
B_avg = torch.where(one_hot < 1, torch.exp(self._s * logits), torch.zeros_like(logits))
B_avg = torch.sum(B_avg) / x.size(0)
# print(B_avg)
theta_med = torch.median(theta[one_hot == 1])
self.s = torch.log(B_avg) / torch.cos(torch.min(math.pi / 4 * torch.ones_like(theta_med), theta_med))
pred_class_logits = one_hot * s_p + (1.0 - one_hot) * s_n
pred_class_logits = self.s * logits
return pred_class_logits, global_feat
@classmethod
def losses(cls, cfg, pred_class_logits, global_feat, gt_classes):
return LinearHead.losses(cfg, pred_class_logits, global_feat, gt_classes)

46
fastreid/modeling/layers/arcface.py 100644
View File

@ -0,0 +1,46 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..losses.loss_utils import one_hot
class Arcface(nn.Module):
def __init__(self, cfg, in_feat):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
self._s = cfg.MODEL.HEADS.SCALE
self._m = cfg.MODEL.HEADS.MARGIN
self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, features, targets):
# get cos(theta)
cosine = F.linear(F.normalize(features), F.normalize(self.weight))
# add margin
theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7))
phi = torch.cos(theta + self._m)
# --------------------------- convert label to one-hot ---------------------------
targets = one_hot(targets, self._num_classes)
pred_class_logits = targets * phi + (1.0 - targets) * cosine
# logits re-scale
pred_class_logits *= self._s
return pred_class_logits

0
fastreid/layers/attention.py → fastreid/modeling/layers/attention.py
View File

0
fastreid/layers/batch_drop.py → fastreid/modeling/layers/batch_drop.py
View File

0
fastreid/layers/batch_norm.py → fastreid/modeling/layers/batch_norm.py
View File

44
fastreid/modeling/layers/circle.py 100644
View File

@ -0,0 +1,44 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..losses.loss_utils import one_hot
class Circle(nn.Module):
def __init__(self, cfg, in_feat):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
self._s = cfg.MODEL.HEADS.SCALE
self._m = cfg.MODEL.HEADS.MARGIN
self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, features, targets):
sim_mat = F.linear(F.normalize(features), F.normalize(self.weight))
alpha_p = F.relu(-sim_mat.detach() + 1 + self._m)
alpha_n = F.relu(sim_mat.detach() + self._m)
delta_p = 1 - self._m
delta_n = self._m
s_p = self._s * alpha_p * (sim_mat - delta_p)
s_n = self._s * alpha_n * (sim_mat - delta_n)
targets = one_hot(targets, self._num_classes)
pred_class_logits = targets * s_p + (1.0 - targets) * s_n
return pred_class_logits

4
fastreid/layers/context_block.py → fastreid/modeling/layers/context_block.py
View File

@ -3,9 +3,9 @@
import torch
from torch import nn
__all__ = ['ContextBlock']
def last_zero_init(m):
if isinstance(m, nn.Sequential):
nn.init.constant_(m[-1].weight, val=0)
@ -23,7 +23,7 @@ class ContextBlock(nn.Module):
inplanes,
ratio,
pooling_type='att',
fusion_types=('channel_add', )):
fusion_types=('channel_add',)):
super(ContextBlock, self).__init__()
assert pooling_type in ['avg', 'att']
assert isinstance(fusion_types, (list, tuple))

0
fastreid/layers/frn.py → fastreid/modeling/layers/frn.py
View File

0
fastreid/layers/gem_pool.py → fastreid/modeling/layers/gem_pool.py
View File

0
fastreid/layers/mish.py → fastreid/modeling/layers/mish.py
View File

92
fastreid/modeling/layers/osm.py 100644
View File

@ -0,0 +1,92 @@
# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from ..model_utils import weights_init_kaiming
from ..layers import *
class OSM(nn.Module):
def __init__(self, cfg, in_feat, pool_layer=nn.AdaptiveAvgPool2d(1)):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
self.pool_layer = nn.Sequential(
pool_layer,
Flatten()
)
# bnneck
self.bnneck = NoBiasBatchNorm1d(in_feat)
self.bnneck.apply(weights_init_kaiming)
# classifier
self.alpha = 1.2 # margin of weighted contrastive loss, as mentioned in the paper
self.l = 0.5 # hyperparameter controlling weights of positive set and the negative set
# I haven't been able to figure out the use of \sigma CAA 0.18
self.osm_sigma = 0.8 # \sigma OSM (0.8) as mentioned in paper
self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, features, targets=None):
global_feat = self.pool_layer(features)
bn_feat = self.bnneck(global_feat)
if not self.training:
return bn_feat
bn_feat = F.normalize(bn_feat)
n = bn_feat.size(0)
# Compute pairwise distance, replace by the official when merged
dist = torch.pow(bn_feat, 2).sum(dim=1, keepdim=True).expand(n, n)
dist = dist + dist.t()
dist.addmm_(1, -2, bn_feat, bn_feat.t())
dist = dist.clamp(min=1e-12).sqrt() # for numerical stability & pairwise distance, dij
S = torch.exp(-1.0 * torch.pow(dist, 2) / (self.osm_sigma * self.osm_sigma))
S_ = torch.clamp(self.alpha - dist, min=1e-12) # max (0 , \alpha - dij) # 1e-12, 0 may result in nan error
p_mask = targets.expand(n, n).eq(targets.expand(n, n).t()) # same label == 1
n_mask = torch.bitwise_not(p_mask) # oposite label == 1
S = S * p_mask.float()
S = S + S_ * n_mask.float()
denominator = torch.exp(F.linear(bn_feat, F.normalize(self.weight)))
A = [] # attention corresponding to each feature fector
for i in range(n):
a_i = denominator[i][targets[i]] / torch.sum(denominator[i])
A.append(a_i)
# a_i's
atten_class = torch.stack(A)
# a_ij's
A = torch.min(atten_class.expand(n, n),
atten_class.view(-1, 1).expand(n, n)) # pairwise minimum of attention weights
W = S * A
W_P = W * p_mask.float()
W_N = W * n_mask.float()
W_P = W_P * (1 - torch.eye(n,
n).float().cuda()) # dist between (xi,xi) not necessarily 0, avoiding precision error
W_N = W_N * (1 - torch.eye(n, n).float().cuda())
L_P = 1.0 / 2 * torch.sum(W_P * torch.pow(dist, 2)) / torch.sum(W_P)
L_N = 1.0 / 2 * torch.sum(W_N * torch.pow(S_, 2)) / torch.sum(W_N)
L = (1 - self.l) * L_P + self.l * L_N
return L, global_feat

29
fastreid/modeling/heads/arcface_head.py → fastreid/modeling/layers/qam.py
View File

@ -6,20 +6,17 @@
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from .build import REID_HEADS_REGISTRY
from .linear_head import LinearHead
from ..layers import *
from ..losses.loss_utils import one_hot
from ..model_utils import weights_init_kaiming
from ...layers import NoBiasBatchNorm1d, Flatten
@REID_HEADS_REGISTRY.register()
class ArcfaceHead(nn.Module):
class QAMHead(nn.Module):
def __init__(self, cfg, in_feat, pool_layer=nn.AdaptiveAvgPool2d(1)):
super().__init__()
self._num_classes = cfg.MODEL.HEADS.NUM_CLASSES
@ -33,10 +30,12 @@ class ArcfaceHead(nn.Module):
self.bnneck.apply(weights_init_kaiming)
# classifier
self._s = cfg.MODEL.HEADS.ARCFACE.SCALE
self._m = cfg.MODEL.HEADS.ARCFACE.MARGIN
# self.adaptive_s = False
self._s = 6.0
self._m = 0.50
self.weight = Parameter(torch.Tensor(self._num_classes, in_feat))
self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
self.reset_parameters()
def reset_parameters(self):
@ -52,19 +51,17 @@ class ArcfaceHead(nn.Module):
cosine = F.linear(F.normalize(bn_feat), F.normalize(self.weight))
# add margin
theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7))
phi = torch.cos(theta + self._m)
theta = torch.acos(torch.clamp(cosine, -1.0 + 1e-7, 1.0 - 1e-7)) # for numerical stability
# --------------------------- convert label to one-hot ---------------------------
targets = one_hot(targets, self._num_classes)
pred_class_logits = targets * phi + (1.0 - targets) * cosine
phi = (2 * np.pi - (theta + self._m)) ** 2
others = (2 * np.pi - theta) ** 2
pred_class_logits = targets * phi + (1.0 - targets) * others
# logits re-scale
pred_class_logits *= self._s
return pred_class_logits, global_feat
@classmethod
def losses(cls, cfg, pred_class_logits, global_feat, gt_classes):
return LinearHead.losses(cfg, pred_class_logits, global_feat, gt_classes)

0
fastreid/layers/se_module.py → fastreid/modeling/layers/se_module.py
View File

79
fastreid/modeling/layers/splat.py 100644
View File

@ -0,0 +1,79 @@
# encoding: utf-8
"""
@author: xingyu liao
@contact: liaoxingyu5@jd.com
"""
import torch
import torch.nn.functional as F
from torch.nn import Conv2d, Module, ReLU
from torch.nn.modules.utils import _pair
class SplAtConv2d(Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0),
dilation=(1, 1), groups=1, bias=True,
radix=2, reduction_factor=4,
rectify=False, rectify_avg=False, norm_layer=None,
dropblock_prob=0.0, **kwargs):
super(SplAtConv2d, self).__init__()
padding = _pair(padding)
self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
self.rectify_avg = rectify_avg
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
from rfconv import RFConv2d
self.conv = RFConv2d(in_channels, channels * radix, kernel_size, stride, padding, dilation,
groups=groups * radix, bias=bias, average_mode=rectify_avg, **kwargs)
else:
self.conv = Conv2d(in_channels, channels * radix, kernel_size, stride, padding, dilation,
groups=groups * radix, bias=bias, **kwargs)
self.use_bn = norm_layer is not None
self.bn0 = norm_layer(channels * radix)
self.relu = ReLU(inplace=True)
self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
self.bn1 = norm_layer(inter_channels)
self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.cardinality)
if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
def forward(self, x):
x = self.conv(x)
if self.use_bn:
x = self.bn0(x)
if self.dropblock_prob > 0.0:
x = self.dropblock(x)
x = self.relu(x)
batch, channel = x.shape[:2]
if self.radix > 1:
splited = torch.split(x, channel // self.radix, dim=1)
gap = sum(splited)
else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
gap = self.fc1(gap)
if self.use_bn:
gap = self.bn1(gap)
gap = self.relu(gap)
atten = self.fc2(gap).view((batch, self.radix, self.channels))
if self.radix > 1:
atten = F.softmax(atten, dim=1).view(batch, -1, 1, 1)
else:
atten = F.sigmoid(atten, dim=1).view(batch, -1, 1, 1)
if self.radix > 1:
atten = torch.split(atten, channel // self.radix, dim=1)
out = sum([att * split for (att, split) in zip(atten, splited)])
else:
out = atten * x
return out.contiguous()

2
fastreid/modeling/losses/__init__.py
View File

@ -4,5 +4,7 @@
@contact: sherlockliao01@gmail.com
"""
from .build_losses import reid_losses
from .cross_entroy_loss import CrossEntropyLoss
from .focal_loss import FocalLoss
from .metric_loss import *

33
fastreid/modeling/losses/build.py
View File

@ -1,33 +0,0 @@
# encoding: utf-8
"""
@author: l1aoxingyu
@contact: sherlockliao01@gmail.com
"""
from ...utils.registry import Registry
LOSS_REGISTRY = Registry("LOSS")
LOSS_REGISTRY.__doc__ = """
Registry for loss, which extract feature maps from images
The registered object must be a callable that accepts two arguments:
It must returns an instance of :class:`Loss`.
"""
def build_criterion(cfg):
"""
Build a loss from `cfg.MODEL.BACKBONE.NAME`.
Returns:
an instance of :class:`Loss`
"""
loss_names = cfg.MODEL.LOSSES.NAME
loss_funcs = [LOSS_REGISTRY.get(loss_name)(cfg) for loss_name in loss_names]
def criterion(*args):
loss_dict = {}
for loss_func in loss_funcs:
loss = loss_func(*args)
loss_dict.update(loss)
return loss_dict
return criterion

20
fastreid/modeling/losses/build_losses.py 100644
View File

@ -0,0 +1,20 @@
# encoding: utf-8
"""
@author: xingyu liao
@contact: liaoxingyu5@jd.com
"""
from .. import losses as Loss
def reid_losses(cfg, pred_class_logits, global_features, gt_classes, prefix='') -> dict:
loss_dict = {}
for loss_name in cfg.MODEL.LOSSES.NAME:
loss = getattr(Loss, loss_name)(cfg)(pred_class_logits, global_features, gt_classes)
loss_dict.update(loss)
# rename
named_loss_dict = {}
for name in loss_dict.keys():
named_loss_dict[prefix + name] = loss_dict[name]
del loss_dict
return named_loss_dict

10
fastreid/modeling/losses/metric_loss.py
View File

@ -124,11 +124,11 @@ class TripletLoss(object):
Loss for Person Re-Identification'."""
def __init__(self, cfg):
self._margin = cfg.MODEL.LOSSES.MARGIN
self._normalize_feature = cfg.MODEL.LOSSES.NORM_FEAT
self._scale = cfg.MODEL.LOSSES.SCALE_TRI
self._hard_mining = cfg.MODEL.LOSSES.HARD_MINING
self._use_cosine_dist = cfg.MODEL.LOSSES.USE_COSINE_DIST
self._margin = cfg.MODEL.LOSSES.TRI.MARGIN
self._normalize_feature = cfg.MODEL.LOSSES.TRI.NORM_FEAT
self._scale = cfg.MODEL.LOSSES.TRI.SCALE
self._hard_mining = cfg.MODEL.LOSSES.TRI.HARD_MINING
self._use_cosine_dist = cfg.MODEL.LOSSES.TRI.USE_COSINE_DIST
if self._margin > 0:
self.ranking_loss = nn.MarginRankingLoss(margin=self._margin)

4
fastreid/modeling/meta_arch/abd_network.py
View File

@ -12,9 +12,9 @@ from torch import nn
from .build import META_ARCH_REGISTRY
from ..backbones import build_backbone
from ..heads import build_reid_heads, BNneckHead
from ..heads import build_reid_heads
from ..model_utils import weights_init_kaiming
from ...layers import CAM_Module, PAM_Module, DANetHead, Flatten, NoBiasBatchNorm1d
from fastreid.modeling.layers import CAM_Module, PAM_Module, DANetHead, Flatten
@META_ARCH_REGISTRY.register()

8
fastreid/modeling/meta_arch/baseline.py
View File

@ -4,13 +4,14 @@
@contact: sherlockliao01@gmail.com
"""
from torch import nn
import torch.nn.functional as F
from torch import nn
from .build import META_ARCH_REGISTRY
from ..backbones import build_backbone
from ..heads import build_reid_heads
from ...layers import GeneralizedMeanPoolingP
from ..layers import GeneralizedMeanPoolingP
from ..losses import reid_losses
@META_ARCH_REGISTRY.register()
@ -52,4 +53,5 @@ class Baseline(nn.Module):
return F.normalize(pred_feat)
def losses(self, outputs):
return self.heads.losses(self._cfg, *outputs)
logits, global_feat, targets = outputs
return reid_losses(self._cfg, logits, global_feat, targets)

4
fastreid/modeling/meta_arch/bdb_network.py
View File

@ -11,9 +11,9 @@ import torch.nn.functional as F
from .build import META_ARCH_REGISTRY
from ..backbones import build_backbone
from ..backbones.resnet import Bottleneck
from ..heads import build_reid_heads, BNneckHead
from ..heads import build_reid_heads
from ..model_utils import weights_init_kaiming
from ...layers import BatchDrop, NoBiasBatchNorm1d, Flatten, GeneralizedMeanPoolingP
from fastreid.modeling.layers import BatchDrop, Flatten, GeneralizedMeanPoolingP
@META_ARCH_REGISTRY.register()

4
fastreid/modeling/meta_arch/mask_model.py
View File

@ -8,10 +8,8 @@ import torch
from torch import nn
import torch.nn.functional as F
from fastreid.modeling.backbones import *
from fastreid.modeling.model_utils import *
from fastreid.modeling.heads import *
from fastreid.layers import NoBiasBatchNorm1d, GeM
from fastreid.modeling.layers import NoBiasBatchNorm1d
class MaskUnit(nn.Module):

4
fastreid/modeling/meta_arch/mf_network.py
View File

@ -11,8 +11,8 @@ import torch.nn.functional as F
from .build import META_ARCH_REGISTRY
from ..model_utils import weights_init_kaiming
from ..backbones import build_backbone
from ..heads import build_reid_heads, BNneckHead
from ...layers import Flatten, NoBiasBatchNorm1d
from ..heads import build_reid_heads
from fastreid.modeling.layers import Flatten
@META_ARCH_REGISTRY.register()

2
fastreid/modeling/meta_arch/mgn.py
View File

@ -13,7 +13,7 @@ from ..backbones import build_backbone
from ..backbones.resnet import Bottleneck
from ..heads import build_reid_heads
from ..model_utils import weights_init_kaiming
from ...layers import GeneralizedMeanPoolingP, Flatten
from fastreid.modeling.layers import GeneralizedMeanPoolingP, Flatten
@META_ARCH_REGISTRY.register()

2
fastreid/modeling/meta_arch/mid_network.py
View File

@ -12,7 +12,7 @@ from .build import META_ARCH_REGISTRY
from ..backbones import build_backbone
from ..heads import build_reid_heads
from ..model_utils import weights_init_kaiming
from ...layers import Flatten, NoBiasBatchNorm1d
from fastreid.modeling.layers import Flatten
@META_ARCH_REGISTRY.register()