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# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
#
# All rights reserved.
# This work should only be used for nonprofit purposes.
#
# By downloading and/or using any of these files, you implicitly agree to all the
# terms of the license, as specified in the document LICENSE.txt
# (included in this package) and online at
# http://www.grip.unina.it/download/LICENSE_OPEN.txt
"""
Edited in September 2022
@author: fabrizio.guillaro, davide.cozzolino
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
from lib.models.cmx.init_func import init_weight
import logging
def preprc_imagenet_torch(x):
mean = torch.Tensor([0.485, 0.456, 0.406]).to(x.device)
std = torch.Tensor([0.229, 0.224, 0.225]).to(x.device)
x = (x-mean[None, :, None, None]) / std[None, :, None, None]
return x
def preprc_xception_torch(x):
return 2.0*x-1.0
def create_backbone(typ, norm_layer):
channels = [64, 128, 320, 512]
if typ == 'mit_b5':
logging.info('Using backbone: Segformer-B5')
from .encoders.dual_segformer import mit_b5 as backbone_
backbone = backbone_(norm_fuse=norm_layer)
elif typ == 'mit_b4':
logging.info('Using backbone: Segformer-B4')
from .encoders.dual_segformer import mit_b4 as backbone_
backbone = backbone_(norm_fuse=norm_layer)
elif typ == 'mit_b2':
logging.info('Using backbone: Segformer-B2')
from .encoders.dual_segformer import mit_b2 as backbone_
backbone = backbone_(norm_fuse=norm_layer)
elif typ == 'mit_b1':
logging.info('Using backbone: Segformer-B1')
from .encoders.dual_segformer import mit_b1 as backbone_
backbone = backbone_(norm_fuse=norm_layer)
elif typ == 'mit_b0':
logging.info('Using backbone: Segformer-B0')
channels = [32, 64, 160, 256]
from .encoders.dual_segformer import mit_b0 as backbone_
backbone = backbone_(norm_fuse=norm_layer)
else:
raise NotImplementedError('Backbone not implemented')
return backbone, channels
class EncoderDecoder(nn.Module):
def __init__(self, cfg=None, norm_layer=nn.BatchNorm2d):
super(EncoderDecoder, self).__init__()
self.norm_layer = norm_layer
self.cfg = cfg.MODEL.EXTRA
self.mods = cfg.MODEL.MODS # input modalities
# setting number of Noiseprint++ output channels
if 'NP_OUT_CHANNELS' in self.cfg:
self.np_out_ch = self.cfg.NP_OUT_CHANNELS
else:
self.np_out_ch = 1
modules_list = ['NP++','backbone','loc_head','conf_head','det_head']
for module in self.cfg.MODULES:
assert module in modules_list
assert 'backbone' in self.cfg.MODULES
for module in self.cfg.FIX_MODULES:
assert module in modules_list
# importing backbone
self.backbone, self.channels = create_backbone(self.cfg.BACKBONE, norm_layer)
# defining heads
self.decode_head = None # localization head
self.decode_head_conf = None # confidence head
self.detection = None # detection head
if self.cfg.DECODER == 'MLPDecoder':
logging.info('Using MLP Decoder')
from .decoders.MLPDecoder import DecoderHead
# localization head
if 'loc_head' in self.cfg.MODULES:
self.decode_head = DecoderHead(
in_channels=self.channels,
num_classes=cfg.DATASET.NUM_CLASSES,
norm_layer=norm_layer,
embed_dim=self.cfg.DECODER_EMBED_DIM)
# confidence head
if 'conf_head' in self.cfg.MODULES:
self.decode_head_conf = DecoderHead(
in_channels=self.channels,
num_classes=1,
norm_layer=norm_layer,
embed_dim=self.cfg.DECODER_EMBED_DIM)
# detection head
self.conf_detection = self.cfg.DETECTION
if 'det_head' in self.cfg.MODULES:
if self.conf_detection == 'confpool':
assert 'conf_head' in self.cfg.MODULES
self.detection = nn.Sequential(
nn.Linear(in_features=8, out_features=128),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(in_features=128, out_features=1),
)
else:
raise NotImplementedError('Detection mechanism not implemented')
else:
raise NotImplementedError('Decoder not implemented')
# Noiseprint++ extractor
from lib.models.DnCNN import make_net
num_levels = 17
out_channel = self.np_out_ch
self.dncnn = make_net(3, kernels=[3, ] * num_levels,
features=[64, ] * (num_levels - 1) + [out_channel],
bns=[False, ] + [True, ] * (num_levels - 2) + [False, ],
acts=['relu', ] * (num_levels - 1) + ['linear', ],
dilats=[1, ] * num_levels,
bn_momentum=0.1, padding=1)
if self.cfg.PREPRC is None or self.cfg.PREPRC == 'none': #RGB01 (0,1)
self.prepro = None
elif self.cfg.PREPRC == 'imagenet': #RGB (mean and variance)
self.prepro = preprc_imagenet_torch
elif self.cfg.PREPRC == 'xception': #RGB0 (-1,1)
self.prepro = preprc_xception_torch
else:
assert False
# pretraining
self.init_weights(pretrained=cfg.MODEL.PRETRAINED)
def init_weights(self, pretrained=None):
# loading Noiseprint++ weights
if 'NP_WEIGHTS' in self.cfg and not self.cfg.NP_WEIGHTS == '' and self.cfg.NP_WEIGHTS is not None:
np_weights = self.cfg.NP_WEIGHTS
assert os.path.isfile(np_weights)
dat = torch.load(np_weights, map_location=torch.device('cpu'))['network']
logging.info(f'Noiseprint++ weights: {np_weights}')
self.dncnn.load_state_dict(dat)
# backbone pretraining
if pretrained:
logging.info('Loading backbone model: {}'.format(pretrained))
assert os.path.isfile(pretrained)
self.backbone.init_weights(pretrained=pretrained)
# initing heads weights
logging.info('Initing heads weights ...')
if self.decode_head:
init_weight(self.decode_head, nn.init.kaiming_normal_,
self.norm_layer, self.cfg.BN_EPS, self.cfg.BN_MOMENTUM,
mode='fan_in', nonlinearity='relu')
if self.decode_head_conf:
init_weight(self.decode_head_conf, nn.init.kaiming_normal_,
self.norm_layer, self.cfg.BN_EPS, self.cfg.BN_MOMENTUM,
mode='fan_in', nonlinearity='relu')
# freezing modules
if 'NP++' in self.cfg.FIX_MODULES:
for param in self.dncnn.parameters():
param.requires_grad = False
if 'backbone' in self.cfg.FIX_MODULES:
for param in self.backbone.parameters():
param.requires_grad = False
if 'loc_head' in self.cfg.FIX_MODULES:
for param in self.decode_head.parameters():
param.requires_grad = False
if 'conf_head' in self.cfg.FIX_MODULES:
for param in self.decode_head_conf.parameters():
param.requires_grad = False
def encode_decode(self, rgb, modal_x):
if rgb is not None:
orisize = rgb.shape
else:
orisize = modal_x.shape
# CMX encoder
if 'backbone' in self.cfg.FIX_MODULES:
with torch.no_grad():
self.backbone.eval()
x = self.backbone(rgb, modal_x)
else:
x = self.backbone(rgb, modal_x)
# anomaly localization
if 'loc_head' in self.cfg.FIX_MODULES:
with torch.no_grad():
self.decode_head.eval()
out = self.decode_head(x)
else:
out = self.decode_head(x)
out = F.interpolate(out, size=orisize[2:], mode='bilinear', align_corners=False)
# confidence estimation
if self.decode_head_conf:
if 'conf_head' in self.cfg.FIX_MODULES:
with torch.no_grad():
self.decode_head_conf.eval()
conf = self.decode_head_conf(x)
else:
conf = self.decode_head_conf(x)
conf = F.interpolate(conf, size=orisize[2:], mode='bilinear', align_corners=False)
else:
conf = None
# detection
if self.detection:
if self.conf_detection == 'confpool':
from .layer_utils import weighted_statistics_pooling
f1 = weighted_statistics_pooling(conf).view(out.shape[0],-1)
f2 = weighted_statistics_pooling(out[:,1:2,:,:]-out[:,0:1,:,:], F.logsigmoid(conf)).view(out.shape[0],-1)
det = self.detection(torch.cat((f1,f2),-1))
else:
assert False
else:
det = None
return out, conf, det
def forward(self, rgb, save_np=False):
# rgb should be a float tensor in the range [0,1], since Noiseprint++ has been trained with this input
# Noiseprint++ extraction
if 'NP++' in self.mods:
if 'NP++' in self.cfg.FIX_MODULES:
with torch.no_grad():
self.dncnn.eval()
modal_x = self.dncnn(rgb)
else:
modal_x = self.dncnn(rgb)
if self.np_out_ch == 1:
modal_x = torch.tile(modal_x, (3, 1, 1))
else:
assert self.np_out_ch == 3
else:
modal_x = None
if 'RGB' not in self.mods:
rgb = None
# from [0,1] to other normalization, before going in the CMX network
elif self.prepro is not None:
rgb = self.prepro(rgb)
# Localization and Detection
out, conf, det = self.encode_decode(rgb, modal_x)
if save_np:
return out, conf, det, modal_x
else:
return out, conf, det, None
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