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import torch
from torch import nn
from addict import Dict
from rscd.models.decoderheads.pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder4ScalesFASeg
from rscd.models.decoderheads.transformer_decoder import MultiScaleMaskedTransformerDecoder_OurDH_v4,MultiScaleMaskedTransformerDecoder_OurDH_v5
from torch.nn import functional as F
class MaskFormerHead(nn.Module):
def __init__(self, input_shape,
num_classes = 1,
num_queries = 10,
dec_layers = 10
):
super().__init__()
self.num_classes = num_classes
self.num_queries = num_queries
self.dec_layers = dec_layers
self.pixel_decoder = self.pixel_decoder_init(input_shape)
self.predictor = self.predictor_init()
def pixel_decoder_init(self, input_shape):
common_stride = 4 # cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
transformer_dropout = 0 # cfg.MODEL.MASK_FORMER.DROPOUT
transformer_nheads = 8 # cfg.MODEL.MASK_FORMER.NHEADS
transformer_dim_feedforward = 1024
transformer_enc_layers = 4 # cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS
conv_dim = 256 # cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
mask_dim = 256 # cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
transformer_in_features = ["res3", "res4", "res5"] # cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES # ["res3", "res4", "res5"]
pixel_decoder = MSDeformAttnPixelDecoder4ScalesFASeg(input_shape,
transformer_dropout,
transformer_nheads,
transformer_dim_feedforward,
transformer_enc_layers,
conv_dim,
mask_dim,
transformer_in_features,
common_stride)
return pixel_decoder
def predictor_init(self):
in_channels = 256 # cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
num_classes = self.num_classes # cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
hidden_dim = 256 # cfg.MODEL.MASK_FORMER.HIDDEN_DIM
num_queries = self.num_queries # cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
nheads = 8 # cfg.MODEL.MASK_FORMER.NHEADS
dim_feedforward = 1024 # cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
dec_layers = self.dec_layers - 1 # cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1
pre_norm = False # cfg.MODEL.MASK_FORMER.PRE_NORM
mask_dim = 256 # cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
enforce_input_project = False
mask_classification = True
predictor = MultiScaleMaskedTransformerDecoder_OurDH_v5(in_channels,
num_classes,
mask_classification,
hidden_dim,
num_queries,
nheads,
dim_feedforward,
dec_layers,
pre_norm,
mask_dim,
enforce_input_project)
return predictor
def forward(self, features, mask=None):
mask_features, transformer_encoder_features, multi_scale_features, pos_list_2d = self.pixel_decoder.forward_features(features)
predictions = self.predictor(multi_scale_features, mask_features, mask, pos_list_2d)
return predictions
def dsconv_3x3(in_channel, out_channel):
return nn.Sequential(
nn.Conv2d(in_channel, in_channel, kernel_size=3, stride=1, padding=1, groups=in_channel),
nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0, groups=1),
nn.BatchNorm2d(out_channel),
nn.ReLU(inplace=True)
)
class SaELayer(nn.Module):
def __init__(self, in_channel, reduction=32):
super(SaELayer, self).__init__()
assert in_channel>=reduction and in_channel%reduction==0,'invalid in_channel in SaElayer'
self.reduction = reduction
self.cardinality=4
self.avg_pool = nn.AdaptiveAvgPool2d(1)
#cardinality 1
self.fc1 = nn.Sequential(
nn.Linear(in_channel,in_channel//self.reduction, bias=False),
nn.ReLU(inplace=True)
)
# cardinality 2
self.fc2 = nn.Sequential(
nn.Linear(in_channel, in_channel // self.reduction, bias=False),
nn.ReLU(inplace=True)
)
# cardinality 3
self.fc3 = nn.Sequential(
nn.Linear(in_channel, in_channel // self.reduction, bias=False),
nn.ReLU(inplace=True)
)
# cardinality 4
self.fc4 = nn.Sequential(
nn.Linear(in_channel, in_channel // self.reduction, bias=False),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
nn.Linear(in_channel//self.reduction*self.cardinality, in_channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y1 = self.fc1(y)
y2 = self.fc2(y)
y3 = self.fc3(y)
y4 = self.fc4(y)
y_concate = torch.cat([y1,y2,y3,y4],dim=1)
y_ex_dim = self.fc(y_concate).view(b,c,1,1)
return y_ex_dim.expand_as(x)
class TFF(nn.Module):
def __init__(self, in_channel, out_channel):
super(TFF, self).__init__()
self.catconvA = dsconv_3x3(in_channel * 2, in_channel)
self.catconvB = dsconv_3x3(in_channel * 2, in_channel)
self.catconv = dsconv_3x3(in_channel * 2, out_channel)
self.convA = nn.Conv2d(in_channel, 1, 1)
self.convB = nn.Conv2d(in_channel, 1, 1)
self.sigmoid = nn.Sigmoid()
self.senetv2 = SaELayer(in_channel)
def forward(self, xA, xB):
x_diff = xA - xB
x_weight = self.senetv2(x_diff)
x_diffA = self.catconvA(torch.cat([x_diff, xA], dim=1))
x_diffB = self.catconvB(torch.cat([x_diff, xB], dim=1))
A_weight = self.sigmoid(self.convA(x_diffA))
B_weight = self.sigmoid(self.convB(x_diffB))
xA = A_weight * xA * x_weight
xB = B_weight * xB * x_weight
x = self.catconv(torch.cat([xA, xB], dim=1))
return x
class MaskFormerModel_sea_ourDH(nn.Module):
def __init__(self, channels,
num_classes = 1,
num_queries = 10,
dec_layers = 14):
super().__init__()
self.channels = channels
self.backbone_feature_shape = dict()
for i, channel in enumerate(self.channels):
self.backbone_feature_shape[f'res{i+2}'] = Dict({'channel': channel, 'stride': 2**(i+2)})
self.tff1 = TFF(self.channels[0], self.channels[0])
self.tff2 = TFF(self.channels[1], self.channels[1])
self.tff3 = TFF(self.channels[2], self.channels[2])
self.tff4 = TFF(self.channels[3], self.channels[3])
self.sem_seg_head = MaskFormerHead(self.backbone_feature_shape, num_classes, num_queries, dec_layers)
def semantic_inference(self, mask_cls, mask_pred):
# mask_cls = F.softmax(mask_cls, dim=-1)
mask_cls = F.softmax(mask_cls, dim=-1)[...,1:]
mask_pred = mask_pred.sigmoid()
semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred).detach()
b, c, h, w = semseg.shape
for i in range(b):
for j in range(c):
minval = semseg[i, j].min()
maxval = semseg[i, j].max()
semseg[i, j] = (semseg[i, j] - minval) / (maxval - minval)
return semseg
def forward(self, inputs):
featuresA, featuresB =inputs
features = [self.tff1(featuresA[0], featuresB[0]),
self.tff2(featuresA[1], featuresB[1]),
self.tff3(featuresA[2], featuresB[2]),
self.tff4(featuresA[3], featuresB[3]),]
features = {
'res2': features[0],
'res3': features[1],
'res4': features[2],
'res5': features[3]
}
outputs = self.sem_seg_head(features)
mask_cls_results = outputs["pred_logits"]
mask_pred_results = outputs["pred_masks"]
mask_pred_results = F.interpolate(
mask_pred_results,
scale_factor=(4,4),
mode="bilinear",
align_corners=False,
)
pred_masks = self.semantic_inference(mask_cls_results, mask_pred_results)
return [pred_masks, outputs]
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