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9
net.py
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@ -7,7 +7,6 @@ from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from einops import rearrange
from componets.SCSA import SCSA
from componets.WTConvCV2 import WTConv2d
def drop_path(x, drop_prob: float = 0., training: bool = False):
@ -249,10 +248,6 @@ class DetailNode(nn.Module):
self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
elif useBlock == 2:
self.theta_phi = WTConv2d(in_channels=32, out_channels=32)
self.theta_rho = WTConv2d(in_channels=32, out_channels=32)
self.theta_eta = WTConv2d(in_channels=32, out_channels=32)
else:
self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
@ -274,7 +269,7 @@ class DetailNode(nn.Module):
class DetailFeatureFusion(nn.Module):
def __init__(self, num_layers=3):
super(DetailFeatureFusion, self).__init__()
INNmodules = [DetailNode(useBlock=2) for _ in range(num_layers)]
INNmodules = [DetailNode() for _ in range(num_layers)]
self.net = nn.Sequential(*INNmodules)
def forward(self, x):
@ -286,7 +281,7 @@ class DetailFeatureFusion(nn.Module):
class DetailFeatureExtraction(nn.Module):
def __init__(self, num_layers=3):
super(DetailFeatureExtraction, self).__init__()
INNmodules = [DetailNode(useBlock=2) for _ in range(num_layers)]
INNmodules = [DetailNode() for _ in range(num_layers)]
self.net = nn.Sequential(*INNmodules)
def forward(self, x):