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mfnet_3d.py
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mfnet_3d.py
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# -*- coding: utf-8 -*-
"""
Original Author: Yunpeng Chen
https://github.com/cypw/PyTorch-MFNet/blob/master/network/mfnet_3d.py
"""
from collections import OrderedDict
import torch.nn as nn
class BN_AC_CONV3D(nn.Module):
def __init__(self, num_in, num_filter,
kernel=(1,1,1), pad=(0,0,0), stride=(1,1,1), g=1, bias=False):
super(BN_AC_CONV3D, self).__init__()
self.bn = nn.BatchNorm3d(num_in)
self.relu = nn.ReLU(inplace=True)
self.conv = nn.Conv3d(num_in, num_filter, kernel_size=kernel, padding=pad,
stride=stride, groups=g, bias=bias)
def forward(self, x):
h = self.relu(self.bn(x))
h = self.conv(h)
return h
class MF_UNIT(nn.Module):
def __init__(self, num_in, num_mid, num_out, g=1, stride=(1,1,1), first_block=False, use_3d=True):
super(MF_UNIT, self).__init__()
num_ix = int(num_mid/4)
kt,pt = (3,1) if use_3d else (1,0)
# prepare input
self.conv_i1 = BN_AC_CONV3D(num_in=num_in, num_filter=num_ix, kernel=(1,1,1), pad=(0,0,0))
self.conv_i2 = BN_AC_CONV3D(num_in=num_ix, num_filter=num_in, kernel=(1,1,1), pad=(0,0,0))
# main part
self.conv_m1 = BN_AC_CONV3D(num_in=num_in, num_filter=num_mid, kernel=(kt,3,3), pad=(pt,1,1), stride=stride, g=g)
if first_block:
self.conv_m2 = BN_AC_CONV3D(num_in=num_mid, num_filter=num_out, kernel=(1,1,1), pad=(0,0,0))
else:
self.conv_m2 = BN_AC_CONV3D(num_in=num_mid, num_filter=num_out, kernel=(1,3,3), pad=(0,1,1), g=g)
# adapter
if first_block:
self.conv_w1 = BN_AC_CONV3D(num_in=num_in, num_filter=num_out, kernel=(1,1,1), pad=(0,0,0), stride=stride)
def forward(self, x):
h = self.conv_i1(x)
x_in = x + self.conv_i2(h)
h = self.conv_m1(x_in)
h = self.conv_m2(h)
if hasattr(self, 'conv_w1'):
x = self.conv_w1(x)
return h + x
class MFNET_3D(nn.Module):
def __init__(self, num_classes, dropout=None, pretrained=False, pretrained_model="", **kwargs):
super(MFNET_3D, self).__init__()
groups = 16
k_sec = { 2: 3, \
3: 4, \
4: 6, \
5: 3 }
# conv1 - x224 (x16)
conv1_num_out = 16
self.conv1 = nn.Sequential(OrderedDict([
('conv', nn.Conv3d( 3, conv1_num_out, kernel_size=(3,5,5), padding=(1,2,2), stride=(1,2,2), bias=False)),
('bn', nn.BatchNorm3d(conv1_num_out)),
('relu', nn.ReLU(inplace=True))
]))
self.maxpool = nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1))
# conv2 - x56 (x8)
num_mid = 96
conv2_num_out = 96
self.conv2 = nn.Sequential(OrderedDict([
("B%02d"%i, MF_UNIT(num_in=conv1_num_out if i==1 else conv2_num_out,
num_mid=num_mid,
num_out=conv2_num_out,
stride=(2,1,1) if i==1 else (1,1,1),
g=groups,
first_block=(i==1))) for i in range(1,k_sec[2]+1)
]))
# conv3 - x28 (x8)
num_mid *= 2
conv3_num_out = 2 * conv2_num_out
self.conv3 = nn.Sequential(OrderedDict([
("B%02d"%i, MF_UNIT(num_in=conv2_num_out if i==1 else conv3_num_out,
num_mid=num_mid,
num_out=conv3_num_out,
stride=(1,2,2) if i==1 else (1,1,1),
g=groups,
first_block=(i==1))) for i in range(1,k_sec[3]+1)
]))
# conv4 - x14 (x8)
num_mid *= 2
conv4_num_out = 2 * conv3_num_out
self.conv4 = nn.Sequential(OrderedDict([
("B%02d"%i, MF_UNIT(num_in=conv3_num_out if i==1 else conv4_num_out,
num_mid=num_mid,
num_out=conv4_num_out,
stride=(1,2,2) if i==1 else (1,1,1),
g=groups,
first_block=(i==1))) for i in range(1,k_sec[4]+1)
]))
# conv5 - x7 (x8)
num_mid *= 2
conv5_num_out = 2 * conv4_num_out
self.conv5 = nn.Sequential(OrderedDict([
("B%02d"%i, MF_UNIT(num_in=conv4_num_out if i==1 else conv5_num_out,
num_mid=num_mid,
num_out=conv5_num_out,
stride=(1,2,2) if i==1 else (1,1,1),
g=groups,
first_block=(i==1))) for i in range(1,k_sec[5]+1)
]))
# final
self.tail = nn.Sequential(OrderedDict([
('bn', nn.BatchNorm3d(conv5_num_out)),
('relu', nn.ReLU(inplace=True))
]))
if dropout:
self.globalpool = nn.Sequential(OrderedDict([
('avg', nn.AvgPool3d(kernel_size=(8,7,7), stride=(1,1,1))),
('dropout', nn.Dropout(p=dropout)),
]))
else:
self.globalpool = nn.Sequential(OrderedDict([
('avg', nn.AvgPool3d(kernel_size=(8,7,7), stride=(1,1,1))),
# ('dropout', nn.Dropout(p=0.5)), only for fine-tuning
]))
self.classifier = nn.Linear(conv5_num_out, num_classes)
def forward(self, x):
assert x.shape[2] == 16
h = self.conv1(x) # x224 -> x112
h = self.maxpool(h) # x112 -> x56
h = self.conv2(h) # x56 -> x56
h = self.conv3(h) # x56 -> x28
h = self.conv4(h) # x28 -> x14
h = self.conv5(h) # x14 -> x7
h = self.tail(h)
layerout = h.detach().cpu()
h = self.globalpool(h)
h = h.view(h.shape[0], -1)
h = self.classifier(h)
return h, layerout