/
few_shot_segmentor.py
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/
few_shot_segmentor.py
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"""Few-Shot_learning Segmentation"""
import numpy as np
import torch
import torch.nn as nn
from nn_common_modules import modules as sm
from utils.data_utils import split_batch
# import torch.nn.functional as F
from squeeze_and_excitation import squeeze_and_excitation as se
class SDnetConditioner(nn.Module):
"""
A conditional branch of few shot learning regressing the parameters for the segmentor
"""
def __init__(self, params):
super(SDnetConditioner, self).__init__()
se_block_type = se.SELayer.SSE
params['num_channels'] = 2
params['num_filters'] = 16
self.encode1 = sm.SDnetEncoderBlock(params)
self.squeeze_conv_e1 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
params['num_channels'] = 16
self.encode2 = sm.SDnetEncoderBlock(params)
self.squeeze_conv_e2 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.encode3 = sm.SDnetEncoderBlock(params)
self.squeeze_conv_e3 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.encode4 = sm.SDnetEncoderBlock(params)
self.squeeze_conv_e4 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.bottleneck = sm.GenericBlock(params)
self.squeeze_conv_bn = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
params['num_channels'] = 16
self.decode1 = sm.SDnetDecoderBlock(params)
self.squeeze_conv_d1 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.decode2 = sm.SDnetDecoderBlock(params)
self.squeeze_conv_d2 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.decode3 = sm.SDnetDecoderBlock(params)
self.squeeze_conv_d3 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
self.decode4 = sm.SDnetDecoderBlock(params)
self.squeeze_conv_d4 = nn.Conv2d(in_channels=params['num_filters'], out_channels=1,
kernel_size=(1, 1),
padding=(0, 0),
stride=1)
params['num_channels'] = 16
self.classifier = sm.ClassifierBlock(params)
self.sigmoid = nn.Sigmoid()
def forward(self, input):
e1, _, ind1 = self.encode1(input)
e_w1 = self.sigmoid(self.squeeze_conv_e1(e1))
e2, out2, ind2 = self.encode2(e1)
e_w2 = self.sigmoid(self.squeeze_conv_e2(e2))
e3, _, ind3 = self.encode3(e2)
e_w3 = self.sigmoid(self.squeeze_conv_e3(e3))
e4, _, ind4 = self.encode3(e3)
e_w4 = self.sigmoid(self.squeeze_conv_e4(e4))
bn = self.bottleneck(e4)
bn_w4 = self.sigmoid(self.squeeze_conv_bn(bn))
d4 = self.decode4(bn, None, ind4)
d_w4 = self.sigmoid(self.squeeze_conv_d4(d4))
d3 = self.decode3(d4, None, ind3)
d_w3 = self.sigmoid(self.squeeze_conv_d3(d3))
d2 = self.decode2(d3, None, ind2)
d_w2 = self.sigmoid(self.squeeze_conv_d2(d2))
d1 = self.decode1(d2, None, ind1)
d_w1 = self.sigmoid(self.squeeze_conv_d1(d1))
space_weights = (e_w1, e_w2, e_w3, e_w4, bn_w4,
d_w4, d_w3, d_w2, d_w1, None)
channel_weights = (None, None, None, None)
return space_weights, channel_weights
class SDnetSegmentor(nn.Module):
"""
Segmentor Code
param ={
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':1
'se_block': True,
'drop_out':0
}
"""
def __init__(self, params):
super(SDnetSegmentor, self).__init__()
params['num_channels'] = 1
params['num_filters'] = 64
self.encode1 = sm.SDnetEncoderBlock(params)
params['num_channels'] = 64
self.encode2 = sm.SDnetEncoderBlock(params)
self.encode3 = sm.SDnetEncoderBlock(params)
self.encode4 = sm.SDnetEncoderBlock(params)
self.bottleneck = sm.GenericBlock(params)
self.decode1 = sm.SDnetDecoderBlock(params)
self.decode2 = sm.SDnetDecoderBlock(params)
self.decode3 = sm.SDnetDecoderBlock(params)
params['num_channels'] = 128
self.decode4 = sm.SDnetDecoderBlock(params)
params['num_channels'] = 64
self.classifier = sm.ClassifierBlock(params)
self.soft_max = nn.Softmax2d()
def forward(self, inpt, weights=None):
space_weights, channel_weights = weights
e_w1, e_w2, e_w3, e_w4, bn_w, d_w4, d_w3, d_w2, d_w1, cls_w = space_weights if space_weights is not None else (
None, None, None, None, None, None, None, None, None, None)
e_c1, e_c2, d_c1, d_c2 = channel_weights
e1, _, ind1 = self.encode1(inpt)
if e_w1 is not None:
e1 = torch.mul(e1, e_w1)
e2, _, ind2 = self.encode2(e1)
if e_w2 is not None:
e2 = torch.mul(e2, e_w2)
e3, _, ind3 = self.encode3(e2)
if e_w3 is not None:
e3 = torch.mul(e3, e_w3)
e4, out4, ind4 = self.encode4(e3)
if e_w4 is not None:
e4 = torch.mul(e4, e_w4)
bn = self.bottleneck(e4)
if bn_w is not None:
bn = torch.mul(bn, bn_w)
d4 = self.decode4(bn, None, ind4)
if d_w4 is not None:
d4 = torch.mul(d4, d_w4)
d3 = self.decode3(d4, None, ind3)
if d_w3 is not None:
d3 = torch.mul(d3, d_w3)
d2 = self.decode2(d3, None, ind2)
if d_w2 is not None:
d2 = torch.mul(d2, d_w2)
d1 = self.decode1(d2, None, ind1)
if d_w1 is not None:
d1 = torch.mul(d1, d_w1)
logit = self.classifier.forward(d1)
if cls_w is not None:
logit = torch.mul(logit, cls_w)
logit = self.soft_max(logit)
return logit
class FewShotSegmentorDoubleSDnet(nn.Module):
'''
Class Combining Conditioner and Segmentor for few shot learning
'''
def __init__(self, params):
super(FewShotSegmentorDoubleSDnet, self).__init__()
self.conditioner = SDnetConditioner(params)
self.segmentor = SDnetSegmentor(params)
def forward(self, input1, input2):
weights = self.conditioner(input1)
segment = self.segmentor(input2, weights)
return segment
def enable_test_dropout(self):
attr_dict = self.__dict__['_modules']
for i in range(1, 5):
encode_block, decode_block = attr_dict['encode' +
str(i)], attr_dict['decode' + str(i)]
encode_block.drop_out = encode_block.drop_out.apply(
nn.Module.train)
decode_block.drop_out = decode_block.drop_out.apply(
nn.Module.train)
@property
def is_cuda(self):
"""
Check if model parameters are allocated on the GPU.
"""
return next(self.parameters()).is_cuda
def save(self, path):
"""
Save model with its parameters to the given path. Conventionally the
path should end with "*.model".
Inputs:
- path: path string
"""
print('Saving model... %s' % path)
torch.save(self, path)
def predict(self, X, y, query_label, device=0, enable_dropout=False):
"""
Predicts the outout after the model is trained.
Inputs:
- X: Volume to be predicted
"""
self.eval()
input1, input2, y2 = split_batch(X, y, query_label)
input1, input2, y2 = to_cuda(input1, device), to_cuda(
input2, device), to_cuda(y2, device)
if enable_dropout:
self.enable_test_dropout()
with torch.no_grad():
out = self.forward(input1, input2)
idx = out > 0.5
idx = idx.data.cpu().numpy()
prediction = np.squeeze(idx)
del X, out, idx
return prediction
def to_cuda(X, device):
if type(X) is np.ndarray:
X = torch.tensor(X, requires_grad=False).type(
torch.FloatTensor).cuda(device, non_blocking=True)
elif type(X) is torch.Tensor and not X.is_cuda:
X = X.type(torch.FloatTensor).cuda(device, non_blocking=True)
return X