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abmil.py
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abmil.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Attention(nn.Module):
def __init__(self, in_size, out_size, confounder_path=False, confounder_learn=False, \
confounder_dim=128, confounder_merge='cat'):
super(Attention, self).__init__()
self.L = in_size
self.D = in_size
self.K = 1
self.confounder_merge = confounder_merge
assert confounder_merge in ['cat', 'add', 'sub']
# self.feature_extractor_part1 = nn.Sequential(
# nn.Conv2d(1, 20, kernel_size=5),
# nn.ReLU(),
# nn.MaxPool2d(2, stride=2),
# nn.Conv2d(20, 50, kernel_size=5),
# nn.ReLU(),
# nn.MaxPool2d(2, stride=2)
# )
# self.feature_extractor_part2 = nn.Sequential(
# nn.Linear(50 * 4 * 4, self.L),
# nn.ReLU(),
# )
# self.attention_1 = nn.Sequential(
# nn.Linear(self.L, self.D),
# nn.Tanh(),
# )
# self.attention_1 = nn.Identity()
# self.attention_2 = nn.Linear(self.D, self.K)
self.attention = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Tanh(),
nn.Linear(self.D, self.K)
)
self.classifier = nn.Linear(self.L*self.K, out_size)
self.confounder_path=None
if confounder_path:
print('deconfounding')
self.confounder_path = confounder_path
conf_list = []
for i in confounder_path:
conf_list.append(torch.from_numpy(np.load(i)).view(-1,in_size).float())
conf_tensor = torch.cat(conf_list, 0)
conf_tensor_dim = conf_tensor.shape[-1]
if confounder_learn:
self.confounder_feat = nn.Parameter(conf_tensor, requires_grad=True)
else:
self.register_buffer("confounder_feat",conf_tensor)
joint_space_dim = confounder_dim
dropout_v = 0.5
# self.confounder_W_q = nn.Linear(in_size, joint_space_dim)
# self.confounder_W_k = nn.Linear(conf_tensor_dim, joint_space_dim)
self.W_q = nn.Linear(in_size, joint_space_dim)
self.W_k = nn.Linear(conf_tensor_dim, joint_space_dim)
if confounder_merge == 'cat':
self.classifier = nn.Linear(self.L*self.K+conf_tensor_dim, out_size)
elif confounder_merge == 'add' or 'sub':
self.classifier = nn.Linear(self.L*self.K, out_size)
self.dropout = nn.Dropout(dropout_v)
def forward(self, x):
# x = x.squeeze(0)
# H = self.feature_extractor_part1(x)
# H = H.view(-1, 50 * 4 * 4)
# H = self.feature_extractor_part2(H) # NxL
# A = self.attention_1(x)
# A = self.attention_2(A) # NxK
A = self.attention(x) # NxK
A = torch.transpose(A, 1, 0) # KxN
A = F.softmax(A, dim=1) # softmax over N
# print('norm')
# A = F.softmax(A/ torch.sqrt(torch.tensor(x.shape[1])), dim=1) # For Vis
M = torch.mm(A, x) # KxL
if self.confounder_path:
device = M.device
# bag_q = self.confounder_W_q(M)
# conf_k = self.confounder_W_k(self.confounder_feat)
bag_q = self.W_q(M)
conf_k = self.W_k(self.confounder_feat)
deconf_A = torch.mm(conf_k, bag_q.transpose(0, 1))
deconf_A = F.softmax( deconf_A / torch.sqrt(torch.tensor(conf_k.shape[1], dtype=torch.float32, device=device)), 0) # normalize attention scores, A in shape N x C,
conf_feats = torch.mm(deconf_A.transpose(0, 1), self.confounder_feat) # compute bag representation, B in shape C x V
if self.confounder_merge == 'cat':
M = torch.cat((M,conf_feats),dim=1)
elif self.confounder_merge == 'add':
M = M + conf_feats
elif self.confounder_merge == 'sub':
M = M - conf_feats
Y_prob = self.classifier(M)
Y_hat = torch.ge(Y_prob, 0.5).float()
if self.confounder_path:
return Y_prob, M, deconf_A
else:
return Y_prob, M, A
# AUXILIARY METHODS
def calculate_classification_error(self, X, Y):
Y = Y.float()
_, Y_hat, _ = self.forward(X)
error = 1. - Y_hat.eq(Y).cpu().float().mean().data.item()
return error, Y_hat
def calculate_objective(self, X, Y):
Y = Y.float()
Y_prob, _, A = self.forward(X)
Y_prob = torch.clamp(Y_prob, min=1e-5, max=1. - 1e-5)
neg_log_likelihood = -1. * (Y * torch.log(Y_prob) + (1. - Y) * torch.log(1. - Y_prob)) # negative log bernoulli
return neg_log_likelihood, A
class GatedAttention(nn.Module):
def __init__(self):
super(GatedAttention, self).__init__()
self.L = 500
self.D = 128
self.K = 1
self.feature_extractor_part1 = nn.Sequential(
nn.Conv2d(1, 20, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(20, 50, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(2, stride=2)
)
self.feature_extractor_part2 = nn.Sequential(
nn.Linear(50 * 4 * 4, self.L),
nn.ReLU(),
)
self.attention_V = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Tanh()
)
self.attention_U = nn.Sequential(
nn.Linear(self.L, self.D),
nn.Sigmoid()
)
self.attention_weights = nn.Linear(self.D, self.K)
self.classifier = nn.Sequential(
nn.Linear(self.L*self.K, 1),
nn.Sigmoid()
)
def forward(self, x):
x = x.squeeze(0)
H = self.feature_extractor_part1(x)
H = H.view(-1, 50 * 4 * 4)
H = self.feature_extractor_part2(H) # NxL
A_V = self.attention_V(H) # NxD
A_U = self.attention_U(H) # NxD
A = self.attention_weights(A_V * A_U) # element wise multiplication # NxK
A = torch.transpose(A, 1, 0) # KxN
A = F.softmax(A, dim=1) # softmax over N
M = torch.mm(A, H) # KxL
Y_prob = self.classifier(M)
Y_hat = torch.ge(Y_prob, 0.5).float()
return Y_prob, Y_hat, A
# AUXILIARY METHODS
def calculate_classification_error(self, X, Y):
Y = Y.float()
_, Y_hat, _ = self.forward(X)
error = 1. - Y_hat.eq(Y).cpu().float().mean().item()
return error, Y_hat
def calculate_objective(self, X, Y):
Y = Y.float()
Y_prob, _, A = self.forward(X)
Y_prob = torch.clamp(Y_prob, min=1e-5, max=1. - 1e-5)
neg_log_likelihood = -1. * (Y * torch.log(Y_prob) + (1. - Y) * torch.log(1. - Y_prob)) # negative log bernoulli
return neg_log_likelihood, A