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prototypicalNet.py
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prototypicalNet.py
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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from tqdm import trange
from time import sleep
import numpy as np
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
class Net(nn.Module):
"""
Image2Vector CNN which takes image of dimension (28x28x3) and return column vector length 64
"""
def sub_block(self, in_channels, out_channels=64, kernel_size=3):
block = torch.nn.Sequential(
torch.nn.Conv2d(kernel_size=kernel_size, in_channels=in_channels,
out_channels=out_channels, padding=1),
torch.nn.BatchNorm2d(out_channels),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2)
)
return block
def __init__(self):
super(Net, self).__init__()
self.convnet1 = self.sub_block(3)
self.convnet2 = self.sub_block(64)
self.convnet3 = self.sub_block(64)
self.convnet4 = self.sub_block(64)
def forward(self, x):
x = self.convnet1(x)
x = self.convnet2(x)
x = self.convnet3(x)
x = self.convnet4(x)
x = torch.flatten(x, start_dim=1)
return x
class PrototypicalNet(nn.Module):
def __init__(self, use_gpu=False):
super(PrototypicalNet, self).__init__()
self.f = Net()
self.gpu = use_gpu
if self.gpu:
self.f = self.f.cuda()
def forward(self, datax, datay, Ns, Nc, Nq, total_classes):
"""
Implementation of one episode in Prototypical Net
datax: Training images
datay: Corresponding labels of datax
Nc: Number of classes per episode
Ns: Number of support data per class
Nq: Number of query data per class
total_classes: Total classes in training set
"""
k = total_classes.shape[0]
K = np.random.choice(total_classes, Nc, replace=False)
Query_x = torch.Tensor()
if(self.gpu):
Query_x = Query_x.cuda()
Query_y = []
Query_y_count = []
centroid_per_class = {}
class_label = {}
label_encoding = 0
for cls in K:
S_cls, Q_cls = self.random_sample_cls(datax, datay, Ns, Nq, cls)
centroid_per_class[cls] = self.get_centroid(S_cls, Nc)
class_label[cls] = label_encoding
label_encoding += 1
# Joining all the query set together
Query_x = torch.cat((Query_x, Q_cls), 0)
Query_y += [cls]
Query_y_count += [Q_cls.shape[0]]
Query_y, Query_y_labels = self.get_query_y(
Query_y, Query_y_count, class_label)
Query_x = self.get_query_x(Query_x, centroid_per_class, Query_y_labels)
return Query_x, Query_y
def random_sample_cls(self, datax, datay, Ns, Nq, cls):
"""
Randomly samples Ns examples as support set and Nq as Query set
"""
data = datax[(datay == cls).nonzero()]
perm = torch.randperm(data.shape[0])
idx = perm[:Ns]
S_cls = data[idx]
idx = perm[Ns: Ns+Nq]
Q_cls = data[idx]
if self.gpu:
S_cls = S_cls.cuda()
Q_cls = Q_cls.cuda()
return S_cls, Q_cls
def get_centroid(self, S_cls, Nc):
"""
Returns a centroid vector of support set for a class
"""
return torch.sum(self.f(S_cls), 0).unsqueeze(1).transpose(0, 1) / Nc
def get_query_y(self, Qy, Qyc, class_label):
"""
Returns labeled representation of classes of Query set and a list of labels.
"""
labels = []
m = len(Qy)
for i in range(m):
labels += [Qy[i]] * Qyc[i]
labels = np.array(labels).reshape(len(labels), 1)
label_encoder = LabelEncoder()
Query_y = torch.Tensor(
label_encoder.fit_transform(labels).astype(int)).long()
if self.gpu:
Query_y = Query_y.cuda()
Query_y_labels = np.unique(labels)
return Query_y, Query_y_labels
def get_centroid_matrix(self, centroid_per_class, Query_y_labels):
"""
Returns the centroid matrix where each column is a centroid of a class.
"""
centroid_matrix = torch.Tensor()
if(self.gpu):
centroid_matrix = centroid_matrix.cuda()
for label in Query_y_labels:
centroid_matrix = torch.cat(
(centroid_matrix, centroid_per_class[label]))
if self.gpu:
centroid_matrix = centroid_matrix.cuda()
return centroid_matrix
def get_query_x(self, Query_x, centroid_per_class, Query_y_labels):
"""
Returns distance matrix from each Query image to each centroid.
"""
centroid_matrix = self.get_centroid_matrix(
centroid_per_class, Query_y_labels)
Query_x = self.f(Query_x)
m = Query_x.size(0)
n = centroid_matrix.size(0)
# The below expressions expand both the matrices such that they become compatible to each other in order to caclulate L2 distance.
# Expanding centroid matrix to "m".
centroid_matrix = centroid_matrix.expand(
m, centroid_matrix.size(0), centroid_matrix.size(1))
Query_matrix = Query_x.expand(n, Query_x.size(0), Query_x.size(
1)).transpose(0, 1) # Expanding Query matrix "n" times
Qx = torch.pairwise_distance(centroid_matrix.transpose(
1, 2), Query_matrix.transpose(1, 2))
return Qx
def train_step(protonet, datax, datay, Ns, Nc, Nq, optimizer):
optimizer.zero_grad()
Qx, Qy = protonet(datax, datay, Ns, Nc, Nq, np.unique(datay))
pred = torch.log_softmax(Qx, dim=-1)
loss = F.nll_loss(pred, Qy)
loss.backward()
optimizer.step()
acc = torch.mean((torch.argmax(pred, 1) == Qy).float())
return loss, acc
def test_step(protonet, datax, datay, Ns, Nc, Nq):
Qx, Qy = protonet(datax, datay, Ns, Nc, Nq, np.unique(datay))
pred = torch.log_softmax(Qx, dim=-1)
loss = F.nll_loss(pred, Qy)
acc = torch.mean((torch.argmax(pred, 1) == Qy).float())
return loss, acc
def load_weights(filename, protonet, use_gpu):
if use_gpu:
protonet.load_state_dict(torch.load(filename))
else:
protonet.load_state_dict(torch.load(filename), map_location='cpu')
return protonet