I tried to implement unsupervised image segmentation using the code available here. https://github.com/kanezaki/pytorch-unsupervised-segmentation/blob/master/demo.py 8 For more description: https://github.com/kanezaki/pytorch-unsupervised-segmentation 7
In the original code, input is a rgb image and segmentation is performed inside the code. However, I wanted to use already segmented image (1 channel) as an input.
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import cv2
import sys
import numpy as np
from skimage import segmentation
import torch.nn.init

use_cuda = torch.cuda.is_available()


parser = argparse.ArgumentParser(description='PyTorch Unsupervised Segmentation')
parser.add_argument('--nChannel', metavar='N', default=100, type=int, 
                    help='number of channels')
parser.add_argument('--maxIter', metavar='T', default=1000, type=int, 
                    help='number of maximum iterations')
parser.add_argument('--minLabels', metavar='minL', default=7, type=int, 
                    help='minimum number of labels')
parser.add_argument('--lr', metavar='LR', default=0.1, type=float, 
                    help='learning rate')
parser.add_argument('--nConv', metavar='M', default=2, type=int, 
                    help='number of convolutional layers')
parser.add_argument('--num_superpixels', metavar='K', default=10000, type=int, 
                    help='number of superpixels')
parser.add_argument('--compactness', metavar='C', default=100, type=float, 
                    help='compactness of superpixels')
parser.add_argument('--visualize', metavar='1 or 0', default=0, type=int, 
                    help='visualization flag')
parser.add_argument('--input', metavar='FILENAME',
                    help='input image file name', required=True)
args = parser.parse_args()

# CNN model
class MyNet(nn.Module):
    def __init__(self,input_dim):
        super(MyNet, self).__init__()
        self.conv1 = nn.Conv2d(input_dim, args.nChannel, kernel_size=3, stride=1, padding=1 )
        self.bn1 = nn.BatchNorm2d(args.nChannel)
        self.conv2 = nn.ModuleList()
        self.bn2 = nn.ModuleList()
        for i in range(args.nConv-1):
            self.conv2.append( nn.Conv2d(args.nChannel, args.nChannel, kernel_size=3, stride=1, padding=1 ) )
            self.bn2.append( nn.BatchNorm2d(args.nChannel) )
        self.conv3 = nn.Conv2d(args.nChannel, args.nChannel, kernel_size=1, stride=1, padding=0 )
        self.bn3 = nn.BatchNorm2d(args.nChannel)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu( x )
        x = self.bn1(x)
        for i in range(args.nConv-1):
            x = self.conv2[i](x)
            x = F.relu( x )
            x = self.bn2[i](x)
        x = self.conv3(x)
        x = self.bn3(x)
        return x

# load image

fi = args.input
im = cv2.imread(args.input)
data = torch.from_numpy( np.array([im.transpose( (2, 0, 1) ).astype('float32')/255.]) )
print (im.shape)
print (data.shape)          


if use_cuda:
    data = data.cuda()
data = Variable(data)

# slic

labels = im.reshape(im.shape[0]*im.shape[1])
print (labels.shape)

u_labels = np.unique(labels)
l_inds = []
for i in range(len(u_labels)):
    l_inds.append( np.where( labels == u_labels[ i ] )[ 0 ] )

# train
model = MyNet( data.size(1) )
if use_cuda:
    model.cuda()
model.train()
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
label_colours = np.random.randint(255,size=(100,1))
for batch_idx in range(args.maxIter):
    # forwarding
    optimizer.zero_grad()
    output = model( data )[ 0 ]
    output = output.permute( 1, 2, 0 ).contiguous().view( -1, args.nChannel )
    ignore, target = torch.max( output, 1 )
    im_target = target.data.cpu().numpy()
    nLabels = len(np.unique(im_target))
    if args.visualize:
        im_target_rgb = np.array([label_colours[ c % 100 ] for c in im_target])
        im_target_rgb = im_target_rgb.reshape( im.shape ).astype( np.uint8 )
        cv2.imshow( "output", im_target_rgb )
        cv2.waitKey(10)

    # superpixel refinement
    # TODO: use Torch Variable instead of numpy for faster calculation
    for i in range(len(l_inds)):
        labels_per_sp = im_target[ l_inds[ i ] ]
        u_labels_per_sp = np.unique( labels_per_sp )
        hist = np.zeros( len(u_labels_per_sp) )
        for j in range(len(hist)):
            hist[ j ] = len( np.where( labels_per_sp == u_labels_per_sp[ j ] )[ 0 ] )
        im_target[ l_inds[ i ] ] = u_labels_per_sp[ np.argmax( hist ) ]
    target = torch.from_numpy( im_target )
    if use_cuda:
        target = target.cuda()
    target = Variable( target )
    loss = loss_fn(output, target)
    loss.backward()
    optimizer.step()

##    print (batch_idx, '/', args.maxIter, ':', nLabels, loss.data[0])
    # for pytorch 1.0
    # print (batch_idx, '/', args.maxIter, ':', nLabels, loss.item())
    if nLabels <= args.minLabels:
        print ("nLabels", nLabels, "reached minLabels", args.minLabels, ".")
        break

# save output image
if not args.visualize:
    output = model( data )[ 0 ]
    output = output.permute( 1, 2, 0 ).contiguous().view( -1, args.nChannel )
    ignore, target = torch.max( output, 1 )
    im_target = target.data.cpu().numpy()
    im_target_rgb = np.array([label_colours[ c % 100 ] for c in im_target])
    im_target_rgb = im_target_rgb.reshape( im.shape ).astype( np.uint8 )            

fo = r"D:\test\out.jpg" 
cv2.imwrite(fo, im_target_rgb )

How can I implement it?