def __init__(self, port, frequency=10, name="Network Loss"):
self.visdom = VisdomLinePlotter(port=port)
self.frequency = frequency
self.name = name
self.epoch_loss = 0
class EpochLog:
def __init__(self, port, frequency=10, name="Network Loss"):
self.visdom = VisdomLinePlotter(port=port)
self.frequency = frequency
self.name = name
def message(self, epoch, loss):
if epoch % self.frequency == 0:
self.visdom.plot("Loss",
"Training Loss",
self.name,
epoch,
loss,
xlabel='epochs')
print("Epoch {} loss: {}".format(epoch, loss))
import argparse
import itertools
import PIL
from torch.utils.data import TensorDataset, DataLoader
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
from plotting import VisdomLinePlotter
from model import Model, VGG16FeatureExtractor
from loss import Loss
import utils
import os
import json
plotter = VisdomLinePlotter()
parser = argparse.ArgumentParser(
description=
'An implementation of the 2018 Image Inpainting for Irregular Holes Using Partial Convolutions paper in PyTorch'
)
parser.add_argument('--datadir',
type=str,
default="images/train",
help='directory for training data')
parser.add_argument('--validdir',
type=str,
default="images/valid",
help='directory for validation data')
parser.add_argument('--maskdir',