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ExVAE.py
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""" tensorMONK's :: ExVAE """
from __future__ import print_function,division
import os
import sys
import timeit
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.utils as show_utils
from core import *
import torch.optim as neuralOptimizer
#==============================================================================#
def trainMONK(args):
if args.Project.lower() == "mnist":
tensor_size = (1, 1, 28, 28)
trDataLoader, teDataLoader, n_labels = NeuralEssentials.MNIST("./data/MNIST", tensor_size, args.BSZ, args.cpus)
elif args.Project.lower() == "cifar10":
tensor_size = (1, 3, 32, 32)
trDataLoader, teDataLoader, n_labels = NeuralEssentials.CIFAR10("./data/CIFAR10", tensor_size, args.BSZ, args.cpus)
file_name = "./models/" + args.Architecture.lower()
if args.Architecture.lower() == "cvae":
autoencoder_net = NeuralArchitectures.ConvolutionalVAE
autoencoder_net_kwargs = {"embedding_layers" : [(3, 32, 2), (3, 64, 2), (3, 128, 2),], "n_latent" : 64,
"decoder_final_activation" : "tanh", "pad" : True, "activation" : "relu", "batch_nm" : False}
elif args.Architecture.lower() == "lvae":
autoencoder_net = NeuralArchitectures.LinearVAE
autoencoder_net_kwargs = {"embedding_layers" : [1024, 512,], "n_latent" : 32,
"decoder_final_activation" : "tanh", "activation" : "relu", }
else:
raise NotImplementedError
Model = NeuralEssentials.MakeAE(file_name, tensor_size, n_labels,
autoencoder_net, autoencoder_net_kwargs,
default_gpu=args.default_gpu, gpus=args.gpus,
ignore_trained=args.ignore_trained)
if args.optimizer.lower() == "adam":
Optimizer = neuralOptimizer.Adam(Model.netAE.parameters())
elif args.optimizer.lower() == "sgd":
Optimizer = neuralOptimizer.SGD(Model.netAE.parameters(), lr= args.learningRate)
else:
raise NotImplementedError
if args.meta_learning:
transformer = NeuralLayers.ObfuscateDecolor(tensor_size, 0.4, 0.6, 0.5)
# Usual training
for _ in range(args.Epochs):
Timer = timeit.default_timer()
Model.netAE.train()
for i,(tensor, targets) in enumerate(trDataLoader):
Model.meterIterations += 1
# forward pass and parameter update
Model.netAE.zero_grad()
if args.meta_learning:
org_tensor = Variable(tensor)
tensor = transformer(org_tensor)
encoded, mu, log_var, latent, decoded, kld, mse = Model.netAE((org_tensor, tensor))
else:
encoded, mu, log_var, latent, decoded, kld, mse = Model.netAE(Variable(tensor))
loss = kld * 0.1 + mse
loss.backward()
Optimizer.step()
# updating all meters
Model.meterLoss.append(float(loss.cpu().data.numpy() if torch.__version__.startswith("0.4") else loss.cpu().data.numpy()[0]))
kld = float(kld.cpu().data.numpy() if torch.__version__.startswith("0.4") else kld.cpu().data.numpy()[0])
mse = float(mse.cpu().data.numpy() if torch.__version__.startswith("0.4") else mse.cpu().data.numpy()[0])
Model.meterSpeed.append(int(float(args.BSZ)/(timeit.default_timer()-Timer)))
Timer = timeit.default_timer()
print("... {:6d} :: Cost {:2.3f}/{:2.3f}/{:2.3f} :: {:4d} I/S ".format(Model.meterIterations,
Model.meterLoss[-1], kld, mse, Model.meterSpeed[-1]),end="\r")
sys.stdout.flush()
if i%100 == 0:
original = tensor[:min(32,tensor.size(0))].cpu()
reconstructed = decoded[:min(32,tensor.size(0))].cpu().data
if original.dim !=4 :
original = original.view(original.size(0), *tensor_size[1:])
if reconstructed.dim !=4 :
reconstructed = reconstructed.view(reconstructed.size(0), *tensor_size[1:])
original = (original - original.min(2, keepdim=True)[0].min(3, keepdim=True)[0]) / \
(original.max(2, keepdim=True)[0].max(3, keepdim=True)[0] - original.min(2, keepdim=True)[0].min(3, keepdim=True)[0])
reconstructed = (reconstructed - reconstructed.min(2, keepdim=True)[0].min(3, keepdim=True)[0]) / \
(reconstructed.max(2, keepdim=True)[0].max(3, keepdim=True)[0] - reconstructed.min(2, keepdim=True)[0].min(3, keepdim=True)[0])
show_utils.save_image(torch.cat([original, reconstructed], 0), "./models/CVAE_train.png", normalize=True)
# save every epoch and print the average of epoch
print("... {:6d} :: Cost {:2.3f}/{:2.3f}/{:2.3f} :: {:4d} I/S ".format(Model.meterIterations,
Model.meterLoss[-1], kld, mse, Model.meterSpeed[-1]))
NeuralEssentials.SaveModel(Model)
# test_top1, test_top5 = [], []
# Model.netAE.eval()
# for i,(tensor, targets) in enumerate(teDataLoader):
#
# Model.netEmbedding.zero_grad()
# Model.netLoss.zero_grad()
# encoded, mu, log_var, latent, decoded, kld, mse = Model.netAE(Variable(tensor))
#
#
#
# test_top1.append(float(top1.cpu().data.numpy() if torch.__version__.startswith("0.4") else top1.cpu().data.numpy()[0]))
# test_top5.append(float(top5.cpu().data.numpy() if torch.__version__.startswith("0.4") else top5.cpu().data.numpy()[0]))
# print("... Test accuracy - {:3.2f}/{:3.2f} ".format(np.mean(test_top1), np.mean(test_top5)))
# Model.netEmbedding.train()
# Model.netLoss.train()
Timer = timeit.default_timer()
print("\nDone with training")
return Model
# ============================================================================ #
def parse_args():
parser = argparse.ArgumentParser(description="VAEs using tensorMONK!!!")
parser.add_argument("-A", "--Architecture", type=str, default="cvae", choices=["cvae", "lvae",])
parser.add_argument("-P", "--Project", type=str, default="mnist", choices=["mnist", "cifar10",])
parser.add_argument("-B", "--BSZ", type=int, default=32)
parser.add_argument("-E", "--Epochs", type=int, default=6)
parser.add_argument("--optimizer", type=str, default="adam", choices=["adam", "sgd",])
parser.add_argument("--learningRate", type=float, default=0.01)
parser.add_argument("--meta_learning", action="store_true")
parser.add_argument("--default_gpu", type=int, default=0)
parser.add_argument("--gpus", type=int, default=1)
parser.add_argument("--cpus", type=int, default=6)
parser.add_argument("-I", "--ignore_trained", action="store_true")
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
Model = trainMONK(args)