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NP_or_ANP_train_2d.py
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NP_or_ANP_train_2d.py
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from data.Image_data_sampler import ImageReader
from module.NP import NeuralProcess as NP
from module.utils import compute_loss, comput_kl_loss, to_numpy, load_plot_data, img_mask_to_np_input, generate_mask, np_input_to_img
import torch
from torch.utils.tensorboard import SummaryWriter
import numpy
from tqdm import tqdm
import time
import matplotlib.pyplot as plt
import os
from torchmeta.datasets import MiniImagenet
from torchmeta.transforms import ClassSplitter
from torchmeta.utils.data import BatchMetaDataLoader
from torchvision.transforms import Compose, Resize, ToTensor
def validation(data_test, model):
total_ll = 0
model.eval()
for i, (img, _) in tqdm(enumerate(data_test)):
context_mask, target_mask = generate_mask(img)
x_context, y_context, x_target, y_target = img_mask_to_np_input(img, context_mask, target_mask, \
include_context=False)
(mean, var), _, _ = model(x_context.to(device), y_context.to(device), x_target.to(device))
loss = compute_loss(mean, var, y_target.to(device))
total_ll += -loss.item()
return total_ll / (i+1)
def validation_meta(data_test, model, VAL_ITER=10):
total_ll = 0
model.eval()
for i, batch in tqdm(enumerate(data_test)):
if i == VAL_ITER :
break
img, _ = batch["test"]
bs, n_shot, c, w, h = img.shape
img = torch.reshape(img, (bs*n_shot, c, w, h))
context_mask, target_mask = generate_mask(img)
x_context, y_context, x_target, y_target = img_mask_to_np_input(img, context_mask, target_mask, \
include_context=False)
(mean, var), _, _ = model(x_context.to(device), y_context.to(device), x_target.to(device))
loss = compute_loss(mean, var, y_target.to(device))
total_ll += -loss.item()
return total_ll / (i + 1)
def save_plot(epoch, data, model, imgsize):
imgsize = list(imgsize)
imgsize[0] = 1 # change batch_size = 1
ax, fig = plt.subplots()
(x_context, y_context) , x_target = data.query
(mean, var), _, _ = model(x_context.to(device), y_context.to(device), x_target.to(device))
# recover prediction
mean_recover = np_input_to_img(x_target, mean.cpu(), imgsize)
var_recover = np_input_to_img(x_target, var.cpu(), imgsize)
# recover x_target
target_recover = np_input_to_img(x_target, data.y_target, imgsize)
# recover x_context
context_recover = np_input_to_img(x_context, y_context, imgsize)
mean_recover += context_recover # fill in context data with prediction
raw_image = target_recover + context_recover # recover raw image
img_recover = torch.cat([raw_image, mean_recover, var_recover], dim=3)
plt.title("epoch:%d" % epoch)
# last squeeze for removing color channel for gray scale image
plt.imshow(to_numpy(img_recover.squeeze(0).permute(1,2,0).squeeze()))
model_path = "saved_fig/" + MODELNAME
kernel_path = os.path.join(model_path, kernel)
if not os.path.exists(model_path):
os.mkdir(model_path)
if not os.path.exists(kernel_path):
os.mkdir(kernel_path)
plt.savefig("saved_fig/"+MODELNAME+"/"+kernel+"/"+"%03d"%(epoch)+".png")
plt.close()
return fig
def main_nonmeta():
TRAINING_ITERATIONS = int(200)
BEST_LOSS = -numpy.inf
MODELNAME = 'NP' # 'NP' or 'ANP'
kernel = 'MNIST' # EQ/ period / MNIST/ SVHN / celebA
# set up tensorboard
time_stamp = time.strftime("%m-%d-%Y_%H:%M:%S", time.localtime())
writer = SummaryWriter('runs/' + kernel + '_' + MODELNAME + '_' + time_stamp)
# load data set
dataset = ImageReader(dataset=kernel, batch_size=64, datapath='/share/scratch/xuesongwang/metadata/')
# load plot dataset for recording training progress
# plot_data = save_plot_data(dataset.valloader, kernel) # generate and save data for the first run
plot_data = load_plot_data(kernel)
np = NP(input_dim=2, latent_dim=128, output_dim=3 if kernel != 'MNIST' else 1, use_attention=MODELNAME == 'ANP').to(
device)
optim = torch.optim.Adam(np.parameters(), lr=3e-4, weight_decay=1e-5)
for epoch in tqdm(range(TRAINING_ITERATIONS)):
for i, (img, _) in tqdm(enumerate(dataset.trainloader)):
context_mask, target_mask = generate_mask(img)
x_context, y_context, x_target, y_target = img_mask_to_np_input(img, context_mask, target_mask, \
include_context=True)
(mean, var), prior, poster = np(x_context.to(device), y_context.to(device), x_target.to(device),
y_target.to(device))
nll_loss = compute_loss(mean, var, y_target.to(device))
kl_loss = comput_kl_loss(prior, poster)
loss = nll_loss + kl_loss
optim.zero_grad()
loss.backward()
optim.step()
writer.add_scalars("Image Log-likelihood", {"train/overall": -loss.item(),
"train/ll": -nll_loss.item(),
"train/kl": kl_loss.item()}, epoch)
val_loss = validation(dataset.valloader, np)
save_plot(epoch, plot_data, np, img.shape)
writer.add_scalars("Image Log-likelihood", {"val": val_loss}, epoch)
if val_loss > BEST_LOSS:
BEST_LOSS = val_loss
print("save module at epoch: %d, val log-likelihood: %.4f" % (epoch, val_loss))
torch.save(np.state_dict(), 'saved_model/' + kernel + '_' + MODELNAME + '.pt')
writer.close()
print("finished training: " + MODELNAME)
def main_meta():
data_name = 'miniImagenet'
MODELNAME = 'ANP' # 'NP' or 'ANP'
dataset = MiniImagenet("/share/scratch/xuesongwang/metadata/",
num_classes_per_task=2,
transform=Compose([Resize(32), ToTensor()]),
meta_train=True,
download=False)
dataset = ClassSplitter(dataset, shuffle=True, num_train_per_class=15, num_test_per_class=15)
dataloader = BatchMetaDataLoader(dataset, batch_size=4, num_workers=8)
np = NP(input_dim=2, latent_dim=128, output_dim=3, use_attention=MODELNAME == 'ANP').to(device)
optim = torch.optim.Adam(np.parameters(), lr=3e-4, weight_decay=1e-5)
TRAINING_ITERATIONS = int(500)
BEST_LOSS = -numpy.inf
for epoch in range(TRAINING_ITERATIONS):
for i, batch in tqdm(enumerate(dataloader)):
img, _ = batch["train"]
bs, n_shot, c, w, h = img.shape
img = torch.reshape(img, (bs * n_shot, c, w, h))
context_mask, target_mask = generate_mask(img)
x_context, y_context, x_target, y_target = img_mask_to_np_input(img, context_mask, target_mask, \
include_context=False)
(mean, var), prior, poster = np(x_context.to(device), y_context.to(device), x_target.to(device),
y_target.to(device))
nll_loss = compute_loss(mean, var, y_target.to(device))
kl_loss = comput_kl_loss(prior, poster)
loss = nll_loss + kl_loss
optim.zero_grad()
loss.backward()
optim.step()
val_loss = validation_meta(dataloader, np, VAL_ITER=50 if MODELNAME == 'ANP' else 10)
if val_loss > BEST_LOSS:
BEST_LOSS = val_loss
print("save module at epoch: %d, val log-likelihood: %.4f" % (epoch, val_loss))
torch.save(np.state_dict(), 'saved_model/' + data_name + '_' + MODELNAME + '.pt')
print("finished training " + MODELNAME +' '+ data_name+'!')
if __name__ == '__main__':
# define hyper parameters
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# main_nonmeta()
main_meta()