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NP_or_ANP_test_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, to_numpy, img_mask_to_np_input, generate_mask, np_input_to_img, compute_MSE
import torch
import numpy
from tqdm import tqdm
import matplotlib.pyplot as plt
from torchvision.utils import make_grid
from torchmeta.datasets import MiniImagenet
from torchmeta.transforms import ClassSplitter
from torchmeta.utils.data import BatchMetaDataLoader
from torchvision.transforms import Compose, Resize, ToTensor
def testing(data_test, model):
total_ll = 0
total_mse = 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))
mse_loss = compute_MSE(mean, y_target.to(device))
total_mse += mse_loss.item()
total_ll += -loss.item()
return total_ll / (i+1), total_mse/ (i+1)
def testing_meta(data_test, model):
total_ll = 0
total_mse = 0
model.eval()
for i, batch in tqdm(enumerate(data_test)):
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))
mse_loss = compute_MSE(mean, y_target.to(device))
total_mse += mse_loss.item()
total_ll += -loss.item()
return total_ll / (i + 1), total_mse/ (i+1)
def plot_sample(data, model):
img, _ = next(iter(data))
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)
imgsize = list(img.shape)
ax, fig = plt.subplots()
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, 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)
# last squeeze for removing color channel for gray scale image
img_recover = make_grid(img_recover, nrow=4, pad_value=1.).permute(1, 2, 0)
plt.imshow(to_numpy(img_recover))
plt.savefig("saved_fig/"+MODELNAME+"_"+kernel+".png")
plt.close()
return fig
def main_nonmeta():
kernel = 'MNIST' # EQ/ period / MNIST/ SVHN / celebA
# load data set
dataset = ImageReader(dataset=kernel, batch_size=64, datapath='/share/scratch/xuesongwang/metadata/')
np = NP(input_dim=2, latent_dim=128, output_dim=3 if kernel != 'MNIST' else 1, use_attention=MODELNAME == 'ANP').to(
device)
np.load_state_dict(torch.load('saved_model/' + kernel + '_' + MODELNAME + '.pt'))
print("successfully load %s module!" % MODELNAME)
total_loss = []
total_mse = []
for _ in range(6):
test_ll, test_mse = testing(dataset.testloader, np)
total_loss.append(test_ll)
total_mse.append(test_mse)
print("for 6 runs, mean: %.4f, std:%.4f" % (numpy.mean(total_loss), numpy.std(total_loss)))
print("for 6 runs, mean: %.4f, std:%.4f" % (numpy.mean(total_mse), numpy.std(total_mse)))
# test_ll, test_mse = testing(dataset.testloader, np)
# print ("CNP loglikelihood on %d samples: %.4f, mse: %.4f"%(len(dataset.testloader), test_ll, test_mse))
# fig = plot_sample(dataset.testloader, np)
# print("save plots!")
def main_meta():
data_name = 'miniImagenet'
dataset = MiniImagenet("/share/scratch/xuesongwang/metadata/",
num_classes_per_task=2,
transform=Compose([Resize(32), ToTensor()]),
meta_test=True,
download=False)
dataset = ClassSplitter(dataset, shuffle=False, num_train_per_class=15, num_test_per_class=15)
testloader = 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)
np.load_state_dict(torch.load('saved_model/' + data_name + '_' + MODELNAME + '.pt'))
print("successfully load %s module!" % MODELNAME)
total_loss = []
total_mse = []
for _ in range(6):
test_ll, test_mse = testing_meta(testloader, np)
total_loss.append(test_ll)
total_mse.append(test_mse)
print("for 6 runs, mean: %.4f, std:%.4f" % (numpy.mean(total_loss), numpy.std(total_loss)))
print("for 6 runs, mean: %.4f, std:%.4f" % (numpy.mean(total_mse), numpy.std(total_mse)))
# test_ll, test_mse = testing_meta(testloader, cnp)
# print ("CNP loglikelihood on miniImageNet: %.4f, mse: %.4f"%(test_ll, test_mse))
if __name__ == '__main__':
# define hyper parameters
device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
MODELNAME = 'ANP' # 'NP' or 'ANP'
main_meta()
# main_nonmeta()