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CNP_test_2d.py
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from data.Image_data_sampler import ImageReader
from module.CNP import ConditionalNeuralProcess as CNP
from module.utils import compute_loss, to_numpy, img_mask_to_np_input, generate_mask, np_input_to_img, compute_MSE
import torch
from torchvision.utils import make_grid
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
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_ll += -loss.item()
total_mse += mse_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_ll += -loss.item()
total_mse += mse_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/CNP_"+kernel+".png")
plt.close()
return fig
def main_nonmeta():
kernel = 'celebA' # use kernel to be consistent with 1d dataset, kernel can be "MNIST", "SVHN", "celebA"
# load data set
dataset = ImageReader(dataset=kernel, batch_size=64, datapath='/share/scratch/xuesongwang/metadata/')
cnp = CNP(input_dim=2, latent_dim=128, output_dim=3 if kernel != 'MNIST' else 1).to(device)
cnp.load_state_dict(torch.load('saved_model/' + kernel + '_CNP.pt'))
print("successfully load CNP module!")
total_loss = []
total_mse = []
for _ in range(6):
test_ll, test_mse = testing(dataset.testloader, cnp)
total_loss.append(test_ll)
total_mse.append(test_mse)
print("for 6 runs, mean: %.4f, std:%.4f" % (np.mean(total_loss), np.std(total_loss)))
print("for 6 runs, mean: %.4f, std:%.4f" % (np.mean(total_mse), np.std(total_mse)))
# test_ll, test_mse = testing(dataset.testloader, cnp)
# print ("CNP loglikelihood on %d samples: %.4f, mse: %.4f"%(len(dataset.testloader), test_ll, test_mse))
# fig = plot_sample(dataset.testloader, cnp)
# 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=32, num_workers=8)
cnp = CNP(input_dim=2, latent_dim=128, output_dim=3).to(device)
cnp.load_state_dict(torch.load('saved_model/' + data_name + '_CNP.pt'))
print("successfully load CNP module!")
total_loss = []
total_mse = []
for _ in range(6):
test_ll, test_mse = testing_meta(testloader, cnp)
total_loss.append(test_ll)
total_mse.append(test_mse)
print("for 6 runs, mean: %.4f, std:%.4f" % (np.mean(total_loss), np.std(total_loss)))
print("for 6 runs, mean: %.4f, std:%.4f" % (np.mean(total_mse), np.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')
# main_nonmeta()
main_meta()