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caae_test.py
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caae_test.py
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import argparse
import os
import numpy as np
import math
import itertools
import pprint
from datetime import date
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from torchvision.utils import save_image
import torchvision
from config import *
from utils import *
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from data import Fashion_attr_prediction, GeneratedDataset
from caae import * # import the model
import torch.nn as nn
import torch.nn.functional as F
import torch
cuda = True if torch.cuda.is_available() else False
today = date.today().strftime("%Y%m%d")
def class_noise(class_num, dim, size):
'''if class_num == CATEGORIES[0]:
mu = 3
else:
mu = -3
mean = np.ones(dim) * mu
cov = np.diag(np.ones(dim))
arr = np.random.multivariate_normal(mean, cov, size)'''
l = class_num
half = int(dim/2)
m1 = 10*np.cos((l*2*np.pi)/10)
m2 = 10*np.sin((l*2*np.pi)/10)
mean = [m1, m2]
mean = np.tile(mean, half)
v1 = [np.cos((l*2*np.pi)/10), np.sin((l*2*np.pi)/10)]
v2 = [-np.sin((l*2*np.pi)/10), np.cos((l*2*np.pi)/10)]
a1 = 8
a2 = .4
M =np.vstack((v1,v2)).T
S = np.array([[a1, 0], [0, a2]])
c = np.dot(np.dot(M, S), np.linalg.inv(M))
cov = np.zeros((dim, dim))
for i in range(half):
cov[i*2:(i+1)*2, i*2:(i+1)*2] = c
#cov = cov*cov.T
vec = np.random.multivariate_normal(mean=mean, cov=cov,
size=size)
return vec
def sample_noise(size):
noise_vector = np.zeros((size, LATENT_DIM))
'''half = int(size/2)
noise_vector[:half,:] = class_noise(CATEGORIES[0], LATENT_DIM, half)
noise_vector[half:,:] = class_noise(CATEGORIES[1], LATENT_DIM, size-half)'''
section = int(size/N_CLASSES)
for i in range(N_CLASSES):
noise_vector[i*section:min((i+1)*section, size), :] = class_noise(i, LATENT_DIM, min(section, size-section*i))
return noise_vector
def test_decoder(ver):
noise = sample_noise(1000)
plt.figure()
for i in range(10):
plt.scatter(noise[i*100:(i+1)*100, 0], noise[i*100:(i+1)*100,1], s=0.5, label=str(CATEGORIES[i]))
plt.xlim(-20, 20)
plt.ylim(-20, 20)
plt.legend()
plt.title("Distribution of Noise Used to Generate 10 Categories")
plt.show()
def _get_base_dataloader(sample=False):
if sample:
_type = "sample"
else:
_type = "test"
return torch.utils.data.DataLoader(
Fashion_attr_prediction(
categories=CATEGORIES,
type=_type,
transform=TEST_TRANSFORM_FN,
crop=True,
),
batch_size=128,
num_workers=NUM_WORKERS,
shuffle=True,
)
def test_encoder(ver):
device = torch.device("cuda" if cuda else "cpu")
# load the model
encoder = Encoder().to(device)
encoder.load_state_dict(load_model("caae_encoder", CONFIG_AS_STR, ver, device))
encoder.eval()
if cuda:
encoder.cuda()
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
sample = False
dataloader = _get_base_dataloader(sample=sample)
print("got data")
points = []
n_classes = len(CATEGORIES)
for i in range(n_classes):
points.append(np.zeros((1, LATENT_DIM)))
for i, (imgs, labels) in enumerate(dataloader):
real_imgs = Variable(imgs.type(Tensor))
encoded_imgs = encoder(real_imgs).cpu().data.numpy()
for j in range(n_classes):
#print(points[j].shape)
#print(encoded_imgs[np.where(labels == CATEGORIES[j]),:][].shape)
points[j] = np.append(points[j], encoded_imgs[np.where(labels == CATEGORIES[j]),:][0], axis=0)
for i in range(n_classes):
plt.figure(i)
plt.scatter(points[i][:,0], points[i][:,1], s=0.5)
plt.title("Encoded Images for Category {}".format(CATEGORIES[i]))
plt.xlim(-20, 20)
plt.ylim(-20, 20)
plt.savefig('10Cat_{}_400.png'.format(i))
#for j in range(5):
plt.figure()
for i in range(n_classes):
plt.scatter(points[i][:,0], points[i][:,1], s=0.1, label=str(CATEGORIES[i]))
plt.xlim(-20, 20)
plt.ylim(-20, 20)
plt.legend()
plt.title("Encoded Images for 10 Categories")
plt.savefig('10Cat_All_400.png')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--ver", type=str, default=today, help="YYYYMMDD format")
#parser.add_argument("--type", type=str, help="model type eg. aae")
#parser.add_argument("--model", type=str, help="generator name eg. decoder or generator")
opt = parser.parse_args()
test_encoder(opt.ver)
test_decoder(opt.ver)