def save_png(self, save_dir, epoch):
if torch.cuda.is_available():
noise = self.noise.cuda()
with torch.no_grad():
gen_data = self.G(noise)
gen_data = gen_data.cpu().detach().numpy()
img_bhwc = np.transpose(gen_data, (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile,
title='CIFAR10 samples ' + str(epoch) +
" epochs")
plotting.plt.savefig(save_dir + "cifar_sample_feature_match_" +
str(epoch) + ".png")
parser.add_argument('--data_dir', type=str, default='~/temp/data')
parser.add_argument('--plot_title', type=str, default=None)
args = parser.parse_args()
print(args)
data_dir = args.data_dir
trainx, trainy = cifar10_data.load(data_dir)
ids = [[] for i in range(10)]
for i in range(trainx.shape[0]):
if len(ids[trainy[i]]) < 10:
ids[trainy[i]].append(i)
if np.alltrue(np.asarray([len(_ids) >= 10 for _ids in ids])):
break
images = np.zeros((10 * 10, 32, 32, 3), dtype='uint8')
for i in range(len(ids)):
for j in range(len(ids[i])):
images[10 * j + i] = trainx[ids[i][j]].transpose([1, 2, 0])
print(ids)
img_tile = plotting.img_tile(images,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(
img_tile, title=args.plot_title if args.plot_title != 'None' else None)
plotting.plt.savefig(args.save_dir + '/cifar10_orig_images.png')
plotting.plt.close('all')
import plotting
import numpy as np
from data_reader import load
from settings import DATA_DIR, DATA_SEED
trainx, trainy = load(DATA_DIR, subset='train')
rng_data = np.random.RandomState(DATA_SEED)
inds = rng_data.permutation(trainx.shape[0])
trainx = trainx[inds]
trainy = trainy[inds]
txs = []
for j in range(10):
txs.append(trainx[trainy == j][:10])
txs = np.concatenate(txs, axis=0)
img_bhwc = np.transpose(txs, (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title='CIFAR10 samples')
plotting.plt.savefig("cifar_sample.png")
c += 1
if c == pat: break
# log progress to console
print(
"Iteration %d, time = %ds, train bits_per_dim = %.4f, test bits_per_dim = %.4f"
% (epoch, time.time() - begin, train_loss_gen, test_loss_gen))
sys.stdout.flush()
if epoch % args.save_interval == -1:
print("Generating samples...")
# generate samples from the model
sampled_x = sample_from_model(sess)
img_tile = plotting.img_tile(sampled_x,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title=args.data_set + ' samples')
plotting.plt.savefig(
os.path.join(args.save_dir,
'%s_sample%d.png' % (args.data_set, epoch)))
plotting.plt.close('all')
# save params
saver.save(sess,
args.save_dir + '/params_' + args.data_set + '.ckpt')
np.savez(args.save_dir + '/test_bpd_' + args.data_set + '.npz',
test_bpd=np.array(test_bpd))
print("online: ", first)
print("offline: ", testl)
loss_unl += lu
train_err += te
train_err2 +=te2
e = train_batch_gen(trainx_unl2[t*args.batch_size:(t+1)*args.batch_size],lr) # disc and gen for unlabeled data are different
gen_loss += float(e)
#clamp_D_fn()
loss_lab /= nr_batches_train
loss_unl /= nr_batches_train
train_err /= nr_batches_train
train_err2 /= nr_batches_train
# test
test_err = 0.
for t in range(nr_batches_test):
test_err += test_batch(testx[t*args.batch_size:(t+1)*args.batch_size],testy[t*args.batch_size:(t+1)*args.batch_size])
test_err /= nr_batches_test
# report
print("Iteration %d, time = %ds, loss_lab = %.4f, loss_unl = %.4f, train err = %.4f, train err2 = %.4f,gen loss = %.4f,test err = %.4f" % (epoch, time.time()-begin, loss_lab, loss_unl, train_err,train_err2,gen_loss,test_err))
sys.stdout.flush()
# generate samples from the model
sample_x = samplefun()
img_bhwc = np.transpose(sample_x[:100,], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc, aspect_ratio=1.0, border_color=1.0, stretch=True)
img = plotting.plot_img(img_tile, title='CIFAR10 samples')
plotting.plt.savefig("cifar_sample_feature_match_50_pai.png")
# save params
np.savez('disc_params.npz', *[p.get_value() for p in disc_params])
np.savez('gen_params.npz', *[p.get_value() for p in gen_params])
x_temp = T.tensor4()
# Test generator in sampling procedure
samplefun = th.function(inputs=[], outputs=gen_dat)
sample_x = []
for k in range(20):
sample_x.append(samplefun())
sample_x = np.concatenate(sample_x, axis=0)
img_bhwc = np.transpose(sample_x[:100, ], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title='')
plotting.plt.savefig(args.data_name + "_patches_gen.png")
img_bhwc = np.transpose(trainx[:100, ], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title='')
plotting.plt.savefig(args.data_name + "_patches_train.png")
evens = [x for x in range(int(test_matched.shape[0] / 2)) if x % 2 == 0]
odds = [x for x in range(int(test_matched.shape[0] / 2)) if x % 2 == 1]
disc_layers.append(ll.GlobalPoolLayer(disc_layers[f_low_dim]))
batch_size_test = 100
test_err = 0.
for t in range(nr_batches_test):
#if t % 100 == 0:
#print(t)
#print(test_batch_out(testx[t*args.batch_size:(t+1)*args.batch_size],testy[t*args.batch_size:(t+1)*args.batch_size]))
test_err += test_batch(testx[t*args.batch_size:(t+1)*args.batch_size],testy[t*args.batch_size:(t+1)*args.batch_size])
test_err /= nr_batches_test
test_err2 = 0.
for t in range(nr_batches_test):
#if t % 100 == 0:
#print(t)
#print(test_batch_out(testx[t*args.batch_size:(t+1)*args.batch_size],testy[t*args.batch_size:(t+1)*args.batch_size]))
test_err2 += test_batch2(testx[t*args.batch_size:(t+1)*args.batch_size],testy[t*args.batch_size:(t+1)*args.batch_size])
test_err2 /= nr_batches_test
# report
print("Iteration %d, time = %ds, loss_lab = %.4f, loss_unl = %.4f, train err = %.4f, test err = %.4f, test err avg. = %.4f" % (epoch, time.time()-begin, loss_lab, loss_unl, train_err, test_err, test_err2))
sys.stdout.flush()
# generate samples from the model
sample_x = samplefun()
img_bhwc = np.transpose(sample_x[:100,], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc, aspect_ratio=1.0, border_color=1.0, stretch=True)
img = plotting.plot_img(img_tile, title='CIFAR10 samples', rgb=False)
plotting.plt.savefig("cxr_sample_feature_match{:}.png".format(epoch))
# save params
#np.savez('disc_params.npz', *[p.get_value() for p in disc_params])
#np.savez('gen_params.npz', *[p.get_value() for p in gen_params])
NNdiff = np.sum(np.sum(np.sum(np.square(
np.expand_dims(sample_x, axis=1) - np.expand_dims(trainx, axis=0)),
axis=2),
axis=2),
axis=2)
NN = trainx[np.argmin(NNdiff, axis=1)]
NN = np.transpose(NN[:100], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
NN_tile = plotting.img_tile(NN,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img_tile = np.concatenate((img_tile, NN_tile), axis=1)
img_tile = img_tile.reshape(img_tile.shape[0], img_tile.shape[1])
img = plotting.plot_img(img_tile, title='sketch samples')
plotting.plt.savefig(args.results_dir + '/bg_sketch_sample_minibatch.png')
if epoch % 50 == 0:
plotting.plt.savefig(args.results_dir + '/' + str(epoch) + '.png')
# save params
np.savez(args.results_dir + '/train/disc_params' + str(epoch) + '.npz',
*[p.get_value() for p in disc_params])
np.savez(args.results_dir + '/train/gen_params' + str(epoch) + '.npz',
*[p.get_value() for p in gen_params])
np.save(args.results_dir + '/train/errors.npy', a)
np.save(args.results_dir + '/train/sig.npy', sigm)
plotting.plt.close('all')
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', type=str, default='./log')
parser.add_argument('--data_dir', type=str, default='/home/tim/data')
parser.add_argument('--plot_title', type=str, default=None)
args = parser.parse_args()
print(args)
data_dir = args.data_dir
trainx, trainy = cifar10_data.load(data_dir)
ids = [[] for i in range(10)]
for i in range(trainx.shape[0]):
if len(ids[trainy[i]]) < 10:
ids[trainy[i]].append(i)
if np.alltrue(np.asarray([len(_ids) >= 10 for _ids in ids])):
break
images = np.zeros((10*10,32,32,3),dtype='uint8')
for i in range(len(ids)):
for j in range(len(ids[i])):
images[10*j+i] = trainx[ids[i][j]].transpose([1,2,0])
print(ids)
img_tile = plotting.img_tile(images, aspect_ratio=1.0, border_color=1.0, stretch=True)
img = plotting.plot_img(img_tile, title=args.plot_title if args.plot_title != 'None' else None)
plotting.plt.savefig(args.save_dir + '/cifar10_orig_images.png')
plotting.plt.close('all')
# report
print(
"Epoch %d, time = %ds, loss_lab = %.3f, learning_rate = %.6f,train err= %.4f, test err = %.4f"
% (epoch, time.time() - begin, loss_lab, learning_rate_var.get_value(),
train_err, test_err))
sys.stdout.flush()
# generate samples from the model
sample_y = np.int32(np.random.choice(num_classes, size=(args.batch_size)))
sample_x = samplefun(sample_y)
img_bhwc = np.transpose(sample_x[:100, ], (0, 2, 3, 1))
img_tile = plotting.img_tile(img_bhwc,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img = plotting.plot_img(img_tile, title='SVHN samples')
plotting.plt.savefig(args.results_dir + args.dataset + '/svhn_sample.png')
if epoch % 20 == 0:
NNdiff = np.sum(np.sum(np.sum(np.square(
np.expand_dims(sample_x, axis=1) - np.expand_dims(trainx, axis=0)),
axis=2),
axis=2),
axis=2)
NN = trainx[np.argmin(NNdiff, axis=1)]
NN = np.transpose(NN[:100], (0, 2, 3, 1))
NN_tile = plotting.img_tile(NN,
aspect_ratio=1.0,
border_color=1.0,
stretch=True)
img_tile = np.concatenate((img_tile, NN_tile), axis=1)
img = plotting.plot_img(img_tile, title='SVHN samples')
def measure_all(i):
# Read in image
bcg_im3 = fits.getdata(bcgdir+bcg_list[i].replace('maps','bcg'))
bcg_im = np.zeros((bcg_im3.shape[0]+2,bcg_im3.shape[1]+2),dtype=bcg_im3.dtype)
bcg_im[1:-1,1:-1]=bcg_im3
# Image size in pixels and Mpc
tsy,tsx = bcg_im.shape
mpc = tsx/0.4 # pix/Mpc
xctr=int(tsx/2); yctr=int(tsy/2)
t0=time.time()
the_res=[] ; colnames=[]
# Compute moments -- input param w_guess
# Weighted moments full image----------------------------------------------
q20,q11,q02 = sl.weighted_higher_order_moments(bcg_im,w_guess,2,xctr,yctr)
q00 = sl.weighted_higher_order_moments(bcg_im,w_guess,0,xctr,yctr)[0]
Tchi = np.around(np.sqrt((q20+q02)/q00),decimals=2)
size_a,size_b,pos_angle = sl.axes_lengths_from_chi_moments(Tchi,(q20-q02)/(q20+q02),2*q11/(q20+q02))
# Save results
the_res += [xctr,yctr,Tchi,size_b/size_a,pos_angle+90]
colnames += ['moment_wghtd_'+str(w_guess)+'_xc','moment_wghtd_'+str(w_guess)+'_yc','moment_wghtd_'+str(w_guess)+'_T','moment_wghtd_'+str(w_guess)+'_q','mom_wgh_'+str(w_guess)+'_pos_ang']
mom_p=((xctr,yctr),Tchi,
np.around((q20-q02)/(q20+q02),decimals=4),
np.around((2*q11)/(q20+q02),decimals=4))
# # Fit Sersic-----------------------------------------------------
# fit_morph = sl.fit_sersic(bcg_im,verbose=False,plot_mod=False)
# q_sersic= (1-fit_morph.sersic_ellip)/(1+fit_morph.sersic_ellip)
# # Save results
# if q_sersic==-1:
# the_res+=[-1,-1,-1,q_sersic,-1]
# else:
# the_res+=[fit_morph.sersic_xc,fit_morph.sersic_yc,fit_morph.sersic_rhalf,
# q_sersic,fit_morph.sersic_theta*180/np.pi + 90]
# colnames+=['sersic_fit_xc','sersic_fit_yc','sersic_fit_r','sersic_fit_q','sersic_fit_pos_ang']
# Measure (longest) contour and its properties -- ((percentile variable used here))-----------
longcon,longcon_props = sl.get_longest_contour_props(bcg_im,contour_intensity=np.percentile(bcg_im,percentile))
a_c,b_c = longcon_props[0][0].major_axis_length,longcon_props[0][0].minor_axis_length
theta_c = 180 - longcon_props[0][0].orientation*180/np.pi
# Save results
if a_c==0:
the_res+=[longcon_props[0][0].centroid[0],longcon_props[0][0].centroid[1],np.sqrt(b_c*a_c),-1, -99,]
else:
the_res+=[longcon_props[0][0].centroid[0],longcon_props[0][0].centroid[1],np.sqrt(b_c*a_c),b_c/a_c, theta_c]
colnames+=['contour_'+str(percentile)+'ptile_xc','contour_'+str(percentile)+'ptile_yc','contour_'+str(percentile)+'ptile_r','contour_'+str(percentile)+'ptile_q','contour_'+str(percentile)+'ptile_pos_ang']
#make visual check images -----------------------------------------------
pl.plot_img(bcg_im,
mom_p,fit_morph,
contour=longcon, contour_props=longcon_props,
zoomins=[0,100,150],
save=args.save_data_dir+'TEST_shapes_%s.pdf' %(bcg_list[i]),
show=False)
print(i,bcg_list[i], time.time()-t0)
return the_res,colnames
test_list.append(test_err)
# report
print("Epoch %d, time = %ds, loss_lab = %.3f, learning_rate = %.6f,train err= %.4f, test err = %.4f" % (epoch, time.time() - begin, loss_lab, learning_rate_var.get_value(),train_err, test_err))
sys.stdout.flush()
# generate samples from the model
if epoch % 20 == 0:
sample_y = np.int32(np.random.choice(num_classes, size=(args.batch_size)))
sample_x = samplefun(sample_y)
img_bhwc = np.transpose(sample_x[:100, ], (0, 2, 3, 1))
# print(img_bhwc.shape)
img_tile = plotting.img_tile(img_bhwc, aspect_ratio=1.0, border_color=1.0, stretch=True)
# print(img_tile.shape)
img_tile = np.squeeze(img_tile)
img = plotting.plot_img(img_tile, title='ddsm samples')
plotting.plt.savefig(args.results_dir + args.dataset + '/ddsm_sample_'+str(epoch)+'.png')
plotting.plt.close('all')
if epoch % 100 == 0:
np.savez(args.results_dir + args.dataset + '/disc_params' + str(epoch) + '.npz', *[p.get_value() for p in disc_params])
np.savez(args.results_dir + args.dataset + '/gen_params' + str(epoch) + '.npz', *[p.get_value() for p in gen_params])
sns.set()
plotting.plt.plot(train_list)
plotting.plt.plot(test_list)
plotting.plt.title('training and testing error')
plotting.plt.ylabel('err')
plotting.plt.xlabel('epoch')
plotting.plt.legend(['training', 'testing'], loc='upper right')
plotting.plt.savefig(args.results_dir + args.dataset + '/error.png')
