#model_name = 'SparseSlowModel_patchsz064_N2048_NN2048_l2_l1_None_2012-02-05_15-29-08/SparseSlowModel_patchsz064_N2048_NN2048_l2_l1_None.model'
# faces
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_11-40-37/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None_2012-02-09_11-47-43/SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_16-38-31/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz032_N512_NN512_l2_l1_None_2012-02-09_19-05-45/SparseSlowModel_patchsz032_N512_NN512_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N768_NN768_l2_l1_None_2012-02-09_19-07-07/SparseSlowModel_patchsz048_N768_NN768_l2_l1_None.model'
#model_name = 'SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None_2012-02-09_19-08-50/SparseSlowModel_patchsz064_N1024_NN1024_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N1024_NN1024_l2_l1_None_2012-02-10_16-23-20/SparseSlowModel_patchsz048_N1024_NN1024_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N1024_NN1024_l2_subspacel1_None_2012-02-10_17-22-55/SparseSlowModel_patchsz048_N1024_NN1024_l2_subspacel1_None.model'
model_name = 'SparseSlowModel_patchsz048_N4096_NN4096_l2_l1_None_2012-03-01_19-15-33/SparseSlowModel_patchsz048_N4096_NN4096_l2_l1_None.model'
model_name = 'SparseSlowModel_patchsz048_N512_NN512_l2_subspacel1_None_2012-03-05_11-42-48/SparseSlowModel_patchsz048_N512_NN512_l2_subspacel1_None.model'
#fname = os.path.join(state_dir,model_name)
#m = SparseSlowModel()
#m.load(fname,reset_theano=False)
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-13_15-24-27/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-15_15-56-39/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
model_name = 'BinocColorModel_patchsz020_N3200_NN3200_l2_subspacel1_dist_2012-03-18_17-35-13/BinocColorModel_patchsz020_N3200_NN3200_l2_subspacel1_dist.model'
fname = os.path.join(state_dir,model_name)
m = BinocColorModel()
m.load(fname,reset_theano=False)
display_final(m)
print m
def explore_marginals_color():
small_value = .001
phase_small_value = np.exp(-3.)
print 'Loading model'
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-13_15-24-27/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
model_name = 'BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist_2012-03-15_15-56-39/BinocColorModel_patchsz016_N512_NN512_l2_subspacel1_dist.model'
datasource = '3Dvideo_color'
fname = os.path.join(state_dir,model_name)
m = BinocColorModel()
m.load(fname)
#m.inference_params['u_init_method'] = 'proj'
#m.inference_params['FISTAargs']['maxiter'] = 40
#m.inference_params['FISTAargs']['maxline'] = 40
#m.inference_params['FISTAargs']['errthres'] = 1e-8
#m.inference_params['FISTAargs']['verbose'] = True
m.lam_sparse.set_value(getattr(np,hdl.models.theano.config.floatX)(m.lam_sparse.get_value()*.1))
#m.lam_sparse.set_value(getattr(np,hdl.models.theano.config.floatX)(.2))
m.reset_functions()
l = BaseLearner(datasource=datasource,model=m)
l.get_databatch()
from hdl.config import fig_dir
savepath = os.path.join(fig_dir,m.model_name + '_' + m.tstring,'explore_distribution')
if not os.path.isdir(savepath): os.makedirs(savepath)
print 'Get data'
display_batches = True
batch_size = 32
batches = 100
u_list = []
snr_list = []
A = m.A.get_value()
for bind in range(batches):
batch = l.get_databatch(batch_size)
u_batch = m.inferlatent(m.preprocess(batch.copy()))
batchhat = np.dot(m.dewhitenmatrix,np.dot(A,u_batch)) + m.inputmean
error = batch - batchhat
snr = -10.*np.log10(np.var(error,0)/np.var(batch,0))
u_list.append(u_batch)
snr_list.append(snr)
print '%d->%d'%(bind*batch_size,(bind+1)*batch_size)
print 'Max SNR: %f, Min SNR: %f'%(snr.max(), snr.min())
#print '%2.2e %2.2e %2.2e'%(error[:,0].mean(), batch[:,0].mean(), batchhat[:,0].mean())
if display_batches:
if not os.path.isdir(os.path.join(savepath,'batch_rec')): os.makedirs(os.path.join(savepath,'batch_rec'))
arr = display_binoc_color_patches(batch,m.patch_sz,1,normalize=False)
fname = os.path.join(savepath,'batch_rec','batch_%d_%d.png'%(bind,batch_size))
toimage(arr).save(fname)
arr = display_binoc_color_patches(batchhat,m.patch_sz,1,normalize=False)
fname = os.path.join(savepath,'batch_rec','batch_%d_%d_rec.png'%(bind,batch_size))
toimage(arr).save(fname)
import sys
sys.exit()
u = np.hstack(u_list)
snr = np.hstack(snr_list)
amp = np.sqrt(u[::2,:]**2 + u[1::2,:]**2)
phase = np.arctan2(u[::2,:], u[1::2,:])
print 'num indices above %f, %d'%(small_value,np.sum(amp>small_value))
print 'num indices above %f, %d'%(small_value,np.sum(amp>phase_small_value))
print 'Save distributions...'
pinds = range(amp.shape[0])
plt.figure(1)
plt.clf()
snr_valid = np.isreal(snr) & np.isfinite(snr)
plt.hist(snr[snr_valid].ravel(),101)
plt.title('SNR of reconstructions')
fname = os.path.join(savepath,'SNR_rec_lam_%2.2e_uinit_%s.png'%(m.lam_sparse.get_value(),m.inference_params['u_init_method']))
plt.savefig(fname)
plt.figure(1)
plt.clf()
plt.subplot(1,2,1)
plt.hist(amp.ravel(),101)
plt.title('amp values')
plt.subplot(1,2,2)
plt.hist(np.log(amp.ravel()[amp.ravel() > 0.]),101)
plt.title('log(amp) values')
fname = os.path.join(savepath,'amp_lam_%2.2e_uinit_%s.png'%(m.lam_sparse.get_value(),m.inference_params['u_init_method']))
plt.savefig(fname)
savedir = os.path.join(savepath,'marginals')
if not os.path.isdir(savedir): os.makedirs(savedir)
for iind, pind in enumerate(pinds):
plt.figure(1)
plt.clf()
valind = amp[pind,:] > small_value
plt.subplot(1,3,1)
hval = np.max(amp[pind,:])*.1
Htemp, xedges, yedges = np.histogram2d(amp[pind,valind]*np.cos(phase[pind,valind]),amp[pind,valind]*np.sin(phase[pind,valind]),bins=np.arange(-hval,hval,2.*hval/31))
Hzero = Htemp == 0
if np.sum(Hzero):
Hmin = Htemp[Hzero].min()
H = np.zeros_like(Htemp)
H[Hzero] = np.log(Hmin)
H[~Hzero] = np.log(Htemp[~Hzero])
else:
H = np.log(Htemp)
plt.imshow(H,interpolation='nearest')
plt.title('real vs. imag %d'%pind)
plt.subplot(1,3,2)
plt.hist(np.log(amp[pind,valind]),bins=16)
plt.title('amp %d dist'%pind)
plt.subplot(1,3,3)
valind = amp[pind,:] > phase_small_value
plt.hist(phase[pind,valind],bins=16)
plt.title('phase %d dist'%pind)
fname = os.path.join(savedir, 'dist_%d.png'%pind)
plt.savefig(fname)
print 'Done with %d, %d/%d'%(pind,iind,len(pinds))
