def show_samples(generator,Noise_Dim,data_obj,filename):
if data_obj.pitch_scale:
pitch_max = 1.0
else:
pitch_max = 108.0
rows = 4
sample_cols = 5
input_noise = np.random.uniform(-1.0,1.0,(rows*sample_cols, Noise_Dim))
samples = generator.predict(input_noise)
topo_samples = samples.reshape(samples.shape[0],4,samples.shape[-1]/4)
#get topological_view
pv = PatchViewer(grid_shape=(rows,sample_cols + 1),patch_shape=(4,samples.shape[-1]/4), \
is_color=False)
X = np.concatenate((data_obj.X_train,data_obj.X_val,data_obj.X_test),axis = 0)
topo_X = X
print('Shape of dataset is {}').format(X.shape)
X = X.reshape(X.shape[0],X.shape[1]*X.shape[2])
for i in xrange(topo_samples.shape[0]):
topo_sample = patch_quantize_01(patch_thresholding(topo_samples[i,:]/pitch_max))
pv.add_patch(topo_sample * 2. -1.,rescale=False)
if(i + 1) % sample_cols ==0:
sample = samples[i,:]
dists = np.square(X - sample).sum(axis = 1)
j = np.argmin(dists)
match = patch_quantize_01(patch_thresholding(topo_X[j,:]/pitch_max))
pv.add_patch(match*2-1,rescale=False,activation = 1)
print "Saving %s ..."%filename
pv.save(filename)
class GenerateAndSave(TrainExtension):
"""
Keeps track of what the generator in a (vanilla) GAN returns for a
particular set of noise values.
"""
def __init__(self, generator, save_prefix, batch_size=20, grid_shape=(5, 4)):
assert isinstance(generator, Generator)
self.batch_sym = T.matrix('generate_batch')
self.generate_f = theano.function([self.batch_sym],
generator.dropout_fprop(self.batch_sym)[0])
self.batch = generator.get_noise(batch_size).eval()
self.save_prefix = save_prefix
self.patch_viewer = PatchViewer(grid_shape=grid_shape, patch_shape=(32, 32),
is_color=True)
def on_monitor(self, model, dataset, algorithm):
samples = self.generate_f(self.batch).swapaxes(0, 3)
self.patch_viewer.clear()
for sample in samples:
self.patch_viewer.add_patch(sample, rescale=True)
fname = self.save_prefix + '.%05i.png' % model.monitor.get_epochs_seen()
self.patch_viewer.save(fname)
c = rng.randint(0, 8)
cropped = X[:, r:r + 32, c:c + 32, i]
other_cropped = other_X[:, r:r + 32, c:c + 32, i]
if rng.randint(2):
cropped = cropped[:, :, ::-1]
other_cropped = other_cropped[:, :, ::-1]
new_X[:, :, :, i] = cropped.copy()
other_new_X[:, :, :, i] = other_cropped.copy()
X = new_X
other_X = other_new_X
b01c = f(X)
max_abs = max(np.abs(b01c).max(), np.abs(other_X).max())
b01c = b01c / max_abs
other_X = other_X / max_abs
other_X = np.transpose(other_X, (3, 1, 2, 0))
print "Formatting"
from pylearn2.gui.patch_viewer import PatchViewer
pv = PatchViewer(grid_shape=(rows, cols), patch_shape=(32, 32), is_color=True)
for i in xrange(m):
pv.add_patch(b01c[i, :, :, :], rescale=False)
pv.add_patch(other_X[i, :, :, :], rescale=False)
print "Showing"
pv.save('/u/goodfeli/vis.png')
wa = np.abs(w)
to_sort = zip(wa, range(N1), w)
s = sorted(to_sort)
for j in xrange(count):
idx = s[N1 - j - 1][1]
mag = s[N1 - j - 1][2]
if mag > 0:
act = (mag, 0)
else:
act = (0, -mag)
pv.add_patch(imgs[idx, ...], rescale=True, activation=act)
if out_prefix is None:
pv.show()
else:
pv.save(out_prefix + ".png")
if __name__ == "__main__":
if len(sys.argv) == 2:
_, model_path = sys.argv
out_prefix = None
else:
_, model_path, out_prefix = sys.argv
cropped = cropped[:, :, ::-1]
other_cropped = other_cropped[:, :, ::-1]
new_X[:,:,:,i] = cropped.copy()
other_new_X[:,:,:,i] = other_cropped.copy()
X = new_X
other_X = other_new_X
b01c = f(X)
max_abs = max(np.abs(b01c).max(), np.abs(other_X).max())
b01c = b01c / max_abs
other_X = other_X / max_abs
other_X = np.transpose(other_X, (3, 1, 2, 0))
print "Formatting"
from pylearn2.gui.patch_viewer import PatchViewer
pv = PatchViewer(grid_shape=(rows, cols), patch_shape=(32, 32), is_color=True)
for i in xrange(m):
pv.add_patch(b01c[i,:,:,:], rescale=False)
pv.add_patch(other_X[i,:,:,:], rescale=False)
print "Showing"
pv.save('/u/goodfeli/vis.png')
total_dimension = space.get_total_dimension()
import numpy as np
num_colors = 1
if total_dimension % 3 == 0:
num_colors = 3
w = int(np.sqrt(total_dimension / num_colors))
from pylearn2.space import Conv2DSpace
desired_space = Conv2DSpace(shape=[w, w], num_channels=num_colors, axes=('b',0,1,'c'))
samples_func = function([], space.format_as(batch=model.generator.mlp.fprop(Z),
space=desired_space))
streams = []
for i in xrange(36):
print "stream",i
Z.set_value(make_Z().astype(Z.dtype))
streams.append(samples_func())
from pylearn2.gui.patch_viewer import PatchViewer
for i in xrange(endpoints * steps_per_point):
print "file ",i
viewer = PatchViewer(grid_shape=(6,6), patch_shape=(streams[0].shape[1], streams[0].shape[2]), is_color=False)
for j in xrange(36):
viewer.add_patch(streams[j][i, :, :, :] * 2. - 1., rescale=False)
number = str(i)
while len(number) < len(str(endpoints * steps_per_point)):
number = '0' + number
path = '/Tmp/video/' + number + '.png'
viewer.save(path)
w = W2[:, i]
wneg = w[w < 0.]
wpos = w[w > 0.]
w /= np.abs(w).max()
wa = np.abs(w)
to_sort = zip(wa,range(N1), w )
s = sorted(to_sort)
for j in xrange(count):
idx = s[N1-j-1][1]
mag = s[N1-j-1][2]
if mag > 0:
act = (mag, 0)
else:
act = (0, -mag)
pv.add_patch( imgs[idx,...], rescale = True, activation = act)
if out_prefix is None:
pv.show()
else:
pv.save(out_prefix+".png")
w = int(np.sqrt(total_dimension / num_colors))
from pylearn2.space import Conv2DSpace
desired_space = Conv2DSpace(shape=[w, w],
num_channels=num_colors,
axes=('b', 0, 1, 'c'))
samples_func = function([],
space.format_as(batch=model.generator.mlp.fprop(Z),
space=desired_space))
streams = []
for i in xrange(36):
print "stream", i
Z.set_value(make_Z().astype(Z.dtype))
streams.append(samples_func())
from pylearn2.gui.patch_viewer import PatchViewer
for i in xrange(endpoints * steps_per_point):
print "file ", i
viewer = PatchViewer(grid_shape=(6, 6),
patch_shape=(streams[0].shape[1],
streams[0].shape[2]),
is_color=False)
for j in xrange(36):
viewer.add_patch(streams[j][i, :, :, :] * 2. - 1., rescale=False)
number = str(i)
while len(number) < len(str(endpoints * steps_per_point)):
number = '0' + number
path = '/Tmp/video/' + number + '.png'
viewer.save(path)
from pylearn2.utils import serial
stl10 = serial.load('/data/lisa/data/stl10/stl10_32x32/train.pkl')
batch = stl10.X[24:25,:]
img = stl10.view_converter.design_mat_to_topo_view(batch)[0,...] / 127.5
from pylearn2.gui.patch_viewer import PatchViewer
pv = PatchViewer((27,27),(6,6),pad=(1,1),is_color=True)
for row in xrange(27):
for col in xrange(27):
pv.add_patch(img[row:row+6,col:col+6], rescale = False)
pv.show()
pv.save('patches.png')
