Python PatchViewer.save方法代碼示例

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
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)

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
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)

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
        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')

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
    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)

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
    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")

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import save [as 別名]
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')