def test_mask_generator():
shape = (15, )
input = Input(shape=shape)
output = tag3d_network_dense([input])
model = Model(input, output)
model.compile('adam', 'mse')
bs = (64, )
x = np.random.sample(bs + shape)
y_depth_map = np.random.sample(bs + (1, 16, 16))
y_mask = np.random.sample(bs + (1, 64, 64))
model.train_on_batch(x, [y_mask, y_depth_map])
def test_mask_generator():
shape = (15, )
input = Input(shape=shape)
output = tag3d_network_dense([input])
model = Model(input, output)
model.compile('adam', 'mse')
bs = (64, )
x = np.random.sample(bs + shape)
y_depth_map = np.random.sample(bs + (1, 16, 16))
y_mask = np.random.sample(bs + (1, 64, 64))
model.train_on_batch(x, [y_mask, y_depth_map])
def test_render_gan_builder_gan_train_on_batch():
builder = RenderGAN(lambda x: tag3d_network_dense(x, nb_units=4),
generator_units=4, discriminator_units=4,
labels_shape=(27,))
bs = 19
z, z_offset, labels = data(builder, bs)
builder.gan.train_on_batch(g_inputs={
'z': z
}, d_inputs={
'real': np.random.uniform(-1, 1, (bs,) + builder.data_shape),
})
def test_render_gan_builder_generator_train_on_batch():
builder = RenderGAN(lambda x: tag3d_network_dense(x, nb_units=4),
generator_units=4, discriminator_units=4,
labels_shape=(27,))
bs = 19
z, z_offset, labels = data(builder, bs)
real = np.zeros((bs,) + builder.data_shape)
builder.generator_given_z.compile('adam', 'mse')
builder.generator_given_z.train_on_batch(z, real)
builder.generator_given_z_and_labels.compile('adam', 'mse')
builder.generator_given_z_and_labels.train_on_batch([z_offset, labels], real)
def test_render_gan_builder_generate():
builder = RenderGAN(lambda x: tag3d_network_dense(x, nb_units=4),
generator_units=4, discriminator_units=4,
labels_shape=(27,))
bs = 19
z, z_offset, labels = data(builder, bs)
fakes = builder.generator_given_z_and_labels.predict([z_offset, labels])
assert fakes.shape == (bs,) + builder.data_shape
fakes = builder.generator_given_z.predict(z)
assert fakes.shape == (bs,) + builder.data_shape
outs = builder.sample_generator_given_z.predict(z)
assert len(outs) == len(builder.sample_generator_given_z_output_names)
def test_render_gan_builder_generator_extended():
labels_shape = (27,)
z_dim_offset = 50
builder = RenderGAN(lambda x: tag3d_network_dense(x, nb_units=4),
generator_units=4, discriminator_units=4,
z_dim_offset=z_dim_offset,
labels_shape=(27,))
bs = 19
z, z_offset, labels = data(builder, bs)
real = np.zeros((bs,) + builder.data_shape)
labels_input = Input(shape=labels_shape)
z = Input(shape=(z_dim_offset,))
fake = builder.generator_given_z_and_labels([z, labels_input])
m = Model([z, labels_input], [fake])
m.compile('adam', 'mse')
m.train_on_batch([z_offset, labels], real)
def run(output_dir, force, tags_3d_hdf5_fname, nb_units, depth, nb_epoch,
filter_size, project_factor, nb_dense):
batch_size = 64
basename = "network_tags3d_n{}_d{}_e{}".format(nb_units, depth, nb_epoch)
output_basename = os.path.join(output_dir, basename)
tag_dataset = DistributionHDF5Dataset(tags_3d_hdf5_fname)
tag_dataset._dataset_created = True
print("Got {} images from the 3d model".format(tag_dataset.nb_samples))
weights_fname = output_basename + ".hdf5"
if os.path.exists(weights_fname) and not force:
raise OSError("File {} already exists. Use --force to override it")
elif os.path.exists(weights_fname) and force:
os.remove(weights_fname)
os.makedirs(output_dir, exist_ok=True)
def generator(batch_size):
for batch in tag_dataset.iter(batch_size):
labels = []
for name in batch['labels'].dtype.names:
labels.append(batch['labels'][name])
assert not np.isnan(batch['tag3d']).any()
assert not np.isnan(batch['depth_map']).any()
labels = np.concatenate(labels, axis=-1)
yield labels, [batch['tag3d'], batch['depth_map']]
labels = next(generator(batch_size))[0]
print("labels.shape ", labels.shape)
print("labels.dtype ", labels.dtype)
nb_input = next(generator(batch_size))[0].shape[1]
x = Input(shape=(nb_input, ))
tag3d, depth_map = tag3d_network_dense(x,
nb_units=nb_units,
depth=depth,
nb_dense_units=nb_dense)
g = Model(x, [tag3d, depth_map])
# optimizer = SGD(momentum=0.8, nesterov=True)
optimizer = Nadam()
g.compile(optimizer, loss=['mse', 'mse'], loss_weights=[1, 1 / 3.])
scheduler = AutomaticLearningRateScheduler(optimizer,
'loss',
epoch_patience=5,
min_improvement=0.0002)
history = HistoryPerBatch()
save = SaveModels({basename + '_snapshot_{epoch:^03}.hdf5': g},
output_dir=output_dir,
hdf5_attrs=tag_dataset.get_distribution_hdf5_attrs())
history_plot = history.plot_callback(fname=output_basename + "_loss.png",
every_nth_epoch=10)
g.fit_generator(generator(batch_size),
samples_per_epoch=800 * batch_size,
nb_epoch=nb_epoch,
verbose=1,
callbacks=[scheduler, save, history, history_plot])
nb_visualize = 18**2
vis_labels, (tags_3d, depth_map) = next(generator(nb_visualize))
predict_tags_3d, predict_depth_map = g.predict(vis_labels)
def zip_and_save(fname, *args):
clipped = list(map(lambda x: np.clip(x, 0, 1)[:, 0], args))
print(clipped[0].shape)
tiled = zip_tile(*clipped)
print(tiled.shape)
scipy.misc.imsave(fname, tiled)
zip_and_save(output_basename + "_predict_tags.png", tags_3d,
predict_tags_3d)
zip_and_save(output_basename + "_predict_depth_map.png", depth_map,
predict_depth_map)
save_model(g,
weights_fname,
attrs=tag_dataset.get_distribution_hdf5_attrs())
with open(output_basename + '.json', 'w+') as f:
f.write(g.to_json())
with open(output_basename + '_loss_history.json', 'w+') as f:
json.dump(history.history, f)
fig, _ = history.plot()
fig.savefig(output_basename + "_loss.png")
print("Saved weights to: {}".format(weights_fname))
def run(output_dir, force, tags_3d_hdf5_fname, nb_units, depth,
nb_epoch, filter_size, project_factor, nb_dense):
batch_size = 64
basename = "network_tags3d_n{}_d{}_e{}".format(nb_units, depth, nb_epoch)
output_basename = os.path.join(output_dir, basename)
tag_dataset = DistributionHDF5Dataset(tags_3d_hdf5_fname)
tag_dataset._dataset_created = True
print("Got {} images from the 3d model".format(tag_dataset.nb_samples))
weights_fname = output_basename + ".hdf5"
if os.path.exists(weights_fname) and not force:
raise OSError("File {} already exists. Use --force to override it")
elif os.path.exists(weights_fname) and force:
os.remove(weights_fname)
os.makedirs(output_dir, exist_ok=True)
def generator(batch_size):
for batch in tag_dataset.iter(batch_size):
labels = []
for name in batch['labels'].dtype.names:
labels.append(batch['labels'][name])
assert not np.isnan(batch['tag3d']).any()
assert not np.isnan(batch['depth_map']).any()
labels = np.concatenate(labels, axis=-1)
yield labels, [batch['tag3d'], batch['depth_map']]
labels = next(generator(batch_size))[0]
print("labels.shape ", labels.shape)
print("labels.dtype ", labels.dtype)
nb_input = next(generator(batch_size))[0].shape[1]
x = Input(shape=(nb_input,))
tag3d, depth_map = tag3d_network_dense(x, nb_units=nb_units, depth=depth,
nb_dense_units=nb_dense)
g = Model(x, [tag3d, depth_map])
# optimizer = SGD(momentum=0.8, nesterov=True)
optimizer = Nadam()
g.compile(optimizer, loss=['mse', 'mse'], loss_weights=[1, 1/3.])
scheduler = AutomaticLearningRateScheduler(
optimizer, 'loss', epoch_patience=5, min_improvement=0.0002)
history = HistoryPerBatch()
save = SaveModels({basename + '_snapshot_{epoch:^03}.hdf5': g}, output_dir=output_dir,
hdf5_attrs=tag_dataset.get_distribution_hdf5_attrs())
history_plot = history.plot_callback(fname=output_basename + "_loss.png",
every_nth_epoch=10)
g.fit_generator(generator(batch_size), samples_per_epoch=800*batch_size,
nb_epoch=nb_epoch, verbose=1,
callbacks=[scheduler, save, history, history_plot])
nb_visualize = 18**2
vis_labels, (tags_3d, depth_map) = next(generator(nb_visualize))
predict_tags_3d, predict_depth_map = g.predict(vis_labels)
def zip_and_save(fname, *args):
clipped = list(map(lambda x: np.clip(x, 0, 1)[:, 0], args))
print(clipped[0].shape)
tiled = zip_tile(*clipped)
print(tiled.shape)
scipy.misc.imsave(fname, tiled)
zip_and_save(output_basename + "_predict_tags.png", tags_3d, predict_tags_3d)
zip_and_save(output_basename + "_predict_depth_map.png", depth_map, predict_depth_map)
save_model(g, weights_fname, attrs=tag_dataset.get_distribution_hdf5_attrs())
with open(output_basename + '.json', 'w+') as f:
f.write(g.to_json())
with open(output_basename + '_loss_history.json', 'w+') as f:
json.dump(history.history, f)
fig, _ = history.plot()
fig.savefig(output_basename + "_loss.png")
print("Saved weights to: {}".format(weights_fname))
