def main():
# tf.enable_eager_execution()
inputs = keras.layers.Input(shape=(100, 100, 1))
x = SparseConv2D(1, [3, 3], 4, padding='same')([inputs, inputs])
x = SparseConv2D(1, [1, 1], 4, padding='same')([x, x])
# x = SparseConv2DTranspose(1, [2, 2], strides=[2, 2], padding='same')([x, x]) # noqa
# x = keras.layers.MaxPool2D()(x)
model = keras.Model(inputs, x)
model.compile(optimizer=tf.train.AdadeltaOptimizer(0.1),
loss='mse',
metrics=['mae'])
images = np.array([
img.open_as_float('../data/disks_100x100/images/1001.png'),
img.open_as_float('../data/disks_100x100/images/1002.png'),
img.open_as_float('../data/disks_100x100/images/1003.png'),
img.open_as_float('../data/disks_100x100/images/1004.png')
])
images = images[:, :, :, np.newaxis]
dataset = tf.data.Dataset.from_tensor_slices((images, images))
dataset = dataset.batch(4).repeat(-1)
model.fit(dataset, epochs=5, steps_per_epoch=1000)
x = images
y = model.predict(images)
vis.plot_image(*x, *y, columns=4, vmin=0., vmax=1.)
vis.show('../figs/sparse_conv2d_example.pdf')
def vis_predict(self):
"""Run prediction on the test set and visualize the output."""
history_files = glob(join(self.model_dir, '*history.json'))
hists = dict(
(fname, utils.json_load(fname)) for fname in history_files)
vis.plot_hist(hists)
test_set = self._load_test()
model = self._load_model()
for image, dist_image, prediction in model.predict_visualization(
test_set):
fig, axes = vis.plot_image(image, image, dist_image, colorbar=True)
axes[0, 1].plot(prediction[:, 1], prediction[:, 0], 'rx')
axes[0, 2].plot(prediction[:, 1], prediction[:, 0], 'rx')
logger.info(f"prediction:\n{prediction}")
vis.show(join(self.figs_dir, 'prediction.pdf'))
if not self.show:
break
def vis_train(self):
"""Visualize the training set. (Mostly for debugging.)"""
train_set = self._load_train()
for batch in train_set.training_input():
for b in range(self.batch_size):
image = batch[0][b]
targets = batch[1]
pose = targets[0][b]
vis.plot_image(image,
None,
None,
pose[:, :, 1],
pose[:, :, 2],
None,
targets[1][b],
targets[2][b],
targets[3][b],
columns=3)
vis.show()
def vis_outputs(self):
"""Run prediction on the test set and visualize the output."""
test_set = self._load_test()
model = self._load_model()
for image, outputs in model.predict_outputs(test_set):
pose_image = outputs[0]
level_outputs = outputs[1:]
columns = max(model.num_levels, model.pose_dim + 1)
fig, axes = vis.plot_image(
image,
*[None for _ in range(columns - 1)],
*[pose_image[:, :, i] for i in range(model.pose_dim + 1)],
*[None for _ in range(columns - model.pose_dim - 1)],
*level_outputs,
*[None for _ in range(columns - model.num_levels)],
colorbar=True,
columns=columns)
vis.show(join(self.figs_dir, 'model_outputs.pdf'))
if not self.show:
break
def vis_history(self):
# todo: fix this for multiple models in the same model_dir
vis.plot_hists_from_dir(self.model_root)
vis.show(join(self.figs_dir, 'history.pdf'))