def visualize(self, n=4):
sample = self.sample(n)
images = sample["image"]
# annotations, annotations_shape, annotations_mask = sample["annotations"]
labels = sample["label"]
if self.n_frames == 0:
images = images[:, None]
fig, *_ = animate(images, labels=labels)
plt.show()
plt.close(fig)
def visualize(self, n=4):
sample = self.sample(n)
images = sample["image"]
actions = sample["action"]
rewards = sample["reward"]
labels = [
"actions={}, rewards={}".format(a, r)
for a, r in zip(actions, rewards)
]
fig, *_ = animate(images, labels=labels)
plt.show()
plt.close(fig)
def _plot_reconstruction(self, updater, fetched):
inp = fetched['inp']
output = fetched['output']
fig_height = 20
fig_width = 4.5 * fig_height
diff = self.normalize_images(np.abs(inp - output).sum(axis=-1, keepdims=True) / output.shape[-1])
xent = self.normalize_images(xent_loss(pred=output, label=inp, tf=False).sum(axis=-1, keepdims=True))
path = self.path_for("animation", updater, ext=None)
fig, axes, anim, path = animate(
inp, output, diff.astype('f'), xent.astype('f'),
figsize=(fig_width, fig_height), path=path)
plt.close()
def __call__(self, updater):
self.fetches = "inp output"
fetched = self._fetch(updater)
fetched = Config(fetched)
inp = fetched['inp']
output = fetched['output']
T = inp.shape[1]
mean_image = np.tile(inp.mean(axis=1, keepdims=True), (1, T, 1, 1, 1))
B = inp.shape[0]
fig_unit_size = 3
fig_height = B * fig_unit_size
fig_width = 7 * fig_unit_size
diff = self.normalize_images(
np.abs(inp - output).sum(axis=-1, keepdims=True))
xent = self.normalize_images(
xent_loss(pred=output, label=inp, tf=False).sum(axis=-1,
keepdims=True))
diff_mean = self.normalize_images(
np.abs(mean_image - output).sum(axis=-1, keepdims=True))
xent_mean = self.normalize_images(
xent_loss(pred=mean_image, label=inp, tf=False).sum(axis=-1,
keepdims=True))
path = self.path_for("animation", updater, ext=None)
fig, axes, anim, path = animate(inp,
output,
diff.astype('f'),
xent.astype('f'),
mean_image,
diff_mean.astype('f'),
xent_mean.astype('f'),
figsize=(fig_width, fig_height),
path=path,
square_grid=False)
plt.close()
def visualize(self, n=None):
sample = self.sample(self.batch_size * self.n_batches)
images = [[] for i in range(self.batch_size)]
for i in range(self.n_batches):
for j in range(self.batch_size):
images[j].append(sample['image'][i * self.batch_size + j])
images = np.array([np.concatenate(stream) for stream in images])
fig, *_ = animate(images)
plt.subplots_adjust(top=0.95,
bottom=0,
left=0,
right=1,
wspace=0.05,
hspace=0.1)
plt.show()
plt.close(fig)
def visualize(self, n=4):
sample = self.sample(n)
images = sample["image"]
actions = sample["action"]
rewards = sample["reward"]
annotations = variable_shape_array_to_list(sample["annotations"])
labels = [
"actions={}, rewards={}".format(a, r)
for a, r in zip(actions, rewards)
]
fig, *_ = animate(images, labels=labels, annotations=annotations)
plt.subplots_adjust(top=0.95,
bottom=0,
left=0,
right=1,
wspace=0.05,
hspace=0.1)
plt.show()
plt.close(fig)
def visualize(self, n=4):
sample = self.sample(n)
images = sample["image"]
pose_r = sample["pose_r"]
pose_t = sample["pose_t"]
indices = sample["idx"]
with numpy_print_options(precision=2):
text = [[
"idx={}\nt={}\npose_r={}\npose_t={}".format(i, t, _pr, _pt)
for t, (_pr, _pt) in enumerate(zip(pr, pt))
] for i, pr, pt in zip(indices, pose_r, pose_t)]
fig, _, anim, _ = animate(images, text=text, fig_unit_size=4)
path = 'gqn_visualization.mp4'
anim.save(path,
writer='ffmpeg',
codec='hevc',
extra_args=['-preset', 'ultrafast'])
plt.show()
plt.close(fig)