def _compute_hardness():
# Load the stanford data to compute the hardness.
if FLAGS.type == '':
args = sna.get_args_for_config(FLAGS.config_name + '+bench_' +
FLAGS.imset)
else:
args = sna.get_args_for_config(FLAGS.type + '.' + FLAGS.config_name +
'+bench_' + FLAGS.imset)
args.navtask.logdir = None
R = lambda: nav_env.get_multiplexer_class(args.navtask, 0)
R = R()
rng_data = [np.random.RandomState(0), np.random.RandomState(0)]
# Sample a room.
h_dists = []
gt_dists = []
for i in range(250):
e = R.sample_env(rng_data)
nodes = e.task.nodes
# Initialize the agent.
init_env_state = e.reset(rng_data)
gt_dist_to_goal = [
e.episode.dist_to_goal[0][j][s]
for j, s in enumerate(e.episode.start_node_ids)
]
for j in range(args.navtask.task_params.batch_size):
start_node_id = e.episode.start_node_ids[j]
end_node_id = e.episode.goal_node_ids[0][j]
h_dist = graph_utils.heuristic_fn_vec(
nodes[[start_node_id], :],
nodes[[end_node_id], :],
n_ori=args.navtask.task_params.n_ori,
step_size=args.navtask.task_params.step_size)[0][0]
gt_dist = e.episode.dist_to_goal[0][j][start_node_id]
h_dists.append(h_dist)
gt_dists.append(gt_dist)
h_dists = np.array(h_dists)
gt_dists = np.array(gt_dists)
e = R.sample_env([np.random.RandomState(0), np.random.RandomState(0)])
input = e.get_common_data()
orig_maps = input['orig_maps'][0, 0, :, :, 0]
return h_dists, gt_dists, orig_maps
def _compute_hardness():
# Load the stanford data to compute the hardness.
if FLAGS.type == '':
args = sna.get_args_for_config(FLAGS.config_name+'+bench_'+FLAGS.imset)
else:
args = sna.get_args_for_config(FLAGS.type+'.'+FLAGS.config_name+'+bench_'+FLAGS.imset)
args.navtask.logdir = None
R = lambda: nav_env.get_multiplexer_class(args.navtask, 0)
R = R()
rng_data = [np.random.RandomState(0), np.random.RandomState(0)]
# Sample a room.
h_dists = []
gt_dists = []
for i in range(250):
e = R.sample_env(rng_data)
nodes = e.task.nodes
# Initialize the agent.
init_env_state = e.reset(rng_data)
gt_dist_to_goal = [e.episode.dist_to_goal[0][j][s]
for j, s in enumerate(e.episode.start_node_ids)]
for j in range(args.navtask.task_params.batch_size):
start_node_id = e.episode.start_node_ids[j]
end_node_id =e.episode.goal_node_ids[0][j]
h_dist = graph_utils.heuristic_fn_vec(
nodes[[start_node_id],:], nodes[[end_node_id], :],
n_ori=args.navtask.task_params.n_ori,
step_size=args.navtask.task_params.step_size)[0][0]
gt_dist = e.episode.dist_to_goal[0][j][start_node_id]
h_dists.append(h_dist)
gt_dists.append(gt_dist)
h_dists = np.array(h_dists)
gt_dists = np.array(gt_dists)
e = R.sample_env([np.random.RandomState(0), np.random.RandomState(0)])
input = e.get_common_data()
orig_maps = input['orig_maps'][0,0,:,:,0]
return h_dists, gt_dists, orig_maps
def plot_trajectory_first_person(dt, orig_maps, out_dir):
out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(),
FLAGS.imset)
fu.makedirs(out_dir)
# Load the model so that we can render.
plt.set_cmap('gray')
samples_per_action = 8; wait_at_action = 0;
Writer = animation.writers['mencoder']
writer = Writer(fps=3*(samples_per_action+wait_at_action),
metadata=dict(artist='anonymous'), bitrate=1800)
args = sna.get_args_for_config(FLAGS.config_name + '+bench_'+FLAGS.imset)
args.navtask.logdir = None
navtask_ = copy.deepcopy(args.navtask)
navtask_.camera_param.modalities = ['rgb']
navtask_.task_params.modalities = ['rgb']
sz = 512
navtask_.camera_param.height = sz
navtask_.camera_param.width = sz
navtask_.task_params.img_height = sz
navtask_.task_params.img_width = sz
R = lambda: nav_env.get_multiplexer_class(navtask_, 0)
R = R()
b = R.buildings[0]
f = [0 for _ in range(wait_at_action)] + \
[float(_)/samples_per_action for _ in range(samples_per_action)];
# Generate things for it to render.
inds_to_do = []
inds_to_do += [1, 4, 10] #1291, 1268, 1273, 1289, 1302, 1426, 1413, 1449, 1399, 1390]
for i in inds_to_do:
fig = plt.figure(figsize=(10,8))
gs = GridSpec(3,4)
gs.update(wspace=0.05, hspace=0.05, left=0.0, top=0.97, right=1.0, bottom=0.)
ax = fig.add_subplot(gs[:,:-1])
ax1 = fig.add_subplot(gs[0,-1])
ax2 = fig.add_subplot(gs[1,-1])
ax3 = fig.add_subplot(gs[2,-1])
axes = [ax, ax1, ax2, ax3]
# ax = fig.add_subplot(gs[:,:])
# axes = [ax]
for ax in axes:
ax.set_axis_off()
node_ids = dt['all_node_ids'][i, :, 0]*1
# Prune so that last node is not repeated more than 3 times?
if np.all(node_ids[-4:] == node_ids[-1]):
while node_ids[-4] == node_ids[-1]:
node_ids = node_ids[:-1]
num_steps = np.minimum(FLAGS.num_steps, len(node_ids))
xyt = b.to_actual_xyt_vec(b.task.nodes[node_ids])
xyt_diff = xyt[1:,:] - xyt[:-1:,:]
xyt_diff[:,2] = np.mod(xyt_diff[:,2], 4)
ind = np.where(xyt_diff[:,2] == 3)[0]
xyt_diff[ind, 2] = -1
xyt_diff = np.expand_dims(xyt_diff, axis=1)
to_cat = [xyt_diff*_ for _ in f]
perturbs_all = np.concatenate(to_cat, axis=1)
perturbs_all = np.concatenate([perturbs_all, np.zeros_like(perturbs_all[:,:,:1])], axis=2)
node_ids_all = np.expand_dims(node_ids, axis=1)*1
node_ids_all = np.concatenate([node_ids_all for _ in f], axis=1)
node_ids_all = np.reshape(node_ids_all[:-1,:], -1)
perturbs_all = np.reshape(perturbs_all, [-1, 4])
imgs = b.render_nodes(b.task.nodes[node_ids_all,:], perturb=perturbs_all)
# Get action at each node.
actions = []
_, action_to_nodes = b.get_feasible_actions(node_ids)
for j in range(num_steps-1):
action_to_node = action_to_nodes[j]
node_to_action = dict(zip(action_to_node.values(), action_to_node.keys()))
actions.append(node_to_action[node_ids[j+1]])
def init_fn():
return fig,
gt_dist_to_goal = []
# Render trajectories.
def worker(j):
# Plot the image.
step_number = j/(samples_per_action + wait_at_action)
img = imgs[j]; ax = axes[0]; ax.clear(); ax.set_axis_off();
img = img.astype(np.uint8); ax.imshow(img);
tt = ax.set_title(
"First Person View\n" +
"Top corners show diagnostics (distance, agents' action) not input to agent.",
fontsize=12)
plt.setp(tt, color='white')
# Distance to goal.
t = 'Dist to Goal:\n{:2d} steps'.format(int(dt['all_d_at_t'][i, step_number]))
t = ax.text(0.01, 0.99, t,
horizontalalignment='left',
verticalalignment='top',
fontsize=20, color='red',
transform=ax.transAxes, alpha=1.0)
t.set_bbox(dict(color='white', alpha=0.85, pad=-0.1))
# Action to take.
action_latex = ['$\odot$ ', '$\curvearrowright$ ', '$\curvearrowleft$ ', r'$\Uparrow$ ']
t = ax.text(0.99, 0.99, action_latex[actions[step_number]],
horizontalalignment='right',
verticalalignment='top',
fontsize=40, color='green',
transform=ax.transAxes, alpha=1.0)
t.set_bbox(dict(color='white', alpha=0.85, pad=-0.1))
# Plot the map top view.
ax = axes[-1]
if j == 0:
# Plot the map
locs = dt['all_locs'][i,:num_steps,:]
goal_loc = dt['all_goal_locs'][i,:,:]
xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0))
xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0))
xy1 = (xymax+xymin)/2. - 0.7*np.maximum(np.max(xymax-xymin), 24)
xy2 = (xymax+xymin)/2. + 0.7*np.maximum(np.max(xymax-xymin), 24)
ax.set_axis_on()
ax.patch.set_facecolor((0.333, 0.333, 0.333))
ax.set_xticks([]); ax.set_yticks([]);
ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0)
ax.plot(goal_loc[:,0], goal_loc[:,1], 'g*', markersize=12)
locs = dt['all_locs'][i,:1,:]
ax.plot(locs[:,0], locs[:,1], 'b.', markersize=12)
ax.set_xlim([xy1[0], xy2[0]])
ax.set_ylim([xy1[1], xy2[1]])
locs = dt['all_locs'][i,step_number,:]
locs = np.expand_dims(locs, axis=0)
ax.plot(locs[:,0], locs[:,1], 'r.', alpha=1.0, linewidth=0, markersize=4)
tt = ax.set_title('Trajectory in topview', fontsize=14)
plt.setp(tt, color='white')
return fig,
line_ani = animation.FuncAnimation(fig, worker,
(num_steps-1)*(wait_at_action+samples_per_action),
interval=500, blit=True, init_func=init_fn)
tmp_file_name = 'tmp.mp4'
line_ani.save(tmp_file_name, writer=writer, savefig_kwargs={'facecolor':'black'})
out_file_name = os.path.join(out_dir, 'vis_{:04d}.mp4'.format(i))
print(out_file_name)
if fu.exists(out_file_name):
gfile.Remove(out_file_name)
gfile.Copy(tmp_file_name, out_file_name)
gfile.Remove(tmp_file_name)
plt.close(fig)
def plot_trajectory_first_person(dt, orig_maps, out_dir):
out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(),
FLAGS.imset)
fu.makedirs(out_dir)
# Load the model so that we can render.
plt.set_cmap('gray')
samples_per_action = 8; wait_at_action = 0;
Writer = animation.writers['mencoder']
writer = Writer(fps=3*(samples_per_action+wait_at_action),
metadata=dict(artist='anonymous'), bitrate=1800)
args = sna.get_args_for_config(FLAGS.config_name + '+bench_'+FLAGS.imset)
args.navtask.logdir = None
navtask_ = copy.deepcopy(args.navtask)
navtask_.camera_param.modalities = ['rgb']
navtask_.task_params.modalities = ['rgb']
sz = 512
navtask_.camera_param.height = sz
navtask_.camera_param.width = sz
navtask_.task_params.img_height = sz
navtask_.task_params.img_width = sz
R = lambda: nav_env.get_multiplexer_class(navtask_, 0)
R = R()
b = R.buildings[0]
f = [0 for _ in range(wait_at_action)] + \
[float(_)/samples_per_action for _ in range(samples_per_action)];
# Generate things for it to render.
inds_to_do = []
inds_to_do += [1, 4, 10] #1291, 1268, 1273, 1289, 1302, 1426, 1413, 1449, 1399, 1390]
for i in inds_to_do:
fig = plt.figure(figsize=(10,8))
gs = GridSpec(3,4)
gs.update(wspace=0.05, hspace=0.05, left=0.0, top=0.97, right=1.0, bottom=0.)
ax = fig.add_subplot(gs[:,:-1])
ax1 = fig.add_subplot(gs[0,-1])
ax2 = fig.add_subplot(gs[1,-1])
ax3 = fig.add_subplot(gs[2,-1])
axes = [ax, ax1, ax2, ax3]
# ax = fig.add_subplot(gs[:,:])
# axes = [ax]
for ax in axes:
ax.set_axis_off()
node_ids = dt['all_node_ids'][i, :, 0]*1
# Prune so that last node is not repeated more than 3 times?
if np.all(node_ids[-4:] == node_ids[-1]):
while node_ids[-4] == node_ids[-1]:
node_ids = node_ids[:-1]
num_steps = np.minimum(FLAGS.num_steps, len(node_ids))
xyt = b.to_actual_xyt_vec(b.task.nodes[node_ids])
xyt_diff = xyt[1:,:] - xyt[:-1:,:]
xyt_diff[:,2] = np.mod(xyt_diff[:,2], 4)
ind = np.where(xyt_diff[:,2] == 3)[0]
xyt_diff[ind, 2] = -1
xyt_diff = np.expand_dims(xyt_diff, axis=1)
to_cat = [xyt_diff*_ for _ in f]
perturbs_all = np.concatenate(to_cat, axis=1)
perturbs_all = np.concatenate([perturbs_all, np.zeros_like(perturbs_all[:,:,:1])], axis=2)
node_ids_all = np.expand_dims(node_ids, axis=1)*1
node_ids_all = np.concatenate([node_ids_all for _ in f], axis=1)
node_ids_all = np.reshape(node_ids_all[:-1,:], -1)
perturbs_all = np.reshape(perturbs_all, [-1, 4])
imgs = b.render_nodes(b.task.nodes[node_ids_all,:], perturb=perturbs_all)
# Get action at each node.
actions = []
_, action_to_nodes = b.get_feasible_actions(node_ids)
for j in range(num_steps-1):
action_to_node = action_to_nodes[j]
node_to_action = dict(zip(action_to_node.values(), action_to_node.keys()))
actions.append(node_to_action[node_ids[j+1]])
def init_fn():
return fig,
gt_dist_to_goal = []
# Render trajectories.
def worker(j):
# Plot the image.
step_number = j/(samples_per_action + wait_at_action)
img = imgs[j]; ax = axes[0]; ax.clear(); ax.set_axis_off();
img = img.astype(np.uint8); ax.imshow(img);
tt = ax.set_title(
"First Person View\n" +
"Top corners show diagnostics (distance, agents' action) not input to agent.",
fontsize=12)
plt.setp(tt, color='white')
# Distance to goal.
t = 'Dist to Goal:\n{:2d} steps'.format(int(dt['all_d_at_t'][i, step_number]))
t = ax.text(0.01, 0.99, t,
horizontalalignment='left',
verticalalignment='top',
fontsize=20, color='red',
transform=ax.transAxes, alpha=1.0)
t.set_bbox(dict(color='white', alpha=0.85, pad=-0.1))
# Action to take.
action_latex = ['$\odot$ ', '$\curvearrowright$ ', '$\curvearrowleft$ ', r'$\Uparrow$ ']
t = ax.text(0.99, 0.99, action_latex[actions[step_number]],
horizontalalignment='right',
verticalalignment='top',
fontsize=40, color='green',
transform=ax.transAxes, alpha=1.0)
t.set_bbox(dict(color='white', alpha=0.85, pad=-0.1))
# Plot the map top view.
ax = axes[-1]
if j == 0:
# Plot the map
locs = dt['all_locs'][i,:num_steps,:]
goal_loc = dt['all_goal_locs'][i,:,:]
xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0))
xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0))
xy1 = (xymax+xymin)/2. - 0.7*np.maximum(np.max(xymax-xymin), 24)
xy2 = (xymax+xymin)/2. + 0.7*np.maximum(np.max(xymax-xymin), 24)
ax.set_axis_on()
ax.patch.set_facecolor((0.333, 0.333, 0.333))
ax.set_xticks([]); ax.set_yticks([]);
ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0)
ax.plot(goal_loc[:,0], goal_loc[:,1], 'g*', markersize=12)
locs = dt['all_locs'][i,:1,:]
ax.plot(locs[:,0], locs[:,1], 'b.', markersize=12)
ax.set_xlim([xy1[0], xy2[0]])
ax.set_ylim([xy1[1], xy2[1]])
locs = dt['all_locs'][i,step_number,:]
locs = np.expand_dims(locs, axis=0)
ax.plot(locs[:,0], locs[:,1], 'r.', alpha=1.0, linewidth=0, markersize=4)
tt = ax.set_title('Trajectory in topview', fontsize=14)
plt.setp(tt, color='white')
return fig,
line_ani = animation.FuncAnimation(fig, worker,
(num_steps-1)*(wait_at_action+samples_per_action),
interval=500, blit=True, init_func=init_fn)
tmp_file_name = 'tmp.mp4'
line_ani.save(tmp_file_name, writer=writer, savefig_kwargs={'facecolor':'black'})
out_file_name = os.path.join(out_dir, 'vis_{:04d}.mp4'.format(i))
print(out_file_name)
if fu.exists(out_file_name):
gfile.Remove(out_file_name)
gfile.Copy(tmp_file_name, out_file_name)
gfile.Remove(tmp_file_name)
plt.close(fig)
