def save_d_at_t(outputs, global_step, output_dir, metric_summary, N):
"""Save distance to goal at all time steps.
Args:
outputs : [gt_dist_to_goal].
global_step : number of iterations.
output_dir : output directory.
metric_summary : to append scalars to summary.
N : number of outputs to process.
"""
d_at_t = np.concatenate(map(lambda x: x[0][:, :, 0] * 1, outputs), axis=0)
fig, axes = utils.subplot(plt, (1, 1), (5, 5))
axes.plot(np.arange(d_at_t.shape[1]), np.mean(d_at_t, axis=0), 'r.')
axes.set_xlabel('time step')
axes.set_ylabel('dist to next goal')
axes.grid('on')
file_name = os.path.join(output_dir,
'dist_at_t_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir,
'dist_at_t_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_at_t], ['d_at_t'], overwrite=True)
plt.close(fig)
return None
def _vis_readout_maps(outputs, global_step, output_dir, metric_summary, N):
# outputs is [gt_map, pred_map]:
if N >= 0:
outputs = outputs[:N]
N = len(outputs)
plt.set_cmap('jet')
fig, axes = utils.subplot(plt, (N, outputs[0][0].shape[4]*2), (5,5))
axes = axes.ravel()[::-1].tolist()
for i in range(N):
gt_map, pred_map = outputs[i]
for j in [0]:
for k in range(gt_map.shape[4]):
# Display something like the midpoint of the trajectory.
id = np.int(gt_map.shape[1]/2)
ax = axes.pop();
ax.imshow(gt_map[j,id,:,:,k], origin='lower', interpolation='none',
vmin=0., vmax=1.)
ax.set_axis_off();
if i == 0: ax.set_title('gt_map')
ax = axes.pop();
ax.imshow(pred_map[j,id,:,:,k], origin='lower', interpolation='none',
vmin=0., vmax=1.)
ax.set_axis_off();
if i == 0: ax.set_title('pred_map')
file_name = os.path.join(output_dir, 'readout_map_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close(fig)
def plot_trajectories(outputs, global_step, output_dir, metric_summary, N):
"""Processes the collected outputs during validation to plot the trajectories
in the top view.
Args:
outputs : [locs, orig_maps, goal_loc].
global_step : global_step.
output_dir : where to store results.
metric_summary : summary object to add summaries to.
N : number of outputs to process.
"""
if N >= 0:
outputs = outputs[:N]
N = len(outputs)
plt.set_cmap('gray')
fig, axes = utils.subplot(plt, (N, outputs[0][1].shape[0]), (5,5))
axes = axes.ravel()[::-1].tolist()
for i in range(N):
locs, orig_maps, goal_loc = outputs[i]
is_semantic = np.isnan(goal_loc[0,0,1])
for j in range(orig_maps.shape[0]):
ax = axes.pop();
ax.plot(locs[j,0,0], locs[j,0,1], 'ys')
# Plot one by one, so that they come in different colors.
for k in range(goal_loc.shape[1]):
if not is_semantic:
ax.plot(goal_loc[j,k,0], goal_loc[j,k,1], 's')
if False:
ax.plot(locs[j,:,0], locs[j,:,1], 'r.', ms=3)
ax.imshow(orig_maps[j,0,:,:,0], origin='lower')
ax.set_axis_off();
else:
ax.scatter(locs[j,:,0], locs[j,:,1], c=np.arange(locs.shape[1]),
cmap='jet', s=10, lw=0)
ax.imshow(orig_maps[j,0,:,:,0], origin='lower', vmin=-1.0, vmax=2.0)
if not is_semantic:
xymin = np.minimum(np.min(goal_loc[j,:,:], axis=0), np.min(locs[j,:,:], axis=0))
xymax = np.maximum(np.max(goal_loc[j,:,:], axis=0), np.max(locs[j,:,:], axis=0))
else:
xymin = np.min(locs[j,:,:], axis=0)
xymax = np.max(locs[j,:,:], axis=0)
xy1 = (xymax+xymin)/2. - np.maximum(np.max(xymax-xymin), 12)
xy2 = (xymax+xymin)/2. + np.maximum(np.max(xymax-xymin), 12)
ax.set_xlim([xy1[0], xy2[0]])
ax.set_ylim([xy1[1], xy2[1]])
ax.set_axis_off()
file_name = os.path.join(output_dir, 'trajectory_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close(fig)
return None
def plot_trajectories(outputs, global_step, output_dir, metric_summary, N):
"""Processes the collected outputs during validation to plot the trajectories
in the top view.
Args:
outputs : [locs, orig_maps, goal_loc].
global_step : global_step.
output_dir : where to store results.
metric_summary : summary object to add summaries to.
N : number of outputs to process.
"""
if N >= 0:
outputs = outputs[:N]
N = len(outputs)
plt.set_cmap('gray')
fig, axes = utils.subplot(plt, (N, outputs[0][1].shape[0]), (5,5))
axes = axes.ravel()[::-1].tolist()
for i in range(N):
locs, orig_maps, goal_loc = outputs[i]
is_semantic = np.isnan(goal_loc[0,0,1])
for j in range(orig_maps.shape[0]):
ax = axes.pop();
ax.plot(locs[j,0,0], locs[j,0,1], 'ys')
# Plot one by one, so that they come in different colors.
for k in range(goal_loc.shape[1]):
if not is_semantic:
ax.plot(goal_loc[j,k,0], goal_loc[j,k,1], 's')
if False:
ax.plot(locs[j,:,0], locs[j,:,1], 'r.', ms=3)
ax.imshow(orig_maps[j,0,:,:,0], origin='lower')
ax.set_axis_off();
else:
ax.scatter(locs[j,:,0], locs[j,:,1], c=np.arange(locs.shape[1]),
cmap='jet', s=10, lw=0)
ax.imshow(orig_maps[j,0,:,:,0], origin='lower', vmin=-1.0, vmax=2.0)
if not is_semantic:
xymin = np.minimum(np.min(goal_loc[j,:,:], axis=0), np.min(locs[j,:,:], axis=0))
xymax = np.maximum(np.max(goal_loc[j,:,:], axis=0), np.max(locs[j,:,:], axis=0))
else:
xymin = np.min(locs[j,:,:], axis=0)
xymax = np.max(locs[j,:,:], axis=0)
xy1 = (xymax+xymin)/2. - np.maximum(np.max(xymax-xymin), 12)
xy2 = (xymax+xymin)/2. + np.maximum(np.max(xymax-xymin), 12)
ax.set_xlim([xy1[0], xy2[0]])
ax.set_ylim([xy1[1], xy2[1]])
ax.set_axis_off()
file_name = os.path.join(output_dir, 'trajectory_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close(fig)
return None
def _vis(outputs, global_step, output_dir, metric_summary, N):
# Plot the value map, goal for various maps to see what if the model is
# learning anything useful.
#
# outputs is [values, goals, maps, occupancy, conf].
#
if N >= 0:
outputs = outputs[:N]
N = len(outputs)
plt.set_cmap('jet')
fig, axes = utils.subplot(plt, (N, outputs[0][0].shape[4]*5), (5,5))
axes = axes.ravel()[::-1].tolist()
for i in range(N):
values, goals, maps, occupancy, conf = outputs[i]
for j in [0]:
for k in range(values.shape[4]):
# Display something like the midpoint of the trajectory.
id = np.int(values.shape[1]/2)
ax = axes.pop();
ax.imshow(goals[j,id,:,:,k], origin='lower', interpolation='none')
ax.set_axis_off();
if i == 0: ax.set_title('goal')
ax = axes.pop();
ax.imshow(occupancy[j,id,:,:,k], origin='lower', interpolation='none')
ax.set_axis_off();
if i == 0: ax.set_title('occupancy')
ax = axes.pop();
ax.imshow(conf[j,id,:,:,k], origin='lower', interpolation='none',
vmin=0., vmax=1.)
ax.set_axis_off();
if i == 0: ax.set_title('conf')
ax = axes.pop();
ax.imshow(values[j,id,:,:,k], origin='lower', interpolation='none')
ax.set_axis_off();
if i == 0: ax.set_title('value')
ax = axes.pop();
ax.imshow(maps[j,id,:,:,k], origin='lower', interpolation='none')
ax.set_axis_off();
if i == 0: ax.set_title('incr map')
file_name = os.path.join(output_dir, 'value_vis_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
plt.close(fig)
def save_d_at_t(outputs, global_step, output_dir, metric_summary, N):
"""Save distance to goal at all time steps.
Args:
outputs : [gt_dist_to_goal].
global_step : number of iterations.
output_dir : output directory.
metric_summary : to append scalars to summary.
N : number of outputs to process.
"""
d_at_t = np.concatenate(map(lambda x: x[0][:,:,0]*1, outputs), axis=0)
fig, axes = utils.subplot(plt, (1,1), (5,5))
axes.plot(np.arange(d_at_t.shape[1]), np.mean(d_at_t, axis=0), 'r.')
axes.set_xlabel('time step')
axes.set_ylabel('dist to next goal')
axes.grid('on')
file_name = os.path.join(output_dir, 'dist_at_t_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir, 'dist_at_t_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_at_t], ['d_at_t'], overwrite=True)
plt.close(fig)
return None
def eval_dist(outputs, global_step, output_dir, metric_summary, N):
"""Processes the collected outputs during validation to
1. Plot the distance over time curve.
2. Compute mean and median distances.
3. Plots histogram of end distances.
Args:
outputs : [locs, goal_loc, gt_dist_to_goal].
global_step : global_step.
output_dir : where to store results.
metric_summary : summary object to add summaries to.
N : number of outputs to process.
"""
SUCCESS_THRESH = 3
if N >= 0:
outputs = outputs[:N]
# Plot distance at time t.
d_at_t = []
for i in range(len(outputs)):
locs, goal_loc, gt_dist_to_goal = outputs[i]
d_at_t.append(gt_dist_to_goal[:, :, 0] * 1)
# Plot the distance.
fig, axes = utils.subplot(plt, (1, 1), (5, 5))
d_at_t = np.concatenate(d_at_t, axis=0)
axes.plot(np.arange(d_at_t.shape[1]), np.mean(d_at_t, axis=0), 'r.')
axes.set_xlabel('time step')
axes.set_ylabel('dist to next goal')
axes.grid('on')
file_name = os.path.join(output_dir,
'dist_at_t_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir,
'dist_at_t_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_at_t], ['d_at_t'], overwrite=True)
plt.close(fig)
# Plot the trajectories and the init_distance and final distance.
d_inits = []
d_ends = []
for i in range(len(outputs)):
locs, goal_loc, gt_dist_to_goal = outputs[i]
d_inits.append(gt_dist_to_goal[:, 0, 0] * 1)
d_ends.append(gt_dist_to_goal[:, -1, 0] * 1)
# Plot the distance.
fig, axes = utils.subplot(plt, (1, 1), (5, 5))
d_inits = np.concatenate(d_inits, axis=0)
d_ends = np.concatenate(d_ends, axis=0)
axes.plot(d_inits + np.random.rand(*(d_inits.shape)) - 0.5,
d_ends + np.random.rand(*(d_ends.shape)) - 0.5,
'.',
mec='red',
mew=1.0)
axes.set_xlabel('init dist')
axes.set_ylabel('final dist')
axes.grid('on')
axes.axis('equal')
title_str = 'mean: {:0.1f}, 50: {:0.1f}, 75: {:0.2f}, s: {:0.1f}'
title_str = title_str.format(np.mean(d_ends), np.median(d_ends),
np.percentile(d_ends, q=75),
100 * (np.mean(d_ends <= SUCCESS_THRESH)))
axes.set_title(title_str)
file_name = os.path.join(output_dir, 'dist_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir, 'dist_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_inits, d_ends], ['d_inits', 'd_ends'],
overwrite=True)
plt.close(fig)
# Plot the histogram of the end_distance.
with plt.style.context('seaborn-white'):
d_ends_ = np.sort(d_ends)
d_inits_ = np.sort(d_inits)
leg = []
fig, ax = utils.subplot(plt, (1, 1), (5, 5))
ax.grid('on')
ax.set_xlabel('Distance from goal')
ax.xaxis.label.set_fontsize(16)
ax.set_ylabel('Fraction of data')
ax.yaxis.label.set_fontsize(16)
ax.plot(d_ends_, np.arange(d_ends_.size) * 1. / d_ends_.size, 'r')
ax.plot(d_inits_, np.arange(d_inits_.size) * 1. / d_inits_.size, 'k')
leg.append('Final')
leg.append('Init')
ax.legend(leg, fontsize='x-large')
ax.set_axis_on()
title_str = 'mean: {:0.1f}, 50: {:0.1f}, 75: {:0.2f}, s: {:0.1f}'
title_str = title_str.format(np.mean(d_ends), np.median(d_ends),
np.percentile(d_ends, q=75),
100 * (np.mean(d_ends <= SUCCESS_THRESH)))
ax.set_title(title_str)
file_name = os.path.join(output_dir,
'dist_hist_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
# Log distance metrics.
tf_utils.add_value_to_summary(metric_summary, 'dists/success_init: ',
100 * (np.mean(d_inits <= SUCCESS_THRESH)))
tf_utils.add_value_to_summary(metric_summary, 'dists/success_end: ',
100 * (np.mean(d_ends <= SUCCESS_THRESH)))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (75): ',
np.percentile(d_inits, q=75))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (75): ',
np.percentile(d_ends, q=75))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (median): ',
np.median(d_inits))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (median): ',
np.median(d_ends))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (mean): ',
np.mean(d_inits))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (mean): ',
np.mean(d_ends))
return np.median(d_inits), np.median(d_ends), np.mean(d_inits), np.mean(d_ends), \
np.percentile(d_inits, q=75), np.percentile(d_ends, q=75), \
100*(np.mean(d_inits) <= SUCCESS_THRESH), 100*(np.mean(d_ends) <= SUCCESS_THRESH)
def eval_dist(outputs, global_step, output_dir, metric_summary, N):
"""Processes the collected outputs during validation to
1. Plot the distance over time curve.
2. Compute mean and median distances.
3. Plots histogram of end distances.
Args:
outputs : [locs, goal_loc, gt_dist_to_goal].
global_step : global_step.
output_dir : where to store results.
metric_summary : summary object to add summaries to.
N : number of outputs to process.
"""
SUCCESS_THRESH = 3
if N >= 0:
outputs = outputs[:N]
# Plot distance at time t.
d_at_t = []
for i in range(len(outputs)):
locs, goal_loc, gt_dist_to_goal = outputs[i]
d_at_t.append(gt_dist_to_goal[:,:,0]*1)
# Plot the distance.
fig, axes = utils.subplot(plt, (1,1), (5,5))
d_at_t = np.concatenate(d_at_t, axis=0)
axes.plot(np.arange(d_at_t.shape[1]), np.mean(d_at_t, axis=0), 'r.')
axes.set_xlabel('time step')
axes.set_ylabel('dist to next goal')
axes.grid('on')
file_name = os.path.join(output_dir, 'dist_at_t_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir, 'dist_at_t_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_at_t], ['d_at_t'], overwrite=True)
plt.close(fig)
# Plot the trajectories and the init_distance and final distance.
d_inits = []
d_ends = []
for i in range(len(outputs)):
locs, goal_loc, gt_dist_to_goal = outputs[i]
d_inits.append(gt_dist_to_goal[:,0,0]*1)
d_ends.append(gt_dist_to_goal[:,-1,0]*1)
# Plot the distance.
fig, axes = utils.subplot(plt, (1,1), (5,5))
d_inits = np.concatenate(d_inits, axis=0)
d_ends = np.concatenate(d_ends, axis=0)
axes.plot(d_inits+np.random.rand(*(d_inits.shape))-0.5,
d_ends+np.random.rand(*(d_ends.shape))-0.5, '.', mec='red', mew=1.0)
axes.set_xlabel('init dist'); axes.set_ylabel('final dist');
axes.grid('on'); axes.axis('equal');
title_str = 'mean: {:0.1f}, 50: {:0.1f}, 75: {:0.2f}, s: {:0.1f}'
title_str = title_str.format(
np.mean(d_ends), np.median(d_ends), np.percentile(d_ends, q=75),
100*(np.mean(d_ends <= SUCCESS_THRESH)))
axes.set_title(title_str)
file_name = os.path.join(output_dir, 'dist_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
file_name = os.path.join(output_dir, 'dist_{:d}.pkl'.format(global_step))
utils.save_variables(file_name, [d_inits, d_ends], ['d_inits', 'd_ends'],
overwrite=True)
plt.close(fig)
# Plot the histogram of the end_distance.
with plt.style.context('seaborn-white'):
d_ends_ = np.sort(d_ends)
d_inits_ = np.sort(d_inits)
leg = [];
fig, ax = utils.subplot(plt, (1,1), (5,5))
ax.grid('on')
ax.set_xlabel('Distance from goal'); ax.xaxis.label.set_fontsize(16);
ax.set_ylabel('Fraction of data'); ax.yaxis.label.set_fontsize(16);
ax.plot(d_ends_, np.arange(d_ends_.size)*1./d_ends_.size, 'r')
ax.plot(d_inits_, np.arange(d_inits_.size)*1./d_inits_.size, 'k')
leg.append('Final'); leg.append('Init');
ax.legend(leg, fontsize='x-large');
ax.set_axis_on()
title_str = 'mean: {:0.1f}, 50: {:0.1f}, 75: {:0.2f}, s: {:0.1f}'
title_str = title_str.format(
np.mean(d_ends), np.median(d_ends), np.percentile(d_ends, q=75),
100*(np.mean(d_ends <= SUCCESS_THRESH)))
ax.set_title(title_str)
file_name = os.path.join(output_dir, 'dist_hist_{:d}.png'.format(global_step))
with fu.fopen(file_name, 'w') as f:
fig.savefig(f, bbox_inches='tight', transparent=True, pad_inches=0)
# Log distance metrics.
tf_utils.add_value_to_summary(metric_summary, 'dists/success_init: ',
100*(np.mean(d_inits <= SUCCESS_THRESH)))
tf_utils.add_value_to_summary(metric_summary, 'dists/success_end: ',
100*(np.mean(d_ends <= SUCCESS_THRESH)))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (75): ',
np.percentile(d_inits, q=75))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (75): ',
np.percentile(d_ends, q=75))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (median): ',
np.median(d_inits))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (median): ',
np.median(d_ends))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_init (mean): ',
np.mean(d_inits))
tf_utils.add_value_to_summary(metric_summary, 'dists/dist_end (mean): ',
np.mean(d_ends))
return np.median(d_inits), np.median(d_ends), np.mean(d_inits), np.mean(d_ends), \
np.percentile(d_inits, q=75), np.percentile(d_ends, q=75), \
100*(np.mean(d_inits) <= SUCCESS_THRESH), 100*(np.mean(d_ends) <= SUCCESS_THRESH)
