def main(args):
# 1. Get datasets
train_ds = scenarios.planning(args.scenario_path)
#val_ds = scenarios.planning(args.scenario_path.replace("train", "val"))
# 2. Define model
model = PlanningNetworkMP(7, (args.batch_size, 6))
# 3. Optimization
optimizer = tf.train.AdamOptimizer(args.eta)
# 4. Restore, Log & Save
experiment_handler = ExperimentHandler(args.working_path, args.out_name, args.log_interval, model, optimizer)
experiment_handler.restore("./monster/last_mix/checkpoints/best-7274")
i = -2
a = 60
x = np.linspace(-12.5, -3., a)
b = 30
y = np.linspace(-21.0, -16., b)
c = 76
#d = 4
#th = np.linspace(-np.pi / d, np.pi / d, c)
th = np.linspace(- 10 * np.pi / 180, 65 * np.pi / 180, c)
X, Y, TH = np.meshgrid(x, y, th)
x, y = np.meshgrid(x, y)
X = X.flatten()
Y = Y.flatten()
TH = TH.flatten()
p0 = np.stack([X, Y, TH], 1).astype(np.float32)
n = a * b * c
map = train_ds[i][2].numpy()[np.newaxis]
map = np.tile(map, (n, 1, 1, 1))
for w in [17]:#, 17]:#range(30):
pk = train_ds[i][1][w].numpy()[np.newaxis]
pk = np.tile(pk, (n, 1))
p0a = train_ds[i][0][w].numpy()
data = (p0, pk, map)
# 5.2.1 Make inference of the model for validation and calculate losses
#dummy_data = (p0[:1], pk[:1], map[:1])
#output, last_ddy = model(dummy_data, None, training=True)
start = time()
output, last_ddy = model(data, None, training=True)
end = time()
print("TIME:", end - start)
model_loss, invalid_loss, overshoot_loss, curvature_loss, non_balanced_loss, x_path, y_path, th_path = plan_loss(
output, last_ddy, data)
#print(invalid_loss, curvature_loss)
l = invalid_loss + curvature_loss + overshoot_loss
gidx = l.numpy()
gidx = np.argwhere(gidx == 0)
l = tf.reshape(l, (-1, c))
color = tf.reduce_sum(tf.cast(tf.equal(l, 0.0), tf.float32), -1)
#for i in range(map.shape[1]):
# for j in range(4):
# fs = map
# plt.plot([fs[0, i, j - 1, 0], fs[0, i, j, 0]], [fs[0, i, j - 1, 1], fs[0, i, j, 1]], zorder=1, color='orange')
#c = 'brown'
plt.fill([-100., 100., 100., -100., -100.], [-100., -100., 100., 100., -100.], 'brown', zorder=1)
plt.xlim(-13.25, 8.25)
plt.ylim(-22., 3.)
m = map[0]
seq = [(1, 3), (1, 0), (0, 3), (0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (2, 0), (1, 3)]
plt.fill([m[s][0] for s in seq], [m[s][1] for s in seq], 'w', zorder=2)
#plt.plot([map[0, 0, -1, 0], map[0, 0, 0, 0]], [map[0, 0, -1, 1], map[0, 0, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 0, 0, 0], map[0, 0, 1, 0]], [map[0, 0, 0, 1], map[0, 0, 1, 1]], zorder=2, color=c)
#plt.plot([map[0, 0, 1, 0], map[0, 0, 2, 0]], [map[0, 0, 1, 1], map[0, 0, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, -1, 0], map[0, 1, 0, 0]], [map[0, 1, -1, 1], map[0, 1, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 0, 0], map[0, 0, -1, 0]], [map[0, 1, 0, 1], map[0, 0, -1, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 0, 0], map[0, 1, 1, 0]], [map[0, 0, 2, 1], map[0, 1, 1, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 1, 0], map[0, 1, 2, 0]], [map[0, 1, 1, 1], map[0, 1, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 2, 0], map[0, 1, 3, 0]], [map[0, 2, 0, 1], map[0, 1, 3, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, -1, 0], map[0, 2, 0, 0]], [map[0, 2, -1, 1], map[0, 2, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, 1, 0], map[0, 2, 2, 0]], [map[0, 2, 1, 1], map[0, 2, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, 2, 0], map[0, 2, 3, 0]], [map[0, 2, 2, 1], map[0, 2, 3, 1]], zorder=2, color=c)
plt.scatter(tf.reshape(x, [-1])[::-1], tf.reshape(y, [-1])[::-1], c=color[::-1], s=1.5*np.ones_like(color), zorder=3, cmap='hot_r')
plt.colorbar()
plt.arrow(pk[0, 0], pk[0, 1], np.cos(pk[0, 2]), np.sin(pk[0, 2]), width=0.1, zorder=10, color='r')
plt.arrow(p0a[0], p0a[1], np.cos(p0a[2]), np.sin(p0a[2]), width=0.2, zorder=11, color='b')
print(w, "P0:", p0a[0], p0a[1])
print(w, "PK:", pk[0, 0], pk[0, 1])
_plot(x_path, y_path, th_path, gidx)
plt.show()
def main(args):
# 1. Get datasets
train_ds, train_size = scenarios.planning_dataset(args.scenario_path)
val_ds, val_size = scenarios.planning_dataset(
args.scenario_path.replace("train", "val"))
val_ds = val_ds \
.batch(args.batch_size) \
.prefetch(args.batch_size)
# 2. Define model
model = PlanningNetworkMP(7, (args.batch_size, 6)) # N = 6
#model = PlanningNetworkMP(7, (args.batch_size, 6)) # N = 4
#model = PlanningNetworkMP(7, (args.batch_size, 6)) # N = 2
# 3. Optimization
optimizer = tf.keras.optimizers.Adam(args.eta)
l2_reg = tf.keras.regularizers.l2(1e-5)
# 4. Restore, Log & Save
experiment_handler = ExperimentHandler(args.working_path, args.out_name,
args.log_interval, model, optimizer)
# 5. Run everything
train_step, val_step = 0, 0
best_accuracy = 0.0
for epoch in range(args.num_epochs):
# workaround for tf problems with shuffling
dataset_epoch = train_ds.shuffle(train_size)
dataset_epoch = dataset_epoch.batch(args.batch_size).prefetch(
args.batch_size)
# 5.1. Training Loop
experiment_handler.log_training()
acc = []
for i, data in _ds('Train', dataset_epoch, train_size, epoch,
args.batch_size):
# 5.1.1. Make inference of the model, calculate losses and record gradients
with tf.GradientTape(persistent=True) as tape:
output, last_ddy = model(data, None, training=True)
model_loss, invalid_loss, overshoot_loss, curvature_loss, total_curvature_loss, _, x_path, y_path, th_path = plan_loss(
output, data, last_ddy)
grads = tape.gradient(model_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# 5.1.3 Calculate statistics
t = tf.reduce_mean(tf.cast(tf.equal(invalid_loss, 0.0),
tf.float32))
s = tf.reduce_mean(
tf.cast(tf.equal(invalid_loss + curvature_loss, 0.0),
tf.float32))
u = tf.reduce_mean(
tf.cast(
tf.equal(invalid_loss + curvature_loss + overshoot_loss,
0.0), tf.float32))
acc.append(
tf.cast(
tf.equal(invalid_loss + curvature_loss + overshoot_loss,
0.0), tf.float32))
# 5.1.4 Save logs for particular interval
with tf.summary.record_if(train_step % args.log_interval == 0):
tf.summary.scalar('metrics/model_loss',
tf.reduce_mean(model_loss),
step=train_step)
tf.summary.scalar('metrics/invalid_loss',
tf.reduce_mean(invalid_loss),
step=train_step)
tf.summary.scalar('metrics/overshoot_loss',
tf.reduce_mean(overshoot_loss),
step=train_step)
tf.summary.scalar('metrics/curvature_loss',
tf.reduce_mean(curvature_loss),
step=train_step)
tf.summary.scalar('metrics/total_curvature_loss',
tf.reduce_mean(total_curvature_loss),
step=train_step)
tf.summary.scalar('metrics/good_paths', t, step=train_step)
tf.summary.scalar('metrics/really_good_paths',
s,
step=train_step)
tf.summary.scalar('metrics/ideal_paths', u, step=train_step)
# 5.1.5 Update meta variables
train_step += 1
if train_step % 100 == 0:
_plot(x_path, y_path, th_path, data, train_step)
#_plot(x_path, y_path, th_path, data, train_step)
epoch_accuracy = tf.reduce_mean(tf.concat(acc, -1))
# 5.1.6 Take statistics over epoch
with tf.summary.record_if(True):
tf.summary.scalar('epoch/good_paths', epoch_accuracy, step=epoch)
# 5.2. Validation Loop
experiment_handler.log_validation()
acc = []
for i, data in _ds('Validation', val_ds, val_size, epoch,
args.batch_size):
# 5.2.1 Make inference of the model for validation and calculate losses
output, last_ddy = model(data, None, training=True)
model_loss, invalid_loss, overshoot_loss, curvature_loss, total_curvature_loss, _, x_path, y_path, th_path = plan_loss(
output, data, last_ddy)
t = tf.reduce_mean(tf.cast(tf.equal(invalid_loss, 0.0),
tf.float32))
s = tf.reduce_mean(
tf.cast(tf.equal(invalid_loss + curvature_loss, 0.0),
tf.float32))
u = tf.reduce_mean(
tf.cast(
tf.equal(invalid_loss + curvature_loss + overshoot_loss,
0.0), tf.float32))
acc.append(
tf.cast(
tf.equal(invalid_loss + curvature_loss + overshoot_loss,
0.0), tf.float32))
# 5.2.3 Print logs for particular interval
with tf.summary.record_if(val_step % args.log_interval == 0):
tf.summary.scalar('metrics/model_loss',
tf.reduce_mean(model_loss),
step=val_step)
tf.summary.scalar('metrics/invalid_loss',
tf.reduce_mean(invalid_loss),
step=val_step)
tf.summary.scalar('metrics/overshoot_loss',
tf.reduce_mean(overshoot_loss),
step=val_step)
tf.summary.scalar('metrics/curvature_loss',
tf.reduce_mean(curvature_loss),
step=val_step)
tf.summary.scalar('metrics/total_curvature_loss',
tf.reduce_mean(total_curvature_loss),
step=val_step)
tf.summary.scalar('metrics/good_paths', t, step=val_step)
tf.summary.scalar('metrics/really_good_paths',
s,
step=val_step)
tf.summary.scalar('metrics/ideal_paths', u, step=val_step)
# 5.2.4 Update meta variables
val_step += 1
epoch_accuracy = tf.reduce_mean(tf.concat(acc, -1))
# 5.2.5 Take statistics over epoch
with tf.summary.record_if(True):
tf.summary.scalar('epoch/good_paths', epoch_accuracy, step=epoch)
# 5.3 Save last and best
if epoch_accuracy > best_accuracy:
experiment_handler.save_best()
best_accuracy = epoch_accuracy
#experiment_handler.save_last()
experiment_handler.flush()
def main(args):
# 1. Get datasets
#train_ds = scenarios.planning(args.scenario_path)
val_ds = scenarios.planning(args.scenario_path.replace("train", "val"))
# 2. Define model
model = PlanningNetworkMP(7, (args.batch_size, 6))
# 3. Optimization
optimizer = tf.train.AdamOptimizer(args.eta)
# 4. Restore, Log & Save
experiment_handler = ExperimentHandler(args.working_path, args.out_name,
args.log_interval, model, optimizer)
experiment_handler.restore("./monster/last_mix/checkpoints/best-7274")
i = -2
a = 60
x = np.linspace(-32.0, -20., a)
b = 40
y = np.linspace(5.0, 0.5, b)
c = 91
d = 4
th = np.linspace(-np.pi / d, np.pi / d, c)
X, Y, TH = np.meshgrid(x, y, th)
x, y = np.meshgrid(x, y)
X = X.flatten()
Y = Y.flatten()
TH = TH.flatten()
p0 = np.stack([X, Y, TH], 1).astype(np.float32)
n = a * b * c
r = 0.1
w = 2.7
map = val_ds[0][2].numpy()[np.newaxis]
map = np.tile(map, (n, 1, 1, 1))
rng = 0.8 + 2 * r
map[:, 1, 0, 1] = rng
map[:, 1, 1, 1] = rng
map[:, 1, 2, 1] = rng + w
map[:, 1, 3, 1] = rng + w
map[:, 0, :2, 1] = 0.
map[:, 0, 2:, 1] = 5.5
map[:, 2, :2, 1] = 0.
map[:, 2, 2:, 1] = 5.5
map[:, 2, 1, 0] = map[:, 2, 2, 0]
map[:, 1, 0, 0] = map[:, 2, 1, 0]
map[:, 1, 3, 0] = map[:, 2, 1, 0]
map[:, 0, 0, 0] = map[:, 0, 3, 0]
map[:, 1, 1, 0] = map[:, 0, 0, 0]
map[:, 1, 2, 0] = map[:, 0, 0, 0]
for w in [17]: #, 17]:#range(30):
pk = np.array([[-3., 1.5, 0.]], dtype=np.float32)
pk = np.tile(pk, (n, 1))
data = (p0, pk, map)
# 5.2.1 Make inference of the model for validation and calculate losses
pp0 = np.array([[-30., 1.5, 0.]], dtype=np.float32)
ppk = np.array([[0., 1.5, 0.]], dtype=np.float32)
dummy_data = (pp0, ppk, map[:1])
output, last_ddy = model(dummy_data, None, training=True)
_, _, _, _, _, px_path, py_path, pth_path = plan_loss(
output, last_ddy, dummy_data)
start = time()
output, last_ddy = model(data, None, training=True)
end = time()
print("TIME:", end - start)
model_loss, invalid_loss, overshoot_loss, curvature_loss, non_balanced_loss, x_path, y_path, th_path = plan_loss(
output, last_ddy, data)
#print(invalid_loss, curvature_loss)
l = invalid_loss + curvature_loss + overshoot_loss
gidx = l.numpy()
gidx = np.argwhere(gidx == 0)
l = tf.reshape(l, (-1, c))
color = tf.reduce_sum(tf.cast(tf.equal(l, 0.0), tf.float32), -1)
#for i in range(map.shape[1]):
# for j in range(4):
# fs = map
# plt.plot([fs[0, i, j - 1, 0], fs[0, i, j, 0]], [fs[0, i, j - 1, 1], fs[0, i, j, 1]], zorder=1, color='orange')
#c = 'brown'
plt.figure(num=None,
figsize=(9, 2),
dpi=300,
facecolor='w',
edgecolor='k')
plt.fill([-100., 100., 100., -100., -100.],
[-100., -100., 100., 100., -100.],
'brown',
zorder=1)
plt.xlim(-33., 4.5)
plt.ylim(-0.25, 5.75)
m = map[0]
#seq = [(1, 3), (1, 0), (0, 3), (0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (2, 0), (1, 3)]
seq = [(0, 0), (0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 2), (2, 3),
(2, 0), (2, 1), (1, 0), (1, 1), (0, 0)]
plt.fill([m[s][0] for s in seq], [m[s][1] for s in seq], 'w', zorder=2)
#plt.plot([map[0, 0, -1, 0], map[0, 0, 0, 0]], [map[0, 0, -1, 1], map[0, 0, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 0, 0, 0], map[0, 0, 1, 0]], [map[0, 0, 0, 1], map[0, 0, 1, 1]], zorder=2, color=c)
#plt.plot([map[0, 0, 1, 0], map[0, 0, 2, 0]], [map[0, 0, 1, 1], map[0, 0, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, -1, 0], map[0, 1, 0, 0]], [map[0, 1, -1, 1], map[0, 1, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 0, 0], map[0, 0, -1, 0]], [map[0, 1, 0, 1], map[0, 0, -1, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 0, 0], map[0, 1, 1, 0]], [map[0, 0, 2, 1], map[0, 1, 1, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 1, 0], map[0, 1, 2, 0]], [map[0, 1, 1, 1], map[0, 1, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 1, 2, 0], map[0, 1, 3, 0]], [map[0, 2, 0, 1], map[0, 1, 3, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, -1, 0], map[0, 2, 0, 0]], [map[0, 2, -1, 1], map[0, 2, 0, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, 1, 0], map[0, 2, 2, 0]], [map[0, 2, 1, 1], map[0, 2, 2, 1]], zorder=2, color=c)
#plt.plot([map[0, 2, 2, 0], map[0, 2, 3, 0]], [map[0, 2, 2, 1], map[0, 2, 3, 1]], zorder=2, color=c)
plt.scatter(tf.reshape(x, [-1])[::-1],
tf.reshape(y, [-1])[::-1],
c=color[::-1],
s=1.5 * np.ones_like(color),
zorder=4,
cmap='hot_r')
plt.colorbar(orientation="horizontal")
print(w, "PK:", pk[0, 0], pk[0, 1])
_plot(px_path, py_path, pth_path)
#plt.show()
#plt.savefig("xD1.pdf")
plt.savefig("2.pdf")
def main(args):
# 1. Get datasets
#train_ds = scenarios.planning(args.scenario_path)
val_ds = scenarios.planning(args.scenario_path.replace("train", "val"))
# 2. Define model
model = PlanningNetworkMP(7, (args.batch_size, 6))
# 3. Optimization
optimizer = tf.train.AdamOptimizer(args.eta)
# 4. Restore, Log & Save
experiment_handler = ExperimentHandler(args.working_path, args.out_name,
args.log_interval, model, optimizer)
experiment_handler.restore("./monster/last_mix/checkpoints/best-7274")
# 5.2. Validation Loop
#for i in range(len(val_ds)):
#q1 = [[-32., -2.], [-19., -2.], [-19., 5.], [-32., 5.]]
#q2 = [[-10., -2.], [4., -2.], [4., 5.], [-10., 5.]]
#y = 0.
#w = 3.0
#q3 = [[-19., y], [-10., y], [-10., y+w], [-19., y+w]]
#map = np.array([q1, q2, q3], dtype=np.float32)[np.newaxis]
n = 20
r = 0.1
y = 0.3
w = 2.7
map = val_ds[0][2].numpy()[np.newaxis]
map = np.tile(map, (n + 1, 1, 1, 1))
rng = np.linspace(y, y + r * n, n + 1)
map[:, 1, 0, 1] = rng
map[:, 1, 1, 1] = rng
map[:, 1, 2, 1] = rng + w
map[:, 1, 3, 1] = rng + w
map[:, 0, :2, 1] = 0.
map[:, 0, 2:, 1] = 5.5
map[:, 2, :2, 1] = 0.
map[:, 2, 2:, 1] = 5.5
map[:, 2, 1, 0] = map[:, 2, 2, 0]
map[:, 1, 0, 0] = map[:, 2, 1, 0]
map[:, 1, 3, 0] = map[:, 2, 1, 0]
map[:, 0, 0, 0] = map[:, 0, 3, 0]
map[:, 1, 1, 0] = map[:, 0, 0, 0]
map[:, 1, 2, 0] = map[:, 0, 0, 0]
p0 = np.array([[-27., 1.5, 0.]], dtype=np.float32)
p0 = np.tile(p0, (n + 1, 1))
pk = np.array([[-3., 1.5, 0.]], dtype=np.float32)
pk = np.tile(pk, (n + 1, 1))
for i in [1]:
data = (p0, pk, map)
# 5.2.1 Make inference of the model for validation and calculate losses
output, last_ddy = model(data, None, training=True)
model_loss, invalid_loss, overshoot_loss, curvature_loss, non_balanced_loss, x_path, y_path, th_path = plan_loss(
output, last_ddy, data)
print(invalid_loss, curvature_loss)
_plot(x_path, y_path, th_path, data, 0, False)