for index, e in enumerate(Predicted_expressions):
Predicted_expressions[index] = e[0:-1].split("$")[0]
print("let us start the optimization party!!")
Target_images = np.concatenate(target_images, 0)
refined_expressions = []
scores = 0
b = 0
distances = 0
beam_scores = []
for index, value in enumerate(Predicted_expressions):
optimized_expression, score = optimize_expression(
value,
Target_images[index // beam_width],
metric="chamfer",
stack_size=max_len // 2 + 1,
steps=max_len,
max_iter=max_iter)
refined_expressions.append(optimized_expression)
beam_scores.append(score)
if b == (beam_width - 1):
scores += np.min(beam_scores)
beam_scores = []
b = 0
else:
b += 1
print(
index,
score,
scores / ((index + beam_width) // beam_width),
plt.close("all")
print("average chamfer distance: {}".format(
CDs / (config.test_size // config.batch_size)),
flush=True)
if REFINE:
Target_images = np.concatenate(Target_images, 0)
tweaked_expressions = []
scores = 0
for index, value in enumerate(pred_expressions):
prog = parser.Parser.parse(value)
if validity(prog, len(prog), len(prog) - 1):
optim_expression, score = optimize_expression(
value,
Target_images[index // beam_width],
metric="chamfer",
max_iter=None)
print(value)
tweaked_expressions.append(optim_expression)
scores += score
else:
# If the predicted program is invalid
tweaked_expressions.append(value)
scores += 16
print("chamfer scores", scores / len(tweaked_expressions))
with open(
tweak_expressions_path +
"chamfer_tweak_expressions_beamwidth_{}.txt".format(
beam_width), "w") as file: