def validation(iteration):
model.eval()
repeat_num = args.eval_repeat_num
all_gt_fix_vectors = []
all_predict_fix_vectors = []
with tqdm(total=len(validation_loader) * repeat_num) as pbar_val:
for i_batch, batch in enumerate(validation_loader):
tmp = [batch["images"], batch["fix_vectors"]]
tmp = [_ if not torch.is_tensor(_) else _.cuda() for _ in tmp]
images, gt_fix_vectors = tmp
N, C, H, W = images.shape
with torch.no_grad():
predict = model(images)
log_normal_mu = predict["log_normal_mu"]
log_normal_sigma2 = predict["log_normal_sigma2"]
all_actions_prob = predict["all_actions_prob"]
for trial in range(repeat_num):
all_gt_fix_vectors.extend(gt_fix_vectors)
samples = sampling.random_sample(all_actions_prob, log_normal_mu, log_normal_sigma2)
prob_sample_actions = samples["selected_actions_probs"]
durations = samples["durations"]
sample_actions = samples["selected_actions"]
sampling_random_predict_fix_vectors, _, _ = sampling.generate_scanpath(
images, prob_sample_actions, durations, sample_actions)
all_predict_fix_vectors.extend(sampling_random_predict_fix_vectors)
pbar_val.update(1)
cur_metrics, cur_metrics_std, _ = evaluation(all_gt_fix_vectors, all_predict_fix_vectors)
# Print and log all evaluation metrics to tensorboard.
logger.info("Evaluation metrics after iteration {iteration}:".format(iteration=iteration))
for metrics_key in cur_metrics.keys():
for (metric_name, metric_value) in cur_metrics[metrics_key].items():
tensorboard_writer.add_scalar(
"metrics/{metrics_key}-{metric_name}".format(metrics_key=metrics_key, metric_name=metric_name),
metric_value, iteration
)
logger.info("{metrics_key:10}-{metric_name:15}: {metric_value:.4f} +- {std:.4f}".format
(metrics_key=metrics_key, metric_name=metric_name, metric_value=metric_value,
std=cur_metrics_std[metrics_key][metric_name]))
return cur_metrics
def train_reader(args):
strategies = args.strategies
seeds = args.seeds[:args.run_cnt]
for idx, (seed, strategy) in enumerate(product(seeds, strategies)):
wandb.init(project="p-stage-3", reinit=True)
args = update_args(args,
strategy) # auto add args.save_path, args.base_path
args.strategy, args.seed = strategy, seed
args.info = Namespace()
set_seed(seed)
checkpoint_dir = glob(p.join(args.path.checkpoint, f"{strategy}*"))
if not checkpoint_dir:
raise FileNotFoundError(
f"{strategy} 전략에 대한 checkpoint가 존재하지 않습니다.")
args.model.model_path = get_last_checkpoint(checkpoint_dir[0])
if args.model.model_path is None:
raise FileNotFoundError(
f"{checkpoint_dir[0]} 경로에 체크포인트가 존재하지 않습니다.")
# run_name: strategy + alias + seed
args.train.run_name = "_".join([strategy, args.alias, str(seed)])
args.train.output_dir = p.join(args.path.checkpoint,
args.train.run_name)
wandb.run.name = args.train.run_name
print("checkpoint_dir: ", args.train.output_dir)
datasets = get_dataset(args, is_train=True)
reader = get_reader(args, eval_answers=datasets["validation"])
retriever = get_retriever(args)
datasets["validation"] = retriever.retrieve(
datasets["validation"], topk=args.retriever.topk)["validation"]
reader.set_dataset(eval_dataset=datasets["validation"])
trainer = reader.get_trainer()
if args.train.do_eval:
metric_results = trainer.evaluate()
results = evaluation(args)
metric_results["predictions"]["exact_match"] = results["EM"][
"value"]
metric_results["predictions"]["f1"] = results["F1"]["value"]
print("EVAL RESULT")
print(metric_results["predictions"])
if args.train.do_eval and args.train.pororo_prediction:
assert metric_results is not None, "trainer.evaluate()가 None을 반환합니다."
results = evaluation(args, prefix="pororo_")
metric_results["pororo_predictions"]["exact_match"] = results[
"EM"]["value"]
metric_results["pororo_predictions"]["f1"] = results["F1"]["value"]
print("PORORO EVAL RESULT")
print(metric_results["pororo_predictions"])
if args.train.do_eval and args.report:
report_reader_to_slack(args,
p.basename(__file__),
metric_results["predictions"],
use_pororo=False)
if args.train.pororo_prediction:
report_reader_to_slack(args,
p.basename(__file__),
metric_results["pororo_predictions"],
use_pororo=True)
def train_reader(args):
strategies = args.strategies
seeds = args.seeds[:args.run_cnt]
for idx, (seed, strategy) in enumerate(product(seeds, strategies)):
wandb.init(project="p-stage-3", reinit=True)
args = update_args(args,
strategy) # auto add args.save_path, args.base_path
args.strategy, args.seed = strategy, seed
args.info = Namespace()
set_seed(seed)
# below codes must run before 'reader.get_trainer()'
# run_name: strategy + alias + seed
args.train.run_name = "_".join([strategy, args.alias, str(seed)])
args.train.output_dir = p.join(args.path.checkpoint,
args.train.run_name)
wandb.run.name = args.train.run_name
print("checkpoint_dir: ", args.train.output_dir)
# retrieve 과정이 없어 top-k를 반환할 수 없음. 무조건 top-1만 반환
# run_mrc.py DOES NOT execute retrieve, so args.retriever.topk cannot be n(>1).
# If topk > 1, post processing function returns mis-bundled predictions.
args.retriever.topk = 1
datasets = get_dataset(args, is_train=True)
reader = get_reader(args, eval_answers=datasets["validation"])
reader.set_dataset(train_dataset=datasets["train"],
eval_dataset=datasets["validation"])
trainer = reader.get_trainer()
if args.train.do_train:
train_results = trainer.train()
print(train_results)
metric_results = None
if args.train.do_eval:
metric_results = trainer.evaluate()
results = evaluation(args)
metric_results["predictions"]["exact_match"] = results["EM"][
"value"]
metric_results["predictions"]["f1"] = results["F1"]["value"]
print("EVAL RESULT")
print(metric_results["predictions"])
if args.train.do_eval and args.train.pororo_prediction:
assert metric_results is not None, "trainer.evaluate()가 None을 반환합니다."
results = evaluation(args, prefix="pororo_")
metric_results["pororo_predictions"]["exact_match"] = results[
"EM"]["value"]
metric_results["pororo_predictions"]["f1"] = results["F1"]["value"]
print("PORORO EVAL RESULT")
print(metric_results["pororo_predictions"])
if args.train.do_eval and args.report:
report_reader_to_slack(args,
p.basename(__file__),
metric_results["predictions"],
use_pororo=False)
if args.train.pororo_prediction:
report_reader_to_slack(args,
p.basename(__file__),
metric_results["pororo_predictions"],
use_pororo=True)
def main():
# load logger
log_dir = args.evaluation_dir
checkpoints_dir = os.path.join(log_dir, "checkpoints")
log_file = os.path.join(log_dir, "log_validation.txt")
predicts_file = os.path.join(log_dir, "validation_predicts.json")
logger = Logger(log_file)
logger.info("The args corresponding to validation process are: ")
for (key, value) in vars(args).items():
logger.info("{key:20}: {value:}".format(key=key, value=value))
validation_dataset = COCO_Search18_evaluation(
args.img_dir,
args.fix_dir,
args.detector_dir,
type="validation",
split="split3",
transform=transform,
detector_threshold=args.detector_threshold)
validation_loader = DataLoader(dataset=validation_dataset,
batch_size=args.batch,
shuffle=False,
num_workers=4,
collate_fn=validation_dataset.collate_func)
object_name = [
"bottle", "bowl", "car", "chair", "clock", "cup", "fork", "keyboard",
"knife", "laptop", "microwave", "mouse", "oven", "potted plant",
"sink", "stop sign", "toilet", "tv"
]
model = baseline(embed_size=512,
convLSTM_length=args.max_length,
min_length=args.min_length).cuda()
sampling = Sampling(convLSTM_length=args.max_length,
min_length=args.min_length)
# Load checkpoint to start evaluation.
# Infer iteration number through file name (it's hacky but very simple), so don't rename
validation_checkpoint = torch.load(
os.path.join(checkpoints_dir, "checkpoint_best.pth"))
for key in validation_checkpoint:
if key == "optimizer":
continue
else:
model.load_state_dict(validation_checkpoint[key], strict=False)
if len(args.gpu_ids) > 1:
model = nn.DataParallel(model, args.gpu_ids)
# get the human baseline score
human_metrics, human_metrics_std, gt_scores_of_each_images = human_evaluation(
validation_loader)
logger.info("The metrics for human performance are: ")
for metrics_key in human_metrics.keys():
for (key, value) in human_metrics[metrics_key].items():
logger.info(
"{metrics_key:10}-{key:15}: {value:.4f} +- {std:.4f}".format(
metrics_key=metrics_key,
key=key,
value=value,
std=human_metrics_std[metrics_key][key]))
model.eval()
repeat_num = args.eval_repeat_num
all_gt_fix_vectors = []
all_predict_fix_vectors = []
predict_results = []
with tqdm(total=len(validation_loader) * repeat_num) as pbar_val:
for i_batch, batch in enumerate(validation_loader):
tmp = [
batch["images"], batch["fix_vectors"], batch["attention_maps"],
batch["tasks"], batch["img_names"]
]
tmp = [_ if not torch.is_tensor(_) else _.cuda() for _ in tmp]
images, gt_fix_vectors, attention_maps, tasks, img_names = tmp
N, C, H, W = images.shape
if args.ablate_attention_info:
attention_maps *= 0
with torch.no_grad():
predict = model(images, attention_maps, tasks)
log_normal_mu = predict["log_normal_mu"]
log_normal_sigma2 = predict["log_normal_sigma2"]
all_actions_prob = predict["all_actions_prob"]
for trial in range(repeat_num):
all_gt_fix_vectors.extend(gt_fix_vectors)
samples = sampling.random_sample(all_actions_prob,
log_normal_mu,
log_normal_sigma2)
prob_sample_actions = samples["selected_actions_probs"]
durations = samples["durations"]
sample_actions = samples["selected_actions"]
sampling_random_predict_fix_vectors, _, _ = sampling.generate_scanpath(
images, prob_sample_actions, durations, sample_actions)
all_predict_fix_vectors.extend(
sampling_random_predict_fix_vectors)
for index in range(N):
predict_result = dict()
one_sampling_random_predict_fix_vectors = sampling_random_predict_fix_vectors[
index]
fix_vector_array = np.array(
one_sampling_random_predict_fix_vectors.tolist())
predict_result["img_names"] = img_names[index]
predict_result["task"] = object_name[tasks[index]]
predict_result["repeat_id"] = trial + 1
predict_result["X"] = list(fix_vector_array[:, 0])
predict_result["Y"] = list(fix_vector_array[:, 1])
predict_result["T"] = list(fix_vector_array[:, 2] * 1000)
predict_result["length"] = len(predict_result["X"])
predict_results.append(predict_result)
pbar_val.update(1)
cur_metrics, cur_metrics_std, _ = evaluation(all_gt_fix_vectors,
all_predict_fix_vectors)
with open(predicts_file, 'w') as f:
json.dump(predict_results, f, indent=2)
logger.info("The metrics for best model performance are: ")
for metrics_key in cur_metrics.keys():
for (metric_name, metric_value) in cur_metrics[metrics_key].items():
logger.info(
"{metrics_key:10}-{metric_name:15}: {metric_value:.4f} +- {std:.4f}"
.format(metrics_key=metrics_key,
metric_name=metric_name,
metric_value=metric_value,
std=cur_metrics_std[metrics_key][metric_name]))
flow = RealNVP(nets,
nett,
num_flows,
prior,
dequantization=True)
# OPTIMIZER
optimizer = torch.optim.Adamax(
[p for p in flow.parameters() if p.requires_grad == True],
lr=lr)
# TRAINING
nll_val = training(name=result_dir + name,
max_patience=max_patience,
num_epochs=num_epochs,
flow=flow,
optimizer=optimizer,
training_loader=training_loader,
val_loader=val_loader)
# EVALUATION
test_loss = evaluation(name=result_dir + name,
test_loader=test_loader)
f = open(result_dir + name + '_test_loss.txt', "w")
f.write(str(test_loss))
f.close()
samples_real(result_dir + name, test_loader)
plot_curve(result_dir + name, nll_val)
def minimax(board, kmd, okmd, mv, depth, c_depth, sente):
if depth == 0:
return evaluation.evaluation(board, kmd, okmd, mv)
movelist = []
y = 0
if c_depth % 2 == 0:
for line in board:
x = 0
for square in line:
if square == "P":
mlist = mv.pawn(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "L":
mlist = mv.lance(x, y)
mlist2 = []
for lx, ly in mlist:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].isupper():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "N":
mlist = mv.knight(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "S":
mlist = mv.silver(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "G":
mlist = mv.gold(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "K":
mlist = mv.king(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "R":
mlist = mv.rook(x, y)
mlist2 = []
#print(mlist)
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].isupper():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "B":
mlist = mv.bishop(x, y)
mlist2 = []
#print(mlist)
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].isupper():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "+P" or square == "+L" or square == "+N" or square == "+S":
mlist = mv.gold(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].isupper(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "+R":
mlist = mv.rook(x, y, dragon=True)
mlist2 = []
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].isupper():
break
else:
mlist2.append((lx, ly))
break
for lx, ly in mlist[4]:
mlist2.append((lx, ly))
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "+B":
mlist = mv.bishop(x, y, horse=True)
mlist2 = []
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].isupper():
break
else:
mlist2.append((lx, ly))
break
for lx, ly in mlist[4]:
mlist2.append((lx, ly))
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
x += 1
y += 1
sfeny = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
for koma in kmd:
for tempy in range(9):
for tempx in range(9):
if board[tempy][tempx] == "":
if sente:
usimove = "{}*{}{}".format(koma.upper(), tempx + 1,
sfeny[tempy])
else:
usimove = "{}*{}{}".format(koma.upper(), 9 - tempx,
sfeny[8 - tempy])
if util.isThisDropLegal(copy.deepcopy(board),
(koma.upper(), tempx, tempy)):
movelist.append(usimove)
else:
for line in board:
x = 0
for square in line:
if square == "p":
mlist = mv.pawn(x, y, me=False)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "l":
mlist = mv.lance(x, y, me=False)
mlist2 = []
for lx, ly in mlist:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].islower():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "n":
mlist = mv.knight(x, y, me=False)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "s":
mlist = mv.silver(x, y, me=False)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "g":
mlist = mv.gold(x, y, me=False)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "k":
mlist = mv.king(x, y)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "r":
mlist = mv.rook(x, y)
mlist2 = []
#print(mlist)
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].islower():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "b":
mlist = mv.bishop(x, y)
mlist2 = []
#print(mlist)
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].islower():
break
else:
mlist2.append((lx, ly))
break
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
if y2 < 3:
usimove += "+"
movelist.append(usimove)
elif square == "+p" or square == "+l" or square == "+n" or square == "+s":
mlist = mv.gold(x, y, me=False)
mlist2 = filter(
lambda xy: not board[xy[1]][xy[0]].islower(), mlist)
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "+r":
mlist = mv.rook(x, y, dragon=True)
mlist2 = []
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].islower():
break
else:
mlist2.append((lx, ly))
break
for lx, ly in mlist[4]:
mlist2.append((lx, ly))
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
elif square == "+b":
mlist = mv.bishop(x, y, horse=True)
mlist2 = []
for mlpart in mlist[:4]:
for lx, ly in mlpart:
if board[ly][lx] == "":
mlist2.append((lx, ly))
elif board[ly][lx].islower():
break
else:
mlist2.append((lx, ly))
break
for lx, ly in mlist[4]:
mlist2.append((lx, ly))
for x2, y2 in mlist2:
if util.isThisMoveLegal(
copy.deepcopy(board),
(square.upper(), x, x2, y, y2)):
usimove = util.rawtousi(square.lower(), x, x2, y,
y2, sente)
movelist.append(usimove)
x += 1
y += 1
sfeny = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
for koma in okmd:
for tempy in range(9):
for tempx in range(9):
if board[tempy][tempx] == "":
if sente:
usimove = "{}*{}{}".format(koma.upper(), tempx + 1,
sfeny[tempy])
else:
usimove = "{}*{}{}".format(koma.upper(), 9 - tempx,
sfeny[8 - tempy])
if util.isThisDropLegal(copy.deepcopy(board),
(koma.upper(), tempx, tempy)):
movelist.append(usimove)
w = len(movelist)
if w == 0:
return evaluation.evaluation(board, kmd, okmd, mv, turn="opponent")
if c_depth % 2 == 0:
maxs = float("-inf")
for i in range(w):
usim = movelist[i]
rawm = util.usitoraw(board, usim, sente)
cboard = copy.deepcopy(board)
legal = util.isThisMoveLegal(cboard, rawm, me=True)
if not legal:
continue
cboard2 = copy.deepcopy(board)
piece = rawm[0]
x1 = rawm[1]
x2 = rawm[2]
y1 = rawm[3]
y2 = rawm[4]
if x1 and y1:
cboard2[y1][x1] = ""
cboard2[y2][x2] = piece
score = minimax(cboard, kmd, okmd, mv, depth - 1, c_depth + 1,
sente)
if score > maxs:
maxs = score
return maxs
else:
mins = float("inf")
for i in range(w):
usim = movelist[i]
rawm = util.usitoraw(board, usim, sente)
cboard = copy.deepcopy(board)
legal = util.isThisMoveLegal(cboard, rawm, me=False)
if not legal:
continue
cboard2 = copy.deepcopy(board)
piece = rawm[0]
x1 = rawm[1]
x2 = rawm[2]
y1 = rawm[3]
y2 = rawm[4]
if x1 and y1:
cboard2[y1][x1] = ""
cboard2[y2][x2] = piece
score = minimax(cboard, kmd, okmd, mv, depth - 1, c_depth + 1,
sente)
if score < mins:
mins = score
return mins