def do_save_inference_model(args):
if args.use_cuda:
dev_count = fluid.core.get_cuda_device_count()
place = fluid.CUDAPlace(0)
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
src_vocab = reader.DataProcessor.load_dict(args.src_vocab_fpath)
trg_vocab = reader.DataProcessor.load_dict(args.trg_vocab_fpath)
args.src_vocab_size = len(src_vocab)
args.trg_vocab_size = len(trg_vocab)
test_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
# define input and reader
input_field_names = desc.encoder_data_input_fields + desc.fast_decoder_data_input_fields
input_descs = desc.get_input_descs(args.args)
input_slots = [{
"name": name,
"shape": input_descs[name][0],
"dtype": input_descs[name][1]
} for name in input_field_names]
input_field = InputField(input_slots)
input_field.build(build_pyreader=True)
# define the network
predictions = create_net(is_training=False,
model_input=input_field,
args=args)
out_ids, out_scores = predictions
# This is used here to set dropout to the test mode.
test_prog = test_prog.clone(for_test=True)
# prepare predicting
## define the executor and program for training
exe = fluid.Executor(place)
exe.run(startup_prog)
assert (
args.init_from_params), "must set init_from_params to load parameters"
load(test_prog, os.path.join(args.init_from_params, "transformer"), exe)
print("finish initing model from params from %s" % (args.init_from_params))
# saving inference model
fluid.io.save_inference_model(args.inference_model_dir,
feeded_var_names=list(input_field_names),
target_vars=[out_ids, out_scores],
executor=exe,
main_program=test_prog,
model_filename="model.pdmodel",
params_filename="params.pdparams")
print("save inference model at %s" % (args.inference_model_dir))
def do_train(args):
if args.use_cuda:
if num_trainers > 1: # for multi-process gpu training
dev_count = 1
else:
dev_count = fluid.core.get_cuda_device_count()
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
# define the data generator
processor = reader.DataProcessor(fpattern=args.training_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size,
device_count=dev_count,
pool_size=args.pool_size,
sort_type=args.sort_type,
shuffle=args.shuffle,
shuffle_batch=args.shuffle_batch,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
max_length=args.max_length,
n_head=args.n_head)
batch_generator = processor.data_generator(phase="train")
if num_trainers > 1: # for multi-process gpu training
batch_generator = fluid.contrib.reader.distributed_batch_reader(
batch_generator)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
train_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
random_seed = eval(str(args.random_seed))
if random_seed is not None:
train_prog.random_seed = random_seed
startup_prog.random_seed = random_seed
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
# define input and reader
input_field_names = desc.encoder_data_input_fields + \
desc.decoder_data_input_fields[:-1] + desc.label_data_input_fields
input_descs = desc.get_input_descs(args.args)
input_slots = [{
"name": name,
"shape": input_descs[name][0],
"dtype": input_descs[name][1]
} for name in input_field_names]
input_field = InputField(input_slots)
input_field.build(build_pyreader=True)
# define the network
sum_cost, avg_cost, token_num = create_net(is_training=True,
model_input=input_field,
args=args)
# define the optimizer
with fluid.default_main_program()._lr_schedule_guard():
learning_rate = fluid.layers.learning_rate_scheduler.noam_decay(
args.d_model, args.warmup_steps) * args.learning_rate
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate,
beta1=args.beta1,
beta2=args.beta2,
epsilon=float(args.eps))
optimizer.minimize(avg_cost)
# prepare training
## decorate the pyreader with batch_generator
input_field.loader.set_batch_generator(batch_generator)
## define the executor and program for training
exe = fluid.Executor(place)
exe.run(startup_prog)
# init position_encoding
for pos_enc_param_name in desc.pos_enc_param_names:
pos_enc_param = fluid.global_scope().find_var(
pos_enc_param_name).get_tensor()
pos_enc_param.set(
position_encoding_init(args.max_length + 1, args.d_model), place)
assert (args.init_from_checkpoint == "") or (args.init_from_pretrain_model
== "")
## init from some checkpoint, to resume the previous training
if args.init_from_checkpoint:
load(train_prog, os.path.join(args.init_from_checkpoint,
"transformer"), exe)
print("finish initing model from checkpoint from %s" %
(args.init_from_checkpoint))
## init from some pretrain models, to better solve the current task
if args.init_from_pretrain_model:
load(train_prog,
os.path.join(args.init_from_pretrain_model, "transformer"), exe)
print("finish initing model from pretrained params from %s" %
(args.init_from_pretrain_model))
build_strategy = fluid.compiler.BuildStrategy()
build_strategy.enable_inplace = True
exec_strategy = fluid.ExecutionStrategy()
if num_trainers > 1:
dist_utils.prepare_for_multi_process(exe, build_strategy, train_prog)
exec_strategy.num_threads = 1
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=avg_cost.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
# the best cross-entropy value with label smoothing
loss_normalizer = -(
(1. - args.label_smooth_eps) * np.log((1. - args.label_smooth_eps)) +
args.label_smooth_eps * np.log(args.label_smooth_eps /
(args.trg_vocab_size - 1) + 1e-20))
# start training
step_idx = 0
total_batch_num = 0 # this is for benchmark
total_batch_token_num = 0 # this is for benchmark word count
for pass_id in range(args.epoch):
pass_start_time = time.time()
input_field.loader.start()
batch_id = 0
while True:
if args.max_iter and total_batch_num == args.max_iter: # this for benchmark
return
try:
outs = exe.run(compiled_train_prog,
fetch_list=[sum_cost.name, token_num.name])
total_batch_token_num += np.asarray(outs[1]).sum()
if step_idx % args.print_step == 0:
sum_cost_val, token_num_val = np.asarray(
outs[0]), np.asarray(outs[1])
# sum the cost from multi-devices
total_sum_cost = sum_cost_val.sum()
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
if step_idx == 0:
logging.info(
"step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f" %
(step_idx, pass_id, batch_id, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)])))
avg_batch_time = time.time()
else:
logging.info(
"step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f, batch speed: %.2f steps/s, ips: %.2f words/sec"
% (step_idx, pass_id, batch_id, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)
]), args.print_step /
(time.time() - avg_batch_time),
total_batch_token_num /
(time.time() - avg_batch_time)))
avg_batch_time = time.time()
total_batch_token_num = 0
if step_idx % args.save_step == 0 and step_idx != 0:
if args.save_model_path:
model_path = os.path.join(args.save_model_path,
"step_" + str(step_idx),
"transformer")
fluid.save(train_prog, model_path)
batch_id += 1
step_idx += 1
total_batch_num = total_batch_num + 1 # this is for benchmark
# profiler tools for benchmark
if args.is_profiler and pass_id == 0 and batch_id == args.print_step:
profiler.start_profiler("All")
elif args.is_profiler and pass_id == 0 and batch_id == args.print_step + 5:
profiler.stop_profiler("total", args.profiler_path)
return
except fluid.core.EOFException:
input_field.loader.reset()
break
time_consumed = time.time() - pass_start_time
if args.save_model_path:
model_path = os.path.join(args.save_model_path, "step_final",
"transformer")
fluid.save(train_prog, model_path)
if args.enable_ce: # For CE
print("kpis\ttrain_cost_card%d\t%f" % (dev_count, total_avg_cost))
print("kpis\ttrain_duration_card%d\t%f" % (dev_count, time_consumed))
from Descriptors.sift import Sift
from Descriptors.matching import MultiMatch
from utils.load import load
from utils.calibrate import calibrate
from SFM.camera import Projection
from SFM.reconstruction import Reconstruction
from utils.display import *
from Descriptors.orb import Orb
im_list = load('dataset/sfm/grillage')
calibration = calibrate('dataset/calib')
detector = Orb(nfeatures=5000)
matcher = MultiMatch(detector, im_list)
matcher.fit()
reconstruction = Reconstruction(matcher, calibration)
X_ba = reconstruction.reconstruct()
#for sfm in reconstruction.sfm_list:
# plot_reprojection(sfm)
plot_reprojection_multi(reconstruction)
P_list = [cam.projection for cam in reconstruction.camera_list]
plot_3D_points(X_ba, P_list)
def do_predict(args):
if args.use_cuda:
dev_count = fluid.core.get_cuda_device_count()
place = fluid.CUDAPlace(0)
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
place = fluid.CPUPlace()
# define the data generator
processor = reader.DataProcessor(fpattern=args.predict_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
use_token_batch=False,
batch_size=args.batch_size,
device_count=dev_count,
pool_size=args.pool_size,
sort_type=reader.SortType.NONE,
shuffle=False,
shuffle_batch=False,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
max_length=args.max_length,
n_head=args.n_head)
batch_generator = processor.data_generator(phase="predict", place=place)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = processor.get_vocab_summary()
trg_idx2word = reader.DataProcessor.load_dict(
dict_path=args.trg_vocab_fpath, reverse=True)
test_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
# define input and reader
input_field_names = desc.encoder_data_input_fields + desc.fast_decoder_data_input_fields
input_descs = desc.get_input_descs(args.args)
input_slots = [{
"name": name,
"shape": input_descs[name][0],
"dtype": input_descs[name][1]
} for name in input_field_names]
input_field = InputField(input_slots)
input_field.build(build_pyreader=True)
# define the network
predictions = create_net(is_training=False,
model_input=input_field,
args=args)
out_ids, out_scores = predictions
# This is used here to set dropout to the test mode.
test_prog = test_prog.clone(for_test=True)
# prepare predicting
## define the executor and program for training
exe = fluid.Executor(place)
exe.run(startup_prog)
assert (
args.init_from_params), "must set init_from_params to load parameters"
load(test_prog, os.path.join(args.init_from_params, "transformer"), exe)
print("finish initing model from params from %s" % (args.init_from_params))
# to avoid a longer length than training, reset the size of position encoding to max_length
for pos_enc_param_name in desc.pos_enc_param_names:
pos_enc_param = fluid.global_scope().find_var(
pos_enc_param_name).get_tensor()
pos_enc_param.set(
position_encoding_init(args.max_length + 1, args.d_model), place)
exe_strategy = fluid.ExecutionStrategy()
# to clear tensor array after each iteration
exe_strategy.num_iteration_per_drop_scope = 1
compiled_test_prog = fluid.CompiledProgram(test_prog).with_data_parallel(
exec_strategy=exe_strategy, places=place)
f = open(args.output_file, "wb")
# start predicting
## decorate the pyreader with batch_generator
input_field.loader.set_batch_generator(batch_generator)
input_field.loader.start()
while True:
try:
seq_ids, seq_scores = exe.run(
compiled_test_prog,
fetch_list=[out_ids.name, out_scores.name],
return_numpy=False)
# How to parse the results:
# Suppose the lod of seq_ids is:
# [[0, 3, 6], [0, 12, 24, 40, 54, 67, 82]]
# then from lod[0]:
# there are 2 source sentences, beam width is 3.
# from lod[1]:
# the first source sentence has 3 hyps; the lengths are 12, 12, 16
# the second source sentence has 3 hyps; the lengths are 14, 13, 15
hyps = [[] for i in range(len(seq_ids.lod()[0]) - 1)]
scores = [[] for i in range(len(seq_scores.lod()[0]) - 1)]
for i in range(len(seq_ids.lod()[0]) -
1): # for each source sentence
start = seq_ids.lod()[0][i]
end = seq_ids.lod()[0][i + 1]
for j in range(end - start): # for each candidate
sub_start = seq_ids.lod()[1][start + j]
sub_end = seq_ids.lod()[1][start + j + 1]
hyps[i].append(b" ".join([
trg_idx2word[idx] for idx in post_process_seq(
np.array(seq_ids)[sub_start:sub_end], args.bos_idx,
args.eos_idx)
]))
scores[i].append(np.array(seq_scores)[sub_end - 1])
f.write(hyps[i][-1] + b"\n")
if len(hyps[i]) >= args.n_best:
break
except fluid.core.EOFException:
break
f.close()
from utils.load import load
import rospkg
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
rospack = rospkg.RosPack()
evaluation_data_prefix = "max_gaussian_mean"
evaluation_data_path = rospack.get_path(
'krp_localization') + '/data/evaluations/'
eval_train = load(evaluation_data_path + evaluation_data_prefix + '_train.p')
eval_test = load(evaluation_data_path + evaluation_data_prefix + '_test.p')
criteria = eval_train.pop(0)
eval_test.pop(0)
eval_train = np.array(eval_train)
eval_test = np.array(eval_test)
mean_train, std_train = np.mean(eval_train, axis=0), np.std(eval_train, axis=0)
mean_test, std_test = np.mean(eval_test, axis=0), np.std(eval_test, axis=0)
x = np.arange(len(criteria)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width / 2,
mean_train,