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models
未验证 提交 830e85ed 编写于 作者: K Kaipeng Deng 提交者： GitHub
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3d use new api (#4275) 

* PointNet++ and PointRCNN use new API
上级 1078acc2
隐藏空白更改
内联 并排
Showing with 155 addition and 132 deletion
PaddleCV/Paddle3D/PointNet++/eval_cls.py
浏览文件 @ 830e85ed
......@@ -55,7 +55,7 @@ def parse_args():
parser.add_argument(
'--num_points',
type=int,
default=4096,
default=2048,
help='number of points in a sample, default: 4096')
parser.add_argument(
'--num_classes',
......@@ -101,7 +101,7 @@ def eval():
eval_model.build_model()
eval_feeds = eval_model.get_feeds()
eval_outputs = eval_model.get_outputs()
eval_pyreader = eval_model.get_pyreader()
eval_loader = eval_model.get_loader()
eval_prog = eval_prog.clone(True)
eval_keys, eval_values = parse_outputs(eval_outputs)
......@@ -109,21 +109,20 @@ def eval():
exe = fluid.Executor(place)
exe.run(startup)
assert os.path.exists(args.weights), "weights {} not exists.".format(args.weights)
def if_exist(var):
return os.path.exists(os.path.join(args.weights, var.name))
fluid.io.load_vars(exe, args.weights, eval_prog, predicate=if_exist)
assert os.path.exists("{}.pdparams".format(args.weights)), \
"Given resume weight {}.pdparams not exist.".format(args.weights)
fluid.load(eval_prog, args.weights, exe)
eval_compile_prog = fluid.compiler.CompiledProgram(eval_prog)
# get reader
modelnet_reader = ModelNet40ClsReader(args.data_dir, mode='test')
eval_reader = modelnet_reader.get_reader(args.batch_size, args.num_points)
eval_pyreader.decorate_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader, place)
eval_stat = Stat()
try:
eval_pyreader.start()
eval_loader.start()
eval_iter = 0
eval_periods = []
while True:
......@@ -141,7 +140,7 @@ def eval():
except fluid.core.EOFException:
logger.info("[EVAL] Eval finished, {}average time: {:.2f}".format(eval_stat.get_mean_log(), np.mean(eval_periods[1:])))
finally:
eval_pyreader.reset()
eval_loader.reset()
if __name__ == "__main__":
......
PaddleCV/Paddle3D/PointNet++/eval_seg.py
浏览文件 @ 830e85ed
......@@ -100,7 +100,7 @@ def eval():
eval_model.build_model()
eval_feeds = eval_model.get_feeds()
eval_outputs = eval_model.get_outputs()
eval_pyreader = eval_model.get_pyreader()
eval_loader = eval_model.get_loader()
eval_prog = eval_prog.clone(True)
eval_keys, eval_values = parse_outputs(eval_outputs)
......@@ -108,21 +108,20 @@ def eval():
exe = fluid.Executor(place)
exe.run(startup)
assert os.path.exists(args.weights), "weights {} not exists.".format(args.weights)
def if_exist(var):
return os.path.exists(os.path.join(args.weights, var.name))
fluid.io.load_vars(exe, args.weights, eval_prog, predicate=if_exist)
assert os.path.exists("{}.pdparams".format(args.weights)), \
"Given resume weight {}.pdparams not exist.".format(args.weights)
fluid.load(eval_prog, args.weights, exe)
eval_compile_prog = fluid.compiler.CompiledProgram(eval_prog)
# get reader
indoor_reader = Indoor3DReader(args.data_dir)
eval_reader = indoor_reader.get_reader(args.batch_size, args.num_points, mode='test')
eval_pyreader.decorate_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader, place)
eval_stat = Stat()
try:
eval_pyreader.start()
eval_loader.start()
eval_iter = 0
eval_periods = []
while True:
......@@ -140,7 +139,7 @@ def eval():
except fluid.core.EOFException:
logger.info("[EVAL] Eval finished, {}average time: {:.2f}".format(eval_stat.get_mean_log(), np.mean(eval_periods[1:])))
finally:
eval_pyreader.reset()
eval_loader.reset()
if __name__ == "__main__":
......
PaddleCV/Paddle3D/PointNet++/models/pointnet2_cls.py
浏览文件 @ 830e85ed
......@@ -35,16 +35,22 @@ class PointNet2Cls(object):
self.num_points = num_points
self.use_xyz = use_xyz
self.out_feature = None
self.pyreader = None
self.loader = None
self.model_config()
def model_config(self):
self.SA_confs = []
def build_input(self):
self.xyz = fluid.layers.data(name='xyz', shape=[self.num_points, 3], dtype='float32', lod_level=0)
self.label = fluid.layers.data(name='label', shape=[1], dtype='int64', lod_level=0)
self.pyreader = fluid.io.PyReader(
self.xyz = fluid.data(name='xyz',
shape=[None, self.num_points, 3],
dtype='float32',
lod_level=0)
self.label = fluid.data(name='label',
shape=[None, 1],
dtype='int64',
lod_level=0)
self.loader = fluid.io.DataLoader.from_generator(
feed_list=[self.xyz, self.label],
capacity=64,
use_double_buffer=True,
......@@ -65,11 +71,11 @@ class PointNet2Cls(object):
**SA_conf)
out = fluid.layers.squeeze(feature, axes=[-1])
out = fc_bn(out,out_channels=512, bn=True, bn_momentum=bn_momentum, name="fc_1")
out = fc_bn(out, out_channels=512, bn=True, bn_momentum=bn_momentum, name="fc_1")
out = fluid.layers.dropout(out, 0.5, dropout_implementation="upscale_in_train")
out = fc_bn(out,out_channels=256, bn=True, bn_momentum=bn_momentum, name="fc_2")
out = fc_bn(out, out_channels=256, bn=True, bn_momentum=bn_momentum, name="fc_2")
out = fluid.layers.dropout(out, 0.5, dropout_implementation="upscale_in_train")
out = fc_bn(out,out_channels=self.num_classes, act=None, name="fc_3")
out = fc_bn(out, out_channels=self.num_classes, act=None, name="fc_3")
pred = fluid.layers.softmax(out)
# calc loss
......@@ -87,8 +93,8 @@ class PointNet2Cls(object):
def get_outputs(self):
return {"loss": self.loss, "accuracy": self.acc1}
def get_pyreader(self):
return self.pyreader
def get_loader(self):
return self.loader
class PointNet2ClsSSG(PointNet2Cls):
......
PaddleCV/Paddle3D/PointNet++/models/pointnet2_seg.py
浏览文件 @ 830e85ed
......@@ -36,7 +36,7 @@ class PointNet2SemSeg(object):
self.use_xyz = use_xyz
self.feed_vars = []
self.out_feature = None
self.pyreader = None
self.loader = None
self.model_config()
def model_config(self):
......@@ -44,10 +44,19 @@ class PointNet2SemSeg(object):
self.FP_confs = []
def build_input(self):
self.xyz = fluid.layers.data(name='xyz', shape=[self.num_points, 3], dtype='float32', lod_level=0)
self.feature = fluid.layers.data(name='feature', shape=[self.num_points, 6], dtype='float32', lod_level=0)
self.label = fluid.layers.data(name='label', shape=[self.num_points, 1], dtype='int64', lod_level=0)
self.pyreader = fluid.io.PyReader(
self.xyz = fluid.data(name='xyz',
shape=[None, self.num_points, 3],
dtype='float32',
lod_level=0)
self.feature = fluid.data(name='feature',
shape=[None, self.num_points, 6],
dtype='float32',
lod_level=0)
self.label = fluid.data(name='label',
shape=[None, self.num_points, 1],
dtype='int64',
lod_level=0)
self.loader = fluid.io.DataLoader.from_generator(
feed_list=[self.xyz, self.feature, self.label],
capacity=64,
use_double_buffer=True,
......@@ -103,8 +112,8 @@ class PointNet2SemSeg(object):
def get_outputs(self):
return {"loss": self.loss, "accuracy": self.acc1}
def get_pyreader(self):
return self.pyreader
def get_loader(self):
return self.loader
class PointNet2SemSegSSG(PointNet2SemSeg):
......
PaddleCV/Paddle3D/PointNet++/train_cls.py
浏览文件 @ 830e85ed
......@@ -54,7 +54,7 @@ def parse_args():
parser.add_argument(
'--num_points',
type=int,
default=4096,
default=2048,
help='number of points in a sample, default: 4096')
parser.add_argument(
'--num_classes',
......@@ -148,7 +148,7 @@ def train():
PointNet2ClsSSG(args.num_classes, args.num_points)
train_model.build_model(bn_momentum=args.bn_momentum)
train_feeds = train_model.get_feeds()
train_pyreader = train_model.get_pyreader()
train_loader = train_model.get_loader()
train_outputs = train_model.get_outputs()
train_loss = train_outputs['loss']
lr = fluid.layers.exponential_decay(
......@@ -157,9 +157,13 @@ def train():
decay_rate=args.lr_decay,
staircase=True)
lr = fluid.layers.clip(lr, 1e-5, args.lr)
params = []
for var in train_prog.list_vars():
if fluid.io.is_parameter(var):
params.append(var.name)
optimizer = fluid.optimizer.Adam(learning_rate=lr,
regularization=fluid.regularizer.L2Decay(args.weight_decay))
optimizer.minimize(train_loss)
optimizer.minimize(train_loss, parameter_list=params)
train_keys, train_values = parse_outputs(train_outputs)
test_prog = fluid.Program()
......@@ -171,7 +175,7 @@ def train():
test_model.build_model()
test_feeds = test_model.get_feeds()
test_outputs = test_model.get_outputs()
test_pyreader = test_model.get_pyreader()
test_loader = test_model.get_loader()
test_prog = test_prog.clone(True)
test_keys, test_values = parse_outputs(test_outputs)
......@@ -180,12 +184,13 @@ def train():
exe.run(startup)
if args.resume:
assert os.path.exists(args.resume), \
"Given resume weight dir {} not exist.".format(args.resume)
def if_exist(var):
return os.path.exists(os.path.join(args.resume, var.name))
fluid.io.load_vars(
exe, args.resume, predicate=if_exist, main_program=train_prog)
assert os.path.exists("{}.pdparams".format(args.resume)), \
"Given resume weight {}.pdparams not exist.".format(args.resume)
assert os.path.exists("{}.pdopt".format(args.resume)), \
"Given resume optimizer state {}.pdopt not exist.".format(args.resume)
assert os.path.exists("{}.pdmodel".format(args.resume)), \
"Given resume model parameter list {}.pdmodel not exist.".format(args.resume)
fluid.load(train_prog, args.resume, exe)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -200,7 +205,7 @@ def train():
if os.path.isdir(path):
shutil.rmtree(path)
logger.info("Save model to {}".format(path))
fluid.io.save_persistables(exe, path, prog)
fluid.save(prog, path)
# get reader
trans_list = [
......@@ -213,10 +218,10 @@ def train():
]
modelnet_reader = ModelNet40ClsReader(args.data_dir, mode='train', transforms=trans_list)
train_reader = modelnet_reader.get_reader(args.batch_size, args.num_points)
train_pyreader.decorate_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader, place)
modelnet_reader = ModelNet40ClsReader(args.data_dir, mode='test', transforms=None)
test_reader = modelnet_reader.get_reader(args.batch_size, args.num_points)
test_pyreader.decorate_sample_list_generator(test_reader, place)
test_loader.set_sample_list_generator(test_reader, place)
train_stat = Stat()
test_stat = Stat()
......@@ -226,7 +231,7 @@ def train():
for epoch_id in range(args.epoch):
try:
train_pyreader.start()
train_loader.start()
train_iter = 0
train_periods = []
while True:
......@@ -251,7 +256,7 @@ def train():
# evaluation
if not args.enable_ce:
try:
test_pyreader.start()
test_loader.start()
test_iter = 0
test_periods = []
while True:
......@@ -269,12 +274,12 @@ def train():
except fluid.core.EOFException:
logger.info("[TEST] Epoch {} finished, {}average time: {:.2f}".format(epoch_id, test_stat.get_mean_log(), np.mean(test_periods[1:])))
finally:
test_pyreader.reset()
test_loader.reset()
test_stat.reset()
test_periods = []
finally:
train_pyreader.reset()
train_loader.reset()
train_stat.reset()
train_periods = []
......
PaddleCV/Paddle3D/PointNet++/train_seg.py
浏览文件 @ 830e85ed
......@@ -147,7 +147,7 @@ def train():
PointNet2SemSegSSG(args.num_classes, args.num_points)
train_model.build_model(bn_momentum=args.bn_momentum)
train_feeds = train_model.get_feeds()
train_pyreader = train_model.get_pyreader()
train_loader = train_model.get_loader()
train_outputs = train_model.get_outputs()
train_loss = train_outputs['loss']
lr = fluid.layers.exponential_decay(
......@@ -156,9 +156,13 @@ def train():
decay_rate=args.lr_decay,
staircase=True)
lr = fluid.layers.clip(lr, 1e-5, args.lr)
params = []
for var in train_prog.list_vars():
if fluid.io.is_parameter(var):
params.append(var.name)
optimizer = fluid.optimizer.Adam(learning_rate=lr,
regularization=fluid.regularizer.L2Decay(args.weight_decay))
optimizer.minimize(train_loss)
optimizer.minimize(train_loss, parameter_list=params)
train_keys, train_values = parse_outputs(train_outputs)
test_prog = fluid.Program()
......@@ -170,7 +174,7 @@ def train():
test_model.build_model()
test_feeds = test_model.get_feeds()
test_outputs = test_model.get_outputs()
test_pyreader = test_model.get_pyreader()
test_loader = test_model.get_loader()
test_prog = test_prog.clone(True)
test_keys, test_values = parse_outputs(test_outputs)
......@@ -179,12 +183,13 @@ def train():
exe.run(startup)
if args.resume:
assert os.path.exists(args.resume), \
"Given resume weight dir {} not exist.".format(args.resume)
def if_exist(var):
return os.path.exists(os.path.join(args.resume, var.name))
fluid.io.load_vars(
exe, args.resume, predicate=if_exist, main_program=train_prog)
assert os.path.exists("{}.pdparams".format(args.resume)), \
"Given resume weight {}.pdparams not exist.".format(args.resume)
assert os.path.exists("{}.pdopt".format(args.resume)), \
"Given resume optimizer state {}.pdopt not exist.".format(args.resume)
assert os.path.exists("{}.pdmodel".format(args.resume)), \
"Given resume model parameter list {}.pdmodel not exist.".format(args.resume)
fluid.load(train_prog, args.resume, exe)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -199,14 +204,14 @@ def train():
if os.path.isdir(path):
shutil.rmtree(path)
logger.info("Save model to {}".format(path))
fluid.io.save_persistables(exe, path, prog)
fluid.save(prog, path)
# get reader
indoor_reader = Indoor3DReader(args.data_dir)
train_reader = indoor_reader.get_reader(args.batch_size, args.num_points, mode='train')
test_reader = indoor_reader.get_reader(args.batch_size, args.num_points, mode='test')
train_pyreader.decorate_sample_list_generator(train_reader, place)
test_pyreader.decorate_sample_list_generator(test_reader, place)
train_loader.set_sample_list_generator(train_reader, place)
test_loader.set_sample_list_generator(test_reader, place)
train_stat = Stat()
test_stat = Stat()
......@@ -216,7 +221,7 @@ def train():
for epoch_id in range(args.epoch):
try:
train_pyreader.start()
train_loader.start()
train_iter = 0
train_periods = []
while True:
......@@ -236,12 +241,12 @@ def train():
except fluid.core.EOFException:
logger.info("[TRAIN] Epoch {} finished, {}average time: {:.2f}".format(epoch_id, train_stat.get_mean_log(), np.mean(train_periods[1:])))
ce_time = np.mean(train_periods[1:])
save_model(exe, train_prog, os.path.join(args.save_dir, str(epoch_id)))
save_model(exe, train_prog, os.path.join(args.save_dir, str(epoch_id), "pointnet2_{}_seg".format(args.model)))
# evaluation
if not args.enable_ce:
try:
test_pyreader.start()
test_loader.start()
test_iter = 0
test_periods = []
while True:
......@@ -259,12 +264,12 @@ def train():
except fluid.core.EOFException:
logger.info("[TEST] Epoch {} finished, {}average time: {:.2f}".format(epoch_id, test_stat.get_mean_log(), np.mean(test_periods[1:])))
finally:
test_pyreader.reset()
test_loader.reset()
test_stat.reset()
test_periods = []
finally:
train_pyreader.reset()
train_loader.reset()
train_stat.reset()
train_periods = []
......
PaddleCV/Paddle3D/PointRCNN/README.md
浏览文件 @ 830e85ed
......@@ -188,14 +188,14 @@ RPN训练checkpoints默认保存在`checkpoints/rpn`目录，也可以通过`--s
python tools/generate_aug_scene.py --class_name 'Car' --split train --aug_times 4
```
保存RPN模型对离线增强数据的输出特征和ROI，可以通过参数`--ckpt_dir`来指定RPN训练最终权重保存路径，RPN权重默认保存在`checkpoints/rpn`目录。
保存RPN模型对离线增强数据的输出特征和ROI，可以通过参数`--weights`来指定RPN训练最终权重保存路径，RPN权重默认保存在`checkpoints/rpn`目录。
保存输出特征和ROI时须指定`TEST.SPLIT`为`train_aug`，指定`TEST.RPN_POST_NMS_TOP_N`为`300`, `TEST.RPN_NMS_THRESH`为`0.85`。
通过`--output_dir`指定保存输出特征和ROI的路径，默认保存到`./output`目录。
```
python eval.py --cfg=cfgs/default.yml \
--eval_mode=rpn \
--ckpt_dir=./checkpoints/rpn/199 \
--weights=./checkpoints/rpn/199 \
--save_rpn_feature \
--output_dir=output \
--set TEST.SPLIT train_aug TEST.RPN_POST_NMS_TOP_N 300 TEST.RPN_NMS_THRESH 0.85
......@@ -260,13 +260,13 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`python -c 'import paddle; print(paddle.
2. 保存RPN模型对评估数据的输出特征和ROI
保存RPN模型对评估数据的输出特征和ROI命令如下，可以通过参数`--ckpt_dir`来指定RPN训练最终权重保存路径，RPN权重默认保存在`checkpoints/rpn`目录。
保存RPN模型对评估数据的输出特征和ROI命令如下，可以通过参数`--weights`来指定RPN训练最终权重保存路径，RPN权重默认保存在`checkpoints/rpn`目录。
通过`--output_dir`指定保存输出特征和ROI的路径，默认保存到`./output`目录。
```
python eval.py --cfg=cfgs/default.yml \
--eval_mode=rpn \
--ckpt_dir=./checkpoints/rpn/199 \
--weights=./checkpoints/rpn/199 \
--save_rpn_feature \
--output_dir=output/val
```
......@@ -280,7 +280,7 @@ python eval.py --cfg=cfgs/default.yml \
```
python eval.py --cfg=cfgs/default.yml \
--eval_mode=rcnn_offline \
--ckpt_dir=./checkpoints/rcnn_offline/29 \
--weights=./checkpoints/rcnn_offline/29 \
--rcnn_eval_roi_dir=output/val/detections/data \
--rcnn_eval_feature_dir=output/val/features \
--save_result
......
PaddleCV/Paddle3D/PointRCNN/eval.py
浏览文件 @ 830e85ed
......@@ -59,10 +59,10 @@ def parse_args():
default=1,
help='evaluation batch size, default 1')
parser.add_argument(
'--ckpt_dir',
'--weights',
type=str,
default='checkpoints/199',
help='specify a ckpt directory to be evaluated if needed')
help='specify weights to be evaluated if needed')
parser.add_argument(
'--data_dir',
type=str,
......@@ -146,7 +146,7 @@ def eval():
with fluid.unique_name.guard():
eval_model = PointRCNN(cfg, args.batch_size, True, 'TEST')
eval_model.build()
eval_pyreader = eval_model.get_pyreader()
eval_loader = eval_model.get_loader()
eval_feeds = eval_model.get_feeds()
eval_outputs = eval_model.get_outputs()
eval_prog = eval_prog.clone(True)
......@@ -164,13 +164,10 @@ def eval():
exe.run(startup)
# load checkpoint
assert os.path.isdir(
args.ckpt_dir), "ckpt_dir {} not a directory".format(args.ckpt_dir)
def if_exist(var):
return os.path.exists(os.path.join(args.ckpt_dir, var.name))
fluid.io.load_vars(exe, args.ckpt_dir, eval_prog, predicate=if_exist)
# load weights
assert os.path.exists("{}.pdparams".format(args.weights)), \
"Given resume weight {}.pdparams not exist.".format(args.weights)
fluid.load(eval_prog, args.weights)
kitti_feature_dir = os.path.join(args.output_dir, 'features')
kitti_output_dir = os.path.join(args.output_dir, 'detections', 'data')
......@@ -207,7 +204,7 @@ def eval():
rcnn_eval_roi_dir=args.rcnn_eval_roi_dir,
rcnn_eval_feature_dir=args.rcnn_eval_feature_dir)
eval_reader = kitti_rcnn_reader.get_multiprocess_reader(args.batch_size, eval_feeds)
eval_pyreader.decorate_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader, place)
thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9]
queue = multiprocessing.Queue(128)
......@@ -249,7 +246,7 @@ def eval():
p_list[-1].start()
try:
eval_pyreader.start()
eval_loader.start()
eval_iter = 0
start_time = time.time()
......@@ -336,7 +333,7 @@ def eval():
"run 'python3 tools/kitti_eval.py' to evaluate KITTI mAP.")
finally:
eval_pyreader.reset()
eval_loader.reset()
if __name__ == "__main__":
......
PaddleCV/Paddle3D/PointRCNN/models/point_rcnn.py
浏览文件 @ 830e85ed
......@@ -39,47 +39,47 @@ class PointRCNN(object):
self.num_points = self.cfg.RPN.NUM_POINTS
self.prog = prog
self.inputs = None
self.pyreader = None
self.loader = None
def build_inputs(self):
self.inputs = OrderedDict()
if self.cfg.RPN.ENABLED:
self.inputs['sample_id'] = fluid.layers.data(name='sample_id', shape=[1], dtype='int32')
self.inputs['pts_input'] = fluid.layers.data(name='pts_input', shape=[self.num_points, 3], dtype='float32')
self.inputs['pts_rect'] = fluid.layers.data(name='pts_rect', shape=[self.num_points, 3], dtype='float32')
self.inputs['pts_features'] = fluid.layers.data(name='pts_features', shape=[self.num_points, 1], dtype='float32')
self.inputs['rpn_cls_label'] = fluid.layers.data(name='rpn_cls_label', shape=[self.num_points], dtype='int32')
self.inputs['rpn_reg_label'] = fluid.layers.data(name='rpn_reg_label', shape=[self.num_points, 7], dtype='float32')
self.inputs['gt_boxes3d'] = fluid.layers.data(name='gt_boxes3d', shape=[7], lod_level=1, dtype='float32')
self.inputs['sample_id'] = fluid.data(name='sample_id', shape=[None, 1], dtype='int32')
self.inputs['pts_input'] = fluid.data(name='pts_input', shape=[None, self.num_points, 3], dtype='float32')
self.inputs['pts_rect'] = fluid.data(name='pts_rect', shape=[None, self.num_points, 3], dtype='float32')
self.inputs['pts_features'] = fluid.data(name='pts_features', shape=[None, self.num_points, 1], dtype='float32')
self.inputs['rpn_cls_label'] = fluid.data(name='rpn_cls_label', shape=[None, self.num_points], dtype='int32')
self.inputs['rpn_reg_label'] = fluid.data(name='rpn_reg_label', shape=[None, self.num_points, 7], dtype='float32')
self.inputs['gt_boxes3d'] = fluid.data(name='gt_boxes3d', shape=[None, 7], lod_level=1, dtype='float32')
if self.cfg.RCNN.ENABLED:
if self.cfg.RCNN.ROI_SAMPLE_JIT:
self.inputs['sample_id'] = fluid.layers.data(name='sample_id', shape=[1], dtype='int32', append_batch_size=False)
self.inputs['rpn_xyz'] = fluid.layers.data(name='rpn_xyz', shape=[self.num_points, 3], dtype='float32', append_batch_size=False)
self.inputs['rpn_features'] = fluid.layers.data(name='rpn_features', shape=[self.num_points,128], dtype='float32', append_batch_size=False)
self.inputs['rpn_intensity'] = fluid.layers.data(name='rpn_intensity', shape=[self.num_points], dtype='float32', append_batch_size=False)
self.inputs['seg_mask'] = fluid.layers.data(name='seg_mask', shape=[self.num_points], dtype='float32', append_batch_size=False)
self.inputs['roi_boxes3d'] = fluid.layers.data(name='roi_boxes3d', shape=[-1, -1, 7], dtype='float32', append_batch_size=False, lod_level=0)
self.inputs['pts_depth'] = fluid.layers.data(name='pts_depth', shape=[self.num_points], dtype='float32', append_batch_size=False)
self.inputs['gt_boxes3d'] = fluid.layers.data(name='gt_boxes3d', shape=[-1, -1, 7], dtype='float32', append_batch_size=False, lod_level=0)
self.inputs['sample_id'] = fluid.data(name='sample_id', shape=[1], dtype='int32')
self.inputs['rpn_xyz'] = fluid.data(name='rpn_xyz', shape=[self.num_points, 3], dtype='float32')
self.inputs['rpn_features'] = fluid.data(name='rpn_features', shape=[self.num_points, 128], dtype='float32')
self.inputs['rpn_intensity'] = fluid.data(name='rpn_intensity', shape=[self.num_points], dtype='float32')
self.inputs['seg_mask'] = fluid.data(name='seg_mask', shape=[self.num_points], dtype='float32')
self.inputs['roi_boxes3d'] = fluid.data(name='roi_boxes3d', shape=[None, None, 7], dtype='float32', lod_level=0)
self.inputs['pts_depth'] = fluid.data(name='pts_depth', shape=[self.num_points], dtype='float32')
self.inputs['gt_boxes3d'] = fluid.data(name='gt_boxes3d', shape=[None, None, 7], dtype='float32', lod_level=0)
else:
self.inputs['sample_id'] = fluid.layers.data(name='sample_id', shape=[-1], dtype='int32', append_batch_size=False)
self.inputs['pts_input'] = fluid.layers.data(name='pts_input', shape=[-1,512,133], dtype='float32', append_batch_size=False)
self.inputs['pts_feature'] = fluid.layers.data(name='pts_feature', shape=[-1,512,128], dtype='float32', append_batch_size=False)
self.inputs['roi_boxes3d'] = fluid.layers.data(name='roi_boxes3d', shape=[-1,7], dtype='float32', append_batch_size=False)
self.inputs['sample_id'] = fluid.data(name='sample_id', shape=[None], dtype='int32')
self.inputs['pts_input'] = fluid.data(name='pts_input', shape=[None, 512, 133], dtype='float32')
self.inputs['pts_feature'] = fluid.data(name='pts_feature', shape=[None, 512, 128], dtype='float32')
self.inputs['roi_boxes3d'] = fluid.data(name='roi_boxes3d', shape=[None,7], dtype='float32')
if self.is_train:
self.inputs['cls_label'] = fluid.layers.data(name='cls_label', shape=[-1], dtype='float32', append_batch_size=False)
self.inputs['reg_valid_mask'] = fluid.layers.data(name='reg_valid_mask', shape=[-1], dtype='float32', append_batch_size=False)
self.inputs['gt_boxes3d_ct'] = fluid.layers.data(name='gt_boxes3d_ct', shape=[-1,7], dtype='float32', append_batch_size=False)
self.inputs['gt_of_rois'] = fluid.layers.data(name='gt_of_rois', shape=[-1,7], dtype='float32', append_batch_size=False)
self.inputs['cls_label'] = fluid.data(name='cls_label', shape=[None], dtype='float32')
self.inputs['reg_valid_mask'] = fluid.data(name='reg_valid_mask', shape=[None], dtype='float32')
self.inputs['gt_boxes3d_ct'] = fluid.data(name='gt_boxes3d_ct', shape=[None, 7], dtype='float32')
self.inputs['gt_of_rois'] = fluid.data(name='gt_of_rois', shape=[None, 7], dtype='float32')
else:
self.inputs['roi_scores'] = fluid.layers.data(name='roi_scores', shape=[-1,], dtype='float32', append_batch_size=False)
self.inputs['gt_iou'] = fluid.layers.data(name='gt_iou', shape=[-1], dtype='float32', append_batch_size=False)
self.inputs['gt_boxes3d'] = fluid.layers.data(name='gt_boxes3d', shape=[-1,-1,7], dtype='float32', append_batch_size=False, lod_level=0)
self.inputs['roi_scores'] = fluid.data(name='roi_scores', shape=[None], dtype='float32')
self.inputs['gt_iou'] = fluid.data(name='gt_iou', shape=[None], dtype='float32')
self.inputs['gt_boxes3d'] = fluid.data(name='gt_boxes3d', shape=[None, None, 7], dtype='float32', lod_level=0)
self.pyreader = fluid.io.PyReader(
self.loader = fluid.io.DataLoader.from_generator(
feed_list=list(self.inputs.values()),
capacity=64,
use_double_buffer=True,
......@@ -120,6 +120,6 @@ class PointRCNN(object):
rcnn_loss, _, _ = self.rcnn.get_loss()
return rpn_loss + rcnn_loss
def get_pyreader(self):
return self.pyreader
def get_loader(self):
return self.loader
PaddleCV/Paddle3D/PointRCNN/train.py
浏览文件 @ 830e85ed
......@@ -22,7 +22,6 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers import control_flow
from paddle.fluid.contrib.extend_optimizer import extend_with_decoupled_weight_decay
import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler
from models.point_rcnn import PointRCNN
......@@ -169,7 +168,7 @@ def train():
with fluid.unique_name.guard():
train_model = PointRCNN(cfg, args.batch_size, True, 'TRAIN')
train_model.build()
train_pyreader = train_model.get_pyreader()
train_loader = train_model.get_loader()
train_feeds = train_model.get_feeds()
train_outputs = train_model.get_outputs()
train_loss = train_outputs['loss']
......@@ -179,7 +178,7 @@ def train():
decay_factor=1e-5,
total_step=steps_per_epoch * args.epoch,
warmup_pct=cfg.TRAIN.PCT_START,
train_program=train_prog,
train_prog=train_prog,
startup_prog=startup,
weight_decay=cfg.TRAIN.WEIGHT_DECAY,
clip_norm=cfg.TRAIN.GRAD_NORM_CLIP)
......@@ -188,13 +187,13 @@ def train():
exe.run(startup)
if args.resume:
assert os.path.exists(args.resume), \
"Given resume weight dir {} not exist.".format(args.resume)
def if_exist(var):
logger.debug("{}: {}".format(var.name, os.path.exists(os.path.join(args.resume, var.name))))
return os.path.exists(os.path.join(args.resume, var.name))
fluid.io.load_vars(
exe, args.resume, predicate=if_exist, main_program=train_prog)
assert os.path.exists("{}.pdparams".format(args.resume)), \
"Given resume weight {}.pdparams not exist.".format(args.resume)
assert os.path.exists("{}.pdopt".format(args.resume)), \
"Given resume optimizer state {}.pdopt not exist.".format(args.resume)
assert os.path.exists("{}.pdmodel".format(args.resume)), \
"Given resume model parameter list {}.pdmodel not exist.".format(args.resume)
fluid.load(train_prog, args.resume, exe)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -208,19 +207,19 @@ def train():
if os.path.isdir(path):
shutil.rmtree(path)
logger.info("Save model to {}".format(path))
fluid.io.save_persistables(exe, path, prog)
fluid.save(prog, path)
# get reader
train_reader = kitti_rcnn_reader.get_multiprocess_reader(args.batch_size,
train_feeds,
proc_num=args.worker_num,
drop_last=True)
train_pyreader.decorate_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader, place)
train_stat = Stat()
for epoch_id in range(args.resume_epoch, args.epoch):
try:
train_pyreader.start()
train_loader.start()
train_iter = 0
train_periods = []
while True:
......@@ -241,7 +240,7 @@ def train():
train_stat.reset()
train_periods = []
finally:
train_pyreader.reset()
train_loader.reset()
if __name__ == "__main__":
......
PaddleCV/Paddle3D/PointRCNN/utils/optimizer.py
浏览文件 @ 830e85ed
......@@ -79,7 +79,7 @@ def optimize(loss,
decay_factor,
total_step,
warmup_pct,
train_program,
train_prog,
startup_prog,
weight_decay,
clip_norm,
......@@ -105,11 +105,15 @@ def optimize(loss,
param_list = dict()
if weight_decay > 0:
for param in train_program.global_block().all_parameters():
for param in train_prog.all_parameters():
param_list[param.name] = param * 1.0
param_list[param.name].stop_gradient = True
_, param_grads = optimizer.minimize(loss)
opt_param_list = []
for var in train_prog.list_vars():
if fluid.io.is_parameter(var):
opt_param_list.append(var.name)
_, param_grads = optimizer.minimize(loss, parameter_list=opt_param_list)
if weight_decay > 0:
for param, grad in param_grads:
......
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