8000 modify save and load to 1.7 api for rrpn by cjt222 · Pull Request #4310 · PaddlePaddle/models · GitHub
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modify save and load to 1.7 api for rrpn #4310

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Merged
merged 2 commits into from
Feb 19, 2020
Merged

modify save and load to 1.7 api for rrpn #4310

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9b229c1
modify save and load to 1.7 api
cjt222 Feb 18, 2020
118a730
add func to load parm
cjt222 Feb 18, 2020
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79 changes: 41 additions & 38 deletions PaddleCV/rrpn/checkpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,19 @@
logger = logging.getLogger(__name__)


def _load_state(path):
if os.path.exists(path + '.pdopt'):
# XXX another hack to ignore the optimizer state
tmp = tempfile.mkdtemp()
dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
shutil.copy(path + '.pdparams', dst + '.pdparams')
state = fluid.io.load_program_state(dst)
shutil.rmtree(tmp)
else:
state = fluid.io.load_program_state(path)
return state


def load_params(exe, prog, path):
"""
Load model from the given path.
Expand Down Expand Up @@ -64,7 +77,7 @@ def save(exe, prog, path):
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)


def load_and_fusebn(exe, prog, path):
Expand All @@ -81,15 +94,6 @@ def load_and_fusebn(exe, prog, path):
if not os.path.exists(path):
raise ValueError("Model path {} does not exists.".format(path))

def _if_exist(var):
b = os.path.exists(os.path.join(path, var.name))

if b:
logger.debug('load weight {}'.format(var.name))
return b

all_vars = list(filter(_if_exist, prog.list_vars()))

# Since the program uses affine-channel, there is no running mean and var
# in the program, here append running mean and var.
# NOTE, the params of batch norm should be like:
Expand All @@ -101,15 +105,25 @@ def _if_exist(var):
mean_variances = set()
bn_vars = []

bn_in_path = True
state = None
if os.path.exists(path + '.pdparams'):
state = _load_state(path)

inner_prog = fluid.Program()
inner_start_prog = fluid.Program()
inner_block = inner_prog.global_block()
with fluid.program_guard(inner_prog, inner_start_prog):
def check_mean_and_bias(prefix):
m = prefix + 'mean'
v = prefix + 'variance'
if state:
return v in state and m in state
else:
return (os.path.exists(os.path.join(path, m)) and
os.path.exists(os.path.join(path, v)))

has_mean_bias = True

with fluid.program_guard(prog, fluid.Program()):
for block in prog.blocks:
ops = list(block.ops)
if not bn_in_path:
if not has_mean_bias:
break
for op in ops:
if op.type == 'affine_channel':
Expand All @@ -119,50 +133,39 @@ def _if_exist(var):
prefix = scale_name[:-5]
mean_name = prefix + 'mean'
variance_name = prefix + 'variance'

if not os.path.exists(os.path.join(path, mean_name)):
bn_in_path = False
break
if not os.path.exists(os.path.join(path, variance_name)):
bn_in_path = False
if not check_mean_and_bias(prefix):
has_mean_bias = False
break

bias = block.var(bias_name)

mean_vb = inner_block.create_var(
mean_vb = block.create_var(
name=mean_name,
type=bias.type,
shape=bias.shape,
dtype=bias.dtype,
persistable=True)
variance_vb = inner_block.create_var(
dtype=bias.dtype)
variance_vb = block.create_var(
name=variance_name,
type=bias.type,
shape=bias.shape,
dtype=bias.dtype,
persistable=True)
dtype=bias.dtype)

mean_variances.add(mean_vb)
mean_variances.add(variance_vb)

bn_vars.append(
[scale_name, bias_name, mean_name, variance_name])

if not bn_in_path:
fluid.io.load_vars(exe, path, prog, vars=all_vars)
if state:
fluid.io.set_program_state(prog, state)
else:
load_params(exe, prog, path)
if not has_mean_bias:
logger.warning(
"There is no paramters of batch norm in model {}. "
"Skip to fuse batch norm. And load paramters done.".format(path))
return

# load running mean and running variance on cpu place into global scope.
place = fluid.CPUPlace()
exe_cpu = fluid.Executor(place)
fluid.io.load_vars(exe_cpu, path, vars=[v for v in mean_variances])

# load params on real place into global scope.
fluid.io.load_vars(exe, path, prog, vars=all_vars)

eps = 1e-5
for names in bn_vars:
scale_name, bias_name, mean_name, var_name = names
Expand Down
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