提交 c5cf3c15 编写于 作者: G gaotingquan

fix export_model to support dygraph

上级 6589b2a8
......@@ -15,7 +15,10 @@
import argparse
from ppcls.modeling import architectures
import paddle.fluid as fluid
from ppcls.utils.save_load import load_dygraph_pretrain
import paddle
import paddle.nn.functional as F
from paddle.jit import to_static
def parse_args():
......@@ -24,54 +27,38 @@ def parse_args():
parser.add_argument("-p", "--pretrained_model", type=str)
parser.add_argument("-o", "--output_path", type=str)
parser.add_argument("--class_dim", type=int, default=1000)
parser.add_argument("--img_size", type=int, default=224)
# parser.add_argument("--img_size", type=int, default=224)
return parser.parse_args()
def create_input(img_size=224):
image = fluid.data(
name='image', shape=[None, 3, img_size, img_size], dtype='float32')
return image
class Net(paddle.nn.Layer):
def __init__(self, net, to_static, class_dim):
super(Net, self).__init__()
self.pre_net = net(class_dim=class_dim)
self.to_static = to_static
def create_model(args, model, input, class_dim=1000):
if args.model == "GoogLeNet":
out, _, _ = model.net(input=input, class_dim=class_dim)
else:
out = model.net(input=input, class_dim=class_dim)
out = fluid.layers.softmax(out)
return out
# 请根据实际需求修改shape
@to_static(input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype='float32')
])
def forward(self, inputs):
x = self.pre_net(inputs)
x = F.softmax(x)
return x
def main():
args = parse_args()
model = architectures.__dict__[args.model]()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
image = create_input(args.img_size)
out = create_model(args, model, image, class_dim=args.class_dim)
infer_prog = infer_prog.clone(for_test=True)
fluid.load(
program=infer_prog, model_path=args.pretrained_model, executor=exe)
paddle.disable_static()
net = architectures.__dict__[args.model]
fluid.io.save_inference_model(
dirname=args.output_path,
feeded_var_names=[image.name],
main_program=infer_prog,
target_vars=out,
executor=exe,
model_filename='model',
params_filename='params')
model = Net(net, to_static, args.class_dim)
para_state_dict = paddle.io.load_program_state(args.pretrained_model)
load_dygraph_pretrain(model, args.pretrained_model, True)
paddle.jit.save(model, args.output_path)
if __name__ == "__main__":
......
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