PaddleClas/ppcls/arch/__init__.py
2021-06-01 11:30:26 +08:00

69 lines
2.2 KiB
Python

#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import copy
import importlib
import paddle.nn as nn
from . import backbone
from . import head
from .backbone import *
from .head import *
from .utils import *
__all__ = ["build_model", "RecModel"]
def build_model(config):
config = copy.deepcopy(config)
model_type = config.pop("name")
mod = importlib.import_module(__name__)
arch = getattr(mod, model_type)(**config)
return arch
class RecModel(nn.Layer):
def __init__(self, **config):
super().__init__()
backbone_config = config["Backbone"]
backbone_name = backbone_config.pop("name")
self.backbone = eval(backbone_name)(**backbone_config)
assert "Stoplayer" in config, "Stoplayer should be specified in retrieval task \
please specified a Stoplayer config"
stop_layer_config = config["Stoplayer"]
self.backbone.stop_after(stop_layer_config["name"])
if stop_layer_config.get("embedding_size", 0) > 0:
self.neck = nn.Linear(stop_layer_config["output_dim"], stop_layer_config["embedding_size"])
embedding_size = stop_layer_config["embedding_size"]
else:
self.neck = None
embedding_size = stop_layer_config["output_dim"]
assert "Head" in config, "Head should be specified in retrieval task \
please specify a Head config"
config["Head"]["embedding_size"] = embedding_size
self.head = build_head(config["Head"])
def forward(self, x, label):
x = self.backbone(x)
if self.neck is not None:
x = self.neck(x)
y = self.head(x, label)
return {"features":x, "logits":y}