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