forked from mindspore-Ecosystem/mindspore
add export file for bgcf,maskrcnn_mobilenetv1,deepfm
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import argparse
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import numpy as np
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from mindspore import Tensor, context, load_checkpoint, export
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from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
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from src.config import config
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parser = argparse.ArgumentParser(description="maskrcnn mobilnetv1 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="maskrcnn_mobilenetv1", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if __name__ == '__main__':
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config.test_batch_size = args.batch_size
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net = Mask_Rcnn_Mobilenetv1(config)
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load_checkpoint(args.ckpt_file, net=net)
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net.set_train(False)
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img_data = Tensor(np.zeros([args.batch_size, 3, config.img_height, config.img_width], np.float16))
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img_metas = Tensor(np.zeros([args.batch_size, 4], np.float16))
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gt_bboxes = Tensor(np.zeros([args.batch_size, config.num_gts, 4], np.float16))
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gt_labels = Tensor(np.zeros([args.batch_size, config.num_gts], np.int32))
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gt_num = Tensor(np.zeros([args.batch_size, config.num_gts], np.bool))
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gt_mask = Tensor(np.zeros([args.batch_size, 1, 1, 1], np.bool))
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input_data = [img_data, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
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export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import argparse
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import numpy as np
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from mindspore import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint
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from src.mobilenet_v1 import mobilenet_v1 as mobilenet
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parser = argparse.ArgumentParser(description="mobilenetv1 export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=256, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--dataset", type=str, default="imagenet2012", help="Dataset, either cifar10 or imagenet2012")
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parser.add_argument("--file_name", type=str, default="mobilenetv1", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
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if args.dataset == "cifar10":
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from src.config import config1 as config
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else:
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from src.config import config2 as config
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if __name__ == "__main__":
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config.batch_size = args.batch_size
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target = args.device_target
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if target != "GPU":
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context.set_context(device_id=args.device_id)
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network = mobilenet(class_num=config.class_num)
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param_dict = load_checkpoint(args.ckpt_file, net=network)
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network.set_train(False)
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input_data = Tensor(np.zeros([config.batch_size, 3, 224, 224]).astype(np.float32))
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export(network, input_data, file_name=args.file_name, file_format=args.file_format)
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""export ckpt to model"""
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import argparse
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import numpy as np
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from mindspore import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint
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from src.bgcf import BGCF
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from src.callback import ForwardBGCF
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parser = argparse.ArgumentParser(description="bgcf export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="bgcf", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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parser.add_argument("--input_dim", type=int, choices=[64, 128], default=64, help="embedding dimension")
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parser.add_argument("--embedded_dimension", type=int, default=64, help="output embedding dimension")
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parser.add_argument("--row_neighs", type=int, default=40, help="num of sampling neighbors in raw graph")
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parser.add_argument("--gnew_neighs", type=int, default=20, help="num of sampling neighbors in sample graph")
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parser.add_argument("--activation", type=str, default="tanh", choices=["relu", "tanh"], help="activation function")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if __name__ == "__main__":
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num_user, num_item = 7068, 3570
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network = BGCF([args.input_dim, num_user, num_item],
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args.embedded_dimension,
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args.activation,
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[0.0, 0.0, 0.0],
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num_user,
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num_item,
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args.input_dim)
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load_checkpoint(args.ckpt_file, net=network)
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forward_net = ForwardBGCF(network)
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users = Tensor(np.zeros([num_user,]).astype(np.int32))
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items = Tensor(np.zeros([num_item,]).astype(np.int32))
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neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32))
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u_test_neighs = Tensor(np.zeros([num_user, args.row_neighs]).astype(np.int32))
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u_test_gnew_neighs = Tensor(np.zeros([num_user, args.gnew_neighs]).astype(np.int32))
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i_test_neighs = Tensor(np.zeros([num_item, args.row_neighs]).astype(np.int32))
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i_test_gnew_neighs = Tensor(np.zeros([num_item, args.gnew_neighs]).astype(np.int32))
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input_data = [users, items, neg_items, u_test_neighs, u_test_gnew_neighs, i_test_neighs, i_test_gnew_neighs]
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export(forward_net, *input_data, file_name=args.file_name, file_format=args.file_format)
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""export ckpt to model"""
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import argparse
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import numpy as np
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from mindspore import context, Tensor
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from mindspore.train.serialization import export, load_checkpoint
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from src.deepfm import ModelBuilder
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from src.config import DataConfig, ModelConfig, TrainConfig
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parser = argparse.ArgumentParser(description="deepfm export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=16000, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--file_name", type=str, default="deepfm", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
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help="device target")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
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if __name__ == "__main__":
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data_config = DataConfig()
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model_builder = ModelBuilder(ModelConfig, TrainConfig)
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_, network = model_builder.get_train_eval_net()
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load_checkpoint(args.ckpt_file, net=network)
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batch_ids = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.int32))
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batch_wts = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.float32))
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labels = Tensor(np.zeros([data_config.batch_size, 1]).astype(np.float32))
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input_data = [batch_ids, batch_wts, labels]
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export(network, *input_data, file_name=args.file_name, file_format=args.file_format)
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