add export file for bgcf,maskrcnn_mobilenetv1,deepfm

model_zoo/official/cv/maskrcnn_mobilenetv1/export.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 argparse
+import numpy as np
+
+from mindspore import Tensor, context, load_checkpoint, export
+
+from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
+from src.config import config
+
+parser = argparse.ArgumentParser(description="maskrcnn mobilnetv1 export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="maskrcnn_mobilenetv1", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+
+if __name__ == '__main__':
+    config.test_batch_size = args.batch_size
+    net = Mask_Rcnn_Mobilenetv1(config)
+    load_checkpoint(args.ckpt_file, net=net)
+
+    net.set_train(False)
+
+    img_data = Tensor(np.zeros([args.batch_size, 3, config.img_height, config.img_width], np.float16))
+    img_metas = Tensor(np.zeros([args.batch_size, 4], np.float16))
+    gt_bboxes = Tensor(np.zeros([args.batch_size, config.num_gts, 4], np.float16))
+    gt_labels = Tensor(np.zeros([args.batch_size, config.num_gts], np.int32))
+    gt_num = Tensor(np.zeros([args.batch_size, config.num_gts], np.bool))
+    gt_mask = Tensor(np.zeros([args.batch_size, 1, 1, 1], np.bool))
+
+    input_data = [img_data, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
+    export(net, *input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/mobilenetv1/export.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 argparse
+import numpy as np
+
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint
+
+from src.mobilenet_v1 import mobilenet_v1 as mobilenet
+
+parser = argparse.ArgumentParser(description="mobilenetv1 export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=256, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--dataset", type=str, default="imagenet2012", help="Dataset, either cifar10 or imagenet2012")
+parser.add_argument("--file_name", type=str, default="mobilenetv1", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
+
+if args.dataset == "cifar10":
+    from src.config import config1 as config
+else:
+    from src.config import config2 as config
+
+if __name__ == "__main__":
+    config.batch_size = args.batch_size
+
+    target = args.device_target
+    if target != "GPU":
+        context.set_context(device_id=args.device_id)
+
+    network = mobilenet(class_num=config.class_num)
+
+    param_dict = load_checkpoint(args.ckpt_file, net=network)
+
+    network.set_train(False)
+
+    input_data = Tensor(np.zeros([config.batch_size, 3, 224, 224]).astype(np.float32))
+
+    export(network, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/gnn/bgcf/export.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""export ckpt to model"""
+import argparse
+import numpy as np
+
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint
+
+from src.bgcf import BGCF
+from src.callback import ForwardBGCF
+
+parser = argparse.ArgumentParser(description="bgcf export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="bgcf", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+parser.add_argument("--input_dim", type=int, choices=[64, 128], default=64, help="embedding dimension")
+parser.add_argument("--embedded_dimension", type=int, default=64, help="output embedding dimension")
+parser.add_argument("--row_neighs", type=int, default=40, help="num of sampling neighbors in raw graph")
+parser.add_argument("--gnew_neighs", type=int, default=20, help="num of sampling neighbors in sample graph")
+parser.add_argument("--activation", type=str, default="tanh", choices=["relu", "tanh"], help="activation function")
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+
+if __name__ == "__main__":
+    num_user, num_item = 7068, 3570
+
+    network = BGCF([args.input_dim, num_user, num_item],
+                   args.embedded_dimension,
+                   args.activation,
+                   [0.0, 0.0, 0.0],
+                   num_user,
+                   num_item,
+                   args.input_dim)
+
+    load_checkpoint(args.ckpt_file, net=network)
+
+    forward_net = ForwardBGCF(network)
+
+    users = Tensor(np.zeros([num_user,]).astype(np.int32))
+    items = Tensor(np.zeros([num_item,]).astype(np.int32))
+    neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32))
+    u_test_neighs = Tensor(np.zeros([num_user, args.row_neighs]).astype(np.int32))
+    u_test_gnew_neighs = Tensor(np.zeros([num_user, args.gnew_neighs]).astype(np.int32))
+    i_test_neighs = Tensor(np.zeros([num_item, args.row_neighs]).astype(np.int32))
+    i_test_gnew_neighs = Tensor(np.zeros([num_item, args.gnew_neighs]).astype(np.int32))
+
+    input_data = [users, items, neg_items, u_test_neighs, u_test_gnew_neighs, i_test_neighs, i_test_gnew_neighs]
+    export(forward_net, *input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/recommend/deepfm/export.py

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+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""export ckpt to model"""
+import argparse
+import numpy as np
+
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint
+
+from src.deepfm import ModelBuilder
+from src.config import DataConfig, ModelConfig, TrainConfig
+
+parser = argparse.ArgumentParser(description="deepfm export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=16000, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="deepfm", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+
+if __name__ == "__main__":
+    data_config = DataConfig()
+
+    model_builder = ModelBuilder(ModelConfig, TrainConfig)
+    _, network = model_builder.get_train_eval_net()
+
+    load_checkpoint(args.ckpt_file, net=network)
+
+    batch_ids = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.int32))
+    batch_wts = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.float32))
+    labels = Tensor(np.zeros([data_config.batch_size, 1]).astype(np.float32))
+
+    input_data = [batch_ids, batch_wts, labels]
+    export(network, *input_data, file_name=args.file_name, file_format=args.file_format)