prompt
stringlengths
94
42.6k
completion
stringlengths
6
120
api
stringlengths
14
68
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
isscalar(b)
megengine.core.tensor.utils.isscalar
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
trace(symbolic=symbolic, capture_as_const=True)
megengine.jit.trace
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.topk(x, 3)
megengine.functional.topk
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.warp_perspective(x, M, (2, 2))
megengine.functional.warp_perspective
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
trace(symbolic=symbolic, capture_as_const=True)
megengine.jit.trace
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.nn.nms(boxes, scores=scores, iou_thresh=0.5, max_output=20)
megengine.functional.nn.nms
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor([2])
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor([2])
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.broadcast_to(x, shape)
megengine.functional.broadcast_to
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.clip(x, lower, upper)
megengine.functional.clip
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Parameter([1.0], dtype=np.float32)
megengine.Parameter
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.exp(x)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
GradManager()
megengine.autodiff.GradManager
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(x)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(x)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor([1, 10], dtype=np.int32)
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.broadcast_to(x, (3, 4, 5))
megengine.functional.broadcast_to
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor(boxes)
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor(scores)
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exclude_from_trace()
megengine.jit.exclude_from_trace
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.nn.nms(boxes, scores=scores, iou_thresh=0.5)
megengine.functional.nn.nms
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor([0])
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
tensor([1])
megengine.tensor
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
F.arange(8)
megengine.functional.arange
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
trace(symbolic=True, symbolic_shape=symbolic_shape)
megengine.jit.trace
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exclude_from_trace()
megengine.jit.exclude_from_trace
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
cgtools.get_owner_opr_inputs(out)
megengine.utils.comp_graph_tools.get_owner_opr_inputs
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(x)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(y)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(x)
megengine.functional.exp
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2020 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
exp(y)
megengine.functional.exp
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0/effnet_b0.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1/effnet_b1.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2/effnet_b2.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3/effnet_b3.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4/effnet_b4.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b5/effnet_b5.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b6/effnet_b6.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b7/effnet_b7.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b8/effnet_b8.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_l2/effnet_l2.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b0_lite/effnet_b0_lite.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b1_lite/effnet_b1_lite.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b2_lite/effnet_b2_lite.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b3_lite/effnet_b3_lite.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnet_b4_lite/effnet_b4_lite.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b0/effnetv2_b0.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b1/effnetv2_b1.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b2/effnetv2_b2.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_b3/effnetv2_b3.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_s/effnetv2_s.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_m/effnetv2_m.pkl" )
megengine.hub.pretrained
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # Copyright (c) 2020 <NAME> # This file has been modified by Megvii ("Megvii Modifications"). # All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved. """EfficientNet Series EfficientNet: `"EfficientNet: Rethinking...
hub.pretrained( "https://data.megengine.org.cn/research/basecls/models/effnet/effnetv2_l/effnetv2_l.pkl" )
megengine.hub.pretrained
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
fill_(x, 5.0)
megengine.module.init.fill_
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Linear(in_features=3, out_features=8)
megengine.module.Linear
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.weight)
megengine.module.init.calculate_fan_in_and_fan_out
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Conv1d(in_channels=2, out_channels=3, kernel_size=5)
megengine.module.Conv1d
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.weight)
megengine.module.init.calculate_fan_in_and_fan_out
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Conv2d(in_channels=2, out_channels=3, kernel_size=(5, 7))
megengine.module.Conv2d
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.weight)
megengine.module.init.calculate_fan_in_and_fan_out
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Conv2d(in_channels=2, out_channels=4, kernel_size=(5, 7), groups=2)
megengine.module.Conv2d
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.weight)
megengine.module.init.calculate_fan_in_and_fan_out
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
Conv3d(in_channels=2, out_channels=4, kernel_size=(5, 7, 9), groups=2)
megengine.module.Conv3d
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.weight)
megengine.module.init.calculate_fan_in_and_fan_out
# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTI...
calculate_fan_in_and_fan_out(l.bias)
megengine.module.init.calculate_fan_in_and_fan_out
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor(0)
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([1, 2])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([3, 4])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.mul(vara, varb)
megengine.functional.mul
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.relu(out)
megengine.functional.relu
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
GraphInference(modified_model)
megengine.utils.comp_graph_tools.GraphInference
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([1, 2])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([3, 4])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.sub(vara, varb)
megengine.functional.sub
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.relu(out1)
megengine.functional.relu
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
GraphInference(modified_model1)
megengine.utils.comp_graph_tools.GraphInference
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.ones((2,))
megengine.functional.ones
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
F.ones((2,))
megengine.functional.ones
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([1, 2])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([3, 4])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
GraphInference(modified_model)
megengine.utils.comp_graph_tools.GraphInference
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([1, 2])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Tensor([3, 4])
megengine.tensor.Tensor
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
trace(symbolic=True, capture_as_const=True)
megengine.jit.tracing.trace
import io import numpy as np import megengine.core.tensor.megbrain_graph as G import megengine.functional as F import megengine.module as M import megengine.utils.network_node as N from megengine.jit.tracing import trace from megengine.tensor import Tensor from megengine.utils.comp_graph_tools import GraphInference f...
Net.load(orig_model)
megengine.utils.network.Network.load