prompt stringlengths 94 42.6k | completion stringlengths 6 120 | api stringlengths 14 68 |
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# -*- 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... | tensor(data) | megengine.tensor |
# -*- 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... | tensor(data) | megengine.tensor |
# -*- 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... | tensor(data1) | megengine.tensor |
# -*- 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... | tensor(data2) | megengine.tensor |
# -*- 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... | tensor(data1) | megengine.tensor |
# -*- 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... | tensor(data2) | megengine.tensor |
# -*- 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... | tensor(data1) | megengine.tensor |
# -*- 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... | tensor(data2) | megengine.tensor |
# -*- 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... | jit.trace(symbolic=is_symbolic) | megengine.jit.trace |
# -*- 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... | tensor(data) | megengine.tensor |
# -*- 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... | jit.trace(symbolic=symbolic) | megengine.jit.trace |
# -*- 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... | tensor(input, dtype=dtype) | 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... | mge.get_logger(__name__) | megengine.get_logger |
# -*- 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... | jit.trace(symbolic=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... | data.dataset.ImageNet(args.data, train=False) | megengine.data.dataset.ImageNet |
# -*- 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... | mge.get_device_count("gpu") | megengine.get_device_count |
# -*- 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... | mge.set_default_device("cpux") | megengine.set_default_device |
# -*- 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... | Q.quantize_qat(model, Q.ema_fakequant_qconfig) | megengine.quantization.quantize_qat |
# -*- 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... | mge.load(args.checkpoint) | megengine.load |
# -*- 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... | Q.quantize(model) | megengine.quantization.quantize |
# -*- 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.cross_entropy_with_softmax(logits, label, label_smooth=0.1) | megengine.functional.cross_entropy_with_softmax |
# -*- 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.accuracy(logits, label, (1, 5)) | megengine.functional.accuracy |
# -*- 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... | dist.is_distributed() | megengine.distributed.is_distributed |
# -*- 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... | dist.all_reduce_sum(loss, "valid_loss") | megengine.distributed.all_reduce_sum |
# -*- 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... | dist.get_world_size() | megengine.distributed.get_world_size |
# -*- 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... | dist.all_reduce_sum(acc1, "valid_acc1") | megengine.distributed.all_reduce_sum |
# -*- 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... | dist.get_world_size() | megengine.distributed.get_world_size |
# -*- 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... | dist.all_reduce_sum(acc5, "valid_acc5") | megengine.distributed.all_reduce_sum |
# -*- 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... | dist.get_world_size() | megengine.distributed.get_world_size |
# -*- 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... | dist.get_rank() | megengine.distributed.get_rank |
# -*- 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... | T.Resize(256) | megengine.data.transform.Resize |
# -*- 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... | T.CenterCrop(224) | megengine.data.transform.CenterCrop |
# -*- 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... | T.Normalize(mean=128) | megengine.data.transform.Normalize |
# -*- 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... | T.ToMode("CHW") | megengine.data.transform.ToMode |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import argparse
import importlib
import json
import multiprocessing as mp
import os
import pathlib
import sys
import megengine as mge
import megengine.distributed as dist
from basecore.config import ConfigDict
from loguru import logger
... | mge.device.get_device_count("gpu") | megengine.device.get_device_count |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import argparse
import importlib
import json
import multiprocessing as mp
import os
import pathlib
import sys
import megengine as mge
import megengine.distributed as dist
from basecore.config import ConfigDict
from loguru import logger
... | mge.functional.debug_param.set_execution_strategy("PROFILE") | megengine.functional.debug_param.set_execution_strategy |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import argparse
import importlib
import json
import multiprocessing as mp
import os
import pathlib
import sys
import megengine as mge
import megengine.distributed as dist
from basecore.config import ConfigDict
from loguru import logger
... | dist.launcher(worker) | megengine.distributed.launcher |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import argparse
import importlib
import json
import multiprocessing as mp
import os
import pathlib
import sys
import megengine as mge
import megengine.distributed as dist
from basecore.config import ConfigDict
from loguru import logger
... | dist.get_rank() | megengine.distributed.get_rank |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | RandomSampler(train_ds, batch_size=params.train_batch_size, drop_last=True) | megengine.data.sampler.RandomSampler |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | DataLoader(train_ds, train_sampler, num_workers=params.num_workers) | megengine.data.DataLoader |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | dist.get_rank() | megengine.distributed.get_rank |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | dist.get_rank() | megengine.distributed.get_rank |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | SequentialSampler(val_ds, batch_size=params.eval_batch_size) | megengine.data.sampler.SequentialSampler |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | DataLoader(val_ds, val_sampler, num_workers=params.num_workers) | megengine.data.DataLoader |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | SequentialSampler(test_ds, batch_size=params.eval_batch_size) | megengine.data.sampler.SequentialSampler |
import logging
import os
import pickle
import numpy as np
import h5py
from megengine.data import DataLoader
from megengine.data.dataset import Dataset
from megengine.data.sampler import RandomSampler, SequentialSampler
import megengine.distributed as dist
from dataset.transformations import fetch_transform
from comm... | DataLoader(test_ds, test_sampler, num_workers=params.num_workers) | megengine.data.DataLoader |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(256*7*7, 1024) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1024, 1024) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.ReLU() | megengine.module.ReLU |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1024, 5 * self.n) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1024, 5 * self.n) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.concat([fc3, pred_boxes], axis=1) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.flatten(poo5, start_axis=1) | megengine.functional.flatten |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.stack([loss0, loss1], axis=1) | megengine.functional.stack |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.max_pool2d(results[0], kernel_size=1, stride=2, padding=0) | megengine.functional.max_pool2d |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1054, 1024) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1024, 5 * self.n) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.Linear(1024, 5 * self.n) | megengine.module.Linear |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.normal_(l.weight, std=0.01) | megengine.module.init.normal_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.fill_(l.bias, 0) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.softmax(scores, axis=1) | megengine.functional.softmax |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.concat([offsets, cls_scores], axis=2) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.softmax(cls_scores, axis=1) | megengine.functional.softmax |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.msra_normal_(lateral_conv.weight, mode="fan_in") | megengine.module.init.msra_normal_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.msra_normal_(output_conv.weight, mode="fan_in") | megengine.module.init.msra_normal_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | mge.tensor(mean) | megengine.tensor |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | mge.tensor(std) | megengine.tensor |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.normal_(l.weight, std=0.01) | megengine.module.init.normal_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.fill_(l.bias, 0) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.stack([a, b], axis=1) | megengine.functional.stack |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.stack([a, b], axis=1) | megengine.functional.stack |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.fill_(lateral_conv.bias, 0) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | M.init.fill_(output_conv.bias, 0) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.expand_dims(pred_boxes, axis=1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.expand_dims(fc2, axis=1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.expand_dims(cls_prob, axis=2) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
from config import config
from backbone.resnet50 import ResNet50
from module.rpn import RPN
from layers.roi_pool import roi_pool
from det_opr.bbox_opr import bbox_transform_inv_opr, restore_bbox
from det_opr.fpn_roi... | F.expand_dims(rcnn_rois[:, 1:5], axis=1) | megengine.functional.expand_dims |
# -*- 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... | AvgPool2d(kernel_size, stride=stride, padding=padding, mode="average") | megengine.module.AvgPool2d |
# -*- 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... | tensor(inp) | 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... | assertTensorClose(a, b) | megengine.test.assertTensorClose |
# -*- 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(data, device="xpux", dtype=np.int32) | megengine.core.tensor |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | M.Linear(feature_dim, num_class, bias=False) | megengine.module.Linear |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.normalize(self.weight, axis=1) | megengine.functional.normalize |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.one_hot(target, self.num_class) | megengine.functional.one_hot |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.loss.cross_entropy(logits, target) | megengine.functional.loss.cross_entropy |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.topk_accuracy(origin_logits, target, topk=1) | megengine.functional.topk_accuracy |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.where(origin_logits >= 0, large_margined_logit, small_margined_logit) | megengine.functional.where |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.where(one_hot_target, margined_logit, origin_logits) | megengine.functional.where |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.loss.cross_entropy(logits, target) | megengine.functional.loss.cross_entropy |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.topk_accuracy(origin_logits, target, topk=1) | megengine.functional.topk_accuracy |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.one_hot(target, self.num_class) | megengine.functional.one_hot |
# Copyright (c) Megvii, Inc. and its affiliates.
import math
import megengine.functional as F
import megengine.module as M
class LogitsFullyConnected(M.Module):
"""single fully connected layer, mapping embedding to logits with normalized weight
"""
def __init__(self, num_class, feature_dim):
su... | F.acos(origin_logits) | megengine.functional.acos |
# -*- 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... | mge.tensor(x, dtype="float32") | 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... | mge.tensor(s, dtype="float32") | 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... | mge.tensor(g_y, dtype="float32") | 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... | tqt_forward(-127, 127, x, s) | megengine.quantization.utils.tqt_forward |
# -*- 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([1.0], dtype=np.float32) | 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([4.0], dtype=np.float32) | 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(inp_data, dtype=np.float32) | 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(inp_data, dtype=np.float32) | megengine.tensor |
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