prompt stringlengths 94 42.6k | completion stringlengths 6 120 | api stringlengths 14 68 |
|---|---|---|
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
... | get_device_count_by_fork("gpu") | megengine.distributed.helper.get_device_count_by_fork |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
... | dist.get_rank() | megengine.distributed.get_rank |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
... | SyncExponentialMovingAverageObserver(momentum=t) | megengine.quantization.observer.SyncExponentialMovingAverageObserver |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
... | get_device_count_by_fork("gpu") | megengine.distributed.helper.get_device_count_by_fork |
import platform
import numpy as np
import pytest
import megengine as mge
import megengine.distributed as dist
from megengine.distributed.helper import get_device_count_by_fork
from megengine.quantization.observer import (
ExponentialMovingAverageObserver,
MinMaxObserver,
Observer,
PassiveObserver,
... | mge.tensor(2.0) | megengine.tensor |
import random
from megengine.data.transform import RandomResizedCrop as mge_RRC
from megengine.data.transform import Resize as mge_resize
from ..registry import PIPELINES
from edit.utils import interp_codes
@PIPELINES.register_module()
class Resize(object):
"""
Args:
size (int|list|tuple): Desired ... | mge_resize(output_size=self.size, interpolation=interp_codes[interpolation]) | megengine.data.transform.Resize |
import random
from megengine.data.transform import RandomResizedCrop as mge_RRC
from megengine.data.transform import Resize as mge_resize
from ..registry import PIPELINES
from edit.utils import interp_codes
@PIPELINES.register_module()
class Resize(object):
"""
Args:
size (int|list|tuple): Desired ... | mge_RRC(output_size=output_size, scale_range=scale, ratio_range=ratio, interpolation=interp_codes[interpolation]) | megengine.data.transform.RandomResizedCrop |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import bisect
import datetime
import math
import os
import pickle
import time
from typing import Optional
import megengine as mge
import megengine.distributed as dist
import megengine.module as M
from basecore.config import ConfigDict
fr... | dist.get_world_size() | megengine.distributed.get_world_size |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import bisect
import datetime
import math
import os
import pickle
import time
from typing import Optional
import megengine as mge
import megengine.distributed as dist
import megengine.module as M
from basecore.config import ConfigDict
fr... | dist.get_rank() | megengine.distributed.get_rank |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import bisect
import datetime
import math
import os
import pickle
import time
from typing import Optional
import megengine as mge
import megengine.distributed as dist
import megengine.module as M
from basecore.config import ConfigDict
fr... | dist.get_rank() | megengine.distributed.get_rank |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.stack([broad_shift_x, broad_shift_y, broad_shift_x, broad_shift_y], axis=1) | megengine.functional.stack |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.concat([rpn_bbox, rpn_cls_scores, rpn_iou_prob], axis=1) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.concat(res, 0) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(labels, axis=2) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Sequential(*cls_subnet) | megengine.module.Sequential |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Sequential(*bbox_subnet) | megengine.module.Sequential |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.init.fill_(self.cls_score.bias, bias_value) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Conv2d(fpn_dim, fpn_dim, kernel_size=3, stride=2, padding=1, bias=use_bias) | megengine.module.Conv2d |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Conv2d(fpn_dim, fpn_dim, kernel_size=3, stride=2, padding=1, bias=use_bias) | megengine.module.Conv2d |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.ReLU() | megengine.module.ReLU |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(anchors, axis=0) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(shifts, axis=1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.concat(rpn_cls_list, axis=1) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.concat(rpn_bbox_list,axis=1) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.concat(rpn_iou_list, axis=1) | megengine.functional.concat |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(ignore_mask, axis=0) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.nn.indexing_one_hot(overlaps, index, 1) | megengine.functional.nn.indexing_one_hot |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.sigmoid(cls_score) | megengine.functional.sigmoid |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | 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
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | 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
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | mge.tensor(np_anchors) | megengine.tensor |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | mge.tensor(mean) | megengine.tensor |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | mge.tensor(std) | megengine.tensor |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(value, axis=1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) | megengine.module.Conv2d |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.ReLU() | megengine.module.ReLU |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) | megengine.module.Conv2d |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.ReLU() | megengine.module.ReLU |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | 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
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | 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
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.linspace(0, width-1, width) | megengine.functional.linspace |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.linspace(0, height -1, height) | megengine.functional.linspace |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(all_anchors, 1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.init.normal_(layer.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
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | M.init.fill_(layer.bias, 0) | megengine.module.init.fill_ |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.expand_dims(all_anchors, 1) | megengine.functional.expand_dims |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.equal(gtboxes[:, 4], config.anchor_ignore_label) | megengine.functional.equal |
import numpy as np
import megengine as mge
import megengine.functional as F
import megengine.module as M
import math
from config import config
from backbone.resnet50 import ResNet50
from module.generate_anchors import generate_anchors
from det_opr.bbox_opr import bbox_transform_inv_opr, box_overlap_opr
from det_opr.uti... | F.ones([a.shape[0], 1]) | megengine.functional.ones |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.zeros((2*B, netG.hidden_channels, h, w)) | megengine.functional.zeros |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.zeros((2*B, netG.hidden_channels, h, w)) | megengine.functional.zeros |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | dist.is_distributed() | megengine.distributed.is_distributed |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.concat([image[:, i, ...], image[:, T-i-1, ...]], axis=0) | megengine.functional.concat |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | mge.tensor(batchdata['lq'], dtype="float32") | megengine.tensor |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | mge.tensor(batchdata['gt'], dtype="float32") | megengine.tensor |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.nn.interpolate(image, scale_factor=4) | megengine.functional.nn.interpolate |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.concat([image[:, i, ...], image[:, T-i-1, ...]], axis=0) | megengine.functional.concat |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.concat([image[:, i-1, ...], image[:, T-i, ...]], axis=0) | megengine.functional.concat |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.concat([image[:, i-1, ...], image[:, T-i, ...]], axis=0) | megengine.functional.concat |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | dist.functional.all_reduce_sum(loss) | megengine.distributed.functional.all_reduce_sum |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | dist.get_world_size() | megengine.distributed.get_world_size |
import os
import time
import numpy as np
import megengine.distributed as dist
import megengine as mge
import megengine.functional as F
from megengine.autodiff import GradManager
from edit.core.hook.evaluation import psnr, ssim
from edit.utils import imwrite, tensor2img, bgr2ycbcr, img_multi_padding, img_de_multi_paddin... | F.expand_dims(inp, axis=0) | megengine.functional.expand_dims |
# -*- 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.mean(x, [2, 3], True) | megengine.functional.mean |
# -*- 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.interpolate(gp, (x.shape[2], x.shape[3])) | megengine.functional.nn.interpolate |
# -*- 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.concat([conv1, conv31, conv32, conv33, gp], axis=1) | megengine.functional.concat |
# -*- 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... | M.Dropout(0.5) | megengine.module.Dropout |
# -*- 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... | M.Conv2d(256, self.num_classes, 1, 1, padding=0) | megengine.module.Conv2d |
# -*- 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.interpolate(up0, scale_factor=self.sub_output_stride) | megengine.functional.nn.interpolate |
# -*- 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.concat([up0, up1], 1) | megengine.functional.concat |
# -*- 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.interpolate(out, scale_factor=4) | megengine.functional.nn.interpolate |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Conv2d(in_channels, out_channels, 1, 1, 0, bias=False) | megengine.module.Conv2d |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Conv2d(out_channels * 5, out_channels, 1, 1, padding=0, bias=False) | megengine.module.Conv2d |
# -*- 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... | M.BatchNorm2d(out_channels) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Conv2d(256, 48, 1, 1, padding=1 // 2, bias=False) | megengine.module.Conv2d |
# -*- 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... | M.BatchNorm2d(48) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Conv2d(256 + 48, 256, 3, 1, padding=1, bias=False) | megengine.module.Conv2d |
# -*- 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... | M.BatchNorm2d(256) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Dropout(0.5) | megengine.module.Dropout |
# -*- 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... | M.Conv2d(256, 256, 3, 1, padding=1, bias=False) | megengine.module.Conv2d |
# -*- 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... | M.BatchNorm2d(256) | megengine.module.BatchNorm2d |
# -*- 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... | M.ReLU() | megengine.module.ReLU |
# -*- 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... | M.Dropout(0.1) | megengine.module.Dropout |
# -*- 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... | M.init.msra_normal_(m.weight, mode="fan_out", nonlinearity="relu") | megengine.module.init.msra_normal_ |
# -*- 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... | M.init.ones_(m.weight) | megengine.module.init.ones_ |
# -*- 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... | M.init.zeros_(m.bias) | megengine.module.init.zeros_ |
import math
import megengine as mge
import megengine.functional as F
import numpy as np
from megengine import Tensor
import pdb
def restore_bbox(rois, deltas, unnormalize=True, config = None):
assert deltas.ndim == 3
if unnormalize:
std_opr = mge.tensor(config.bbox_normalize_stds.reshape(1, 1, -1))
... | F.minimum(dw, max_delta) | megengine.functional.minimum |
import math
import megengine as mge
import megengine.functional as F
import numpy as np
from megengine import Tensor
import pdb
def restore_bbox(rois, deltas, unnormalize=True, config = None):
assert deltas.ndim == 3
if unnormalize:
std_opr = mge.tensor(config.bbox_normalize_stds.reshape(1, 1, -1))
... | F.minimum(dh, max_delta) | megengine.functional.minimum |
import math
import megengine as mge
import megengine.functional as F
import numpy as np
from megengine import Tensor
import pdb
def restore_bbox(rois, deltas, unnormalize=True, config = None):
assert deltas.ndim == 3
if unnormalize:
std_opr = mge.tensor(config.bbox_normalize_stds.reshape(1, 1, -1))
... | F.log(gt_width / bbox_width) | megengine.functional.log |
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