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# -*- 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... | set_conv_execution_strategy("PROFILE") | megengine.functional.debug_param.set_conv_execution_strategy |
# -*- 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.init_process_group(args.server, args.port, num_gpu, device, device) | megengine.distributed.init_process_group |
# -*- 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) | 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... | get_default_graph() | megengine.core.graph.get_default_graph |
# 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 ARRANTIES OR CONDITIONS OF ANY ... | mgb.dtype.get_scale(inp.dtype) | megengine._internal.dtype.get_scale |
# 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 ARRANTIES OR CONDITIONS OF ANY ... | mgb.dtype.get_scale(self.weight.dtype) | megengine._internal.dtype.get_scale |
# 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 ARRANTIES OR CONDITIONS OF ANY ... | mgb.dtype.qint32(inp_scale * w_scale) | megengine._internal.dtype.qint32 |
import os
import math
import argparse
from multiprocessing import Process, Queue
from tqdm import tqdm
import numpy as np
import megengine as mge
from megengine import jit
from config import config
import network
import dataset
import misc_utils
if_set_nms = True
def eval_all(args):
# model_path
saveDir = c... | jit.trace(symbolic=False) | megengine.jit.trace |
import os
import math
import argparse
from multiprocessing import Process, Queue
from tqdm import tqdm
import numpy as np
import megengine as mge
from megengine import jit
from config import config
import network
import dataset
import misc_utils
if_set_nms = True
def eval_all(args):
# model_path
saveDir = c... | mge.load(model_file) | megengine.load |
import megengine as mge
import megengine.module as M
import pytest
from basecls.models.snet import SNV2Block, SNV2XceptionBlock
@pytest.mark.parametrize("w_in", [32, 48])
@pytest.mark.parametrize("w_out", [64])
@pytest.mark.parametrize("w_mid", [32, 24])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parame... | mge.random.normal(size=(2, w_in * 2 // stride, 8, 8)) | megengine.random.normal |
import megengine as mge
import megengine.module as M
import pytest
from basecls.models.snet import SNV2Block, SNV2XceptionBlock
@pytest.mark.parametrize("w_in", [32, 48])
@pytest.mark.parametrize("w_out", [64])
@pytest.mark.parametrize("w_mid", [32, 24])
@pytest.mark.parametrize("stride", [1, 2])
@pytest.mark.parame... | mge.random.normal(size=(2, w_in * 2 // stride, 8, 8)) | megengine.random.normal |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(x) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(y) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(x) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(y) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(x) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(oy) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(x) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(y) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(x) | megengine.Tensor |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine as mge
import megengine.module as M
import numpy as np
import pytest
import torch
import torch.nn as nn
from basecls.configs import BaseConfig
from basecls.layers import BinaryCrossEntropy, CrossEntropy, build_loss
@py... | mge.Tensor(oy) | megengine.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | tensor(x, device=device) | megengine.tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | cgtools.GraphInference(file) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | VarNode(x.var) | megengine.utils.network_node.VarNode |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | trace(symbolic=True, capture_as_const=True) | megengine.jit.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | tensor(r) | megengine.tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine import tensor
from megengine.jit import trace
from megengine.utils.network_node import VarNode
def _default_compare_fn(x, y):
if isinstance(x, np.ndarray):
np.t... | trace(symbolic=symbolic) | megengine.jit.trace |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.ConvRelu2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size//2), groups=in_channels) | megengine.module.ConvRelu2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0) | megengine.module.Conv2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.ConvRelu2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0) | megengine.module.ConvRelu2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size//2), groups=out_channels) | megengine.module.Conv2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.ConvRelu2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size//2)) | megengine.module.ConvRelu2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size//2)) | megengine.module.Conv2d |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Sequential(*layers) | megengine.module.Sequential |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Sequential(*layers) | megengine.module.Sequential |
import numpy as np
import megengine
import megengine.module as M
import megengine.functional as F
from edit.models.common import ShuffleV2Block, CoordAtt
import math
from . import default_init_weights
class MobileNeXt(M.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
... | M.Sequential(*layers) | megengine.module.Sequential |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat(batch_proposals_list, axis=0) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat(batch_probs_list, axis=0) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.argsort(batch_probs, descending=True) | megengine.functional.argsort |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat([batch_proposals, batch_probs], axis=1) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat([batch_inds, batch_rois[:, :-1]], axis=1) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat(return_rois, axis=0) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.concat(return_probs, axis=0) | megengine.functional.concat |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | tensor(config.bbox_normalize_stds[None, :]) | megengine.core.tensor |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | tensor(config.bbox_normalize_means[None, :]) | megengine.core.tensor |
import megengine as mge
import megengine.functional as F
from megengine.core import tensor
from layers.nms import gpu_nms
from config import config
from det_opr.bbox_opr import bbox_transform_inv_opr, clip_boxes_opr, \
filter_boxes_opr
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,... | F.softmax(probs) | megengine.functional.softmax |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.stack([ptrx, ptry], axis=1) | megengine.functional.stack |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.concat([gt_boxes, dummy], axis=0) | megengine.functional.concat |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(anchor_centers, axis=1) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(gtboxes_centers, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.abs(an_centers - gt_centers) | megengine.functional.abs |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(valid_mask, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.concat(ious_list, axis=0) | megengine.functional.concat |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.std(ious, 0) | megengine.functional.std |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.maximum(iou_thresh_per_gt, 0.2) | megengine.functional.maximum |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.stack([l, r, t, b], axis=2) | megengine.functional.stack |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.argsort(ious, 1) | megengine.functional.argsort |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.gather(ious, 1, sorted_index) | megengine.functional.gather |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(anchor_centers, axis=1) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(gtboxes_centers, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.abs(an_centers - gt_centers) | megengine.functional.abs |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.concat(ious_list, axis=0) | megengine.functional.concat |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.stack([l, r, t, b], axis=2) | megengine.functional.stack |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.zeros(labels.shape[0]) | megengine.functional.zeros |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(valid_mask, axis=1) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.cond_take(all_anchors[:, 4] == l, all_anchors[:, 4]) | megengine.functional.cond_take |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.argsort(level_dist, descending=False) | megengine.functional.argsort |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(pos_area, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(valid_mask, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.ones(n) | megengine.functional.ones |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(anchors, 1) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(gtboxes, 0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.maximum(max_off[:, :, 2] - max_off[:, :, 0] + 1, 0) | megengine.functional.maximum |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.maximum(p_pred[:, :, 2] - p_pred[:, :, 0] + 1, 0) | megengine.functional.maximum |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(ignore_mask, 0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.ones(a_shp0) | megengine.functional.ones |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(iou_thresh_per_gt, axis=0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(rpn_labels, 0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(rpn_target_boxes, 0) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.maximum(p_pred[:,:, :2], p_gt[:,:,:2]) | megengine.functional.maximum |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.minimum(p_pred[:, :, 2:4], p_gt[:, :, 2:4]) | megengine.functional.minimum |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.ones([1, gt_boxes.shape[1]]) | megengine.functional.ones |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.pow(distance, 2) | megengine.functional.pow |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.gather(ious, 1, sorted_index[:, :n]) | megengine.functional.gather |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.ones(2 * n) | megengine.functional.ones |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(all_anchors, axis=1) | megengine.functional.expand_dims |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.equal(labels, -1) | megengine.functional.equal |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.pow(distance, 2) | megengine.functional.pow |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.equal(labels, 0) | megengine.functional.equal |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.equal(labels, -1) | megengine.functional.equal |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.equal(rpn_labels, 0) | megengine.functional.equal |
import os, sys
import numpy as np
from config import config
from det_opr.bbox_opr import box_overlap_opr, bbox_transform_opr
import megengine as mge
from megengine import functional as F
import pdb
def _compute_center(boxes):
ptrx = 0.5 * (boxes[:, 0] + boxes[:, 2])
ptry = 0.5 * (boxes[:, 1] + boxes[:, 3])
... | F.expand_dims(ignore_label < 0, 1) | megengine.functional.expand_dims |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | is_distributed() | megengine.distributed.group.is_distributed |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | is_distributed() | megengine.distributed.group.is_distributed |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | dist.get_rank() | megengine.distributed.get_rank |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | dist.get_world_size() | megengine.distributed.get_world_size |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | is_distributed() | megengine.distributed.group.is_distributed |
import os
import time
from megengine.distributed.group import is_distributed
import megengine.distributed as dist
from megengine.data.dataloader import DataLoader
from edit.core.hook import Hook
from edit.utils import to_list, is_list_of, get_logger, mkdir_or_exist
class EvalIterHook(Hook):
"""evaluation hook by ... | dist.group_barrier() | megengine.distributed.group_barrier |
# -*- 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... | F.sort(x) | megengine.functional.sort |
# -*- 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 |
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