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HELP！many issuea，but i flow your tips #37

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Aurora-Rong opened this issue Oct 7, 2021 · 1 comment
Open

HELP！many issuea，but i flow your tips #37

Aurora-Rong opened this issue Oct 7, 2021 · 1 comment

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@Aurora-Rong
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$ bash scripts/run_detnas_coco_fpn_300M_search.sh

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

Traceback (most recent call last):
File "tools/train_net.py", line 19, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .faster_rcnn import *
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
_compile_and_register_class(obj, _rcb, qualified_name)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

Traceback (most recent call last):
File "tools/train_net.py", line 19, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .faster_rcnn import *
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
_compile_and_register_class(obj, _rcb, qualified_name)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

Traceback (most recent call last):
File "tools/train_net.py", line 19, in 

from maskrcnn_benchmark.data import make_data_loader

File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .faster_rcnn import *
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
Traceback (most recent call last):
File "tools/train_net.py", line 19, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
_compile_and_register_class(obj, _rcb, qualified_name)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .faster_rcnn import *
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
return torch.jit.script(fn, _rcb=rcb)
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

_compile_and_register_class(obj, _rcb, qualified_name)

File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

Traceback (most recent call last):
File "tools/train_net.py", line 19, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .faster_rcnn import *
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
Traceback (most recent call last):
Traceback (most recent call last):
File "tools/train_net.py", line 19, in
_compile_and_register_class(obj, _rcb, qualified_name)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
Traceback (most recent call last):
File "tools/train_net.py", line 19, in
File "tools/train_net.py", line 19, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from maskrcnn_benchmark.data import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/init.py", line 2, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from .build import make_data_loader
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
_jit_script_class_compile(qualified_name, ast, rcb)
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
from .coco import COCODataset
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
from .build import make_data_loader
import torchvision
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/build.py", line 11, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
import torchvision
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from torchvision import models
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
from . import datasets as D
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/init.py", line 3, in
from torchvision import models
from . import detection
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
from .coco import COCODataset
from .faster_rcnn import *from . import detection

File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
File "/home/mist/DetNAS-master/maskrcnn_benchmark/data/datasets/coco.py", line 3, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
from .faster_rcnn import * File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in 

import torchvision  File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in <module>

File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/init.py", line 3, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetworkfrom torchvision import models

File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/init.py", line 12, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
class BalancedPositiveNegativeSampler(object):
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
from . import detectionfrom . import _utils as det_utils

File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/init.py", line 1, in
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
class BalancedPositiveNegativeSampler(object):
from .faster_rcnn import * File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script

File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py", line 13, in
from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/rpn.py", line 11, in
from . import _utils as det_utils
File "/mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 19, in
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError_compile_and_register_class(obj, _rcb, qualified_name):

builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class

class BalancedPositiveNegativeSampler(object):

File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1219, in script
_compile_and_register_class(obj, _rcb, qualified_name)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
_compile_and_register_class(obj, _rcb, qualified_name)return torch.jit.script(fn, _rcb=rcb)
_jit_script_class_compile(qualified_name, ast, rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn

File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1076, in _compile_and_register_class
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError: _jit_script_class_compile(qualified_name, ast, rcb)
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/_recursive.py", line 222, in try_compile_fn
return torch.jit.script(fn, _rcb=rcb)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/jit/init.py", line 1226, in script
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))

RuntimeError:
builtin cannot be used as a value:
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:14:56
def zeros_like(tensor, dtype):
# type: (Tensor, int) -> Tensor
return torch.zeros_like(tensor, dtype=dtype, layout=tensor.layout,
~~~~~~~~~~~~~ <--- HERE
device=tensor.device, pin_memory=tensor.is_pinned())
'zeros_like' is being compiled since it was called from 'torch.torchvision.models.detection._utils.BalancedPositiveNegativeSampler.call'
at /mistgpu/miniconda/lib/python3.7/site-packages/torchvision/models/detection/_utils.py:72:12

        # randomly select positive and negative examples
        perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
        perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg]

        pos_idx_per_image = positive[perm1]
        neg_idx_per_image = negative[perm2]

        # create binary mask from indices
        pos_idx_per_image_mask = zeros_like(
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...  <--- HERE
            matched_idxs_per_image, dtype=torch.uint8
        )
        neg_idx_per_image_mask = zeros_like(
            matched_idxs_per_image, dtype=torch.uint8
        )

        pos_idx_per_image_mask[pos_idx_per_image] = torch.tensor(1, dtype=torch.uint8)
        neg_idx_per_image_mask[neg_idx_per_image] = torch.tensor(1, dtype=torch.uint8)

Traceback (most recent call last):
File "/mistgpu/miniconda/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/mistgpu/miniconda/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/distributed/launch.py", line 253, in
main()
File "/mistgpu/miniconda/lib/python3.7/site-packages/torch/distributed/launch.py", line 249, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/mistgpu/miniconda/bin/python3', '-u', 'tools/train_net.py', '--local_rank=7', '--config-file', 'configs/e2e_faster_rcnn_DETNAS_COCO_FPN_300M_search.yaml', 'OUTPUT_DIR', 'models/DETNAS_COCO_FPN_300M_1x_search']' returned non-zero exit status 1.
@yukang2017
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Sorry for the late reply. I thinks this issue might comes from the installation. Would you please show your torch and torchvision version?

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@yukang2017 @Aurora-Rong