# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/dl/cdl63wpyh2qbdvol3rnwt477ib45bzxuxyqip4uo7tpclvu7kphq.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[2048, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1536
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 384
    y1 = (yindex // 384)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (384*x2) + (1572864*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/lz/clza44aawh56ovvj2bxwrrs5awyxtdsljsznobv25cfir7zmva5m.py
# Topologically Sorted Source Nodes: [cls_score], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   cls_score => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[8, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 2
    y1 = (yindex // 2)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (2*x2) + (8192*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2e/c2epl7psn644lunhwnw7qf56ft5crbfr7hpx3vuhs26gxjg6depu.py
# Topologically Sorted Source Nodes: [bbox_pred], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   bbox_pred => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 56
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 14
    y1 = (yindex // 14)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (14*x2) + (57344*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/pn/cpngdclw7lzjwmfou3d3kip6qoxuvwgrgxlt5gs2kd5udjsfz5ny.py
# Topologically Sorted Source Nodes: [dir_cls_preds], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   dir_cls_preds => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16, 4096], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16384*y1)), ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4096*y3)), tmp2, ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (2, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_2, (2, ), (1, ))
    assert_size_stride(primals_3, (4, 384, 64, 64), (1572864, 4096, 64, 1))
    assert_size_stride(primals_4, (14, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_5, (14, ), (1, ))
    assert_size_stride(primals_6, (4, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 384, 64, 64), (1572864, 1, 24576, 384), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_3, buf0, 1536, 4096, grid=grid(1536, 4096), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [cls_score], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 64, 64), (8192, 1, 128, 2))
        buf2 = empty_strided_cuda((4, 2, 64, 64), (8192, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cls_score], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf1, primals_2, buf2, 8, 4096, grid=grid(8, 4096), stream=stream0)
        del buf1
        del primals_2
        # Topologically Sorted Source Nodes: [bbox_pred], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 14, 64, 64), (57344, 1, 896, 14))
        buf4 = empty_strided_cuda((4, 14, 64, 64), (57344, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bbox_pred], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf3, primals_5, buf4, 56, 4096, grid=grid(56, 4096), stream=stream0)
        del buf3
        del primals_5
        # Topologically Sorted Source Nodes: [dir_cls_preds], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf0, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf6 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dir_cls_preds], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf5, primals_7, buf6, 16, 4096, grid=grid(16, 4096), stream=stream0)
        del buf5
        del primals_7
    return (buf2, buf4, buf6, primals_1, buf0, primals_4, primals_6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((2, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 384, 64, 64), (1572864, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((14, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((14, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 384, 1, 1), (384, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np
import torch.nn as nn
import torch.utils.dlpack


def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-06):
    """Calculate overlap between two set of bboxes.

    If ``is_aligned `` is ``False``, then calculate the overlaps between each
    bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
    pair of bboxes1 and bboxes2.

    Args:
        bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
        bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
            B indicates the batch dim, in shape (B1, B2, ..., Bn).
            If ``is_aligned `` is ``True``, then m and n must be equal.
        mode (str): "iou" (intersection over union) or "iof" (intersection over
            foreground).
        is_aligned (bool, optional): If True, then m and n must be equal.
            Default False.
        eps (float, optional): A value added to the denominator for numerical
            stability. Default 1e-6.

    Returns:
        Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)

    Example:
        >>> bboxes1 = torch.FloatTensor([
        >>>     [0, 0, 10, 10],
        >>>     [10, 10, 20, 20],
        >>>     [32, 32, 38, 42],
        >>> ])
        >>> bboxes2 = torch.FloatTensor([
        >>>     [0, 0, 10, 20],
        >>>     [0, 10, 10, 19],
        >>>     [10, 10, 20, 20],
        >>> ])
        >>> overlaps = bbox_overlaps(bboxes1, bboxes2)
        >>> assert overlaps.shape == (3, 3)
        >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True)
        >>> assert overlaps.shape == (3, )

    Example:
        >>> empty = torch.empty(0, 4)
        >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]])
        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
    """
    assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}'
    assert bboxes1.size(-1) == 4 or bboxes1.size(0) == 0
    assert bboxes2.size(-1) == 4 or bboxes2.size(0) == 0
    assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
    batch_shape = bboxes1.shape[:-2]
    rows = bboxes1.size(-2)
    cols = bboxes2.size(-2)
    if is_aligned:
        assert rows == cols
    if rows * cols == 0:
        if is_aligned:
            return bboxes1.new(batch_shape + (rows,))
        else:
            return bboxes1.new(batch_shape + (rows, cols))
    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] -
        bboxes1[..., 1])
    area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] -
        bboxes2[..., 1])
    if is_aligned:
        lt = torch.max(bboxes1[..., :2], bboxes2[..., :2])
        rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:])
        wh = (rb - lt).clamp(min=0)
        overlap = wh[..., 0] * wh[..., 1]
        if mode in ['iou', 'giou']:
            union = area1 + area2 - overlap
        else:
            union = area1
        if mode == 'giou':
            enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2])
            enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:])
    else:
        lt = torch.max(bboxes1[..., :, None, :2], bboxes2[..., None, :, :2])
        rb = torch.min(bboxes1[..., :, None, 2:], bboxes2[..., None, :, 2:])
        wh = (rb - lt).clamp(min=0)
        overlap = wh[..., 0] * wh[..., 1]
        if mode in ['iou', 'giou']:
            union = area1[..., None] + area2[..., None, :] - overlap
        else:
            union = area1[..., None]
        if mode == 'giou':
            enclosed_lt = torch.min(bboxes1[..., :, None, :2], bboxes2[...,
                None, :, :2])
            enclosed_rb = torch.max(bboxes1[..., :, None, 2:], bboxes2[...,
                None, :, 2:])
    eps = union.new_tensor([eps])
    union = torch.max(union, eps)
    ious = overlap / union
    if mode in ['iou', 'iof']:
        return ious
    enclose_wh = (enclosed_rb - enclosed_lt).clamp(min=0)
    enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
    enclose_area = torch.max(enclose_area, eps)
    gious = ious - (enclose_area - union) / enclose_area
    return gious


def box3d_to_bev(boxes3d):
    """Convert rotated 3d boxes in XYZWHDR format to BEV in XYWHR format.

    Args:
        boxes3d (torch.Tensor): Rotated boxes in XYZWHDR format.

    Returns:
        torch.Tensor: Converted BEV boxes in XYWHR format.
    """
    return boxes3d[:, [0, 1, 3, 4, 6]]


def limit_period(val, offset=0.5, period=np.pi):
    """Limit the value into a period for periodic function.

    Args:
        val (torch.Tensor): The value to be converted.
        offset (float, optional): Offset to set the value range.             Defaults to 0.5.
        period ([type], optional): Period of the value. Defaults to np.pi.

    Returns:
        torch.Tensor: Value in the range of             [-offset * period, (1-offset) * period]
    """
    return val - torch.floor(val / period + offset) * period


def box3d_to_bev2d(boxes3d):
    """Convert rotated 3d boxes in XYZWHDR format to neareset BEV without
    rotation.

    Args:
        boxes3d (torch.Tensor): Rotated boxes in XYZWHDR format.

    Returns:
        torch.Tensor: Converted BEV boxes in XYWH format.
    """
    bev_rotated_boxes = box3d_to_bev(boxes3d)
    rotations = bev_rotated_boxes[:, -1]
    normed_rotations = torch.abs(limit_period(rotations, 0.5, np.pi))
    conditions = (normed_rotations > np.pi / 4)[..., None]
    bboxes_xywh = torch.where(conditions, bev_rotated_boxes[:, [0, 1, 3, 2]
        ], bev_rotated_boxes[:, :4])
    centers = bboxes_xywh[:, :2]
    dims = bboxes_xywh[:, 2:]
    bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
    return bev_boxes


def xywhr_to_xyxyr(boxes_xywhr):
    """Convert rotated boxes in XYWHR format to XYXYR format.

    Args:
        boxes_xywhr (torch.Tensor): Rotated boxes in XYWHR format.

    Returns:
        torch.Tensor: Converted boxes in XYXYR format.
    """
    boxes = torch.zeros_like(boxes_xywhr)
    half_w = boxes_xywhr[:, 2] / 2
    half_h = boxes_xywhr[:, 3] / 2
    boxes[:, 0] = boxes_xywhr[:, 0] - half_w
    boxes[:, 1] = boxes_xywhr[:, 1] - half_h
    boxes[:, 2] = boxes_xywhr[:, 0] + half_w
    boxes[:, 3] = boxes_xywhr[:, 1] + half_h
    boxes[:, 4] = boxes_xywhr[:, 4]
    return boxes


def multiclass_nms(boxes, scores, score_thr):
    """Multi-class nms for 3D boxes.

    Args:
        boxes (torch.Tensor): Multi-level boxes with shape (N, M).
            M is the dimensions of boxes.
        scores (torch.Tensor): Multi-level boxes with shape
            (N, ). N is the number of boxes.
        score_thr (float): Score threshold to filter boxes with low
            confidence.

    Returns:
        list[torch.Tensor]: Return a list of indices after nms,
            with an entry for each class.
    """
    idxs = []
    for i in range(scores.shape[1]):
        cls_inds = scores[:, i] > score_thr
        if not cls_inds.any():
            idxs.append(torch.tensor([], dtype=torch.long, device=cls_inds.
                device))
            continue
        orig_idx = torch.arange(cls_inds.shape[0], device=cls_inds.device,
            dtype=torch.long)[cls_inds]
        _scores = scores[cls_inds, i]
        _boxes = boxes[cls_inds, :]
        _bev = xywhr_to_xyxyr(box3d_to_bev(_boxes))
        idx = nms(_bev, _scores, 0.01)
        idxs.append(orig_idx[idx])
    return idxs


class Anchor3DRangeGenerator(object):
    """3D Anchor Generator by range.

    This anchor generator generates anchors by the given range in different
    feature levels.

    Args:
        ranges (list[list[float]]): Ranges of different anchors.
            The ranges are the same across different feature levels. But may
            vary for different anchor sizes if size_per_range is True.
        sizes (list[list[float]]): 3D sizes of anchors.
        rotations (list[float]): Rotations of anchors in a feature grid.
    """

    def __init__(self, ranges, sizes=[[1.6, 3.9, 1.56]], rotations=[0, 
        1.5707963]):
        if len(sizes) != len(ranges):
            assert len(ranges) == 1
            ranges = ranges * len(sizes)
        assert len(ranges) == len(sizes)
        self.sizes = sizes
        self.ranges = ranges
        self.rotations = rotations

    @property
    def num_base_anchors(self):
        """list[int]: Total number of base anchors in a feature grid."""
        num_rot = len(self.rotations)
        num_size = torch.tensor(self.sizes).reshape(-1, 3).size(0)
        return num_rot * num_size

    def grid_anchors(self, featmap_size, device='cuda'):
        """Generate grid anchors of a single level feature map.

        This function is usually called by method ``self.grid_anchors``.

        Args:
            featmap_size (tuple[int]): Size of the feature map.
            device (str, optional): Device the tensor will be put on.
                Defaults to 'cuda'.

        Returns:
            torch.Tensor: Anchors in the overall feature map.
        """
        mr_anchors = []
        for anchor_range, anchor_size in zip(self.ranges, self.sizes):
            mr_anchors.append(self.anchors_single_range(featmap_size,
                anchor_range, anchor_size, self.rotations, device=device))
        mr_anchors = torch.cat(mr_anchors, dim=-3)
        return mr_anchors

    def anchors_single_range(self, feature_size, anchor_range, sizes=[[1.6,
        3.9, 1.56]], rotations=[0, 1.5707963], device='cuda'):
        """Generate anchors in a single range.

        Args:
            feature_size (list[float] | tuple[float]): Feature map size. It is
                either a list of a tuple of [D, H, W](in order of z, y, and x).
            anchor_range (torch.Tensor | list[float]): Range of anchors with
                shape [6]. The order is consistent with that of anchors, i.e.,
                (x_min, y_min, z_min, x_max, y_max, z_max).
            sizes (list[list] | np.ndarray | torch.Tensor): Anchor size with
                shape [N, 3], in order of x, y, z.
            rotations (list[float] | np.ndarray | torch.Tensor): Rotations of
                anchors in a single feature grid.
            device (str): Devices that the anchors will be put on.

        Returns:
            torch.Tensor: Anchors with shape                 [*feature_size, num_sizes, num_rots, 7].
        """
        if len(feature_size) == 2:
            feature_size = [1, feature_size[0], feature_size[1]]
        anchor_range = torch.tensor(anchor_range, device=device)
        z_centers = torch.linspace(anchor_range[2], anchor_range[5],
            feature_size[0], device=device)
        y_centers = torch.linspace(anchor_range[1], anchor_range[4],
            feature_size[1], device=device)
        x_centers = torch.linspace(anchor_range[0], anchor_range[3],
            feature_size[2], device=device)
        sizes = torch.tensor(sizes, device=device).reshape(-1, 3)
        rotations = torch.tensor(rotations, device=device)
        rets = torch.meshgrid(x_centers, y_centers, z_centers, rotations)
        rets = list(rets)
        for i in range(len(rets)):
            rets[i] = rets[i].unsqueeze(-2).unsqueeze(-1)
        sizes = sizes.reshape([1, 1, 1, 1, 1, 3])
        tile_size_shape = list(rets[0].shape)
        tile_size_shape[3] = 1
        sizes = sizes.repeat(tile_size_shape)
        rets.insert(3, sizes)
        ret = torch.cat(rets, dim=-1).permute([2, 1, 0, 3, 4, 5])
        return ret


class BBoxCoder(object):
    """Bbox Coder for 3D boxes.

    Args:
        code_size (int): The dimension of boxes to be encoded.
    """

    def __init__(self):
        super(BBoxCoder, self).__init__()

    @staticmethod
    def encode(src_boxes, dst_boxes):
        """Get box regression transformation deltas (dx, dy, dz, dw, dh, dl, dr,
        dv*) that can be used to transform the `src_boxes` into the
        `target_boxes`.

        Args:
            src_boxes (torch.Tensor): source boxes, e.g., object proposals.
            dst_boxes (torch.Tensor): target of the transformation, e.g.,
                ground-truth boxes.

        Returns:
            torch.Tensor: Box transformation deltas.
        """
        xa, ya, za, wa, la, ha, ra = torch.split(src_boxes, 1, dim=-1)
        xg, yg, zg, wg, lg, hg, rg = torch.split(dst_boxes, 1, dim=-1)
        za = za + ha / 2
        zg = zg + hg / 2
        diagonal = torch.sqrt(la ** 2 + wa ** 2)
        xt = (xg - xa) / diagonal
        yt = (yg - ya) / diagonal
        zt = (zg - za) / ha
        lt = torch.log(lg / la)
        wt = torch.log(wg / wa)
        ht = torch.log(hg / ha)
        rt = rg - ra
        return torch.cat([xt, yt, zt, wt, lt, ht, rt], dim=-1)

    @staticmethod
    def decode(anchors, deltas):
        """Apply transformation `deltas` (dx, dy, dz, dw, dh, dl, dr, dv*) to
        `boxes`.

        Args:
            anchors (torch.Tensor): Parameters of anchors with shape (N, 7).
            deltas (torch.Tensor): Encoded boxes with shape
                (N, 7+n) [x, y, z, w, l, h, r, velo*].

        Returns:
            torch.Tensor: Decoded boxes.
        """
        xa, ya, za, wa, la, ha, ra = torch.split(anchors, 1, dim=-1)
        xt, yt, zt, wt, lt, ht, rt = torch.split(deltas, 1, dim=-1)
        za = za + ha / 2
        diagonal = torch.sqrt(la ** 2 + wa ** 2)
        xg = xt * diagonal + xa
        yg = yt * diagonal + ya
        zg = zt * ha + za
        lg = torch.exp(lt) * la
        wg = torch.exp(wt) * wa
        hg = torch.exp(ht) * ha
        rg = rt + ra
        zg = zg - hg / 2
        return torch.cat([xg, yg, zg, wg, lg, hg, rg], dim=-1)


class Anchor3DHead(nn.Module):

    def __init__(self, num_classes=1, in_channels=384, feat_channels=384,
        nms_pre=100, score_thr=0.1, dir_offset=0, ranges=[[0, -40.0, -3, 
        70.0, 40.0, 1]], sizes=[[0.6, 1.0, 1.5]], rotations=[0, 1.57],
        iou_thr=[[0.35, 0.5]]):
        super().__init__()
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.feat_channels = feat_channels
        self.nms_pre = nms_pre
        self.score_thr = score_thr
        self.dir_offset = dir_offset
        self.iou_thr = iou_thr
        if len(self.iou_thr) != num_classes:
            assert len(self.iou_thr) == 1
            self.iou_thr = self.iou_thr * num_classes
        assert len(self.iou_thr) == num_classes
        self.anchor_generator = Anchor3DRangeGenerator(ranges=ranges, sizes
            =sizes, rotations=rotations)
        self.num_anchors = self.anchor_generator.num_base_anchors
        self.bbox_coder = BBoxCoder()
        self.box_code_size = 7
        self.fp16_enabled = False
        self.cls_out_channels = self.num_anchors * self.num_classes
        self.conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
        self.conv_reg = nn.Conv2d(self.feat_channels, self.num_anchors *
            self.box_code_size, 1)
        self.conv_dir_cls = nn.Conv2d(self.feat_channels, self.num_anchors *
            2, 1)
        self.init_weights()

    @staticmethod
    def bias_init_with_prob(prior_prob):
        """Initialize conv/fc bias value according to giving probablity."""
        bias_init = float(-np.log((1 - prior_prob) / prior_prob))
        return bias_init

    @staticmethod
    def normal_init(module, mean=0, std=1, bias=0):
        nn.init.normal_(module.weight, mean, std)
        if hasattr(module, 'bias') and module.bias is not None:
            nn.init.constant_(module.bias, bias)

    def init_weights(self):
        """Initialize the weights of head."""
        bias_cls = self.bias_init_with_prob(0.01)
        self.normal_init(self.conv_cls, std=0.01, bias=bias_cls)
        self.normal_init(self.conv_reg, std=0.01)

    def forward(self, x):
        """Forward function on a feature map.

        Args:
            x (torch.Tensor): Input features.

        Returns:
            tuple[torch.Tensor]: Contain score of each class, bbox                 regression and direction classification predictions.
        """
        cls_score = self.conv_cls(x)
        bbox_pred = self.conv_reg(x)
        dir_cls_preds = None
        dir_cls_preds = self.conv_dir_cls(x)
        return cls_score, bbox_pred, dir_cls_preds

    def assign_bboxes(self, pred_bboxes, target_bboxes):
        """Assigns target bboxes to given anchors.

        Args:
            pred_bboxes (torch.Tensor): Bbox predictions (anchors).
            target_bboxes (torch.Tensor): Bbox targets.

        Returns:
            torch.Tensor: Assigned target bboxes for each given anchor.
            torch.Tensor: Flat index of matched targets.
            torch.Tensor: Index of positive matches.
            torch.Tensor: Index of negative matches.
        """
        anchors = [self.anchor_generator.grid_anchors(pred_bboxes.shape[-2:
            ], device=pred_bboxes.device) for _ in range(len(target_bboxes))]
        anchors_cnt = torch.tensor(anchors[0].shape[:-1]).prod()
        rot_angles = anchors[0].shape[-2]
        assigned_bboxes, target_idxs, pos_idxs, neg_idxs = [], [], [], []

        def flatten_idx(idx, j):
            """Inject class dimension in the given indices (...

            z * rot_angles + x) --> (.. z * num_classes * rot_angles + j * rot_angles + x)
            """
            z = idx // rot_angles
            x = idx % rot_angles
            return z * self.num_classes * rot_angles + j * rot_angles + x
        idx_off = 0
        for i in range(len(target_bboxes)):
            for j, (neg_th, pos_th) in enumerate(self.iou_thr):
                anchors_stride = anchors[i][..., j, :, :].reshape(-1, self.
                    box_code_size)
                if target_bboxes[i].shape[0] == 0:
                    assigned_bboxes.append(torch.zeros((0, 7), device=
                        pred_bboxes.device))
                    target_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    pos_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    neg_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    continue
                overlaps = bbox_overlaps(box3d_to_bev2d(target_bboxes[i]),
                    box3d_to_bev2d(anchors_stride))
                max_overlaps, argmax_overlaps = overlaps.max(dim=0)
                gt_max_overlaps, _ = overlaps.max(dim=1)
                pos_idx = max_overlaps >= pos_th
                neg_idx = (max_overlaps >= 0) & (max_overlaps < neg_th)
                for k in range(len(target_bboxes[i])):
                    if gt_max_overlaps[k] >= neg_th:
                        pos_idx[overlaps[k, :] == gt_max_overlaps[k]] = True
                assigned_bboxes.append(self.bbox_coder.encode(
                    anchors_stride[pos_idx], target_bboxes[i][
                    argmax_overlaps[pos_idx]]))
                target_idxs.append(argmax_overlaps[pos_idx] + idx_off)
                pos_idx = flatten_idx(pos_idx.nonzero(as_tuple=False).
                    squeeze(-1), j) + i * anchors_cnt
                neg_idx = flatten_idx(neg_idx.nonzero(as_tuple=False).
                    squeeze(-1), j) + i * anchors_cnt
                pos_idxs.append(pos_idx)
                neg_idxs.append(neg_idx)
            idx_off += len(target_bboxes[i])
        return torch.cat(assigned_bboxes, axis=0), torch.cat(target_idxs,
            axis=0), torch.cat(pos_idxs, axis=0), torch.cat(neg_idxs, axis=0)

    def get_bboxes(self, cls_scores, bbox_preds, dir_preds):
        """Get bboxes of anchor head.

        Args:
            cls_scores (list[torch.Tensor]): Class scores.
            bbox_preds (list[torch.Tensor]): Bbox predictions.
            dir_cls_preds (list[torch.Tensor]): Direction
                class predictions.

        Returns:
            tuple[torch.Tensor]: Prediction results of batches
                (bboxes, scores, labels).
        """
        bboxes, scores, labels = [], [], []
        for cls_score, bbox_pred, dir_pred in zip(cls_scores, bbox_preds,
            dir_preds):
            b, s, l = self.get_bboxes_single(cls_score, bbox_pred, dir_pred)
            bboxes.append(b)
            scores.append(s)
            labels.append(l)
        return bboxes, scores, labels

    def get_bboxes_single(self, cls_scores, bbox_preds, dir_preds):
        """Get bboxes of anchor head.

        Args:
            cls_scores (list[torch.Tensor]): Class scores.
            bbox_preds (list[torch.Tensor]): Bbox predictions.
            dir_cls_preds (list[torch.Tensor]): Direction
                class predictions.

        Returns:
            tuple[torch.Tensor]: Prediction results of batches
                (bboxes, scores, labels).
        """
        assert cls_scores.size()[-2:] == bbox_preds.size()[-2:]
        assert cls_scores.size()[-2:] == dir_preds.size()[-2:]
        anchors = self.anchor_generator.grid_anchors(cls_scores.shape[-2:],
            device=cls_scores.device)
        anchors = anchors.reshape(-1, self.box_code_size)
        dir_preds = dir_preds.permute(1, 2, 0).reshape(-1, 2)
        dir_scores = torch.max(dir_preds, dim=-1)[1]
        cls_scores = cls_scores.permute(1, 2, 0).reshape(-1, self.num_classes)
        scores = cls_scores.sigmoid()
        bbox_preds = bbox_preds.permute(1, 2, 0).reshape(-1, self.box_code_size
            )
        if scores.shape[0] > self.nms_pre:
            max_scores, _ = scores.max(dim=1)
            _, topk_inds = max_scores.topk(self.nms_pre)
            anchors = anchors[topk_inds, :]
            bbox_preds = bbox_preds[topk_inds, :]
            scores = scores[topk_inds, :]
            dir_scores = dir_scores[topk_inds]
        bboxes = self.bbox_coder.decode(anchors, bbox_preds)
        idxs = multiclass_nms(bboxes, scores, self.score_thr)
        labels = [torch.full((len(idxs[i]),), i, dtype=torch.long) for i in
            range(self.num_classes)]
        labels = torch.cat(labels)
        scores = [scores[idxs[i], i] for i in range(self.num_classes)]
        scores = torch.cat(scores)
        idxs = torch.cat(idxs)
        bboxes = bboxes[idxs]
        dir_scores = dir_scores[idxs]
        if bboxes.shape[0] > 0:
            dir_rot = limit_period(bboxes[..., 6] - self.dir_offset, 1, np.pi)
            bboxes[..., 6] = dir_rot + self.dir_offset + np.pi * dir_scores
        return bboxes, scores, labels


def get_inputs():
    return [torch.rand([4, 384, 64, 64])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
import torch.utils.dlpack
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 1536
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 384
    y1 = yindex // 384
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 384 * x2 + 1572864 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 8
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 2
    y1 = yindex // 2
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 2 * x2 + 8192 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


@triton.jit
def triton_poi_fused_convolution_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 56
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 14
    y1 = yindex // 14
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 14 * x2 + 57344 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (2, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_2, (2,), (1,))
    assert_size_stride(primals_3, (4, 384, 64, 64), (1572864, 4096, 64, 1))
    assert_size_stride(primals_4, (14, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_5, (14,), (1,))
    assert_size_stride(primals_6, (4, 384, 1, 1), (384, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 384, 64, 64), (1572864, 1, 24576, 384
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1536, 4096)](primals_3, buf0, 1536, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 64, 64), (8192, 1, 128, 2))
        buf2 = empty_strided_cuda((4, 2, 64, 64), (8192, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_1[grid(8, 4096)](buf1, primals_2, buf2,
            8, 4096, XBLOCK=128, YBLOCK=8, num_warps=4, num_stages=1)
        del buf1
        del primals_2
        buf3 = extern_kernels.convolution(buf0, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 14, 64, 64), (57344, 1, 896, 14))
        buf4 = empty_strided_cuda((4, 14, 64, 64), (57344, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_2[grid(56, 4096)](buf3, primals_5,
            buf4, 56, 4096, XBLOCK=64, YBLOCK=64, num_warps=8, num_stages=1)
        del buf3
        del primals_5
        buf5 = extern_kernels.convolution(buf0, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf6 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_3[grid(16, 4096)](buf5, primals_7,
            buf6, 16, 4096, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf5
        del primals_7
    return buf2, buf4, buf6, primals_1, buf0, primals_4, primals_6


def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-06):
    """Calculate overlap between two set of bboxes.

    If ``is_aligned `` is ``False``, then calculate the overlaps between each
    bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
    pair of bboxes1 and bboxes2.

    Args:
        bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
        bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
            B indicates the batch dim, in shape (B1, B2, ..., Bn).
            If ``is_aligned `` is ``True``, then m and n must be equal.
        mode (str): "iou" (intersection over union) or "iof" (intersection over
            foreground).
        is_aligned (bool, optional): If True, then m and n must be equal.
            Default False.
        eps (float, optional): A value added to the denominator for numerical
            stability. Default 1e-6.

    Returns:
        Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)

    Example:
        >>> bboxes1 = torch.FloatTensor([
        >>>     [0, 0, 10, 10],
        >>>     [10, 10, 20, 20],
        >>>     [32, 32, 38, 42],
        >>> ])
        >>> bboxes2 = torch.FloatTensor([
        >>>     [0, 0, 10, 20],
        >>>     [0, 10, 10, 19],
        >>>     [10, 10, 20, 20],
        >>> ])
        >>> overlaps = bbox_overlaps(bboxes1, bboxes2)
        >>> assert overlaps.shape == (3, 3)
        >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True)
        >>> assert overlaps.shape == (3, )

    Example:
        >>> empty = torch.empty(0, 4)
        >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]])
        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
    """
    assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}'
    assert bboxes1.size(-1) == 4 or bboxes1.size(0) == 0
    assert bboxes2.size(-1) == 4 or bboxes2.size(0) == 0
    assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
    batch_shape = bboxes1.shape[:-2]
    rows = bboxes1.size(-2)
    cols = bboxes2.size(-2)
    if is_aligned:
        assert rows == cols
    if rows * cols == 0:
        if is_aligned:
            return bboxes1.new(batch_shape + (rows,))
        else:
            return bboxes1.new(batch_shape + (rows, cols))
    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] -
        bboxes1[..., 1])
    area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] -
        bboxes2[..., 1])
    if is_aligned:
        lt = torch.max(bboxes1[..., :2], bboxes2[..., :2])
        rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:])
        wh = (rb - lt).clamp(min=0)
        overlap = wh[..., 0] * wh[..., 1]
        if mode in ['iou', 'giou']:
            union = area1 + area2 - overlap
        else:
            union = area1
        if mode == 'giou':
            enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2])
            enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:])
    else:
        lt = torch.max(bboxes1[..., :, None, :2], bboxes2[..., None, :, :2])
        rb = torch.min(bboxes1[..., :, None, 2:], bboxes2[..., None, :, 2:])
        wh = (rb - lt).clamp(min=0)
        overlap = wh[..., 0] * wh[..., 1]
        if mode in ['iou', 'giou']:
            union = area1[..., None] + area2[..., None, :] - overlap
        else:
            union = area1[..., None]
        if mode == 'giou':
            enclosed_lt = torch.min(bboxes1[..., :, None, :2], bboxes2[...,
                None, :, :2])
            enclosed_rb = torch.max(bboxes1[..., :, None, 2:], bboxes2[...,
                None, :, 2:])
    eps = union.new_tensor([eps])
    union = torch.max(union, eps)
    ious = overlap / union
    if mode in ['iou', 'iof']:
        return ious
    enclose_wh = (enclosed_rb - enclosed_lt).clamp(min=0)
    enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
    enclose_area = torch.max(enclose_area, eps)
    gious = ious - (enclose_area - union) / enclose_area
    return gious


def box3d_to_bev(boxes3d):
    """Convert rotated 3d boxes in XYZWHDR format to BEV in XYWHR format.

    Args:
        boxes3d (torch.Tensor): Rotated boxes in XYZWHDR format.

    Returns:
        torch.Tensor: Converted BEV boxes in XYWHR format.
    """
    return boxes3d[:, [0, 1, 3, 4, 6]]


def limit_period(val, offset=0.5, period=np.pi):
    """Limit the value into a period for periodic function.

    Args:
        val (torch.Tensor): The value to be converted.
        offset (float, optional): Offset to set the value range.             Defaults to 0.5.
        period ([type], optional): Period of the value. Defaults to np.pi.

    Returns:
        torch.Tensor: Value in the range of             [-offset * period, (1-offset) * period]
    """
    return val - torch.floor(val / period + offset) * period


def box3d_to_bev2d(boxes3d):
    """Convert rotated 3d boxes in XYZWHDR format to neareset BEV without
    rotation.

    Args:
        boxes3d (torch.Tensor): Rotated boxes in XYZWHDR format.

    Returns:
        torch.Tensor: Converted BEV boxes in XYWH format.
    """
    bev_rotated_boxes = box3d_to_bev(boxes3d)
    rotations = bev_rotated_boxes[:, -1]
    normed_rotations = torch.abs(limit_period(rotations, 0.5, np.pi))
    conditions = (normed_rotations > np.pi / 4)[..., None]
    bboxes_xywh = torch.where(conditions, bev_rotated_boxes[:, [0, 1, 3, 2]
        ], bev_rotated_boxes[:, :4])
    centers = bboxes_xywh[:, :2]
    dims = bboxes_xywh[:, 2:]
    bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
    return bev_boxes


def xywhr_to_xyxyr(boxes_xywhr):
    """Convert rotated boxes in XYWHR format to XYXYR format.

    Args:
        boxes_xywhr (torch.Tensor): Rotated boxes in XYWHR format.

    Returns:
        torch.Tensor: Converted boxes in XYXYR format.
    """
    boxes = torch.zeros_like(boxes_xywhr)
    half_w = boxes_xywhr[:, 2] / 2
    half_h = boxes_xywhr[:, 3] / 2
    boxes[:, 0] = boxes_xywhr[:, 0] - half_w
    boxes[:, 1] = boxes_xywhr[:, 1] - half_h
    boxes[:, 2] = boxes_xywhr[:, 0] + half_w
    boxes[:, 3] = boxes_xywhr[:, 1] + half_h
    boxes[:, 4] = boxes_xywhr[:, 4]
    return boxes


def multiclass_nms(boxes, scores, score_thr):
    """Multi-class nms for 3D boxes.

    Args:
        boxes (torch.Tensor): Multi-level boxes with shape (N, M).
            M is the dimensions of boxes.
        scores (torch.Tensor): Multi-level boxes with shape
            (N, ). N is the number of boxes.
        score_thr (float): Score threshold to filter boxes with low
            confidence.

    Returns:
        list[torch.Tensor]: Return a list of indices after nms,
            with an entry for each class.
    """
    idxs = []
    for i in range(scores.shape[1]):
        cls_inds = scores[:, i] > score_thr
        if not cls_inds.any():
            idxs.append(torch.tensor([], dtype=torch.long, device=cls_inds.
                device))
            continue
        orig_idx = torch.arange(cls_inds.shape[0], device=cls_inds.device,
            dtype=torch.long)[cls_inds]
        _scores = scores[cls_inds, i]
        _boxes = boxes[cls_inds, :]
        _bev = xywhr_to_xyxyr(box3d_to_bev(_boxes))
        idx = nms(_bev, _scores, 0.01)
        idxs.append(orig_idx[idx])
    return idxs


class Anchor3DRangeGenerator(object):
    """3D Anchor Generator by range.

    This anchor generator generates anchors by the given range in different
    feature levels.

    Args:
        ranges (list[list[float]]): Ranges of different anchors.
            The ranges are the same across different feature levels. But may
            vary for different anchor sizes if size_per_range is True.
        sizes (list[list[float]]): 3D sizes of anchors.
        rotations (list[float]): Rotations of anchors in a feature grid.
    """

    def __init__(self, ranges, sizes=[[1.6, 3.9, 1.56]], rotations=[0, 
        1.5707963]):
        if len(sizes) != len(ranges):
            assert len(ranges) == 1
            ranges = ranges * len(sizes)
        assert len(ranges) == len(sizes)
        self.sizes = sizes
        self.ranges = ranges
        self.rotations = rotations

    @property
    def num_base_anchors(self):
        """list[int]: Total number of base anchors in a feature grid."""
        num_rot = len(self.rotations)
        num_size = torch.tensor(self.sizes).reshape(-1, 3).size(0)
        return num_rot * num_size

    def grid_anchors(self, featmap_size, device='cuda'):
        """Generate grid anchors of a single level feature map.

        This function is usually called by method ``self.grid_anchors``.

        Args:
            featmap_size (tuple[int]): Size of the feature map.
            device (str, optional): Device the tensor will be put on.
                Defaults to 'cuda'.

        Returns:
            torch.Tensor: Anchors in the overall feature map.
        """
        mr_anchors = []
        for anchor_range, anchor_size in zip(self.ranges, self.sizes):
            mr_anchors.append(self.anchors_single_range(featmap_size,
                anchor_range, anchor_size, self.rotations, device=device))
        mr_anchors = torch.cat(mr_anchors, dim=-3)
        return mr_anchors

    def anchors_single_range(self, feature_size, anchor_range, sizes=[[1.6,
        3.9, 1.56]], rotations=[0, 1.5707963], device='cuda'):
        """Generate anchors in a single range.

        Args:
            feature_size (list[float] | tuple[float]): Feature map size. It is
                either a list of a tuple of [D, H, W](in order of z, y, and x).
            anchor_range (torch.Tensor | list[float]): Range of anchors with
                shape [6]. The order is consistent with that of anchors, i.e.,
                (x_min, y_min, z_min, x_max, y_max, z_max).
            sizes (list[list] | np.ndarray | torch.Tensor): Anchor size with
                shape [N, 3], in order of x, y, z.
            rotations (list[float] | np.ndarray | torch.Tensor): Rotations of
                anchors in a single feature grid.
            device (str): Devices that the anchors will be put on.

        Returns:
            torch.Tensor: Anchors with shape                 [*feature_size, num_sizes, num_rots, 7].
        """
        if len(feature_size) == 2:
            feature_size = [1, feature_size[0], feature_size[1]]
        anchor_range = torch.tensor(anchor_range, device=device)
        z_centers = torch.linspace(anchor_range[2], anchor_range[5],
            feature_size[0], device=device)
        y_centers = torch.linspace(anchor_range[1], anchor_range[4],
            feature_size[1], device=device)
        x_centers = torch.linspace(anchor_range[0], anchor_range[3],
            feature_size[2], device=device)
        sizes = torch.tensor(sizes, device=device).reshape(-1, 3)
        rotations = torch.tensor(rotations, device=device)
        rets = torch.meshgrid(x_centers, y_centers, z_centers, rotations)
        rets = list(rets)
        for i in range(len(rets)):
            rets[i] = rets[i].unsqueeze(-2).unsqueeze(-1)
        sizes = sizes.reshape([1, 1, 1, 1, 1, 3])
        tile_size_shape = list(rets[0].shape)
        tile_size_shape[3] = 1
        sizes = sizes.repeat(tile_size_shape)
        rets.insert(3, sizes)
        ret = torch.cat(rets, dim=-1).permute([2, 1, 0, 3, 4, 5])
        return ret


class BBoxCoder(object):
    """Bbox Coder for 3D boxes.

    Args:
        code_size (int): The dimension of boxes to be encoded.
    """

    def __init__(self):
        super(BBoxCoder, self).__init__()

    @staticmethod
    def encode(src_boxes, dst_boxes):
        """Get box regression transformation deltas (dx, dy, dz, dw, dh, dl, dr,
        dv*) that can be used to transform the `src_boxes` into the
        `target_boxes`.

        Args:
            src_boxes (torch.Tensor): source boxes, e.g., object proposals.
            dst_boxes (torch.Tensor): target of the transformation, e.g.,
                ground-truth boxes.

        Returns:
            torch.Tensor: Box transformation deltas.
        """
        xa, ya, za, wa, la, ha, ra = torch.split(src_boxes, 1, dim=-1)
        xg, yg, zg, wg, lg, hg, rg = torch.split(dst_boxes, 1, dim=-1)
        za = za + ha / 2
        zg = zg + hg / 2
        diagonal = torch.sqrt(la ** 2 + wa ** 2)
        xt = (xg - xa) / diagonal
        yt = (yg - ya) / diagonal
        zt = (zg - za) / ha
        lt = torch.log(lg / la)
        wt = torch.log(wg / wa)
        ht = torch.log(hg / ha)
        rt = rg - ra
        return torch.cat([xt, yt, zt, wt, lt, ht, rt], dim=-1)

    @staticmethod
    def decode(anchors, deltas):
        """Apply transformation `deltas` (dx, dy, dz, dw, dh, dl, dr, dv*) to
        `boxes`.

        Args:
            anchors (torch.Tensor): Parameters of anchors with shape (N, 7).
            deltas (torch.Tensor): Encoded boxes with shape
                (N, 7+n) [x, y, z, w, l, h, r, velo*].

        Returns:
            torch.Tensor: Decoded boxes.
        """
        xa, ya, za, wa, la, ha, ra = torch.split(anchors, 1, dim=-1)
        xt, yt, zt, wt, lt, ht, rt = torch.split(deltas, 1, dim=-1)
        za = za + ha / 2
        diagonal = torch.sqrt(la ** 2 + wa ** 2)
        xg = xt * diagonal + xa
        yg = yt * diagonal + ya
        zg = zt * ha + za
        lg = torch.exp(lt) * la
        wg = torch.exp(wt) * wa
        hg = torch.exp(ht) * ha
        rg = rt + ra
        zg = zg - hg / 2
        return torch.cat([xg, yg, zg, wg, lg, hg, rg], dim=-1)


class Anchor3DHeadNew(nn.Module):

    def __init__(self, num_classes=1, in_channels=384, feat_channels=384,
        nms_pre=100, score_thr=0.1, dir_offset=0, ranges=[[0, -40.0, -3, 
        70.0, 40.0, 1]], sizes=[[0.6, 1.0, 1.5]], rotations=[0, 1.57],
        iou_thr=[[0.35, 0.5]]):
        super().__init__()
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.feat_channels = feat_channels
        self.nms_pre = nms_pre
        self.score_thr = score_thr
        self.dir_offset = dir_offset
        self.iou_thr = iou_thr
        if len(self.iou_thr) != num_classes:
            assert len(self.iou_thr) == 1
            self.iou_thr = self.iou_thr * num_classes
        assert len(self.iou_thr) == num_classes
        self.anchor_generator = Anchor3DRangeGenerator(ranges=ranges, sizes
            =sizes, rotations=rotations)
        self.num_anchors = self.anchor_generator.num_base_anchors
        self.bbox_coder = BBoxCoder()
        self.box_code_size = 7
        self.fp16_enabled = False
        self.cls_out_channels = self.num_anchors * self.num_classes
        self.conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
        self.conv_reg = nn.Conv2d(self.feat_channels, self.num_anchors *
            self.box_code_size, 1)
        self.conv_dir_cls = nn.Conv2d(self.feat_channels, self.num_anchors *
            2, 1)
        self.init_weights()

    @staticmethod
    def bias_init_with_prob(prior_prob):
        """Initialize conv/fc bias value according to giving probablity."""
        bias_init = float(-np.log((1 - prior_prob) / prior_prob))
        return bias_init

    @staticmethod
    def normal_init(module, mean=0, std=1, bias=0):
        nn.init.normal_(module.weight, mean, std)
        if hasattr(module, 'bias') and module.bias is not None:
            nn.init.constant_(module.bias, bias)

    def init_weights(self):
        """Initialize the weights of head."""
        bias_cls = self.bias_init_with_prob(0.01)
        self.normal_init(self.conv_cls, std=0.01, bias=bias_cls)
        self.normal_init(self.conv_reg, std=0.01)

    def assign_bboxes(self, pred_bboxes, target_bboxes):
        """Assigns target bboxes to given anchors.

        Args:
            pred_bboxes (torch.Tensor): Bbox predictions (anchors).
            target_bboxes (torch.Tensor): Bbox targets.

        Returns:
            torch.Tensor: Assigned target bboxes for each given anchor.
            torch.Tensor: Flat index of matched targets.
            torch.Tensor: Index of positive matches.
            torch.Tensor: Index of negative matches.
        """
        anchors = [self.anchor_generator.grid_anchors(pred_bboxes.shape[-2:
            ], device=pred_bboxes.device) for _ in range(len(target_bboxes))]
        anchors_cnt = torch.tensor(anchors[0].shape[:-1]).prod()
        rot_angles = anchors[0].shape[-2]
        assigned_bboxes, target_idxs, pos_idxs, neg_idxs = [], [], [], []

        def flatten_idx(idx, j):
            """Inject class dimension in the given indices (...

            z * rot_angles + x) --> (.. z * num_classes * rot_angles + j * rot_angles + x)
            """
            z = idx // rot_angles
            x = idx % rot_angles
            return z * self.num_classes * rot_angles + j * rot_angles + x
        idx_off = 0
        for i in range(len(target_bboxes)):
            for j, (neg_th, pos_th) in enumerate(self.iou_thr):
                anchors_stride = anchors[i][..., j, :, :].reshape(-1, self.
                    box_code_size)
                if target_bboxes[i].shape[0] == 0:
                    assigned_bboxes.append(torch.zeros((0, 7), device=
                        pred_bboxes.device))
                    target_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    pos_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    neg_idxs.append(torch.zeros((0,), dtype=torch.long,
                        device=pred_bboxes.device))
                    continue
                overlaps = bbox_overlaps(box3d_to_bev2d(target_bboxes[i]),
                    box3d_to_bev2d(anchors_stride))
                max_overlaps, argmax_overlaps = overlaps.max(dim=0)
                gt_max_overlaps, _ = overlaps.max(dim=1)
                pos_idx = max_overlaps >= pos_th
                neg_idx = (max_overlaps >= 0) & (max_overlaps < neg_th)
                for k in range(len(target_bboxes[i])):
                    if gt_max_overlaps[k] >= neg_th:
                        pos_idx[overlaps[k, :] == gt_max_overlaps[k]] = True
                assigned_bboxes.append(self.bbox_coder.encode(
                    anchors_stride[pos_idx], target_bboxes[i][
                    argmax_overlaps[pos_idx]]))
                target_idxs.append(argmax_overlaps[pos_idx] + idx_off)
                pos_idx = flatten_idx(pos_idx.nonzero(as_tuple=False).
                    squeeze(-1), j) + i * anchors_cnt
                neg_idx = flatten_idx(neg_idx.nonzero(as_tuple=False).
                    squeeze(-1), j) + i * anchors_cnt
                pos_idxs.append(pos_idx)
                neg_idxs.append(neg_idx)
            idx_off += len(target_bboxes[i])
        return torch.cat(assigned_bboxes, axis=0), torch.cat(target_idxs,
            axis=0), torch.cat(pos_idxs, axis=0), torch.cat(neg_idxs, axis=0)

    def get_bboxes(self, cls_scores, bbox_preds, dir_preds):
        """Get bboxes of anchor head.

        Args:
            cls_scores (list[torch.Tensor]): Class scores.
            bbox_preds (list[torch.Tensor]): Bbox predictions.
            dir_cls_preds (list[torch.Tensor]): Direction
                class predictions.

        Returns:
            tuple[torch.Tensor]: Prediction results of batches
                (bboxes, scores, labels).
        """
        bboxes, scores, labels = [], [], []
        for cls_score, bbox_pred, dir_pred in zip(cls_scores, bbox_preds,
            dir_preds):
            b, s, l = self.get_bboxes_single(cls_score, bbox_pred, dir_pred)
            bboxes.append(b)
            scores.append(s)
            labels.append(l)
        return bboxes, scores, labels

    def get_bboxes_single(self, cls_scores, bbox_preds, dir_preds):
        """Get bboxes of anchor head.

        Args:
            cls_scores (list[torch.Tensor]): Class scores.
            bbox_preds (list[torch.Tensor]): Bbox predictions.
            dir_cls_preds (list[torch.Tensor]): Direction
                class predictions.

        Returns:
            tuple[torch.Tensor]: Prediction results of batches
                (bboxes, scores, labels).
        """
        assert cls_scores.size()[-2:] == bbox_preds.size()[-2:]
        assert cls_scores.size()[-2:] == dir_preds.size()[-2:]
        anchors = self.anchor_generator.grid_anchors(cls_scores.shape[-2:],
            device=cls_scores.device)
        anchors = anchors.reshape(-1, self.box_code_size)
        dir_preds = dir_preds.permute(1, 2, 0).reshape(-1, 2)
        dir_scores = torch.max(dir_preds, dim=-1)[1]
        cls_scores = cls_scores.permute(1, 2, 0).reshape(-1, self.num_classes)
        scores = cls_scores.sigmoid()
        bbox_preds = bbox_preds.permute(1, 2, 0).reshape(-1, self.box_code_size
            )
        if scores.shape[0] > self.nms_pre:
            max_scores, _ = scores.max(dim=1)
            _, topk_inds = max_scores.topk(self.nms_pre)
            anchors = anchors[topk_inds, :]
            bbox_preds = bbox_preds[topk_inds, :]
            scores = scores[topk_inds, :]
            dir_scores = dir_scores[topk_inds]
        bboxes = self.bbox_coder.decode(anchors, bbox_preds)
        idxs = multiclass_nms(bboxes, scores, self.score_thr)
        labels = [torch.full((len(idxs[i]),), i, dtype=torch.long) for i in
            range(self.num_classes)]
        labels = torch.cat(labels)
        scores = [scores[idxs[i], i] for i in range(self.num_classes)]
        scores = torch.cat(scores)
        idxs = torch.cat(idxs)
        bboxes = bboxes[idxs]
        dir_scores = dir_scores[idxs]
        if bboxes.shape[0] > 0:
            dir_rot = limit_period(bboxes[..., 6] - self.dir_offset, 1, np.pi)
            bboxes[..., 6] = dir_rot + self.dir_offset + np.pi * dir_scores
        return bboxes, scores, labels

    def forward(self, input_0):
        primals_1 = self.conv_cls.weight
        primals_2 = self.conv_cls.bias
        primals_4 = self.conv_reg.weight
        primals_5 = self.conv_reg.bias
        primals_6 = self.conv_dir_cls.weight
        primals_7 = self.conv_dir_cls.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1], output[2]
