# AOT ID: ['0_inference']
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_4/inductor_cache/zo/czo4qx55eft4wejbquo4junlsy5kxbkxixlmevzpz7lnkhcjvbvn.py
# Topologically Sorted Source Nodes: [Dilate_y_pred, Dilate_y_true, sub, MissImg, sum_1, sub_1, RedunImg, sum_2, add, sum_3, Loss], Original ATen: [aten.max_pool2d_with_indices, aten.sub, aten.clamp, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   Dilate_y_pred => _low_memory_max_pool2d_with_offsets
#   Dilate_y_true => _low_memory_max_pool2d_with_offsets_1
#   Loss => div
#   MissImg => clamp_max, clamp_min
#   RedunImg => clamp_max_1, clamp_min_1
#   add => add
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [3, 3], [1, 1], [1, 1], [1, 1], False), kwargs = {})
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg1_1, [3, 3], [1, 1], [1, 1], [1, 1], False), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %getitem), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%clamp_max,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %getitem_2), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 0), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%clamp_max_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%arg1_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %sum_3), kwargs = {})
triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 20, 'num_reduction': 3, '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}
)
@triton.jit
def triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = (rindex // 4) % 4
    r0 = rindex % 4
    r3 = rindex
    tmp69 = tl.load(in_ptr1 + (r3), None)
    tmp81 = tl.load(in_ptr0 + (r3), None)
    tmp0 = (-1) + r1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + r0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (tl.broadcast_to((-5) + r3, [RBLOCK])), tmp10, other=float("-inf"))
    tmp12 = r0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (tl.broadcast_to((-4) + r3, [RBLOCK])), tmp16, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + r0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (tl.broadcast_to((-3) + r3, [RBLOCK])), tmp23, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = r1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (tl.broadcast_to((-1) + r3, [RBLOCK])), tmp30, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (tl.broadcast_to(r3, [RBLOCK])), tmp33, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (tl.broadcast_to(1 + r3, [RBLOCK])), tmp36, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + r1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (tl.broadcast_to(3 + r3, [RBLOCK])), tmp43, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (tl.broadcast_to(4 + r3, [RBLOCK])), tmp46, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (tl.broadcast_to(5 + r3, [RBLOCK])), tmp49, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tl.load(in_ptr1 + (tl.broadcast_to((-5) + r3, [RBLOCK])), tmp10, other=float("-inf"))
    tmp53 = tl.load(in_ptr1 + (tl.broadcast_to((-4) + r3, [RBLOCK])), tmp16, other=float("-inf"))
    tmp54 = triton_helpers.maximum(tmp53, tmp52)
    tmp55 = tl.load(in_ptr1 + (tl.broadcast_to((-3) + r3, [RBLOCK])), tmp23, other=float("-inf"))
    tmp56 = triton_helpers.maximum(tmp55, tmp54)
    tmp57 = tl.load(in_ptr1 + (tl.broadcast_to((-1) + r3, [RBLOCK])), tmp30, other=float("-inf"))
    tmp58 = triton_helpers.maximum(tmp57, tmp56)
    tmp59 = tl.load(in_ptr1 + (tl.broadcast_to(r3, [RBLOCK])), tmp33, other=float("-inf"))
    tmp60 = triton_helpers.maximum(tmp59, tmp58)
    tmp61 = tl.load(in_ptr1 + (tl.broadcast_to(1 + r3, [RBLOCK])), tmp36, other=float("-inf"))
    tmp62 = triton_helpers.maximum(tmp61, tmp60)
    tmp63 = tl.load(in_ptr1 + (tl.broadcast_to(3 + r3, [RBLOCK])), tmp43, other=float("-inf"))
    tmp64 = triton_helpers.maximum(tmp63, tmp62)
    tmp65 = tl.load(in_ptr1 + (tl.broadcast_to(4 + r3, [RBLOCK])), tmp46, other=float("-inf"))
    tmp66 = triton_helpers.maximum(tmp65, tmp64)
    tmp67 = tl.load(in_ptr1 + (tl.broadcast_to(5 + r3, [RBLOCK])), tmp49, other=float("-inf"))
    tmp68 = triton_helpers.maximum(tmp67, tmp66)
    tmp70 = tmp69 - tmp51
    tmp71 = 0.0
    tmp72 = triton_helpers.maximum(tmp70, tmp71)
    tmp73 = 1.0
    tmp74 = triton_helpers.minimum(tmp72, tmp73)
    tmp75 = tl.broadcast_to(tmp74, [RBLOCK])
    tmp77 = triton_helpers.promote_to_tensor(tl.sum(tmp75, 0))
    tmp78 = tl.broadcast_to(tmp69, [RBLOCK])
    tmp80 = triton_helpers.promote_to_tensor(tl.sum(tmp78, 0))
    tmp82 = tmp81 - tmp68
    tmp83 = triton_helpers.maximum(tmp82, tmp71)
    tmp84 = triton_helpers.minimum(tmp83, tmp73)
    tmp85 = tl.broadcast_to(tmp84, [RBLOCK])
    tmp87 = triton_helpers.promote_to_tensor(tl.sum(tmp85, 0))
    tmp88 = tmp77 + tmp87
    tmp89 = tmp88 / tmp80
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp89, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [Dilate_y_pred, Dilate_y_true, sub, MissImg, sum_1, sub_1, RedunImg, sum_2, add, sum_3, Loss], Original ATen: [aten.max_pool2d_with_indices, aten.sub, aten.clamp, aten.sum, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0.run(buf5, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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.nn.functional as F


class DilateContourLoss(nn.Module):

    def __init__(self):
        super(DilateContourLoss, self).__init__()
        self.kernel = np.ones((3, 3), np.uint8)

    def forward(self, y_pred, y_true):
        assert y_pred.size() == y_true.size()
        Dilate_y_pred = F.max_pool2d(y_pred, kernel_size=3, stride=1, padding=1
            )
        MissImg = torch.clamp(y_true - Dilate_y_pred, 0, 1)
        Dilate_y_true = F.max_pool2d(y_true, kernel_size=3, stride=1, padding=1
            )
        RedunImg = torch.clamp(y_pred - Dilate_y_true, 0, 1)
        Loss = (MissImg.sum() + RedunImg.sum()) / y_true.sum()
        return Loss


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


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

import torch
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
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex // 4 % 4
    r0 = rindex % 4
    r3 = rindex
    tmp69 = tl.load(in_ptr1 + r3, None)
    tmp81 = tl.load(in_ptr0 + r3, None)
    tmp0 = -1 + r1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + r0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + tl.broadcast_to(-5 + r3, [RBLOCK]), tmp10,
        other=float('-inf'))
    tmp12 = r0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + tl.broadcast_to(-4 + r3, [RBLOCK]), tmp16,
        other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + r0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + tl.broadcast_to(-3 + r3, [RBLOCK]), tmp23,
        other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = r1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + tl.broadcast_to(-1 + r3, [RBLOCK]), tmp30,
        other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + tl.broadcast_to(r3, [RBLOCK]), tmp33, other=
        float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + tl.broadcast_to(1 + r3, [RBLOCK]), tmp36,
        other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + r1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + tl.broadcast_to(3 + r3, [RBLOCK]), tmp43,
        other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + tl.broadcast_to(4 + r3, [RBLOCK]), tmp46,
        other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + tl.broadcast_to(5 + r3, [RBLOCK]), tmp49,
        other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tl.load(in_ptr1 + tl.broadcast_to(-5 + r3, [RBLOCK]), tmp10,
        other=float('-inf'))
    tmp53 = tl.load(in_ptr1 + tl.broadcast_to(-4 + r3, [RBLOCK]), tmp16,
        other=float('-inf'))
    tmp54 = triton_helpers.maximum(tmp53, tmp52)
    tmp55 = tl.load(in_ptr1 + tl.broadcast_to(-3 + r3, [RBLOCK]), tmp23,
        other=float('-inf'))
    tmp56 = triton_helpers.maximum(tmp55, tmp54)
    tmp57 = tl.load(in_ptr1 + tl.broadcast_to(-1 + r3, [RBLOCK]), tmp30,
        other=float('-inf'))
    tmp58 = triton_helpers.maximum(tmp57, tmp56)
    tmp59 = tl.load(in_ptr1 + tl.broadcast_to(r3, [RBLOCK]), tmp33, other=
        float('-inf'))
    tmp60 = triton_helpers.maximum(tmp59, tmp58)
    tmp61 = tl.load(in_ptr1 + tl.broadcast_to(1 + r3, [RBLOCK]), tmp36,
        other=float('-inf'))
    tmp62 = triton_helpers.maximum(tmp61, tmp60)
    tmp63 = tl.load(in_ptr1 + tl.broadcast_to(3 + r3, [RBLOCK]), tmp43,
        other=float('-inf'))
    tmp64 = triton_helpers.maximum(tmp63, tmp62)
    tmp65 = tl.load(in_ptr1 + tl.broadcast_to(4 + r3, [RBLOCK]), tmp46,
        other=float('-inf'))
    tmp66 = triton_helpers.maximum(tmp65, tmp64)
    tmp67 = tl.load(in_ptr1 + tl.broadcast_to(5 + r3, [RBLOCK]), tmp49,
        other=float('-inf'))
    tmp68 = triton_helpers.maximum(tmp67, tmp66)
    tmp70 = tmp69 - tmp51
    tmp71 = 0.0
    tmp72 = triton_helpers.maximum(tmp70, tmp71)
    tmp73 = 1.0
    tmp74 = triton_helpers.minimum(tmp72, tmp73)
    tmp75 = tl.broadcast_to(tmp74, [RBLOCK])
    tmp77 = triton_helpers.promote_to_tensor(tl.sum(tmp75, 0))
    tmp78 = tl.broadcast_to(tmp69, [RBLOCK])
    tmp80 = triton_helpers.promote_to_tensor(tl.sum(tmp78, 0))
    tmp82 = tmp81 - tmp68
    tmp83 = triton_helpers.maximum(tmp82, tmp71)
    tmp84 = triton_helpers.minimum(tmp83, tmp73)
    tmp85 = tl.broadcast_to(tmp84, [RBLOCK])
    tmp87 = triton_helpers.promote_to_tensor(tl.sum(tmp85, 0))
    tmp88 = tmp77 + tmp87
    tmp89 = tmp88 / tmp80
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp89, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_clamp_div_max_pool2d_with_indices_sub_sum_0[grid
            (1)](buf5, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf5,


class DilateContourLossNew(nn.Module):

    def __init__(self):
        super(DilateContourLossNew, self).__init__()
        self.kernel = np.ones((3, 3), np.uint8)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
