# 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/4s/c4s63j6mvfuvext2t2cidkkt5ddb6evlex2lwuvnabga4rsw5ie6.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view_1, -1), kwargs = {})
triton_per_fused_max_0 = async_compile.triton('triton_per_fused_max_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    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_per_fused_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_max_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lf/clfo3mphlvnztdakpdf7ifdc4l6windpzh4ftdzoo5klhhwnwc2f.py
# Topologically Sorted Source Nodes: [summed, gt, targets_fc, neg_idx, pos_idx], Original ATen: [aten.sum, aten.gt, aten._to_copy, aten.lt]
# Source node to ATen node mapping:
#   gt => gt
#   neg_idx => lt
#   pos_idx => gt_1
#   summed => sum_1
#   targets_fc => convert_element_type
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view, [-1]), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
#   %convert_element_type : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%convert_element_type, 0.5), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convert_element_type, 0.5), kwargs = {})
triton_per_fused__to_copy_gt_lt_sum_1 = async_compile.triton('triton_per_fused__to_copy_gt_lt_sum_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*i1', 4: 'i32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_gt_lt_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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__to_copy_gt_lt_sum_1(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = tmp6.to(tl.float32)
    tmp8 = 0.5
    tmp9 = tmp7 < tmp8
    tmp10 = tmp7 > tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp7, xmask)
    tl.store(out_ptr0 + (x0), tmp9, xmask)
    tl.store(out_ptr1 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5y/c5ylsky5zklny7vsj4hyne4sx4rf64metmwxtz3o4gq72fz3bqbl.py
# Topologically Sorted Source Nodes: [n_pos], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   n_pos => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
triton_per_fused_sum_2 = async_compile.triton('triton_per_fused_sum_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_sum_2(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp3, 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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        stream0 = get_raw_stream(0)
        triton_per_fused_max_0.run(arg1_1, buf0, 16, 16, grid=grid(16), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [summed, gt, targets_fc, neg_idx, pos_idx], Original ATen: [aten.sum, aten.gt, aten._to_copy, aten.lt]
        triton_per_fused__to_copy_gt_lt_sum_1.run(buf3, arg0_1, buf4, buf6, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf5 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [n_pos], Original ATen: [aten.sum]
        triton_per_fused_sum_2.run(buf3, buf5, 1, 16, grid=grid(1), stream=stream0)
    return (buf0, buf4, buf3, buf5, buf6, )


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 torch.nn as nn


class SoftDiceLoss(nn.Module):
    """Differentiable soft dice loss.

    Note: Sigmoid is automatically applied here!
    """

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

    def forward(self, logits, targets):
        eps = 1e-09
        num = targets.size(0)
        probs = torch.sigmoid(logits)
        m1 = probs.view(num, -1)
        m2 = targets.view(num, -1).float()
        intersection = torch.sum(m1 * m2, 1)
        union = torch.sum(m1, dim=1) + torch.sum(m2, dim=1)
        score = (2 * intersection + eps) / (union + eps)
        score = (1 - score).mean()
        return score


class MultiLabelDiceLoss(nn.Module):
    """The average dice across multiple classes.

    Note: Sigmoid is automatically applied here!
    """

    def __init__(self):
        super(MultiLabelDiceLoss, self).__init__()
        self.dice_loss = SoftDiceLoss()

    def forward(self, logits, targets):
        loss = 0
        num_classes = targets.size(1)
        for class_nr in range(num_classes):
            loss += self.dice_loss(logits[:, class_nr, :, :], targets[:,
                class_nr, :, :])
        return loss / num_classes


class ComboLoss(nn.Module):
    """Weighted classification and segmentation loss.

    Attributes:
        weights (list):
        activation:
        bce: with logits loss
        dice_loss: soft dice loss (all classes)

    """

    def __init__(self, weights=[0.1, 0, 1], activation=None):
        """
        Args:
            weights (list): [image_cls, pixel_seg, pixel_cls]
            activation: One of ['sigmoid', None]
        """
        super(ComboLoss, self).__init__()
        self.weights = weights
        self.activation = activation
        assert self.activation in ['sigmoid', None
            ], "`activation` must be one of ['sigmoid', None]."
        self.bce = nn.BCEWithLogitsLoss(reduce=True)
        self.dice_loss = MultiLabelDiceLoss()

    def create_fc_tensors(self, logits, targets):
        """Creates the classification tensors from the segmentation ones.
        """
        batch_size, num_classes, _, _ = targets.shape
        summed = targets.view(batch_size, num_classes, -1).sum(-1)
        targets_fc = (summed > 0).float()
        logits_fc = logits.view(batch_size, num_classes, -1)
        logits_fc = torch.max(logits_fc, -1)[0]
        return logits_fc, targets_fc

    def forward(self, logits, targets):
        logits_fc, targets_fc = self.create_fc_tensors(logits, targets)
        p = torch.sigmoid(logits) if self.activation == 'sigmoid' else logits
        if self.weights[0]:
            loss_fc = self.weights[0] * self.bce(logits_fc, targets_fc)
        else:
            loss_fc = torch.tensor(0)
        if self.weights[1] or self.weights[2]:
            loss_seg_dice = self.weights[1] * self.dice_loss(p, targets)
            loss_seg_bce = self.weights[2] * self.bce(logits, targets)
        else:
            loss_seg_dice = torch.tensor(0)
            loss_seg_bce = torch.tensor(0)
        loss = loss_fc + loss_seg_bce + loss_seg_dice
        return loss


class ComboLossOnlyPos(ComboLoss):
    """Weighted classification and segmentation loss. (Considers only positive)

    This loss is only calculated on labels with ROIs in them to maximize
    the foreground class prediction capability.

    Attributes:
        weights (list):
        activation:
        bce: with logits loss
        dice_loss: soft dice loss (per class)

    """

    def __init__(self, weights=[0.1, 0, 1], activation=None):
        """
        Args:
            weights (list): [image_cls, pixel_seg, pixel_cls]
            activation: One of ['sigmoid', None]
        """
        super().__init__(weights=weights, activation=activation)
        self.dice_loss = SoftDiceLoss()

    def forward(self, logits, targets):
        logits_fc, targets_fc = self.create_fc_tensors(logits, targets)
        n_pos = targets_fc.sum()
        pos_idx = targets_fc > 0.5
        neg_idx = targets_fc < 0.5
        if self.weights[0]:
            loss_fc = self.weights[0] * self.bce(logits_fc[neg_idx],
                targets_fc[neg_idx])
        else:
            loss_fc = torch.tensor(0)
        if self.weights[1] or self.weights[2]:
            logits_pos, targets_pos = logits[pos_idx], targets[pos_idx]
            if n_pos == 0:
                loss_seg_dice = torch.tensor(0)
            else:
                loss_seg_dice = self.weights[1] * self.dice_loss(logits_pos,
                    targets_pos)
            loss_seg_bce = self.weights[2] * self.bce(logits_pos, targets_pos)
        else:
            loss_seg_dice = torch.tensor(0)
            loss_seg_bce = torch.tensor(0)
        loss = loss_fc + loss_seg_bce + loss_seg_dice
        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 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_max_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused__to_copy_gt_lt_sum_1(in_out_ptr0, in_ptr0, out_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = tmp6.to(tl.float32)
    tmp8 = 0.5
    tmp9 = tmp7 < tmp8
    tmp10 = tmp7 > tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr0 + x0, tmp9, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)


@triton.jit
def triton_per_fused_sum_2(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, 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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_max_0[grid(16)](arg1_1, buf0, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_per_fused__to_copy_gt_lt_sum_1[grid(16)](buf3, arg0_1, buf4,
            buf6, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf5 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_sum_2[grid(1)](buf3, buf5, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
    return buf0, buf4, buf3, buf5, buf6


class SoftDiceLoss(nn.Module):
    """Differentiable soft dice loss.

    Note: Sigmoid is automatically applied here!
    """

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

    def forward(self, logits, targets):
        eps = 1e-09
        num = targets.size(0)
        probs = torch.sigmoid(logits)
        m1 = probs.view(num, -1)
        m2 = targets.view(num, -1).float()
        intersection = torch.sum(m1 * m2, 1)
        union = torch.sum(m1, dim=1) + torch.sum(m2, dim=1)
        score = (2 * intersection + eps) / (union + eps)
        score = (1 - score).mean()
        return score


class MultiLabelDiceLoss(nn.Module):
    """The average dice across multiple classes.

    Note: Sigmoid is automatically applied here!
    """

    def __init__(self):
        super(MultiLabelDiceLoss, self).__init__()
        self.dice_loss = SoftDiceLoss()

    def forward(self, logits, targets):
        loss = 0
        num_classes = targets.size(1)
        for class_nr in range(num_classes):
            loss += self.dice_loss(logits[:, class_nr, :, :], targets[:,
                class_nr, :, :])
        return loss / num_classes


class ComboLoss(nn.Module):
    """Weighted classification and segmentation loss.

    Attributes:
        weights (list):
        activation:
        bce: with logits loss
        dice_loss: soft dice loss (all classes)

    """

    def __init__(self, weights=[0.1, 0, 1], activation=None):
        """
        Args:
            weights (list): [image_cls, pixel_seg, pixel_cls]
            activation: One of ['sigmoid', None]
        """
        super(ComboLoss, self).__init__()
        self.weights = weights
        self.activation = activation
        assert self.activation in ['sigmoid', None
            ], "`activation` must be one of ['sigmoid', None]."
        self.bce = nn.BCEWithLogitsLoss(reduce=True)
        self.dice_loss = MultiLabelDiceLoss()

    def create_fc_tensors(self, logits, targets):
        """Creates the classification tensors from the segmentation ones.
        """
        batch_size, num_classes, _, _ = targets.shape
        summed = targets.view(batch_size, num_classes, -1).sum(-1)
        targets_fc = (summed > 0).float()
        logits_fc = logits.view(batch_size, num_classes, -1)
        logits_fc = torch.max(logits_fc, -1)[0]
        return logits_fc, targets_fc

    def forward(self, logits, targets):
        logits_fc, targets_fc = self.create_fc_tensors(logits, targets)
        p = torch.sigmoid(logits) if self.activation == 'sigmoid' else logits
        if self.weights[0]:
            loss_fc = self.weights[0] * self.bce(logits_fc, targets_fc)
        else:
            loss_fc = torch.tensor(0)
        if self.weights[1] or self.weights[2]:
            loss_seg_dice = self.weights[1] * self.dice_loss(p, targets)
            loss_seg_bce = self.weights[2] * self.bce(logits, targets)
        else:
            loss_seg_dice = torch.tensor(0)
            loss_seg_bce = torch.tensor(0)
        loss = loss_fc + loss_seg_bce + loss_seg_dice
        return loss


class ComboLossOnlyPosNew(ComboLoss):
    """Weighted classification and segmentation loss. (Considers only positive)

    This loss is only calculated on labels with ROIs in them to maximize
    the foreground class prediction capability.

    Attributes:
        weights (list):
        activation:
        bce: with logits loss
        dice_loss: soft dice loss (per class)

    """

    def __init__(self, weights=[0.1, 0, 1], activation=None):
        """
        Args:
            weights (list): [image_cls, pixel_seg, pixel_cls]
            activation: One of ['sigmoid', None]
        """
        super().__init__(weights=weights, activation=activation)
        self.dice_loss = SoftDiceLoss()

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