# 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_4/inductor_cache/7b/c7bwvkzrfyqe7on7r6rupptsqxo3x6vxvpuiow36csr3chlibccz.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/u7/cu7omvlo3u5eumdt3syjzrt4dpgkb7ra5377otoflgd62iyasjpk.py
# Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_6 => convolution_3
#   x_7 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [1, 1], [4, 4], [4, 4], False, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jd/cjdedjwqwok3veyekms6vsvzizqpg3ta7allmwzvhslkqb2q4e2i.py
# Topologically Sorted Source Nodes: [x_cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %relu_1, %relu_2, %relu_3, %relu_4], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 16) % 256
    x0 = xindex % 16
    x2 = (xindex // 4096)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (1024*x2)), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-64) + x1)) + (1024*x2)), tmp9, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 192, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (16*((-128) + x1)) + (1024*x2)), tmp14, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 224, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr3 + (x0 + (16*((-192) + x1)) + (512*x2)), tmp19, other=0.0)
    tmp21 = tmp0 >= tmp17
    tmp22 = tl.full([1], 256, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tl.load(in_ptr4 + (x0 + (16*((-224) + x1)) + (512*x2)), tmp21, other=0.0)
    tmp25 = tl.load(in_ptr5 + ((-224) + x1), tmp21, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp21, tmp28, tmp29)
    tmp31 = tl.where(tmp19, tmp20, tmp30)
    tmp32 = tl.where(tmp14, tmp15, tmp31)
    tmp33 = tl.where(tmp9, tmp10, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tl.store(out_ptr0 + (x3), tmp34, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uk/cukn5clwce25cwtgsk34lhhie5xjhd4s7kcgvlclefsgtceneixz.py
# Topologically Sorted Source Nodes: [x_out, x_out_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_out => convolution_5
#   x_out_1 => relu_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_5 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_3', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zw/czwp3zxikbzpn27bftm4h4xfeumnwrsrmzo3g36y5stomelytcev.py
# Topologically Sorted Source Nodes: [paf], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   paf => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_4 = async_compile.triton('triton_poi_fused_convolution_4', '''
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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], '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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 896
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 14
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2x/c2x6cucvhtkvtkj3cigyhort5u3qrtm36pjcjl72tovxzw7zlm4y.py
# Topologically Sorted Source Nodes: [heatmap], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   heatmap => convolution_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_5', '''
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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_5', 'mutated_arg_names': ['in_out_ptr0'], '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_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 9
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vb/cvbt7wqpxwgpm2qp2bqlusncosmvfnisulz23hbgvni4dcxyzgmo.py
# Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_8 => convolution_4
#   x_9 => relu_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [8, 8], [8, 8], False, [0, 0], 1), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_6 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_6', '''
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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_relu_threshold_backward_6', '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_relu_threshold_backward_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 16) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, None)
''', 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, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17 = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32, ), (1, ))
    assert_size_stride(primals_12, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_13, (128, ), (1, ))
    assert_size_stride(primals_14, (14, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_15, (14, ), (1, ))
    assert_size_stride(primals_16, (9, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_17, (9, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 4096, grid=grid(4096), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 4096, grid=grid(4096), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(2, 2), dilation=(2, 2), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf5, primals_7, 4096, grid=grid(4096), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf7, primals_9, 2048, grid=grid(2048), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(8, 8), dilation=(8, 8), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 4, 4), (512, 16, 4, 1))
        buf9 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf1, buf3, buf5, buf7, buf8, primals_11, buf9, 16384, grid=grid(16384), stream=stream0)
        # Topologically Sorted Source Nodes: [x_out], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_out, x_out_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf11, primals_13, 8192, grid=grid(8192), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [paf], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 14, 4, 4), (224, 16, 4, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [paf], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_4.run(buf13, primals_15, 896, grid=grid(896), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [heatmap], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf11, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 9, 4, 4), (144, 16, 4, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [heatmap], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf15, primals_17, 576, grid=grid(576), stream=stream0)
        del primals_17
        buf16 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_6.run(buf8, primals_11, buf16, 2048, grid=grid(2048), stream=stream0)
        del buf8
        del primals_11
    return (buf13, buf15, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, buf1, buf3, buf5, buf7, buf9, buf11, buf16, )


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((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((14, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((14, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((9, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((9, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17])
    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.onnx
import torch
import torch.nn as nn
import torch.utils.data


class dilation_layer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, padding=
        'same_padding', dilation=1):
        super(dilation_layer, self).__init__()
        if padding == 'same_padding':
            padding = int((kernel_size - 1) / 2 * dilation)
        self.Dconv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding,
            dilation=dilation)
        self.Drelu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.Dconv(x)
        x = self.Drelu(x)
        return x


class stage_block(nn.Module):
    """This class makes sure the paf and heatmap branch out in every stage"""

    def __init__(self, in_channels, out_channels):
        super(stage_block, self).__init__()
        self.Dconv_1 = dilation_layer(in_channels, out_channels=64)
        self.Dconv_2 = dilation_layer(in_channels=64, out_channels=64)
        self.Dconv_3 = dilation_layer(in_channels=64, out_channels=64,
            dilation=2)
        self.Dconv_4 = dilation_layer(in_channels=64, out_channels=32,
            dilation=4)
        self.Dconv_5 = dilation_layer(in_channels=32, out_channels=32,
            dilation=8)
        self.Mconv_6 = nn.Conv2d(in_channels=256, out_channels=128,
            kernel_size=1, padding=0)
        self.Mrelu_6 = nn.ReLU(inplace=True)
        self.paf = nn.Conv2d(in_channels=128, out_channels=14, kernel_size=
            1, padding=0)
        self.heatmap = nn.Conv2d(in_channels=128, out_channels=9,
            kernel_size=1, padding=0)

    def forward(self, x):
        x_1 = self.Dconv_1(x)
        x_2 = self.Dconv_2(x_1)
        x_3 = self.Dconv_3(x_2)
        x_4 = self.Dconv_4(x_3)
        x_5 = self.Dconv_5(x_4)
        x_cat = torch.cat([x_1, x_2, x_3, x_4, x_5], 1)
        x_out = self.Mconv_6(x_cat)
        x_out = self.Mrelu_6(x_out)
        paf = self.paf(x_out)
        heatmap = self.heatmap(x_out)
        return paf, heatmap


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

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
from torch._inductor.runtime import triton_helpers
import torch.onnx
import torch
import torch.nn as nn
import torch.utils.data
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 256
    x0 = xindex % 16
    x2 = xindex // 4096
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 1024 * x2), tmp4, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 128, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-64 + x1) + 1024 * x2), tmp9,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 192, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 16 * (-128 + x1) + 1024 * x2), tmp14,
        other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 224, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr3 + (x0 + 16 * (-192 + x1) + 512 * x2), tmp19,
        other=0.0)
    tmp21 = tmp0 >= tmp17
    tl.full([1], 256, tl.int64)
    tmp24 = tl.load(in_ptr4 + (x0 + 16 * (-224 + x1) + 512 * x2), tmp21,
        other=0.0)
    tmp25 = tl.load(in_ptr5 + (-224 + x1), tmp21, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp21, tmp28, tmp29)
    tmp31 = tl.where(tmp19, tmp20, tmp30)
    tmp32 = tl.where(tmp14, tmp15, tmp31)
    tmp33 = tl.where(tmp9, tmp10, tmp32)
    tmp34 = tl.where(tmp4, tmp5, tmp33)
    tl.store(out_ptr0 + x3, tmp34, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 896
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 14
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 9
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (14, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_15, (14,), (1,))
    assert_size_stride(primals_16, (9, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_17, (9,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(4096)](buf1, primals_2, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(4096)](buf3, primals_5, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(2, 2), dilation=(2, 2), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_0[grid(4096)](buf5, primals_7, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 4, 4), (512, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_1[grid(2048)](buf7, primals_9, 
            2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(8, 8), dilation=(8, 8), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 4, 4), (512, 16, 4, 1))
        buf9 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.
            float32)
        triton_poi_fused_cat_2[grid(16384)](buf1, buf3, buf5, buf7, buf8,
            primals_11, buf9, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 128, 4, 4), (2048, 16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_3[grid(8192)](buf11, primals_13, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf12 = extern_kernels.convolution(buf11, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 14, 4, 4), (224, 16, 4, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_4[grid(896)](buf13, primals_15, 896,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf14 = extern_kernels.convolution(buf11, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 9, 4, 4), (144, 16, 4, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_5[grid(576)](buf15, primals_17, 576,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_17
        buf16 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(2048)](buf8
            , primals_11, buf16, 2048, XBLOCK=128, num_warps=4, num_stages=1)
        del buf8
        del primals_11
    return (buf13, buf15, primals_1, primals_3, primals_4, primals_6,
        primals_8, primals_10, primals_12, primals_14, primals_16, buf1,
        buf3, buf5, buf7, buf9, buf11, buf16)


class dilation_layer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=3, padding=
        'same_padding', dilation=1):
        super(dilation_layer, self).__init__()
        if padding == 'same_padding':
            padding = int((kernel_size - 1) / 2 * dilation)
        self.Dconv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, padding=padding,
            dilation=dilation)
        self.Drelu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.Dconv(x)
        x = self.Drelu(x)
        return x


class stage_blockNew(nn.Module):
    """This class makes sure the paf and heatmap branch out in every stage"""

    def __init__(self, in_channels, out_channels):
        super(stage_blockNew, self).__init__()
        self.Dconv_1 = dilation_layer(in_channels, out_channels=64)
        self.Dconv_2 = dilation_layer(in_channels=64, out_channels=64)
        self.Dconv_3 = dilation_layer(in_channels=64, out_channels=64,
            dilation=2)
        self.Dconv_4 = dilation_layer(in_channels=64, out_channels=32,
            dilation=4)
        self.Dconv_5 = dilation_layer(in_channels=32, out_channels=32,
            dilation=8)
        self.Mconv_6 = nn.Conv2d(in_channels=256, out_channels=128,
            kernel_size=1, padding=0)
        self.Mrelu_6 = nn.ReLU(inplace=True)
        self.paf = nn.Conv2d(in_channels=128, out_channels=14, kernel_size=
            1, padding=0)
        self.heatmap = nn.Conv2d(in_channels=128, out_channels=9,
            kernel_size=1, padding=0)

    def forward(self, input_0):
        primals_1 = self.Dconv_1.Dconv.weight
        primals_2 = self.Dconv_1.Dconv.bias
        primals_4 = self.Dconv_2.Dconv.weight
        primals_5 = self.Dconv_2.Dconv.bias
        primals_6 = self.Dconv_3.Dconv.weight
        primals_7 = self.Dconv_3.Dconv.bias
        primals_8 = self.Dconv_4.Dconv.weight
        primals_9 = self.Dconv_4.Dconv.bias
        primals_10 = self.Dconv_5.Dconv.weight
        primals_11 = self.Dconv_5.Dconv.bias
        primals_12 = self.Mconv_6.weight
        primals_13 = self.Mconv_6.bias
        primals_14 = self.paf.weight
        primals_15 = self.paf.bias
        primals_16 = self.heatmap.weight
        primals_17 = self.heatmap.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0], output[1]
