# 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_6/inductor_cache/nr/cnru7rb4xzo5pa3w63qs75ksp7nzl3xzuaii7g4loq47qlnrcr6y.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=[4096, 16], 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), 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 = 4096
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (4*x2) + (36*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/j5/cj5nf2owtsdm2zwcezqxpyn63iwddjyadpotkhm2ua52inoqxdcl.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[16, 16], 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_1', '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yf/cyfns4i5e43ce7igjhyd67rvmkai7nsuzd7onmsf6lbqdyycyb6o.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   input_1 => convolution
#   input_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [4, 4], [4, 4], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_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=[65536], 
    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_2', '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_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), 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 + (x2), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ot/cotioyfh5danry2jyd4tlyqer6hmdlkzbvh6alq7li4falxipog7.py
# Topologically Sorted Source Nodes: [input_7, input_14, add, add_1], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   input_14 => convolution_5
#   input_7 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %convolution_5), kwargs = {})
triton_poi_fused_add_convolution_3 = async_compile.triton('triton_poi_fused_add_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=[64, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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_poi_fused_add_convolution_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_add_convolution_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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 = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 16
    y1 = (yindex // 16)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + (y0 + (16*x2) + (64*y1)), tmp8, xmask & 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, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (1024, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_5, (1024, ), (1, ))
    assert_size_stride(primals_6, (4, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (1024, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (1024, ), (1, ))
    assert_size_stride(primals_10, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_11, (1024, ), (1, ))
    assert_size_stride(primals_12, (4, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 16, 16, grid=grid(16, 16), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((1024, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_8, buf2, 4096, 9, grid=grid(4096, 9), stream=stream0)
        del primals_8
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf4, primals_2, 65536, grid=grid(65536), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [input_4, input_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf6, primals_5, 65536, grid=grid(65536), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [input_7], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 1, 16, 4))
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf1, buf2, stride=(1, 1), padding=(4, 4), dilation=(4, 4), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [input_8, input_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf9, primals_9, 65536, grid=grid(65536), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [input_11, input_12], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf11, primals_11, 65536, grid=grid(65536), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [input_14], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 1, 16, 4))
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_7, input_14, add, add_1], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_3.run(buf7, primals_7, buf12, primals_13, buf13, 64, 4, grid=grid(64, 4), stream=stream0)
        del buf12
        del buf7
        del primals_13
        del primals_7
    return (buf13, buf0, buf1, primals_4, primals_6, buf2, primals_10, primals_12, buf4, buf6, buf9, buf11, )


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((1024, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1024, ), (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((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1024, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1024, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    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
from torch import nn


class FCReLUDrop(nn.Sequential):

    def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
        layer_idx, branch_idx):
        super(FCReLUDrop, self).__init__()
        self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
            out_ch, kernel_size, stride=1, padding=padding, dilation=dilation))
        self.add_module(f'relu{layer_idx}_{branch_idx}', nn.ReLU(inplace=True))
        self.add_module(f'drop{layer_idx}_{branch_idx}', nn.Dropout(p=0.5))


class VGGASPPBranch(nn.Sequential):

    def __init__(self, in_ch, num_classes, rate, start_layer_idx,
        branch_idx, net_id):
        super(VGGASPPBranch, self).__init__()
        self.add_module(f'aspp_layer{start_layer_idx}_{branch_idx}',
            FCReLUDrop(in_ch, out_ch=1024, kernel_size=3, dilation=rate,
            padding=rate, layer_idx=start_layer_idx, branch_idx=branch_idx))
        self.add_module(f'aspp_layer{start_layer_idx + 1}_{branch_idx}',
            FCReLUDrop(in_ch=1024, out_ch=1024, kernel_size=1, dilation=1,
            padding=0, layer_idx=start_layer_idx + 1, branch_idx=branch_idx))
        self.add_module(f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}',
            nn.Conv2d(in_channels=1024, out_channels=num_classes,
            kernel_size=1))
        fc_logit = eval('self.' +
            f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}')
        nn.init.normal_(fc_logit.weight, mean=0.0, std=0.01)
        nn.init.constant_(fc_logit.bias, 0.0)


class VGGASPP(nn.Module):

    def __init__(self, in_ch, num_classes, rates, start_layer_idx, net_id=
        'pascal'):
        super(VGGASPP, self).__init__()
        for rate, branch_idx in zip(rates, range(1, len(rates) + 1)):
            self.add_module(f'aspp_branch{branch_idx}', VGGASPPBranch(in_ch,
                num_classes, rate, start_layer_idx, branch_idx, net_id))

    def forward(self, x):
        return sum([branch(x) for branch in self.children()])


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


def get_init_inputs():
    return [[], {'in_ch': 4, 'num_classes': 4, 'rates': [4, 4],
        'start_layer_idx': 1}]

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
from torch import 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_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_2(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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, None)


@triton.jit
def triton_poi_fused_add_convolution_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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 = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + (y0 + 16 * x2 + 64 * y1), tmp8, xmask & ymask)


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) = args
    args.clear()
    assert_size_stride(primals_1, (1024, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (1024,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (4, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1024, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_9, (1024,), (1,))
    assert_size_stride(primals_10, (1024, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_11, (1024,), (1,))
    assert_size_stride(primals_12, (4, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1024, 4, 3, 3), (36, 1, 12, 4), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(4096, 9)](primals_1, buf0, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16,
            YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((1024, 4, 3, 3), (36, 1, 12, 4), torch.
            float32)
        triton_poi_fused_0[grid(4096, 9)](primals_8, buf2, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_8
        buf3 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_2[grid(65536)](buf4, primals_2, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_2
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_relu_2[grid(65536)](buf6, primals_5, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 1, 16, 4))
        buf8 = extern_kernels.convolution(buf1, buf2, stride=(1, 1),
            padding=(4, 4), dilation=(4, 4), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_2[grid(65536)](buf9, primals_9, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_relu_2[grid(65536)](buf11, primals_11,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_11
        buf12 = extern_kernels.convolution(buf11, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 4, 4), (64, 1, 16, 4))
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_3[grid(64, 4)](buf7, primals_7,
            buf12, primals_13, buf13, 64, 4, XBLOCK=4, YBLOCK=64, num_warps
            =4, num_stages=1)
        del buf12
        del buf7
        del primals_13
        del primals_7
    return (buf13, buf0, buf1, primals_4, primals_6, buf2, primals_10,
        primals_12, buf4, buf6, buf9, buf11)


class FCReLUDrop(nn.Sequential):

    def __init__(self, in_ch, out_ch, kernel_size, dilation, padding,
        layer_idx, branch_idx):
        super(FCReLUDrop, self).__init__()
        self.add_module(f'fc{layer_idx}_{branch_idx}', nn.Conv2d(in_ch,
            out_ch, kernel_size, stride=1, padding=padding, dilation=dilation))
        self.add_module(f'relu{layer_idx}_{branch_idx}', nn.ReLU(inplace=True))
        self.add_module(f'drop{layer_idx}_{branch_idx}', nn.Dropout(p=0.5))


class VGGASPPBranch(nn.Sequential):

    def __init__(self, in_ch, num_classes, rate, start_layer_idx,
        branch_idx, net_id):
        super(VGGASPPBranch, self).__init__()
        self.add_module(f'aspp_layer{start_layer_idx}_{branch_idx}',
            FCReLUDrop(in_ch, out_ch=1024, kernel_size=3, dilation=rate,
            padding=rate, layer_idx=start_layer_idx, branch_idx=branch_idx))
        self.add_module(f'aspp_layer{start_layer_idx + 1}_{branch_idx}',
            FCReLUDrop(in_ch=1024, out_ch=1024, kernel_size=1, dilation=1,
            padding=0, layer_idx=start_layer_idx + 1, branch_idx=branch_idx))
        self.add_module(f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}',
            nn.Conv2d(in_channels=1024, out_channels=num_classes,
            kernel_size=1))
        fc_logit = eval('self.' +
            f'fc{start_layer_idx + 2}_{net_id}_{branch_idx}')
        nn.init.normal_(fc_logit.weight, mean=0.0, std=0.01)
        nn.init.constant_(fc_logit.bias, 0.0)


class VGGASPPNew(nn.Module):

    def __init__(self, in_ch, num_classes, rates, start_layer_idx, net_id=
        'pascal'):
        super(VGGASPPNew, self).__init__()
        for rate, branch_idx in zip(rates, range(1, len(rates) + 1)):
            self.add_module(f'aspp_branch{branch_idx}', VGGASPPBranch(in_ch,
                num_classes, rate, start_layer_idx, branch_idx, net_id))

    def forward(self, input_0):
        primals_1 = self.aspp_branch1.aspp_layer1_1.fc1_1.weight
        primals_2 = self.aspp_branch1.aspp_layer1_1.fc1_1.bias
        primals_4 = self.aspp_branch1.aspp_layer2_1.fc2_1.weight
        primals_5 = self.aspp_branch1.aspp_layer2_1.fc2_1.bias
        primals_6 = self.aspp_branch1.fc3_pascal_1.weight
        primals_7 = self.aspp_branch1.fc3_pascal_1.bias
        primals_8 = self.aspp_branch2.aspp_layer1_2.fc1_2.weight
        primals_9 = self.aspp_branch2.aspp_layer1_2.fc1_2.bias
        primals_10 = self.aspp_branch2.aspp_layer2_2.fc2_2.weight
        primals_11 = self.aspp_branch2.aspp_layer2_2.fc2_2.bias
        primals_12 = self.aspp_branch2.fc3_pascal_2.weight
        primals_13 = self.aspp_branch2.fc3_pascal_2.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])
        return output[0]
