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.nn as nn
from itertools import product as product
from math import sqrt as sqrt
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 2
    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_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    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)


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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (2, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (2,), (1,))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(256)](buf3, primals_5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 2, 4, 4), (32, 16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_1[grid(128)](buf5, primals_7, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf1, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_0[grid(256)](buf7, primals_9, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_2[grid(256)](buf9, primals_11, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
    return (buf5, buf9, primals_1, primals_3, primals_4, primals_6,
        primals_8, primals_10, buf1, buf3, buf7)


def conv1x1(in_channels, out_channels):
    """1x1 convolution"""
    return nn.Conv2d(in_channels, out_channels, 1, bias=True)


class BranchNetNew(nn.Module):
    """
    The branch of NaiveNet is the network output and
    only consists of conv 1×1 and ReLU.
    """

    def __init__(self, inplanes, planes):
        super(BranchNetNew, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.conv2_score = conv1x1(planes, planes)
        self.conv3_score = conv1x1(planes, 2)
        self.conv2_bbox = conv1x1(planes, planes)
        self.conv3_bbox = conv1x1(planes, 4)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2_score.weight
        primals_5 = self.conv2_score.bias
        primals_6 = self.conv3_score.weight
        primals_7 = self.conv3_score.bias
        primals_8 = self.conv2_bbox.weight
        primals_9 = self.conv2_bbox.bias
        primals_10 = self.conv3_bbox.weight
        primals_11 = self.conv3_bbox.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])
        return output[0], output[1]


Measured latency_ms: 0.041212