| program(1.0) | |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.2.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.2"}})] | |
| { | |
| func main<ios16>(tensor<fp32, [1, 3, 1024, 1024]> x) { | |
| tensor<fp32, [128]> encoder_conv_in_bias = const()[name = tensor<string, []>("encoder_conv_in_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; | |
| tensor<fp32, [128, 3, 3, 3]> encoder_conv_in_weight = const()[name = tensor<string, []>("encoder_conv_in_weight"), val = tensor<fp32, [128, 3, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(640)))]; | |
| tensor<fp32, [128]> encoder_down_blocks_0_resnets_0_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14528)))]; | |
| tensor<fp32, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15104)))]; | |
| tensor<fp32, [128]> encoder_down_blocks_0_resnets_0_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(604992)))]; | |
| tensor<fp32, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(605568)))]; | |
| tensor<fp32, [128]> encoder_down_blocks_0_resnets_1_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1195456)))]; | |
| tensor<fp32, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1196032)))]; | |
| tensor<fp32, [128]> encoder_down_blocks_0_resnets_1_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1785920)))]; | |
| tensor<fp32, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786496)))]; | |
| tensor<fp32, [128]> encoder_down_blocks_0_downsamplers_0_conv_bias = const()[name = tensor<string, []>("encoder_down_blocks_0_downsamplers_0_conv_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2376384)))]; | |
| tensor<fp32, [128, 128, 3, 3]> encoder_down_blocks_0_downsamplers_0_conv_weight = const()[name = tensor<string, []>("encoder_down_blocks_0_downsamplers_0_conv_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2376960)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_resnets_0_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2966848)))]; | |
| tensor<fp32, [256, 128, 3, 3]> encoder_down_blocks_1_resnets_0_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv1_weight"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2967936)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_resnets_0_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4147648)))]; | |
| tensor<fp32, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_0_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4148736)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6508096)))]; | |
| tensor<fp32, [256, 128, 1, 1]> encoder_down_blocks_1_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6509184)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_resnets_1_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6640320)))]; | |
| tensor<fp32, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6641408)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_resnets_1_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9000768)))]; | |
| tensor<fp32, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9001856)))]; | |
| tensor<fp32, [256]> encoder_down_blocks_1_downsamplers_0_conv_bias = const()[name = tensor<string, []>("encoder_down_blocks_1_downsamplers_0_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11361216)))]; | |
| tensor<fp32, [256, 256, 3, 3]> encoder_down_blocks_1_downsamplers_0_conv_weight = const()[name = tensor<string, []>("encoder_down_blocks_1_downsamplers_0_conv_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11362304)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_resnets_0_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13721664)))]; | |
| tensor<fp32, [512, 256, 3, 3]> encoder_down_blocks_2_resnets_0_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv1_weight"), val = tensor<fp32, [512, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13723776)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_resnets_0_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18442432)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_0_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18444544)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27881792)))]; | |
| tensor<fp32, [512, 256, 1, 1]> encoder_down_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [512, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27883904)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_resnets_1_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28408256)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28410368)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_resnets_1_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37847616)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37849728)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_2_downsamplers_0_conv_bias = const()[name = tensor<string, []>("encoder_down_blocks_2_downsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47286976)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_2_downsamplers_0_conv_weight = const()[name = tensor<string, []>("encoder_down_blocks_2_downsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47289088)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_3_resnets_0_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56726336)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56728448)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_3_resnets_0_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66165696)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66167808)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_3_resnets_1_conv1_bias = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75605056)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv1_weight = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75607168)))]; | |
| tensor<fp32, [512]> encoder_down_blocks_3_resnets_1_conv2_bias = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85044416)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv2_weight = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85046528)))]; | |
| tensor<fp32, [512]> encoder_mid_block_resnets_0_conv1_bias = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94483776)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv1_weight = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(94485888)))]; | |
| tensor<fp32, [512]> encoder_mid_block_resnets_0_conv2_bias = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103923136)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv2_weight = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103925248)))]; | |
| tensor<fp32, [512]> encoder_mid_block_attentions_0_to_q_bias = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113362496)))]; | |
| tensor<fp32, [512, 512]> encoder_mid_block_attentions_0_to_q_weight = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113364608)))]; | |
| tensor<fp32, [512]> encoder_mid_block_attentions_0_to_k_bias = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114413248)))]; | |
| tensor<fp32, [512, 512]> encoder_mid_block_attentions_0_to_k_weight = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114415360)))]; | |
| tensor<fp32, [512]> encoder_mid_block_attentions_0_to_v_bias = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115464000)))]; | |
| tensor<fp32, [512, 512]> encoder_mid_block_attentions_0_to_v_weight = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115466112)))]; | |
| tensor<fp32, [512]> encoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116514752)))]; | |
| tensor<fp32, [512, 512]> encoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_to_out_0_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116516864)))]; | |
| tensor<fp32, [512]> encoder_mid_block_resnets_1_conv1_bias = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117565504)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv1_weight = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117567616)))]; | |
| tensor<fp32, [512]> encoder_mid_block_resnets_1_conv2_bias = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127004864)))]; | |
| tensor<fp32, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv2_weight = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127006976)))]; | |
| tensor<fp32, [8]> encoder_conv_out_bias = const()[name = tensor<string, []>("encoder_conv_out_bias"), val = tensor<fp32, [8]>([0x1.7f4p-6, -0x1.5dcp-5, 0x1.84cp-3, 0x1.e84p-3, 0x1.e34p-3, 0x1.0c8p-4, 0x1.718p-5, -0x1.998p-3])]; | |
| tensor<fp32, [8, 512, 3, 3]> encoder_conv_out_weight = const()[name = tensor<string, []>("encoder_conv_out_weight"), val = tensor<fp32, [8, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136444224)))]; | |
| tensor<fp32, [8]> quant_conv_bias = const()[name = tensor<string, []>("quant_conv_bias"), val = tensor<fp32, [8]>([0x1.f48p-4, 0x1.088p-4, -0x1.e48p-3, -0x1.bf8p-2, -0x1.56cp+4, -0x1.598p+4, -0x1.62p+4, -0x1.664p+4])]; | |
| tensor<fp32, [8, 8, 1, 1]> quant_conv_weight = const()[name = tensor<string, []>("quant_conv_weight"), val = tensor<fp32, [8, 8, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136591744)))]; | |
| tensor<int32, []> var_15 = const()[name = tensor<string, []>("op_15"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [2]> var_33 = const()[name = tensor<string, []>("op_33"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_35 = const()[name = tensor<string, []>("op_35"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_1 = conv(bias = encoder_conv_in_bias, dilations = var_35, groups = var_15, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_33, weight = encoder_conv_in_weight, x = x)[name = tensor<string, []>("input_1")]; | |
| tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_0 = reshape(shape = reshape_0_shape_0, x = input_1)[name = tensor<string, []>("reshape_0")]; | |
| tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor<string, []>("reduce_mean_0")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor<string, []>("sub_0")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_0 = square(x = sub_0)[name = tensor<string, []>("square_0")]; | |
| tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor<string, []>("reduce_mean_2")]; | |
| tensor<fp32, []> add_0_y_0 = const()[name = tensor<string, []>("add_0_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor<string, []>("add_0")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_0 = sqrt(x = add_0)[name = tensor<string, []>("sqrt_0")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor<string, []>("real_div_0")]; | |
| tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor<string, []>("reshape_1")]; | |
| tensor<fp32, [128]> add_1_mean_0 = const()[name = tensor<string, []>("add_1_mean_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136592064)))]; | |
| tensor<fp32, [128]> add_1_variance_0 = const()[name = tensor<string, []>("add_1_variance_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136592640)))]; | |
| tensor<fp32, [128]> add_1_gamma_0 = const()[name = tensor<string, []>("add_1_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136593216)))]; | |
| tensor<fp32, [128]> add_1_beta_0 = const()[name = tensor<string, []>("add_1_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136593792)))]; | |
| tensor<fp32, []> add_1_epsilon_0 = const()[name = tensor<string, []>("add_1_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 128, 1024, 1024]> add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor<string, []>("add_1")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_5 = silu(x = add_1)[name = tensor<string, []>("input_5")]; | |
| tensor<int32, [2]> var_54 = const()[name = tensor<string, []>("op_54"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_56 = const()[name = tensor<string, []>("op_56"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_7 = conv(bias = encoder_down_blocks_0_resnets_0_conv1_bias, dilations = var_56, groups = var_15, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_54, weight = encoder_down_blocks_0_resnets_0_conv1_weight, x = input_5)[name = tensor<string, []>("input_7")]; | |
| tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_4 = reshape(shape = reshape_4_shape_0, x = input_7)[name = tensor<string, []>("reshape_4")]; | |
| tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor<string, []>("reduce_mean_3")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor<string, []>("sub_2")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_1 = square(x = sub_2)[name = tensor<string, []>("square_1")]; | |
| tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor<string, []>("reduce_mean_5")]; | |
| tensor<fp32, []> add_2_y_0 = const()[name = tensor<string, []>("add_2_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor<string, []>("add_2")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_1 = sqrt(x = add_2)[name = tensor<string, []>("sqrt_1")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor<string, []>("real_div_1")]; | |
| tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor<string, []>("reshape_5")]; | |
| tensor<fp32, [128]> add_3_gamma_0 = const()[name = tensor<string, []>("add_3_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136594368)))]; | |
| tensor<fp32, [128]> add_3_beta_0 = const()[name = tensor<string, []>("add_3_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136594944)))]; | |
| tensor<fp32, []> add_3_epsilon_0 = const()[name = tensor<string, []>("add_3_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 128, 1024, 1024]> add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor<string, []>("add_3")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_11 = silu(x = add_3)[name = tensor<string, []>("input_11")]; | |
| tensor<int32, [2]> var_66 = const()[name = tensor<string, []>("op_66"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_68 = const()[name = tensor<string, []>("op_68"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_1 = conv(bias = encoder_down_blocks_0_resnets_0_conv2_bias, dilations = var_68, groups = var_15, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_66, weight = encoder_down_blocks_0_resnets_0_conv2_weight, x = input_11)[name = tensor<string, []>("hidden_states_1")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> var_71 = add(x = input_1, y = hidden_states_1)[name = tensor<string, []>("op_71")]; | |
| tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_8 = reshape(shape = reshape_8_shape_0, x = var_71)[name = tensor<string, []>("reshape_8")]; | |
| tensor<int32, [3]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor<string, []>("reduce_mean_6")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor<string, []>("sub_4")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_2 = square(x = sub_4)[name = tensor<string, []>("square_2")]; | |
| tensor<int32, [3]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor<string, []>("reduce_mean_8")]; | |
| tensor<fp32, []> add_4_y_0 = const()[name = tensor<string, []>("add_4_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor<string, []>("add_4")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_2 = sqrt(x = add_4)[name = tensor<string, []>("sqrt_2")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor<string, []>("real_div_2")]; | |
| tensor<int32, [4]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor<string, []>("reshape_9")]; | |
| tensor<fp32, [128]> add_5_gamma_0 = const()[name = tensor<string, []>("add_5_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136595520)))]; | |
| tensor<fp32, [128]> add_5_beta_0 = const()[name = tensor<string, []>("add_5_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136596096)))]; | |
| tensor<fp32, []> add_5_epsilon_0 = const()[name = tensor<string, []>("add_5_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 128, 1024, 1024]> add_5 = batch_norm(beta = add_5_beta_0, epsilon = add_5_epsilon_0, gamma = add_5_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_9)[name = tensor<string, []>("add_5")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_19 = silu(x = add_5)[name = tensor<string, []>("input_19")]; | |
| tensor<int32, [2]> var_84 = const()[name = tensor<string, []>("op_84"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_21_pad_type_0 = const()[name = tensor<string, []>("input_21_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_21_pad_0 = const()[name = tensor<string, []>("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_21 = conv(bias = encoder_down_blocks_0_resnets_1_conv1_bias, dilations = var_86, groups = var_15, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_84, weight = encoder_down_blocks_0_resnets_1_conv1_weight, x = input_19)[name = tensor<string, []>("input_21")]; | |
| tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_12 = reshape(shape = reshape_12_shape_0, x = input_21)[name = tensor<string, []>("reshape_12")]; | |
| tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor<string, []>("reduce_mean_9")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor<string, []>("sub_6")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_3 = square(x = sub_6)[name = tensor<string, []>("square_3")]; | |
| tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor<string, []>("reduce_mean_11")]; | |
| tensor<fp32, []> add_6_y_0 = const()[name = tensor<string, []>("add_6_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor<string, []>("add_6")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_3 = sqrt(x = add_6)[name = tensor<string, []>("sqrt_3")]; | |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor<string, []>("real_div_3")]; | |
| tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor<string, []>("reshape_13")]; | |
| tensor<fp32, [128]> add_7_gamma_0 = const()[name = tensor<string, []>("add_7_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136596672)))]; | |
| tensor<fp32, [128]> add_7_beta_0 = const()[name = tensor<string, []>("add_7_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136597248)))]; | |
| tensor<fp32, []> add_7_epsilon_0 = const()[name = tensor<string, []>("add_7_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 128, 1024, 1024]> add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor<string, []>("add_7")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> input_25 = silu(x = add_7)[name = tensor<string, []>("input_25")]; | |
| tensor<int32, [2]> var_96 = const()[name = tensor<string, []>("op_96"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_98 = const()[name = tensor<string, []>("op_98"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_3_pad_type_0 = const()[name = tensor<string, []>("hidden_states_3_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = tensor<string, []>("hidden_states_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_3 = conv(bias = encoder_down_blocks_0_resnets_1_conv2_bias, dilations = var_98, groups = var_15, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_96, weight = encoder_down_blocks_0_resnets_1_conv2_weight, x = input_25)[name = tensor<string, []>("hidden_states_3")]; | |
| tensor<fp32, [1, 128, 1024, 1024]> var_101 = add(x = var_71, y = hidden_states_3)[name = tensor<string, []>("op_101")]; | |
| tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [8]> hidden_states_7_pad_0 = const()[name = tensor<string, []>("hidden_states_7_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])]; | |
| tensor<string, []> hidden_states_7_mode_0 = const()[name = tensor<string, []>("hidden_states_7_mode_0"), val = tensor<string, []>("constant")]; | |
| tensor<fp32, [1, 128, 1025, 1025]> hidden_states_7 = pad(constant_val = const_0, mode = hidden_states_7_mode_0, pad = hidden_states_7_pad_0, x = var_101)[name = tensor<string, []>("hidden_states_7")]; | |
| tensor<int32, [2]> var_109 = const()[name = tensor<string, []>("op_109"), val = tensor<int32, [2]>([2, 2])]; | |
| tensor<int32, [2]> var_111 = const()[name = tensor<string, []>("op_111"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_29_pad_type_0 = const()[name = tensor<string, []>("input_29_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 128, 512, 512]> input_29 = conv(bias = encoder_down_blocks_0_downsamplers_0_conv_bias, dilations = var_111, groups = var_15, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = var_109, weight = encoder_down_blocks_0_downsamplers_0_conv_weight, x = hidden_states_7)[name = tensor<string, []>("input_29")]; | |
| tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; | |
| tensor<fp32, [1, 32, 4, 512, 512]> reshape_16 = reshape(shape = reshape_16_shape_0, x = input_29)[name = tensor<string, []>("reshape_16")]; | |
| tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = tensor<string, []>("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_12_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_12_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor<string, []>("reduce_mean_12")]; | |
| tensor<fp32, [1, 32, 4, 512, 512]> sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor<string, []>("sub_8")]; | |
| tensor<fp32, [1, 32, 4, 512, 512]> square_4 = square(x = sub_8)[name = tensor<string, []>("square_4")]; | |
| tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = tensor<string, []>("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_14_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_14_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor<string, []>("reduce_mean_14")]; | |
| tensor<fp32, []> add_8_y_0 = const()[name = tensor<string, []>("add_8_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor<string, []>("add_8")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_4 = sqrt(x = add_8)[name = tensor<string, []>("sqrt_4")]; | |
| tensor<fp32, [1, 32, 4, 512, 512]> real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor<string, []>("real_div_4")]; | |
| tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; | |
| tensor<fp32, [1, 128, 512, 512]> reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor<string, []>("reshape_17")]; | |
| tensor<fp32, [128]> add_9_gamma_0 = const()[name = tensor<string, []>("add_9_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136597824)))]; | |
| tensor<fp32, [128]> add_9_beta_0 = const()[name = tensor<string, []>("add_9_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136598400)))]; | |
| tensor<fp32, []> add_9_epsilon_0 = const()[name = tensor<string, []>("add_9_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 128, 512, 512]> add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor<string, []>("add_9")]; | |
| tensor<fp32, [1, 128, 512, 512]> input_33 = silu(x = add_9)[name = tensor<string, []>("input_33")]; | |
| tensor<int32, [2]> var_131 = const()[name = tensor<string, []>("op_131"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_133 = const()[name = tensor<string, []>("op_133"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 256, 512, 512]> input_35 = conv(bias = encoder_down_blocks_1_resnets_0_conv1_bias, dilations = var_133, groups = var_15, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_131, weight = encoder_down_blocks_1_resnets_0_conv1_weight, x = input_33)[name = tensor<string, []>("input_35")]; | |
| tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_20 = reshape(shape = reshape_20_shape_0, x = input_35)[name = tensor<string, []>("reshape_20")]; | |
| tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor<string, []>("reduce_mean_15")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor<string, []>("sub_10")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> square_5 = square(x = sub_10)[name = tensor<string, []>("square_5")]; | |
| tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor<string, []>("reduce_mean_17")]; | |
| tensor<fp32, []> add_10_y_0 = const()[name = tensor<string, []>("add_10_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor<string, []>("add_10")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_5 = sqrt(x = add_10)[name = tensor<string, []>("sqrt_5")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor<string, []>("real_div_5")]; | |
| tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; | |
| tensor<fp32, [1, 256, 512, 512]> reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor<string, []>("reshape_21")]; | |
| tensor<fp32, [256]> add_11_mean_0 = const()[name = tensor<string, []>("add_11_mean_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136598976)))]; | |
| tensor<fp32, [256]> add_11_variance_0 = const()[name = tensor<string, []>("add_11_variance_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136600064)))]; | |
| tensor<fp32, [256]> add_11_gamma_0 = const()[name = tensor<string, []>("add_11_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136601152)))]; | |
| tensor<fp32, [256]> add_11_beta_0 = const()[name = tensor<string, []>("add_11_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136602240)))]; | |
| tensor<fp32, []> add_11_epsilon_0 = const()[name = tensor<string, []>("add_11_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 256, 512, 512]> add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_21)[name = tensor<string, []>("add_11")]; | |
| tensor<fp32, [1, 256, 512, 512]> input_39 = silu(x = add_11)[name = tensor<string, []>("input_39")]; | |
| tensor<int32, [2]> var_143 = const()[name = tensor<string, []>("op_143"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_145 = const()[name = tensor<string, []>("op_145"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_9_pad_type_0 = const()[name = tensor<string, []>("hidden_states_9_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = tensor<string, []>("hidden_states_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_9 = conv(bias = encoder_down_blocks_1_resnets_0_conv2_bias, dilations = var_145, groups = var_15, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_143, weight = encoder_down_blocks_1_resnets_0_conv2_weight, x = input_39)[name = tensor<string, []>("hidden_states_9")]; | |
| tensor<int32, [2]> var_150 = const()[name = tensor<string, []>("op_150"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_tensor_1_pad_type_0 = const()[name = tensor<string, []>("input_tensor_1_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = tensor<string, []>("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 256, 512, 512]> input_tensor_1 = conv(bias = encoder_down_blocks_1_resnets_0_conv_shortcut_bias, dilations = var_152, groups = var_15, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_150, weight = encoder_down_blocks_1_resnets_0_conv_shortcut_weight, x = input_29)[name = tensor<string, []>("input_tensor_1")]; | |
| tensor<fp32, [1, 256, 512, 512]> var_155 = add(x = input_tensor_1, y = hidden_states_9)[name = tensor<string, []>("op_155")]; | |
| tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_24 = reshape(shape = reshape_24_shape_0, x = var_155)[name = tensor<string, []>("reshape_24")]; | |
| tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor<string, []>("reduce_mean_18")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor<string, []>("sub_12")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> square_6 = square(x = sub_12)[name = tensor<string, []>("square_6")]; | |
| tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor<string, []>("reduce_mean_20")]; | |
| tensor<fp32, []> add_12_y_0 = const()[name = tensor<string, []>("add_12_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor<string, []>("add_12")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_6 = sqrt(x = add_12)[name = tensor<string, []>("sqrt_6")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor<string, []>("real_div_6")]; | |
| tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; | |
| tensor<fp32, [1, 256, 512, 512]> reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor<string, []>("reshape_25")]; | |
| tensor<fp32, [256]> add_13_gamma_0 = const()[name = tensor<string, []>("add_13_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136603328)))]; | |
| tensor<fp32, [256]> add_13_beta_0 = const()[name = tensor<string, []>("add_13_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136604416)))]; | |
| tensor<fp32, []> add_13_epsilon_0 = const()[name = tensor<string, []>("add_13_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 256, 512, 512]> add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_25)[name = tensor<string, []>("add_13")]; | |
| tensor<fp32, [1, 256, 512, 512]> input_47 = silu(x = add_13)[name = tensor<string, []>("input_47")]; | |
| tensor<int32, [2]> var_168 = const()[name = tensor<string, []>("op_168"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_170 = const()[name = tensor<string, []>("op_170"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 256, 512, 512]> input_49 = conv(bias = encoder_down_blocks_1_resnets_1_conv1_bias, dilations = var_170, groups = var_15, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = var_168, weight = encoder_down_blocks_1_resnets_1_conv1_weight, x = input_47)[name = tensor<string, []>("input_49")]; | |
| tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_28 = reshape(shape = reshape_28_shape_0, x = input_49)[name = tensor<string, []>("reshape_28")]; | |
| tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor<string, []>("reduce_mean_21")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor<string, []>("sub_14")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> square_7 = square(x = sub_14)[name = tensor<string, []>("square_7")]; | |
| tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor<string, []>("reduce_mean_23")]; | |
| tensor<fp32, []> add_14_y_0 = const()[name = tensor<string, []>("add_14_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor<string, []>("add_14")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_7 = sqrt(x = add_14)[name = tensor<string, []>("sqrt_7")]; | |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor<string, []>("real_div_7")]; | |
| tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; | |
| tensor<fp32, [1, 256, 512, 512]> reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor<string, []>("reshape_29")]; | |
| tensor<fp32, [256]> add_15_gamma_0 = const()[name = tensor<string, []>("add_15_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136605504)))]; | |
| tensor<fp32, [256]> add_15_beta_0 = const()[name = tensor<string, []>("add_15_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136606592)))]; | |
| tensor<fp32, []> add_15_epsilon_0 = const()[name = tensor<string, []>("add_15_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 256, 512, 512]> add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_29)[name = tensor<string, []>("add_15")]; | |
| tensor<fp32, [1, 256, 512, 512]> input_53 = silu(x = add_15)[name = tensor<string, []>("input_53")]; | |
| tensor<int32, [2]> var_180 = const()[name = tensor<string, []>("op_180"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_182 = const()[name = tensor<string, []>("op_182"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_11_pad_type_0 = const()[name = tensor<string, []>("hidden_states_11_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = tensor<string, []>("hidden_states_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_11 = conv(bias = encoder_down_blocks_1_resnets_1_conv2_bias, dilations = var_182, groups = var_15, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_180, weight = encoder_down_blocks_1_resnets_1_conv2_weight, x = input_53)[name = tensor<string, []>("hidden_states_11")]; | |
| tensor<fp32, [1, 256, 512, 512]> var_185 = add(x = var_155, y = hidden_states_11)[name = tensor<string, []>("op_185")]; | |
| tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [8]> hidden_states_15_pad_0 = const()[name = tensor<string, []>("hidden_states_15_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])]; | |
| tensor<string, []> hidden_states_15_mode_0 = const()[name = tensor<string, []>("hidden_states_15_mode_0"), val = tensor<string, []>("constant")]; | |
| tensor<fp32, [1, 256, 513, 513]> hidden_states_15 = pad(constant_val = const_1, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_185)[name = tensor<string, []>("hidden_states_15")]; | |
| tensor<int32, [2]> var_193 = const()[name = tensor<string, []>("op_193"), val = tensor<int32, [2]>([2, 2])]; | |
| tensor<int32, [2]> var_195 = const()[name = tensor<string, []>("op_195"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 256, 256, 256]> input_57 = conv(bias = encoder_down_blocks_1_downsamplers_0_conv_bias, dilations = var_195, groups = var_15, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = var_193, weight = encoder_down_blocks_1_downsamplers_0_conv_weight, x = hidden_states_15)[name = tensor<string, []>("input_57")]; | |
| tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; | |
| tensor<fp32, [1, 32, 8, 256, 256]> reshape_32 = reshape(shape = reshape_32_shape_0, x = input_57)[name = tensor<string, []>("reshape_32")]; | |
| tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = tensor<string, []>("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_24_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_24_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor<string, []>("reduce_mean_24")]; | |
| tensor<fp32, [1, 32, 8, 256, 256]> sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor<string, []>("sub_16")]; | |
| tensor<fp32, [1, 32, 8, 256, 256]> square_8 = square(x = sub_16)[name = tensor<string, []>("square_8")]; | |
| tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = tensor<string, []>("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_26_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_26_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor<string, []>("reduce_mean_26")]; | |
| tensor<fp32, []> add_16_y_0 = const()[name = tensor<string, []>("add_16_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor<string, []>("add_16")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_8 = sqrt(x = add_16)[name = tensor<string, []>("sqrt_8")]; | |
| tensor<fp32, [1, 32, 8, 256, 256]> real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor<string, []>("real_div_8")]; | |
| tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; | |
| tensor<fp32, [1, 256, 256, 256]> reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor<string, []>("reshape_33")]; | |
| tensor<fp32, [256]> add_17_gamma_0 = const()[name = tensor<string, []>("add_17_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136607680)))]; | |
| tensor<fp32, [256]> add_17_beta_0 = const()[name = tensor<string, []>("add_17_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136608768)))]; | |
| tensor<fp32, []> add_17_epsilon_0 = const()[name = tensor<string, []>("add_17_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 256, 256, 256]> add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_33)[name = tensor<string, []>("add_17")]; | |
| tensor<fp32, [1, 256, 256, 256]> input_61 = silu(x = add_17)[name = tensor<string, []>("input_61")]; | |
| tensor<int32, [2]> var_215 = const()[name = tensor<string, []>("op_215"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_217 = const()[name = tensor<string, []>("op_217"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 256, 256]> input_63 = conv(bias = encoder_down_blocks_2_resnets_0_conv1_bias, dilations = var_217, groups = var_15, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_215, weight = encoder_down_blocks_2_resnets_0_conv1_weight, x = input_61)[name = tensor<string, []>("input_63")]; | |
| tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_36 = reshape(shape = reshape_36_shape_0, x = input_63)[name = tensor<string, []>("reshape_36")]; | |
| tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor<string, []>("reduce_mean_27")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor<string, []>("sub_18")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> square_9 = square(x = sub_18)[name = tensor<string, []>("square_9")]; | |
| tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor<string, []>("reduce_mean_29")]; | |
| tensor<fp32, []> add_18_y_0 = const()[name = tensor<string, []>("add_18_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor<string, []>("add_18")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_9 = sqrt(x = add_18)[name = tensor<string, []>("sqrt_9")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor<string, []>("real_div_9")]; | |
| tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; | |
| tensor<fp32, [1, 512, 256, 256]> reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor<string, []>("reshape_37")]; | |
| tensor<fp32, [512]> add_19_mean_0 = const()[name = tensor<string, []>("add_19_mean_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136609856)))]; | |
| tensor<fp32, [512]> add_19_variance_0 = const()[name = tensor<string, []>("add_19_variance_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136611968)))]; | |
| tensor<fp32, [512]> add_19_gamma_0 = const()[name = tensor<string, []>("add_19_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136614080)))]; | |
| tensor<fp32, [512]> add_19_beta_0 = const()[name = tensor<string, []>("add_19_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136616192)))]; | |
| tensor<fp32, []> add_19_epsilon_0 = const()[name = tensor<string, []>("add_19_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 256, 256]> add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_37)[name = tensor<string, []>("add_19")]; | |
| tensor<fp32, [1, 512, 256, 256]> input_67 = silu(x = add_19)[name = tensor<string, []>("input_67")]; | |
| tensor<int32, [2]> var_227 = const()[name = tensor<string, []>("op_227"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_17 = conv(bias = encoder_down_blocks_2_resnets_0_conv2_bias, dilations = var_229, groups = var_15, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_227, weight = encoder_down_blocks_2_resnets_0_conv2_weight, x = input_67)[name = tensor<string, []>("hidden_states_17")]; | |
| tensor<int32, [2]> var_234 = const()[name = tensor<string, []>("op_234"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_236 = const()[name = tensor<string, []>("op_236"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_tensor_pad_type_0 = const()[name = tensor<string, []>("input_tensor_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_tensor_pad_0 = const()[name = tensor<string, []>("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 512, 256, 256]> input_tensor = conv(bias = encoder_down_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_236, groups = var_15, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_234, weight = encoder_down_blocks_2_resnets_0_conv_shortcut_weight, x = input_57)[name = tensor<string, []>("input_tensor")]; | |
| tensor<fp32, [1, 512, 256, 256]> var_239 = add(x = input_tensor, y = hidden_states_17)[name = tensor<string, []>("op_239")]; | |
| tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_40 = reshape(shape = reshape_40_shape_0, x = var_239)[name = tensor<string, []>("reshape_40")]; | |
| tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = tensor<string, []>("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_30_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_30_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor<string, []>("reduce_mean_30")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor<string, []>("sub_20")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> square_10 = square(x = sub_20)[name = tensor<string, []>("square_10")]; | |
| tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = tensor<string, []>("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_32_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_32_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor<string, []>("reduce_mean_32")]; | |
| tensor<fp32, []> add_20_y_0 = const()[name = tensor<string, []>("add_20_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor<string, []>("add_20")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_10 = sqrt(x = add_20)[name = tensor<string, []>("sqrt_10")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor<string, []>("real_div_10")]; | |
| tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; | |
| tensor<fp32, [1, 512, 256, 256]> reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor<string, []>("reshape_41")]; | |
| tensor<fp32, [512]> add_21_gamma_0 = const()[name = tensor<string, []>("add_21_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136618304)))]; | |
| tensor<fp32, [512]> add_21_beta_0 = const()[name = tensor<string, []>("add_21_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136620416)))]; | |
| tensor<fp32, []> add_21_epsilon_0 = const()[name = tensor<string, []>("add_21_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 256, 256]> add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_41)[name = tensor<string, []>("add_21")]; | |
| tensor<fp32, [1, 512, 256, 256]> input_75 = silu(x = add_21)[name = tensor<string, []>("input_75")]; | |
| tensor<int32, [2]> var_252 = const()[name = tensor<string, []>("op_252"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_254 = const()[name = tensor<string, []>("op_254"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_77_pad_type_0 = const()[name = tensor<string, []>("input_77_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_77_pad_0 = const()[name = tensor<string, []>("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 256, 256]> input_77 = conv(bias = encoder_down_blocks_2_resnets_1_conv1_bias, dilations = var_254, groups = var_15, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_252, weight = encoder_down_blocks_2_resnets_1_conv1_weight, x = input_75)[name = tensor<string, []>("input_77")]; | |
| tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_44 = reshape(shape = reshape_44_shape_0, x = input_77)[name = tensor<string, []>("reshape_44")]; | |
| tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor<string, []>("reduce_mean_33")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor<string, []>("sub_22")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> square_11 = square(x = sub_22)[name = tensor<string, []>("square_11")]; | |
| tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor<string, []>("reduce_mean_35")]; | |
| tensor<fp32, []> add_22_y_0 = const()[name = tensor<string, []>("add_22_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor<string, []>("add_22")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_11 = sqrt(x = add_22)[name = tensor<string, []>("sqrt_11")]; | |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor<string, []>("real_div_11")]; | |
| tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; | |
| tensor<fp32, [1, 512, 256, 256]> reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor<string, []>("reshape_45")]; | |
| tensor<fp32, [512]> add_23_gamma_0 = const()[name = tensor<string, []>("add_23_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136622528)))]; | |
| tensor<fp32, [512]> add_23_beta_0 = const()[name = tensor<string, []>("add_23_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136624640)))]; | |
| tensor<fp32, []> add_23_epsilon_0 = const()[name = tensor<string, []>("add_23_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 256, 256]> add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_45)[name = tensor<string, []>("add_23")]; | |
| tensor<fp32, [1, 512, 256, 256]> input_81 = silu(x = add_23)[name = tensor<string, []>("input_81")]; | |
| tensor<int32, [2]> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_266 = const()[name = tensor<string, []>("op_266"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_19 = conv(bias = encoder_down_blocks_2_resnets_1_conv2_bias, dilations = var_266, groups = var_15, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_264, weight = encoder_down_blocks_2_resnets_1_conv2_weight, x = input_81)[name = tensor<string, []>("hidden_states_19")]; | |
| tensor<fp32, [1, 512, 256, 256]> var_269 = add(x = var_239, y = hidden_states_19)[name = tensor<string, []>("op_269")]; | |
| tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x0p+0)]; | |
| tensor<int32, [8]> hidden_states_23_pad_0 = const()[name = tensor<string, []>("hidden_states_23_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])]; | |
| tensor<string, []> hidden_states_23_mode_0 = const()[name = tensor<string, []>("hidden_states_23_mode_0"), val = tensor<string, []>("constant")]; | |
| tensor<fp32, [1, 512, 257, 257]> hidden_states_23 = pad(constant_val = const_2, mode = hidden_states_23_mode_0, pad = hidden_states_23_pad_0, x = var_269)[name = tensor<string, []>("hidden_states_23")]; | |
| tensor<int32, [2]> var_277 = const()[name = tensor<string, []>("op_277"), val = tensor<int32, [2]>([2, 2])]; | |
| tensor<int32, [2]> var_279 = const()[name = tensor<string, []>("op_279"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_85_pad_type_0 = const()[name = tensor<string, []>("input_85_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 512, 128, 128]> input_85 = conv(bias = encoder_down_blocks_2_downsamplers_0_conv_bias, dilations = var_279, groups = var_15, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_277, weight = encoder_down_blocks_2_downsamplers_0_conv_weight, x = hidden_states_23)[name = tensor<string, []>("input_85")]; | |
| tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_48 = reshape(shape = reshape_48_shape_0, x = input_85)[name = tensor<string, []>("reshape_48")]; | |
| tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor<string, []>("reduce_mean_36")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor<string, []>("sub_24")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_12 = square(x = sub_24)[name = tensor<string, []>("square_12")]; | |
| tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor<string, []>("reduce_mean_38")]; | |
| tensor<fp32, []> add_24_y_0 = const()[name = tensor<string, []>("add_24_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor<string, []>("add_24")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_12 = sqrt(x = add_24)[name = tensor<string, []>("sqrt_12")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor<string, []>("real_div_12")]; | |
| tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor<string, []>("reshape_49")]; | |
| tensor<fp32, [512]> add_25_gamma_0 = const()[name = tensor<string, []>("add_25_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136626752)))]; | |
| tensor<fp32, [512]> add_25_beta_0 = const()[name = tensor<string, []>("add_25_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136628864)))]; | |
| tensor<fp32, []> add_25_epsilon_0 = const()[name = tensor<string, []>("add_25_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_49)[name = tensor<string, []>("add_25")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_89 = silu(x = add_25)[name = tensor<string, []>("input_89")]; | |
| tensor<int32, [2]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_298 = const()[name = tensor<string, []>("op_298"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> input_91 = conv(bias = encoder_down_blocks_3_resnets_0_conv1_bias, dilations = var_298, groups = var_15, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_296, weight = encoder_down_blocks_3_resnets_0_conv1_weight, x = input_89)[name = tensor<string, []>("input_91")]; | |
| tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_52 = reshape(shape = reshape_52_shape_0, x = input_91)[name = tensor<string, []>("reshape_52")]; | |
| tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor<string, []>("reduce_mean_39")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor<string, []>("sub_26")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_13 = square(x = sub_26)[name = tensor<string, []>("square_13")]; | |
| tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor<string, []>("reduce_mean_41")]; | |
| tensor<fp32, []> add_26_y_0 = const()[name = tensor<string, []>("add_26_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor<string, []>("add_26")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_13 = sqrt(x = add_26)[name = tensor<string, []>("sqrt_13")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor<string, []>("real_div_13")]; | |
| tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor<string, []>("reshape_53")]; | |
| tensor<fp32, [512]> add_27_gamma_0 = const()[name = tensor<string, []>("add_27_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136630976)))]; | |
| tensor<fp32, [512]> add_27_beta_0 = const()[name = tensor<string, []>("add_27_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136633088)))]; | |
| tensor<fp32, []> add_27_epsilon_0 = const()[name = tensor<string, []>("add_27_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_53)[name = tensor<string, []>("add_27")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_95 = silu(x = add_27)[name = tensor<string, []>("input_95")]; | |
| tensor<int32, [2]> var_308 = const()[name = tensor<string, []>("op_308"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_25_pad_type_0 = const()[name = tensor<string, []>("hidden_states_25_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = tensor<string, []>("hidden_states_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_25 = conv(bias = encoder_down_blocks_3_resnets_0_conv2_bias, dilations = var_310, groups = var_15, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_308, weight = encoder_down_blocks_3_resnets_0_conv2_weight, x = input_95)[name = tensor<string, []>("hidden_states_25")]; | |
| tensor<fp32, [1, 512, 128, 128]> var_313 = add(x = input_85, y = hidden_states_25)[name = tensor<string, []>("op_313")]; | |
| tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_56 = reshape(shape = reshape_56_shape_0, x = var_313)[name = tensor<string, []>("reshape_56")]; | |
| tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = tensor<string, []>("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_42_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_42_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor<string, []>("reduce_mean_42")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor<string, []>("sub_28")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_14 = square(x = sub_28)[name = tensor<string, []>("square_14")]; | |
| tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = tensor<string, []>("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_44_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_44_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor<string, []>("reduce_mean_44")]; | |
| tensor<fp32, []> add_28_y_0 = const()[name = tensor<string, []>("add_28_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor<string, []>("add_28")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_14 = sqrt(x = add_28)[name = tensor<string, []>("sqrt_14")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor<string, []>("real_div_14")]; | |
| tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor<string, []>("reshape_57")]; | |
| tensor<fp32, [512]> add_29_gamma_0 = const()[name = tensor<string, []>("add_29_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136635200)))]; | |
| tensor<fp32, [512]> add_29_beta_0 = const()[name = tensor<string, []>("add_29_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136637312)))]; | |
| tensor<fp32, []> add_29_epsilon_0 = const()[name = tensor<string, []>("add_29_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_57)[name = tensor<string, []>("add_29")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_103 = silu(x = add_29)[name = tensor<string, []>("input_103")]; | |
| tensor<int32, [2]> var_326 = const()[name = tensor<string, []>("op_326"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> input_105 = conv(bias = encoder_down_blocks_3_resnets_1_conv1_bias, dilations = var_328, groups = var_15, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_326, weight = encoder_down_blocks_3_resnets_1_conv1_weight, x = input_103)[name = tensor<string, []>("input_105")]; | |
| tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_60 = reshape(shape = reshape_60_shape_0, x = input_105)[name = tensor<string, []>("reshape_60")]; | |
| tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor<string, []>("reduce_mean_45")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor<string, []>("sub_30")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_15 = square(x = sub_30)[name = tensor<string, []>("square_15")]; | |
| tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor<string, []>("reduce_mean_47")]; | |
| tensor<fp32, []> add_30_y_0 = const()[name = tensor<string, []>("add_30_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor<string, []>("add_30")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_15 = sqrt(x = add_30)[name = tensor<string, []>("sqrt_15")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor<string, []>("real_div_15")]; | |
| tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor<string, []>("reshape_61")]; | |
| tensor<fp32, [512]> add_31_gamma_0 = const()[name = tensor<string, []>("add_31_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136639424)))]; | |
| tensor<fp32, [512]> add_31_beta_0 = const()[name = tensor<string, []>("add_31_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136641536)))]; | |
| tensor<fp32, []> add_31_epsilon_0 = const()[name = tensor<string, []>("add_31_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_61)[name = tensor<string, []>("add_31")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_109 = silu(x = add_31)[name = tensor<string, []>("input_109")]; | |
| tensor<int32, [2]> var_338 = const()[name = tensor<string, []>("op_338"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_340 = const()[name = tensor<string, []>("op_340"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_27_pad_type_0 = const()[name = tensor<string, []>("hidden_states_27_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = tensor<string, []>("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_27 = conv(bias = encoder_down_blocks_3_resnets_1_conv2_bias, dilations = var_340, groups = var_15, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_338, weight = encoder_down_blocks_3_resnets_1_conv2_weight, x = input_109)[name = tensor<string, []>("hidden_states_27")]; | |
| tensor<fp32, [1, 512, 128, 128]> var_343 = add(x = var_313, y = hidden_states_27)[name = tensor<string, []>("op_343")]; | |
| tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_64 = reshape(shape = reshape_64_shape_0, x = var_343)[name = tensor<string, []>("reshape_64")]; | |
| tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = tensor<string, []>("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_48_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_48_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor<string, []>("reduce_mean_48")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor<string, []>("sub_32")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_16 = square(x = sub_32)[name = tensor<string, []>("square_16")]; | |
| tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = tensor<string, []>("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_50_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_50_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor<string, []>("reduce_mean_50")]; | |
| tensor<fp32, []> add_32_y_0 = const()[name = tensor<string, []>("add_32_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor<string, []>("add_32")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_16 = sqrt(x = add_32)[name = tensor<string, []>("sqrt_16")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor<string, []>("real_div_16")]; | |
| tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor<string, []>("reshape_65")]; | |
| tensor<fp32, [512]> add_33_gamma_0 = const()[name = tensor<string, []>("add_33_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136643648)))]; | |
| tensor<fp32, [512]> add_33_beta_0 = const()[name = tensor<string, []>("add_33_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136645760)))]; | |
| tensor<fp32, []> add_33_epsilon_0 = const()[name = tensor<string, []>("add_33_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_65)[name = tensor<string, []>("add_33")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_117 = silu(x = add_33)[name = tensor<string, []>("input_117")]; | |
| tensor<int32, [2]> var_362 = const()[name = tensor<string, []>("op_362"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_364 = const()[name = tensor<string, []>("op_364"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> input_119 = conv(bias = encoder_mid_block_resnets_0_conv1_bias, dilations = var_364, groups = var_15, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = var_362, weight = encoder_mid_block_resnets_0_conv1_weight, x = input_117)[name = tensor<string, []>("input_119")]; | |
| tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_68 = reshape(shape = reshape_68_shape_0, x = input_119)[name = tensor<string, []>("reshape_68")]; | |
| tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor<string, []>("reduce_mean_51")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor<string, []>("sub_34")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_17 = square(x = sub_34)[name = tensor<string, []>("square_17")]; | |
| tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor<string, []>("reduce_mean_53")]; | |
| tensor<fp32, []> add_34_y_0 = const()[name = tensor<string, []>("add_34_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor<string, []>("add_34")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_17 = sqrt(x = add_34)[name = tensor<string, []>("sqrt_17")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor<string, []>("real_div_17")]; | |
| tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor<string, []>("reshape_69")]; | |
| tensor<fp32, [512]> add_35_gamma_0 = const()[name = tensor<string, []>("add_35_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136647872)))]; | |
| tensor<fp32, [512]> add_35_beta_0 = const()[name = tensor<string, []>("add_35_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136649984)))]; | |
| tensor<fp32, []> add_35_epsilon_0 = const()[name = tensor<string, []>("add_35_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_69)[name = tensor<string, []>("add_35")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_123 = silu(x = add_35)[name = tensor<string, []>("input_123")]; | |
| tensor<int32, [2]> var_374 = const()[name = tensor<string, []>("op_374"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_376 = const()[name = tensor<string, []>("op_376"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_29_pad_type_0 = const()[name = tensor<string, []>("hidden_states_29_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = tensor<string, []>("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_29 = conv(bias = encoder_mid_block_resnets_0_conv2_bias, dilations = var_376, groups = var_15, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_374, weight = encoder_mid_block_resnets_0_conv2_weight, x = input_123)[name = tensor<string, []>("hidden_states_29")]; | |
| tensor<fp32, [1, 512, 128, 128]> var_379 = add(x = var_343, y = hidden_states_29)[name = tensor<string, []>("op_379")]; | |
| tensor<int32, [4]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 16384])]; | |
| tensor<fp32, [1, 32, 16, 16384]> reshape_72 = reshape(shape = reshape_72_shape_0, x = var_379)[name = tensor<string, []>("reshape_72")]; | |
| tensor<int32, [2]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [2]>([2, 3])]; | |
| tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1]> reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor<string, []>("reduce_mean_54")]; | |
| tensor<fp32, [1, 32, 16, 16384]> sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor<string, []>("sub_36")]; | |
| tensor<fp32, [1, 32, 16, 16384]> square_18 = square(x = sub_36)[name = tensor<string, []>("square_18")]; | |
| tensor<int32, [2]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [2]>([2, 3])]; | |
| tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1]> reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor<string, []>("reduce_mean_56")]; | |
| tensor<fp32, []> add_36_y_0 = const()[name = tensor<string, []>("add_36_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1]> add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor<string, []>("add_36")]; | |
| tensor<fp32, [1, 32, 1, 1]> sqrt_18 = sqrt(x = add_36)[name = tensor<string, []>("sqrt_18")]; | |
| tensor<fp32, [1, 32, 16, 16384]> real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor<string, []>("real_div_18")]; | |
| tensor<int32, [3]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [3]>([1, 512, 16384])]; | |
| tensor<fp32, [1, 512, 16384]> reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor<string, []>("reshape_73")]; | |
| tensor<fp32, [1, 512, 1]> reshape_74 = const()[name = tensor<string, []>("reshape_74"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136652096)))]; | |
| tensor<fp32, [1, 512, 16384]> mul_18 = mul(x = reshape_73, y = reshape_74)[name = tensor<string, []>("mul_18")]; | |
| tensor<fp32, [1, 512, 1]> reshape_75 = const()[name = tensor<string, []>("reshape_75"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136654208)))]; | |
| tensor<fp32, [1, 512, 16384]> add_37 = add(x = mul_18, y = reshape_75)[name = tensor<string, []>("add_37")]; | |
| tensor<int32, [3]> input_129_perm_0 = const()[name = tensor<string, []>("input_129_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; | |
| tensor<fp32, [1, 16384, 512]> transpose_11 = transpose(perm = input_129_perm_0, x = add_37)[name = tensor<string, []>("transpose_11")]; | |
| tensor<fp32, [1, 16384, 512]> linear_0 = linear(bias = encoder_mid_block_attentions_0_to_q_bias, weight = encoder_mid_block_attentions_0_to_q_weight, x = transpose_11)[name = tensor<string, []>("linear_0")]; | |
| tensor<fp32, [1, 16384, 512]> linear_1 = linear(bias = encoder_mid_block_attentions_0_to_k_bias, weight = encoder_mid_block_attentions_0_to_k_weight, x = transpose_11)[name = tensor<string, []>("linear_1")]; | |
| tensor<fp32, [1, 16384, 512]> linear_2 = linear(bias = encoder_mid_block_attentions_0_to_v_bias, weight = encoder_mid_block_attentions_0_to_v_weight, x = transpose_11)[name = tensor<string, []>("linear_2")]; | |
| tensor<int32, [4]> var_420 = const()[name = tensor<string, []>("op_420"), val = tensor<int32, [4]>([1, -1, 1, 512])]; | |
| tensor<fp32, [1, 16384, 1, 512]> var_421 = reshape(shape = var_420, x = linear_0)[name = tensor<string, []>("op_421")]; | |
| tensor<int32, [4]> var_423 = const()[name = tensor<string, []>("op_423"), val = tensor<int32, [4]>([1, -1, 1, 512])]; | |
| tensor<fp32, [1, 16384, 1, 512]> var_424 = reshape(shape = var_423, x = linear_1)[name = tensor<string, []>("op_424")]; | |
| tensor<int32, [4]> var_426 = const()[name = tensor<string, []>("op_426"), val = tensor<int32, [4]>([1, -1, 1, 512])]; | |
| tensor<fp32, [1, 16384, 1, 512]> var_427 = reshape(shape = var_426, x = linear_2)[name = tensor<string, []>("op_427")]; | |
| tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<fp32, []> mul_19_y_0 = const()[name = tensor<string, []>("mul_19_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-5)]; | |
| tensor<fp32, [1, 16384, 1, 512]> mul_19 = mul(x = var_421, y = mul_19_y_0)[name = tensor<string, []>("mul_19")]; | |
| tensor<bool, []> matmul_0_transpose_y_0 = const()[name = tensor<string, []>("matmul_0_transpose_y_0"), val = tensor<bool, []>(true)]; | |
| tensor<bool, []> matmul_0_transpose_x_0 = const()[name = tensor<string, []>("matmul_0_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<fp32, [1, 1, 16384, 512]> transpose_8 = transpose(perm = transpose_5_perm_0, x = var_424)[name = tensor<string, []>("transpose_8")]; | |
| tensor<fp32, [1, 1, 16384, 512]> transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_19)[name = tensor<string, []>("transpose_9")]; | |
| tensor<fp32, [1, 1, 16384, 16384]> matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor<string, []>("matmul_0")]; | |
| tensor<int32, []> softmax_0_axis_0 = const()[name = tensor<string, []>("softmax_0_axis_0"), val = tensor<int32, []>(-1)]; | |
| tensor<fp32, [1, 1, 16384, 16384]> softmax_0 = softmax(axis = softmax_0_axis_0, x = matmul_0)[name = tensor<string, []>("softmax_0")]; | |
| tensor<bool, []> hidden_states_35_transpose_x_0 = const()[name = tensor<string, []>("hidden_states_35_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> hidden_states_35_transpose_y_0 = const()[name = tensor<string, []>("hidden_states_35_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 1, 16384, 512]> transpose_10 = transpose(perm = value_perm_0, x = var_427)[name = tensor<string, []>("transpose_10")]; | |
| tensor<fp32, [1, 1, 16384, 512]> hidden_states_35 = matmul(transpose_x = hidden_states_35_transpose_x_0, transpose_y = hidden_states_35_transpose_y_0, x = softmax_0, y = transpose_10)[name = tensor<string, []>("hidden_states_35")]; | |
| tensor<int32, [4]> var_430_perm_0 = const()[name = tensor<string, []>("op_430_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_434 = const()[name = tensor<string, []>("op_434"), val = tensor<int32, [3]>([1, -1, 512])]; | |
| tensor<fp32, [1, 16384, 1, 512]> transpose_7 = transpose(perm = var_430_perm_0, x = hidden_states_35)[name = tensor<string, []>("transpose_7")]; | |
| tensor<fp32, [1, 16384, 512]> hidden_states_37 = reshape(shape = var_434, x = transpose_7)[name = tensor<string, []>("hidden_states_37")]; | |
| tensor<fp32, [1, 16384, 512]> linear_3 = linear(bias = encoder_mid_block_attentions_0_to_out_0_bias, weight = encoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_37)[name = tensor<string, []>("linear_3")]; | |
| tensor<int32, [3]> var_441_perm_0 = const()[name = tensor<string, []>("op_441_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; | |
| tensor<int32, [4]> var_442 = const()[name = tensor<string, []>("op_442"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 16384]> transpose_6 = transpose(perm = var_441_perm_0, x = linear_3)[name = tensor<string, []>("transpose_6")]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_41 = reshape(shape = var_442, x = transpose_6)[name = tensor<string, []>("hidden_states_41")]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_43 = add(x = hidden_states_41, y = var_379)[name = tensor<string, []>("hidden_states_43")]; | |
| tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_76 = reshape(shape = reshape_76_shape_0, x = hidden_states_43)[name = tensor<string, []>("reshape_76")]; | |
| tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor<string, []>("reduce_mean_57")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor<string, []>("sub_38")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_19 = square(x = sub_38)[name = tensor<string, []>("square_19")]; | |
| tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor<string, []>("reduce_mean_59")]; | |
| tensor<fp32, []> add_38_y_0 = const()[name = tensor<string, []>("add_38_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor<string, []>("add_38")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_19 = sqrt(x = add_38)[name = tensor<string, []>("sqrt_19")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor<string, []>("real_div_19")]; | |
| tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor<string, []>("reshape_77")]; | |
| tensor<fp32, [512]> add_39_gamma_0 = const()[name = tensor<string, []>("add_39_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136656320)))]; | |
| tensor<fp32, [512]> add_39_beta_0 = const()[name = tensor<string, []>("add_39_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136658432)))]; | |
| tensor<fp32, []> add_39_epsilon_0 = const()[name = tensor<string, []>("add_39_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_77)[name = tensor<string, []>("add_39")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_139 = silu(x = add_39)[name = tensor<string, []>("input_139")]; | |
| tensor<int32, [2]> var_457 = const()[name = tensor<string, []>("op_457"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_459 = const()[name = tensor<string, []>("op_459"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_141_pad_type_0 = const()[name = tensor<string, []>("input_141_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_141_pad_0 = const()[name = tensor<string, []>("input_141_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> input_141 = conv(bias = encoder_mid_block_resnets_1_conv1_bias, dilations = var_459, groups = var_15, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_457, weight = encoder_mid_block_resnets_1_conv1_weight, x = input_139)[name = tensor<string, []>("input_141")]; | |
| tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_80 = reshape(shape = reshape_80_shape_0, x = input_141)[name = tensor<string, []>("reshape_80")]; | |
| tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = tensor<string, []>("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_60_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_60_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor<string, []>("reduce_mean_60")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor<string, []>("sub_40")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_20 = square(x = sub_40)[name = tensor<string, []>("square_20")]; | |
| tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = tensor<string, []>("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_62_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_62_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor<string, []>("reduce_mean_62")]; | |
| tensor<fp32, []> add_40_y_0 = const()[name = tensor<string, []>("add_40_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor<string, []>("add_40")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_20 = sqrt(x = add_40)[name = tensor<string, []>("sqrt_20")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor<string, []>("real_div_20")]; | |
| tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor<string, []>("reshape_81")]; | |
| tensor<fp32, [512]> add_41_gamma_0 = const()[name = tensor<string, []>("add_41_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136660544)))]; | |
| tensor<fp32, [512]> add_41_beta_0 = const()[name = tensor<string, []>("add_41_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136662656)))]; | |
| tensor<fp32, []> add_41_epsilon_0 = const()[name = tensor<string, []>("add_41_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_81)[name = tensor<string, []>("add_41")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_145 = silu(x = add_41)[name = tensor<string, []>("input_145")]; | |
| tensor<int32, [2]> var_469 = const()[name = tensor<string, []>("op_469"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_471 = const()[name = tensor<string, []>("op_471"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 512, 128, 128]> hidden_states = conv(bias = encoder_mid_block_resnets_1_conv2_bias, dilations = var_471, groups = var_15, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_469, weight = encoder_mid_block_resnets_1_conv2_weight, x = input_145)[name = tensor<string, []>("hidden_states")]; | |
| tensor<fp32, [1, 512, 128, 128]> var_474 = add(x = hidden_states_43, y = hidden_states)[name = tensor<string, []>("op_474")]; | |
| tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_84 = reshape(shape = reshape_84_shape_0, x = var_474)[name = tensor<string, []>("reshape_84")]; | |
| tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor<string, []>("reduce_mean_63")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor<string, []>("sub_42")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> square_21 = square(x = sub_42)[name = tensor<string, []>("square_21")]; | |
| tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; | |
| tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor<string, []>("reduce_mean_65")]; | |
| tensor<fp32, []> add_42_y_0 = const()[name = tensor<string, []>("add_42_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor<string, []>("add_42")]; | |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_21 = sqrt(x = add_42)[name = tensor<string, []>("sqrt_21")]; | |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor<string, []>("real_div_21")]; | |
| tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; | |
| tensor<fp32, [1, 512, 128, 128]> reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor<string, []>("reshape_85")]; | |
| tensor<fp32, [512]> add_43_gamma_0 = const()[name = tensor<string, []>("add_43_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136664768)))]; | |
| tensor<fp32, [512]> add_43_beta_0 = const()[name = tensor<string, []>("add_43_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136666880)))]; | |
| tensor<fp32, []> add_43_epsilon_0 = const()[name = tensor<string, []>("add_43_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, [1, 512, 128, 128]> add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_85)[name = tensor<string, []>("add_43")]; | |
| tensor<fp32, [1, 512, 128, 128]> input_153 = silu(x = add_43)[name = tensor<string, []>("input_153")]; | |
| tensor<int32, [2]> var_483 = const()[name = tensor<string, []>("op_483"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> input_pad_type_0 = const()[name = tensor<string, []>("input_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> input_pad_0 = const()[name = tensor<string, []>("input_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; | |
| tensor<fp32, [1, 8, 128, 128]> input = conv(bias = encoder_conv_out_bias, dilations = var_485, groups = var_15, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_483, weight = encoder_conv_out_weight, x = input_153)[name = tensor<string, []>("input")]; | |
| tensor<int32, []> var_491 = const()[name = tensor<string, []>("op_491"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [2]> var_494 = const()[name = tensor<string, []>("op_494"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<int32, [2]> var_496 = const()[name = tensor<string, []>("op_496"), val = tensor<int32, [2]>([1, 1])]; | |
| tensor<string, []> var_498_pad_type_0 = const()[name = tensor<string, []>("op_498_pad_type_0"), val = tensor<string, []>("custom")]; | |
| tensor<int32, [4]> var_498_pad_0 = const()[name = tensor<string, []>("op_498_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; | |
| tensor<fp32, [1, 8, 128, 128]> latent = conv(bias = quant_conv_bias, dilations = var_496, groups = var_491, pad = var_498_pad_0, pad_type = var_498_pad_type_0, strides = var_494, weight = quant_conv_weight, x = input)[name = tensor<string, []>("op_498")]; | |
| } -> (latent); | |
| } |