| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.2.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios16>(tensor<fp32, [1, 4, 128, 128]> z) { |
| tensor<fp32, [4]> post_quant_conv_bias = const()[name = tensor<string, []>("post_quant_conv_bias"), val = tensor<fp32, [4]>([-0x1.d8p-5, 0x1.dp-3, -0x1.c6p-4, 0x1.acp-3])]; |
| tensor<fp32, [4, 4, 1, 1]> post_quant_conv_weight = const()[name = tensor<string, []>("post_quant_conv_weight"), val = tensor<fp32, [4, 4, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp32, [512]> decoder_conv_in_bias = const()[name = tensor<string, []>("decoder_conv_in_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192)))]; |
| tensor<fp32, [512, 4, 3, 3]> decoder_conv_in_weight = const()[name = tensor<string, []>("decoder_conv_in_weight"), val = tensor<fp32, [512, 4, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2304)))]; |
| tensor<fp32, [512]> decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76096)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_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, []>(78208)))]; |
| tensor<fp32, [512]> decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9515456)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_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, []>(9517568)))]; |
| tensor<fp32, [512]> decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18954816)))]; |
| tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18956928)))]; |
| tensor<fp32, [512]> decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20005568)))]; |
| tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20007680)))]; |
| tensor<fp32, [512]> decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21056320)))]; |
| tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21058432)))]; |
| tensor<fp32, [512]> decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor<string, []>("decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22107072)))]; |
| tensor<fp32, [512, 512]> decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor<string, []>("decoder_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, []>(22109184)))]; |
| tensor<fp32, [512]> decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23157824)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_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, []>(23159936)))]; |
| tensor<fp32, [512]> decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_mid_block_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32597184)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_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, []>(32599296)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42036544)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42038656)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51475904)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51478016)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60915264)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60917376)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70354624)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70356736)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79793984)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79796096)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89233344)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89235456)))]; |
| tensor<fp32, [512]> decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98672704)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98674816)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108112064)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108114176)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117551424)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117553536)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126990784)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126992896)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136430144)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136432256)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145869504)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145871616)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155308864)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155310976)))]; |
| tensor<fp32, [512]> decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164748224)))]; |
| tensor<fp32, [512, 512, 3, 3]> decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164750336)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174187584)))]; |
| tensor<fp32, [256, 512, 3, 3]> decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor<fp32, [256, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174188672)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178907328)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178908416)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181267776)))]; |
| tensor<fp32, [256, 512, 1, 1]> decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [256, 512, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181268864)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181793216)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(181794304)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184153664)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184154752)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186514112)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186515200)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188874560)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188875648)))]; |
| tensor<fp32, [256]> decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191235008)))]; |
| tensor<fp32, [256, 256, 3, 3]> decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor<string, []>("decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191236096)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193595456)))]; |
| tensor<fp32, [128, 256, 3, 3]> decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor<fp32, [128, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193596032)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194775744)))]; |
| tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194776320)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195366208)))]; |
| tensor<fp32, [128, 256, 1, 1]> decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [128, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195366784)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195497920)))]; |
| tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195498496)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196088384)))]; |
| tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196088960)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196678848)))]; |
| tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196679424)))]; |
| tensor<fp32, [128]> decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197269312)))]; |
| tensor<fp32, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor<string, []>("decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197269888)))]; |
| tensor<fp32, [3]> decoder_conv_out_bias = const()[name = tensor<string, []>("decoder_conv_out_bias"), val = tensor<fp32, [3]>([0x1.f8p-4, 0x1.5p-4, 0x1.a2p-5])]; |
| tensor<fp32, [3, 128, 3, 3]> decoder_conv_out_weight = const()[name = tensor<string, []>("decoder_conv_out_weight"), val = tensor<fp32, [3, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197859776)))]; |
| tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_1_strides_0 = const()[name = tensor<string, []>("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_1_dilations_0 = const()[name = tensor<string, []>("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_1_groups_0 = const()[name = tensor<string, []>("input_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 4, 128, 128]> input_1 = conv(bias = post_quant_conv_bias, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = post_quant_conv_weight, x = z)[name = tensor<string, []>("input_1")]; |
| tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_3 = conv(bias = decoder_conv_in_bias, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = decoder_conv_in_weight, x = input_1)[name = tensor<string, []>("input_3")]; |
| tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_0 = reshape(shape = reshape_0_shape_0, x = input_3)[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, 16, 128, 128]> sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor<string, []>("sub_0")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor<string, []>("reshape_1")]; |
| tensor<fp32, [512]> add_1_mean_0 = const()[name = tensor<string, []>("add_1_mean_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197873664)))]; |
| tensor<fp32, [512]> add_1_variance_0 = const()[name = tensor<string, []>("add_1_variance_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197875776)))]; |
| tensor<fp32, [512]> add_1_gamma_0 = const()[name = tensor<string, []>("add_1_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197877888)))]; |
| tensor<fp32, [512]> add_1_beta_0 = const()[name = tensor<string, []>("add_1_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197880000)))]; |
| tensor<fp32, []> add_1_epsilon_0 = const()[name = tensor<string, []>("add_1_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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, 512, 128, 128]> hidden_states_1 = silu(x = add_1)[name = tensor<string, []>("hidden_states_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<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_7 = conv(bias = decoder_mid_block_resnets_0_conv1_bias, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = decoder_mid_block_resnets_0_conv1_weight, x = hidden_states_1)[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, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor<string, []>("sub_2")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor<string, []>("reshape_5")]; |
| tensor<fp32, [512]> add_3_gamma_0 = const()[name = tensor<string, []>("add_3_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197882112)))]; |
| tensor<fp32, [512]> add_3_beta_0 = const()[name = tensor<string, []>("add_3_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197884224)))]; |
| tensor<fp32, []> add_3_epsilon_0 = const()[name = tensor<string, []>("add_3_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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, 512, 128, 128]> input_11 = silu(x = add_3)[name = tensor<string, []>("input_11")]; |
| tensor<string, []> hidden_states_5_pad_type_0 = const()[name = tensor<string, []>("hidden_states_5_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_5_pad_0 = const()[name = tensor<string, []>("hidden_states_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_5_strides_0 = const()[name = tensor<string, []>("hidden_states_5_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_5_dilations_0 = const()[name = tensor<string, []>("hidden_states_5_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_5_groups_0 = const()[name = tensor<string, []>("hidden_states_5_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_5 = conv(bias = decoder_mid_block_resnets_0_conv2_bias, dilations = hidden_states_5_dilations_0, groups = hidden_states_5_groups_0, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = hidden_states_5_strides_0, weight = decoder_mid_block_resnets_0_conv2_weight, x = input_11)[name = tensor<string, []>("hidden_states_5")]; |
| tensor<fp32, [1, 512, 128, 128]> var_82 = add(x = input_3, y = hidden_states_5)[name = tensor<string, []>("op_82")]; |
| tensor<int32, [4]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 16384])]; |
| tensor<fp32, [1, 32, 16, 16384]> reshape_8 = reshape(shape = reshape_8_shape_0, x = var_82)[name = tensor<string, []>("reshape_8")]; |
| tensor<int32, [2]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| 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]> 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, 16, 16384]> sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor<string, []>("sub_4")]; |
| tensor<fp32, [1, 32, 16, 16384]> square_2 = square(x = sub_4)[name = tensor<string, []>("square_2")]; |
| tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| 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]> 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]> add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor<string, []>("add_4")]; |
| tensor<fp32, [1, 32, 1, 1]> sqrt_2 = sqrt(x = add_4)[name = tensor<string, []>("sqrt_2")]; |
| tensor<fp32, [1, 32, 16, 16384]> real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor<string, []>("real_div_2")]; |
| tensor<int32, [3]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 512, 16384])]; |
| tensor<fp32, [1, 512, 16384]> reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor<string, []>("reshape_9")]; |
| tensor<fp32, [1, 512, 1]> reshape_10 = const()[name = tensor<string, []>("reshape_10"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197886336)))]; |
| tensor<fp32, [1, 512, 16384]> mul_2 = mul(x = reshape_9, y = reshape_10)[name = tensor<string, []>("mul_2")]; |
| tensor<fp32, [1, 512, 1]> reshape_11 = const()[name = tensor<string, []>("reshape_11"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197888448)))]; |
| tensor<fp32, [1, 512, 16384]> add_5 = add(x = mul_2, y = reshape_11)[name = tensor<string, []>("add_5")]; |
| tensor<int32, [3]> input_15_perm_0 = const()[name = tensor<string, []>("input_15_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp32, [1, 16384, 512]> input_15 = transpose(perm = input_15_perm_0, x = add_5)[name = tensor<string, []>("transpose_11")]; |
| tensor<fp32, [1, 16384, 512]> query_1 = linear(bias = decoder_mid_block_attentions_0_to_q_bias, weight = decoder_mid_block_attentions_0_to_q_weight, x = input_15)[name = tensor<string, []>("linear_0")]; |
| tensor<fp32, [1, 16384, 512]> key_1 = linear(bias = decoder_mid_block_attentions_0_to_k_bias, weight = decoder_mid_block_attentions_0_to_k_weight, x = input_15)[name = tensor<string, []>("linear_1")]; |
| tensor<fp32, [1, 16384, 512]> value_1 = linear(bias = decoder_mid_block_attentions_0_to_v_bias, weight = decoder_mid_block_attentions_0_to_v_weight, x = input_15)[name = tensor<string, []>("linear_2")]; |
| tensor<int32, [4]> var_123 = const()[name = tensor<string, []>("op_123"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_124 = reshape(shape = var_123, x = query_1)[name = tensor<string, []>("op_124")]; |
| tensor<int32, [4]> var_126 = const()[name = tensor<string, []>("op_126"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_127 = reshape(shape = var_126, x = key_1)[name = tensor<string, []>("op_127")]; |
| tensor<int32, [4]> var_129 = const()[name = tensor<string, []>("op_129"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_130 = reshape(shape = var_129, x = value_1)[name = tensor<string, []>("op_130")]; |
| tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp32, []> mul_3_y_0 = const()[name = tensor<string, []>("mul_3_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-5)]; |
| tensor<fp32, [1, 16384, 1, 512]> mul_3 = mul(x = var_124, y = mul_3_y_0)[name = tensor<string, []>("mul_3")]; |
| 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_5 = transpose(perm = transpose_5_perm_0, x = var_127)[name = tensor<string, []>("transpose_8")]; |
| tensor<fp32, [1, 1, 16384, 512]> transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_3)[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_4, y = transpose_5)[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_11_transpose_x_0 = const()[name = tensor<string, []>("hidden_states_11_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> hidden_states_11_transpose_y_0 = const()[name = tensor<string, []>("hidden_states_11_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 1, 16384, 512]> value = transpose(perm = value_perm_0, x = var_130)[name = tensor<string, []>("transpose_10")]; |
| tensor<fp32, [1, 1, 16384, 512]> hidden_states_11 = matmul(transpose_x = hidden_states_11_transpose_x_0, transpose_y = hidden_states_11_transpose_y_0, x = softmax_0, y = value)[name = tensor<string, []>("hidden_states_11")]; |
| tensor<int32, [4]> var_133_perm_0 = const()[name = tensor<string, []>("op_133_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_137 = const()[name = tensor<string, []>("op_137"), val = tensor<int32, [3]>([1, -1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_133 = transpose(perm = var_133_perm_0, x = hidden_states_11)[name = tensor<string, []>("transpose_7")]; |
| tensor<fp32, [1, 16384, 512]> hidden_states_13 = reshape(shape = var_137, x = var_133)[name = tensor<string, []>("hidden_states_13")]; |
| tensor<fp32, [1, 16384, 512]> input_19 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias, weight = decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_13)[name = tensor<string, []>("linear_3")]; |
| tensor<int32, [3]> var_144_perm_0 = const()[name = tensor<string, []>("op_144_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, [4]> var_145 = const()[name = tensor<string, []>("op_145"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 16384]> var_144 = transpose(perm = var_144_perm_0, x = input_19)[name = tensor<string, []>("transpose_6")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_17 = reshape(shape = var_145, x = var_144)[name = tensor<string, []>("hidden_states_17")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_19 = add(x = hidden_states_17, y = var_82)[name = tensor<string, []>("hidden_states_19")]; |
| tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_19)[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, 16, 128, 128]> sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor<string, []>("sub_6")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor<string, []>("reshape_13")]; |
| tensor<fp32, [512]> add_7_gamma_0 = const()[name = tensor<string, []>("add_7_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197890560)))]; |
| tensor<fp32, [512]> add_7_beta_0 = const()[name = tensor<string, []>("add_7_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197892672)))]; |
| tensor<fp32, []> add_7_epsilon_0 = const()[name = tensor<string, []>("add_7_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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, 512, 128, 128]> hidden_states_21 = silu(x = add_7)[name = tensor<string, []>("hidden_states_21")]; |
| tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_25 = conv(bias = decoder_mid_block_resnets_1_conv1_bias, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = decoder_mid_block_resnets_1_conv1_weight, x = hidden_states_21)[name = tensor<string, []>("input_25")]; |
| tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_16 = reshape(shape = reshape_16_shape_0, x = input_25)[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, 16, 128, 128]> sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor<string, []>("sub_8")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor<string, []>("reshape_17")]; |
| tensor<fp32, [512]> add_9_gamma_0 = const()[name = tensor<string, []>("add_9_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197894784)))]; |
| tensor<fp32, [512]> add_9_beta_0 = const()[name = tensor<string, []>("add_9_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197896896)))]; |
| tensor<fp32, []> add_9_epsilon_0 = const()[name = tensor<string, []>("add_9_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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, 512, 128, 128]> input_29 = silu(x = add_9)[name = tensor<string, []>("input_29")]; |
| 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<int32, [2]> hidden_states_25_strides_0 = const()[name = tensor<string, []>("hidden_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_25_dilations_0 = const()[name = tensor<string, []>("hidden_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_25_groups_0 = const()[name = tensor<string, []>("hidden_states_25_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_25 = conv(bias = decoder_mid_block_resnets_1_conv2_bias, dilations = hidden_states_25_dilations_0, groups = hidden_states_25_groups_0, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = hidden_states_25_strides_0, weight = decoder_mid_block_resnets_1_conv2_weight, x = input_29)[name = tensor<string, []>("hidden_states_25")]; |
| tensor<fp32, [1, 512, 128, 128]> var_177 = add(x = hidden_states_19, y = hidden_states_25)[name = tensor<string, []>("op_177")]; |
| tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_20 = reshape(shape = reshape_20_shape_0, x = var_177)[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, 16, 128, 128]> sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor<string, []>("sub_10")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor<string, []>("reshape_21")]; |
| tensor<fp32, [512]> add_11_gamma_0 = const()[name = tensor<string, []>("add_11_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197899008)))]; |
| tensor<fp32, [512]> add_11_beta_0 = const()[name = tensor<string, []>("add_11_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197901120)))]; |
| tensor<fp32, []> add_11_epsilon_0 = const()[name = tensor<string, []>("add_11_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_21)[name = tensor<string, []>("add_11")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_27 = silu(x = add_11)[name = tensor<string, []>("hidden_states_27")]; |
| 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<int32, [2]> input_35_strides_0 = const()[name = tensor<string, []>("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_35_dilations_0 = const()[name = tensor<string, []>("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_35_groups_0 = const()[name = tensor<string, []>("input_35_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_35 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = decoder_up_blocks_0_resnets_0_conv1_weight, x = hidden_states_27)[name = tensor<string, []>("input_35")]; |
| tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_24 = reshape(shape = reshape_24_shape_0, x = input_35)[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, 16, 128, 128]> sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor<string, []>("sub_12")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor<string, []>("reshape_25")]; |
| tensor<fp32, [512]> add_13_gamma_0 = const()[name = tensor<string, []>("add_13_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197903232)))]; |
| tensor<fp32, [512]> add_13_beta_0 = const()[name = tensor<string, []>("add_13_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197905344)))]; |
| tensor<fp32, []> add_13_epsilon_0 = const()[name = tensor<string, []>("add_13_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_25)[name = tensor<string, []>("add_13")]; |
| tensor<fp32, [1, 512, 128, 128]> input_39 = silu(x = add_13)[name = tensor<string, []>("input_39")]; |
| tensor<string, []> hidden_states_31_pad_type_0 = const()[name = tensor<string, []>("hidden_states_31_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = tensor<string, []>("hidden_states_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_31_strides_0 = const()[name = tensor<string, []>("hidden_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_31_dilations_0 = const()[name = tensor<string, []>("hidden_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_31_groups_0 = const()[name = tensor<string, []>("hidden_states_31_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_31 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias, dilations = hidden_states_31_dilations_0, groups = hidden_states_31_groups_0, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = hidden_states_31_strides_0, weight = decoder_up_blocks_0_resnets_0_conv2_weight, x = input_39)[name = tensor<string, []>("hidden_states_31")]; |
| tensor<fp32, [1, 512, 128, 128]> var_216 = add(x = var_177, y = hidden_states_31)[name = tensor<string, []>("op_216")]; |
| tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_28 = reshape(shape = reshape_28_shape_0, x = var_216)[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, 16, 128, 128]> sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor<string, []>("sub_14")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor<string, []>("reshape_29")]; |
| tensor<fp32, [512]> add_15_gamma_0 = const()[name = tensor<string, []>("add_15_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197907456)))]; |
| tensor<fp32, [512]> add_15_beta_0 = const()[name = tensor<string, []>("add_15_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197909568)))]; |
| tensor<fp32, []> add_15_epsilon_0 = const()[name = tensor<string, []>("add_15_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_29)[name = tensor<string, []>("add_15")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_33 = silu(x = add_15)[name = tensor<string, []>("hidden_states_33")]; |
| tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_45 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = decoder_up_blocks_0_resnets_1_conv1_weight, x = hidden_states_33)[name = tensor<string, []>("input_45")]; |
| tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_32 = reshape(shape = reshape_32_shape_0, x = input_45)[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, 16, 128, 128]> sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor<string, []>("sub_16")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 16, 128, 128]> 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, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor<string, []>("reshape_33")]; |
| tensor<fp32, [512]> add_17_gamma_0 = const()[name = tensor<string, []>("add_17_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197911680)))]; |
| tensor<fp32, [512]> add_17_beta_0 = const()[name = tensor<string, []>("add_17_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197913792)))]; |
| tensor<fp32, []> add_17_epsilon_0 = const()[name = tensor<string, []>("add_17_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_33)[name = tensor<string, []>("add_17")]; |
| tensor<fp32, [1, 512, 128, 128]> input_49 = silu(x = add_17)[name = tensor<string, []>("input_49")]; |
| tensor<string, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = tensor<string, []>("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = tensor<string, []>("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_37_groups_0 = const()[name = tensor<string, []>("hidden_states_37_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_37 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias, dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = decoder_up_blocks_0_resnets_1_conv2_weight, x = input_49)[name = tensor<string, []>("hidden_states_37")]; |
| tensor<fp32, [1, 512, 128, 128]> var_246 = add(x = var_216, y = hidden_states_37)[name = tensor<string, []>("op_246")]; |
| tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_36 = reshape(shape = reshape_36_shape_0, x = var_246)[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, 128, 128]> sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor<string, []>("sub_18")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 128, 128]> 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, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor<string, []>("reshape_37")]; |
| 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, []>(197915904)))]; |
| 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, []>(197918016)))]; |
| tensor<fp32, []> add_19_epsilon_0 = const()[name = tensor<string, []>("add_19_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_37)[name = tensor<string, []>("add_19")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_39 = silu(x = add_19)[name = tensor<string, []>("hidden_states_39")]; |
| tensor<string, []> input_55_pad_type_0 = const()[name = tensor<string, []>("input_55_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_55_pad_0 = const()[name = tensor<string, []>("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_55_strides_0 = const()[name = tensor<string, []>("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_55_dilations_0 = const()[name = tensor<string, []>("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_55_groups_0 = const()[name = tensor<string, []>("input_55_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_55 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = decoder_up_blocks_0_resnets_2_conv1_weight, x = hidden_states_39)[name = tensor<string, []>("input_55")]; |
| tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_40 = reshape(shape = reshape_40_shape_0, x = input_55)[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, 128, 128]> sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor<string, []>("sub_20")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> 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, 128, 128]> 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, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> 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, []>(197920128)))]; |
| 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, []>(197922240)))]; |
| tensor<fp32, []> add_21_epsilon_0 = const()[name = tensor<string, []>("add_21_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_41)[name = tensor<string, []>("add_21")]; |
| tensor<fp32, [1, 512, 128, 128]> input_59 = silu(x = add_21)[name = tensor<string, []>("input_59")]; |
| tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = tensor<string, []>("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = tensor<string, []>("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_43_groups_0 = const()[name = tensor<string, []>("hidden_states_43_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_43 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias, dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = decoder_up_blocks_0_resnets_2_conv2_weight, x = input_59)[name = tensor<string, []>("hidden_states_43")]; |
| tensor<fp32, [1, 512, 128, 128]> var_276 = add(x = var_246, y = hidden_states_43)[name = tensor<string, []>("op_276")]; |
| tensor<fp32, []> hidden_states_47_scale_factor_height_0 = const()[name = tensor<string, []>("hidden_states_47_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, []> hidden_states_47_scale_factor_width_0 = const()[name = tensor<string, []>("hidden_states_47_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_47 = upsample_nearest_neighbor(scale_factor_height = hidden_states_47_scale_factor_height_0, scale_factor_width = hidden_states_47_scale_factor_width_0, x = var_276)[name = tensor<string, []>("hidden_states_47")]; |
| tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_61_strides_0 = const()[name = tensor<string, []>("input_61_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_61_dilations_0 = const()[name = tensor<string, []>("input_61_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_61_groups_0 = const()[name = tensor<string, []>("input_61_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_61 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = decoder_up_blocks_0_upsamplers_0_conv_weight, x = hidden_states_47)[name = tensor<string, []>("input_61")]; |
| 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_61)[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, []>(197924352)))]; |
| 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, []>(197926464)))]; |
| 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_1_mean_0, variance = add_1_variance_0, x = reshape_45)[name = tensor<string, []>("add_23")]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_49 = silu(x = add_23)[name = tensor<string, []>("hidden_states_49")]; |
| tensor<string, []> input_65_pad_type_0 = const()[name = tensor<string, []>("input_65_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_65_pad_0 = const()[name = tensor<string, []>("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_65_strides_0 = const()[name = tensor<string, []>("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_65_dilations_0 = const()[name = tensor<string, []>("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_65_groups_0 = const()[name = tensor<string, []>("input_65_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_65 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = decoder_up_blocks_1_resnets_0_conv1_weight, x = hidden_states_49)[name = tensor<string, []>("input_65")]; |
| tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_48 = reshape(shape = reshape_48_shape_0, x = input_65)[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, 256, 256]> sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor<string, []>("sub_24")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> 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, 256, 256]> 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, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> 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, []>(197928576)))]; |
| 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, []>(197930688)))]; |
| tensor<fp32, []> add_25_epsilon_0 = const()[name = tensor<string, []>("add_25_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_49)[name = tensor<string, []>("add_25")]; |
| tensor<fp32, [1, 512, 256, 256]> input_69 = silu(x = add_25)[name = tensor<string, []>("input_69")]; |
| tensor<string, []> hidden_states_53_pad_type_0 = const()[name = tensor<string, []>("hidden_states_53_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = tensor<string, []>("hidden_states_53_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = tensor<string, []>("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = tensor<string, []>("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_53_groups_0 = const()[name = tensor<string, []>("hidden_states_53_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_53 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias, dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = decoder_up_blocks_1_resnets_0_conv2_weight, x = input_69)[name = tensor<string, []>("hidden_states_53")]; |
| tensor<fp32, [1, 512, 256, 256]> var_324 = add(x = input_61, y = hidden_states_53)[name = tensor<string, []>("op_324")]; |
| tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_52 = reshape(shape = reshape_52_shape_0, x = var_324)[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, 256, 256]> sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor<string, []>("sub_26")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> 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, 256, 256]> 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, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> 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, []>(197932800)))]; |
| 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, []>(197934912)))]; |
| tensor<fp32, []> add_27_epsilon_0 = const()[name = tensor<string, []>("add_27_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_53)[name = tensor<string, []>("add_27")]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_55 = silu(x = add_27)[name = tensor<string, []>("hidden_states_55")]; |
| tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_75_strides_0 = const()[name = tensor<string, []>("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_75_dilations_0 = const()[name = tensor<string, []>("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_75_groups_0 = const()[name = tensor<string, []>("input_75_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_75 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = decoder_up_blocks_1_resnets_1_conv1_weight, x = hidden_states_55)[name = tensor<string, []>("input_75")]; |
| tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_56 = reshape(shape = reshape_56_shape_0, x = input_75)[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, 256, 256]> sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor<string, []>("sub_28")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> 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, 256, 256]> 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, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> 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, []>(197937024)))]; |
| 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, []>(197939136)))]; |
| tensor<fp32, []> add_29_epsilon_0 = const()[name = tensor<string, []>("add_29_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_57)[name = tensor<string, []>("add_29")]; |
| tensor<fp32, [1, 512, 256, 256]> input_79 = silu(x = add_29)[name = tensor<string, []>("input_79")]; |
| tensor<string, []> hidden_states_59_pad_type_0 = const()[name = tensor<string, []>("hidden_states_59_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_59_pad_0 = const()[name = tensor<string, []>("hidden_states_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_59_strides_0 = const()[name = tensor<string, []>("hidden_states_59_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_59_dilations_0 = const()[name = tensor<string, []>("hidden_states_59_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_59_groups_0 = const()[name = tensor<string, []>("hidden_states_59_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_59 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias, dilations = hidden_states_59_dilations_0, groups = hidden_states_59_groups_0, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = hidden_states_59_strides_0, weight = decoder_up_blocks_1_resnets_1_conv2_weight, x = input_79)[name = tensor<string, []>("hidden_states_59")]; |
| tensor<fp32, [1, 512, 256, 256]> var_354 = add(x = var_324, y = hidden_states_59)[name = tensor<string, []>("op_354")]; |
| tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_60 = reshape(shape = reshape_60_shape_0, x = var_354)[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, 256, 256]> sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor<string, []>("sub_30")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> 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, 256, 256]> 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, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> 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, []>(197941248)))]; |
| 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, []>(197943360)))]; |
| tensor<fp32, []> add_31_epsilon_0 = const()[name = tensor<string, []>("add_31_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_61)[name = tensor<string, []>("add_31")]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_61 = silu(x = add_31)[name = tensor<string, []>("hidden_states_61")]; |
| 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]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_85_strides_0 = const()[name = tensor<string, []>("input_85_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_85_dilations_0 = const()[name = tensor<string, []>("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_85_groups_0 = const()[name = tensor<string, []>("input_85_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_85 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = decoder_up_blocks_1_resnets_2_conv1_weight, x = hidden_states_61)[name = tensor<string, []>("input_85")]; |
| tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_64 = reshape(shape = reshape_64_shape_0, x = input_85)[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, 256, 256]> sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor<string, []>("sub_32")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> 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, 256, 256]> 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, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> 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, []>(197945472)))]; |
| 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, []>(197947584)))]; |
| tensor<fp32, []> add_33_epsilon_0 = const()[name = tensor<string, []>("add_33_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_65)[name = tensor<string, []>("add_33")]; |
| tensor<fp32, [1, 512, 256, 256]> input_89 = silu(x = add_33)[name = tensor<string, []>("input_89")]; |
| tensor<string, []> hidden_states_65_pad_type_0 = const()[name = tensor<string, []>("hidden_states_65_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_65_pad_0 = const()[name = tensor<string, []>("hidden_states_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_65_strides_0 = const()[name = tensor<string, []>("hidden_states_65_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_65_dilations_0 = const()[name = tensor<string, []>("hidden_states_65_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_65_groups_0 = const()[name = tensor<string, []>("hidden_states_65_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_65 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias, dilations = hidden_states_65_dilations_0, groups = hidden_states_65_groups_0, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = hidden_states_65_strides_0, weight = decoder_up_blocks_1_resnets_2_conv2_weight, x = input_89)[name = tensor<string, []>("hidden_states_65")]; |
| tensor<fp32, [1, 512, 256, 256]> var_384 = add(x = var_354, y = hidden_states_65)[name = tensor<string, []>("op_384")]; |
| tensor<fp32, []> hidden_states_69_scale_factor_height_0 = const()[name = tensor<string, []>("hidden_states_69_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, []> hidden_states_69_scale_factor_width_0 = const()[name = tensor<string, []>("hidden_states_69_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, [1, 512, 512, 512]> hidden_states_69 = upsample_nearest_neighbor(scale_factor_height = hidden_states_69_scale_factor_height_0, scale_factor_width = hidden_states_69_scale_factor_width_0, x = var_384)[name = tensor<string, []>("hidden_states_69")]; |
| 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<int32, [2]> input_91_strides_0 = const()[name = tensor<string, []>("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_91_dilations_0 = const()[name = tensor<string, []>("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_91_groups_0 = const()[name = tensor<string, []>("input_91_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 512, 512]> input_91 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = decoder_up_blocks_1_upsamplers_0_conv_weight, x = hidden_states_69)[name = tensor<string, []>("input_91")]; |
| tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 512, 512])]; |
| tensor<fp32, [1, 32, 16, 512, 512]> reshape_68 = reshape(shape = reshape_68_shape_0, x = input_91)[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, 512, 512]> sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor<string, []>("sub_34")]; |
| tensor<fp32, [1, 32, 16, 512, 512]> 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, 512, 512]> 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, 512, 512])]; |
| tensor<fp32, [1, 512, 512, 512]> 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, []>(197949696)))]; |
| 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, []>(197951808)))]; |
| tensor<fp32, []> add_35_epsilon_0 = const()[name = tensor<string, []>("add_35_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 512, 512]> add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_69)[name = tensor<string, []>("add_35")]; |
| tensor<fp32, [1, 512, 512, 512]> hidden_states_71 = silu(x = add_35)[name = tensor<string, []>("hidden_states_71")]; |
| tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_95_strides_0 = const()[name = tensor<string, []>("input_95_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_95_dilations_0 = const()[name = tensor<string, []>("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_95_groups_0 = const()[name = tensor<string, []>("input_95_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_95 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = decoder_up_blocks_2_resnets_0_conv1_weight, x = hidden_states_71)[name = tensor<string, []>("input_95")]; |
| tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_72 = reshape(shape = reshape_72_shape_0, x = input_95)[name = tensor<string, []>("reshape_72")]; |
| tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| 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, 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, 8, 512, 512]> sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor<string, []>("sub_36")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_18 = square(x = sub_36)[name = tensor<string, []>("square_18")]; |
| tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| 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, 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, 1]> add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor<string, []>("add_36")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_18 = sqrt(x = add_36)[name = tensor<string, []>("sqrt_18")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor<string, []>("real_div_18")]; |
| tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor<string, []>("reshape_73")]; |
| tensor<fp32, [256]> add_37_mean_0 = const()[name = tensor<string, []>("add_37_mean_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197953920)))]; |
| tensor<fp32, [256]> add_37_variance_0 = const()[name = tensor<string, []>("add_37_variance_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197955008)))]; |
| tensor<fp32, [256]> add_37_gamma_0 = const()[name = tensor<string, []>("add_37_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197956096)))]; |
| tensor<fp32, [256]> add_37_beta_0 = const()[name = tensor<string, []>("add_37_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197957184)))]; |
| tensor<fp32, []> add_37_epsilon_0 = const()[name = tensor<string, []>("add_37_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_73)[name = tensor<string, []>("add_37")]; |
| tensor<fp32, [1, 256, 512, 512]> input_99 = silu(x = add_37)[name = tensor<string, []>("input_99")]; |
| tensor<string, []> hidden_states_75_pad_type_0 = const()[name = tensor<string, []>("hidden_states_75_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_75_pad_0 = const()[name = tensor<string, []>("hidden_states_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_75_strides_0 = const()[name = tensor<string, []>("hidden_states_75_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_75_dilations_0 = const()[name = tensor<string, []>("hidden_states_75_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_75_groups_0 = const()[name = tensor<string, []>("hidden_states_75_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_75 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias, dilations = hidden_states_75_dilations_0, groups = hidden_states_75_groups_0, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = hidden_states_75_strides_0, weight = decoder_up_blocks_2_resnets_0_conv2_weight, x = input_99)[name = tensor<string, []>("hidden_states_75")]; |
| tensor<string, []> input_tensor_1_pad_type_0 = const()[name = tensor<string, []>("input_tensor_1_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_tensor_1_strides_0 = const()[name = tensor<string, []>("input_tensor_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| 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<int32, [2]> input_tensor_1_dilations_0 = const()[name = tensor<string, []>("input_tensor_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_tensor_1_groups_0 = const()[name = tensor<string, []>("input_tensor_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_tensor_1 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = input_tensor_1_dilations_0, groups = input_tensor_1_groups_0, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = input_tensor_1_strides_0, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_91)[name = tensor<string, []>("input_tensor_1")]; |
| tensor<fp32, [1, 256, 512, 512]> var_440 = add(x = input_tensor_1, y = hidden_states_75)[name = tensor<string, []>("op_440")]; |
| tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_76 = reshape(shape = reshape_76_shape_0, x = var_440)[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, 8, 512, 512]> sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor<string, []>("sub_38")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> 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, 8, 512, 512]> 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, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor<string, []>("reshape_77")]; |
| tensor<fp32, [256]> add_39_gamma_0 = const()[name = tensor<string, []>("add_39_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197958272)))]; |
| tensor<fp32, [256]> add_39_beta_0 = const()[name = tensor<string, []>("add_39_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197959360)))]; |
| tensor<fp32, []> add_39_epsilon_0 = const()[name = tensor<string, []>("add_39_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_77)[name = tensor<string, []>("add_39")]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_77 = silu(x = add_39)[name = tensor<string, []>("hidden_states_77")]; |
| 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<int32, [2]> input_105_strides_0 = const()[name = tensor<string, []>("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_105_dilations_0 = const()[name = tensor<string, []>("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_105_groups_0 = const()[name = tensor<string, []>("input_105_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_105 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = decoder_up_blocks_2_resnets_1_conv1_weight, x = hidden_states_77)[name = tensor<string, []>("input_105")]; |
| tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_80 = reshape(shape = reshape_80_shape_0, x = input_105)[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, 8, 512, 512]> sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor<string, []>("sub_40")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> 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, 8, 512, 512]> 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, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor<string, []>("reshape_81")]; |
| tensor<fp32, [256]> add_41_gamma_0 = const()[name = tensor<string, []>("add_41_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197960448)))]; |
| tensor<fp32, [256]> add_41_beta_0 = const()[name = tensor<string, []>("add_41_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197961536)))]; |
| tensor<fp32, []> add_41_epsilon_0 = const()[name = tensor<string, []>("add_41_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_81)[name = tensor<string, []>("add_41")]; |
| tensor<fp32, [1, 256, 512, 512]> input_109 = silu(x = add_41)[name = tensor<string, []>("input_109")]; |
| tensor<string, []> hidden_states_81_pad_type_0 = const()[name = tensor<string, []>("hidden_states_81_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_81_pad_0 = const()[name = tensor<string, []>("hidden_states_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_81_strides_0 = const()[name = tensor<string, []>("hidden_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_81_dilations_0 = const()[name = tensor<string, []>("hidden_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_81_groups_0 = const()[name = tensor<string, []>("hidden_states_81_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_81 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias, dilations = hidden_states_81_dilations_0, groups = hidden_states_81_groups_0, pad = hidden_states_81_pad_0, pad_type = hidden_states_81_pad_type_0, strides = hidden_states_81_strides_0, weight = decoder_up_blocks_2_resnets_1_conv2_weight, x = input_109)[name = tensor<string, []>("hidden_states_81")]; |
| tensor<fp32, [1, 256, 512, 512]> var_470 = add(x = var_440, y = hidden_states_81)[name = tensor<string, []>("op_470")]; |
| tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_84 = reshape(shape = reshape_84_shape_0, x = var_470)[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, 8, 512, 512]> sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor<string, []>("sub_42")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> 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, 8, 512, 512]> 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, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor<string, []>("reshape_85")]; |
| tensor<fp32, [256]> add_43_gamma_0 = const()[name = tensor<string, []>("add_43_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197962624)))]; |
| tensor<fp32, [256]> add_43_beta_0 = const()[name = tensor<string, []>("add_43_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197963712)))]; |
| tensor<fp32, []> add_43_epsilon_0 = const()[name = tensor<string, []>("add_43_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_85)[name = tensor<string, []>("add_43")]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_83 = silu(x = add_43)[name = tensor<string, []>("hidden_states_83")]; |
| tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_115_strides_0 = const()[name = tensor<string, []>("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_115_dilations_0 = const()[name = tensor<string, []>("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_115_groups_0 = const()[name = tensor<string, []>("input_115_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_115 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = decoder_up_blocks_2_resnets_2_conv1_weight, x = hidden_states_83)[name = tensor<string, []>("input_115")]; |
| tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_88 = reshape(shape = reshape_88_shape_0, x = input_115)[name = tensor<string, []>("reshape_88")]; |
| tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = tensor<string, []>("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_66_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_66_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = tensor<string, []>("reduce_mean_66")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = tensor<string, []>("sub_44")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_22 = square(x = sub_44)[name = tensor<string, []>("square_22")]; |
| tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = tensor<string, []>("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_68_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_68_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = tensor<string, []>("reduce_mean_68")]; |
| tensor<fp32, []> add_44_y_0 = const()[name = tensor<string, []>("add_44_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = tensor<string, []>("add_44")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_22 = sqrt(x = add_44)[name = tensor<string, []>("sqrt_22")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = tensor<string, []>("real_div_22")]; |
| tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = tensor<string, []>("reshape_89")]; |
| tensor<fp32, [256]> add_45_gamma_0 = const()[name = tensor<string, []>("add_45_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197964800)))]; |
| tensor<fp32, [256]> add_45_beta_0 = const()[name = tensor<string, []>("add_45_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197965888)))]; |
| tensor<fp32, []> add_45_epsilon_0 = const()[name = tensor<string, []>("add_45_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_89)[name = tensor<string, []>("add_45")]; |
| tensor<fp32, [1, 256, 512, 512]> input_119 = silu(x = add_45)[name = tensor<string, []>("input_119")]; |
| tensor<string, []> hidden_states_87_pad_type_0 = const()[name = tensor<string, []>("hidden_states_87_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = tensor<string, []>("hidden_states_87_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = tensor<string, []>("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = tensor<string, []>("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_87_groups_0 = const()[name = tensor<string, []>("hidden_states_87_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_87 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias, dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = decoder_up_blocks_2_resnets_2_conv2_weight, x = input_119)[name = tensor<string, []>("hidden_states_87")]; |
| tensor<fp32, [1, 256, 512, 512]> var_500 = add(x = var_470, y = hidden_states_87)[name = tensor<string, []>("op_500")]; |
| tensor<fp32, []> hidden_states_91_scale_factor_height_0 = const()[name = tensor<string, []>("hidden_states_91_scale_factor_height_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, []> hidden_states_91_scale_factor_width_0 = const()[name = tensor<string, []>("hidden_states_91_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+1)]; |
| tensor<fp32, [1, 256, 1024, 1024]> hidden_states_91 = upsample_nearest_neighbor(scale_factor_height = hidden_states_91_scale_factor_height_0, scale_factor_width = hidden_states_91_scale_factor_width_0, x = var_500)[name = tensor<string, []>("hidden_states_91")]; |
| tensor<string, []> input_121_pad_type_0 = const()[name = tensor<string, []>("input_121_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_121_pad_0 = const()[name = tensor<string, []>("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_121_strides_0 = const()[name = tensor<string, []>("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_121_dilations_0 = const()[name = tensor<string, []>("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_121_groups_0 = const()[name = tensor<string, []>("input_121_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 1024, 1024]> input_121 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = decoder_up_blocks_2_upsamplers_0_conv_weight, x = hidden_states_91)[name = tensor<string, []>("input_121")]; |
| tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 1024, 1024])]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> reshape_92 = reshape(shape = reshape_92_shape_0, x = input_121)[name = tensor<string, []>("reshape_92")]; |
| tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = tensor<string, []>("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_69_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_69_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = tensor<string, []>("reduce_mean_69")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = tensor<string, []>("sub_46")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> square_23 = square(x = sub_46)[name = tensor<string, []>("square_23")]; |
| tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = tensor<string, []>("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_71_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_71_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = tensor<string, []>("reduce_mean_71")]; |
| tensor<fp32, []> add_46_y_0 = const()[name = tensor<string, []>("add_46_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = tensor<string, []>("add_46")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_23 = sqrt(x = add_46)[name = tensor<string, []>("sqrt_23")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = tensor<string, []>("real_div_23")]; |
| tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([1, 256, 1024, 1024])]; |
| tensor<fp32, [1, 256, 1024, 1024]> reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = tensor<string, []>("reshape_93")]; |
| tensor<fp32, [256]> add_47_gamma_0 = const()[name = tensor<string, []>("add_47_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197966976)))]; |
| tensor<fp32, [256]> add_47_beta_0 = const()[name = tensor<string, []>("add_47_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197968064)))]; |
| tensor<fp32, []> add_47_epsilon_0 = const()[name = tensor<string, []>("add_47_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 1024, 1024]> add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_93)[name = tensor<string, []>("add_47")]; |
| tensor<fp32, [1, 256, 1024, 1024]> hidden_states_93 = silu(x = add_47)[name = tensor<string, []>("hidden_states_93")]; |
| tensor<string, []> input_125_pad_type_0 = const()[name = tensor<string, []>("input_125_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_125_pad_0 = const()[name = tensor<string, []>("input_125_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_125_strides_0 = const()[name = tensor<string, []>("input_125_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_125_dilations_0 = const()[name = tensor<string, []>("input_125_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_125_groups_0 = const()[name = tensor<string, []>("input_125_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_125 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = decoder_up_blocks_3_resnets_0_conv1_weight, x = hidden_states_93)[name = tensor<string, []>("input_125")]; |
| tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_96 = reshape(shape = reshape_96_shape_0, x = input_125)[name = tensor<string, []>("reshape_96")]; |
| tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = tensor<string, []>("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_72_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_72_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = tensor<string, []>("reduce_mean_72")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = tensor<string, []>("sub_48")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_24 = square(x = sub_48)[name = tensor<string, []>("square_24")]; |
| tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = tensor<string, []>("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_74_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_74_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = tensor<string, []>("reduce_mean_74")]; |
| tensor<fp32, []> add_48_y_0 = const()[name = tensor<string, []>("add_48_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = tensor<string, []>("add_48")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_24 = sqrt(x = add_48)[name = tensor<string, []>("sqrt_24")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = tensor<string, []>("real_div_24")]; |
| tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = tensor<string, []>("reshape_97")]; |
| tensor<fp32, [128]> add_49_mean_0 = const()[name = tensor<string, []>("add_49_mean_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197969152)))]; |
| tensor<fp32, [128]> add_49_variance_0 = const()[name = tensor<string, []>("add_49_variance_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197969728)))]; |
| tensor<fp32, [128]> add_49_gamma_0 = const()[name = tensor<string, []>("add_49_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197970304)))]; |
| tensor<fp32, [128]> add_49_beta_0 = const()[name = tensor<string, []>("add_49_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197970880)))]; |
| tensor<fp32, []> add_49_epsilon_0 = const()[name = tensor<string, []>("add_49_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_97)[name = tensor<string, []>("add_49")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_129 = silu(x = add_49)[name = tensor<string, []>("input_129")]; |
| tensor<string, []> hidden_states_97_pad_type_0 = const()[name = tensor<string, []>("hidden_states_97_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = tensor<string, []>("hidden_states_97_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = tensor<string, []>("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = tensor<string, []>("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_97_groups_0 = const()[name = tensor<string, []>("hidden_states_97_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_97 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias, dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = decoder_up_blocks_3_resnets_0_conv2_weight, x = input_129)[name = tensor<string, []>("hidden_states_97")]; |
| tensor<string, []> input_tensor_pad_type_0 = const()[name = tensor<string, []>("input_tensor_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [2]> input_tensor_strides_0 = const()[name = tensor<string, []>("input_tensor_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_tensor_pad_0 = const()[name = tensor<string, []>("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_tensor_dilations_0 = const()[name = tensor<string, []>("input_tensor_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_tensor_groups_0 = const()[name = tensor<string, []>("input_tensor_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_tensor = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = input_tensor_dilations_0, groups = input_tensor_groups_0, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = input_tensor_strides_0, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_121)[name = tensor<string, []>("input_tensor")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_554 = add(x = input_tensor, y = hidden_states_97)[name = tensor<string, []>("op_554")]; |
| tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_100 = reshape(shape = reshape_100_shape_0, x = var_554)[name = tensor<string, []>("reshape_100")]; |
| tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = tensor<string, []>("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_75_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_75_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor<string, []>("reduce_mean_75")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = tensor<string, []>("sub_50")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_25 = square(x = sub_50)[name = tensor<string, []>("square_25")]; |
| tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = tensor<string, []>("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_77_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_77_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = tensor<string, []>("reduce_mean_77")]; |
| tensor<fp32, []> add_50_y_0 = const()[name = tensor<string, []>("add_50_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = tensor<string, []>("add_50")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_25 = sqrt(x = add_50)[name = tensor<string, []>("sqrt_25")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = tensor<string, []>("real_div_25")]; |
| tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = tensor<string, []>("reshape_101")]; |
| tensor<fp32, [128]> add_51_gamma_0 = const()[name = tensor<string, []>("add_51_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197971456)))]; |
| tensor<fp32, [128]> add_51_beta_0 = const()[name = tensor<string, []>("add_51_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197972032)))]; |
| tensor<fp32, []> add_51_epsilon_0 = const()[name = tensor<string, []>("add_51_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_101)[name = tensor<string, []>("add_51")]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_99 = silu(x = add_51)[name = tensor<string, []>("hidden_states_99")]; |
| tensor<string, []> input_135_pad_type_0 = const()[name = tensor<string, []>("input_135_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_135_pad_0 = const()[name = tensor<string, []>("input_135_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_135_strides_0 = const()[name = tensor<string, []>("input_135_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_135_dilations_0 = const()[name = tensor<string, []>("input_135_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_135_groups_0 = const()[name = tensor<string, []>("input_135_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_135 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = decoder_up_blocks_3_resnets_1_conv1_weight, x = hidden_states_99)[name = tensor<string, []>("input_135")]; |
| tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_104 = reshape(shape = reshape_104_shape_0, x = input_135)[name = tensor<string, []>("reshape_104")]; |
| tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = tensor<string, []>("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_78_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_78_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = tensor<string, []>("reduce_mean_78")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = tensor<string, []>("sub_52")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_26 = square(x = sub_52)[name = tensor<string, []>("square_26")]; |
| tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = tensor<string, []>("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_80_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_80_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = tensor<string, []>("reduce_mean_80")]; |
| tensor<fp32, []> add_52_y_0 = const()[name = tensor<string, []>("add_52_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = tensor<string, []>("add_52")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_26 = sqrt(x = add_52)[name = tensor<string, []>("sqrt_26")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = tensor<string, []>("real_div_26")]; |
| tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = tensor<string, []>("reshape_105")]; |
| tensor<fp32, [128]> add_53_gamma_0 = const()[name = tensor<string, []>("add_53_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197972608)))]; |
| tensor<fp32, [128]> add_53_beta_0 = const()[name = tensor<string, []>("add_53_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197973184)))]; |
| tensor<fp32, []> add_53_epsilon_0 = const()[name = tensor<string, []>("add_53_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_105)[name = tensor<string, []>("add_53")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_139 = silu(x = add_53)[name = tensor<string, []>("input_139")]; |
| tensor<string, []> hidden_states_103_pad_type_0 = const()[name = tensor<string, []>("hidden_states_103_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = tensor<string, []>("hidden_states_103_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = tensor<string, []>("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = tensor<string, []>("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_103_groups_0 = const()[name = tensor<string, []>("hidden_states_103_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_103 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias, dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = decoder_up_blocks_3_resnets_1_conv2_weight, x = input_139)[name = tensor<string, []>("hidden_states_103")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_584 = add(x = var_554, y = hidden_states_103)[name = tensor<string, []>("op_584")]; |
| tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_108 = reshape(shape = reshape_108_shape_0, x = var_584)[name = tensor<string, []>("reshape_108")]; |
| tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = tensor<string, []>("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_81_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_81_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor<string, []>("reduce_mean_81")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = tensor<string, []>("sub_54")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_27 = square(x = sub_54)[name = tensor<string, []>("square_27")]; |
| tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = tensor<string, []>("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_83_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_83_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = tensor<string, []>("reduce_mean_83")]; |
| tensor<fp32, []> add_54_y_0 = const()[name = tensor<string, []>("add_54_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = tensor<string, []>("add_54")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_27 = sqrt(x = add_54)[name = tensor<string, []>("sqrt_27")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = tensor<string, []>("real_div_27")]; |
| tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = tensor<string, []>("reshape_109")]; |
| tensor<fp32, [128]> add_55_gamma_0 = const()[name = tensor<string, []>("add_55_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197973760)))]; |
| tensor<fp32, [128]> add_55_beta_0 = const()[name = tensor<string, []>("add_55_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197974336)))]; |
| tensor<fp32, []> add_55_epsilon_0 = const()[name = tensor<string, []>("add_55_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_109)[name = tensor<string, []>("add_55")]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_105 = silu(x = add_55)[name = tensor<string, []>("hidden_states_105")]; |
| tensor<string, []> input_145_pad_type_0 = const()[name = tensor<string, []>("input_145_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> input_145_pad_0 = const()[name = tensor<string, []>("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_145_strides_0 = const()[name = tensor<string, []>("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_145_dilations_0 = const()[name = tensor<string, []>("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> input_145_groups_0 = const()[name = tensor<string, []>("input_145_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_145 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = decoder_up_blocks_3_resnets_2_conv1_weight, x = hidden_states_105)[name = tensor<string, []>("input_145")]; |
| tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_112 = reshape(shape = reshape_112_shape_0, x = input_145)[name = tensor<string, []>("reshape_112")]; |
| tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = tensor<string, []>("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_84_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_84_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = tensor<string, []>("reduce_mean_84")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = tensor<string, []>("sub_56")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_28 = square(x = sub_56)[name = tensor<string, []>("square_28")]; |
| tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = tensor<string, []>("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_86_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_86_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = tensor<string, []>("reduce_mean_86")]; |
| tensor<fp32, []> add_56_y_0 = const()[name = tensor<string, []>("add_56_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = tensor<string, []>("add_56")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_28 = sqrt(x = add_56)[name = tensor<string, []>("sqrt_28")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = tensor<string, []>("real_div_28")]; |
| tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = tensor<string, []>("reshape_113")]; |
| tensor<fp32, [128]> add_57_gamma_0 = const()[name = tensor<string, []>("add_57_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197974912)))]; |
| tensor<fp32, [128]> add_57_beta_0 = const()[name = tensor<string, []>("add_57_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197975488)))]; |
| tensor<fp32, []> add_57_epsilon_0 = const()[name = tensor<string, []>("add_57_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_113)[name = tensor<string, []>("add_57")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_149 = silu(x = add_57)[name = tensor<string, []>("input_149")]; |
| 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<int32, [2]> hidden_states_strides_0 = const()[name = tensor<string, []>("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_dilations_0 = const()[name = tensor<string, []>("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> hidden_states_groups_0 = const()[name = tensor<string, []>("hidden_states_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = decoder_up_blocks_3_resnets_2_conv2_weight, x = input_149)[name = tensor<string, []>("hidden_states")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_614 = add(x = var_584, y = hidden_states)[name = tensor<string, []>("op_614")]; |
| tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_116 = reshape(shape = reshape_116_shape_0, x = var_614)[name = tensor<string, []>("reshape_116")]; |
| tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = tensor<string, []>("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_87_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_87_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor<string, []>("reduce_mean_87")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = tensor<string, []>("sub_58")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_29 = square(x = sub_58)[name = tensor<string, []>("square_29")]; |
| tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = tensor<string, []>("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| tensor<bool, []> reduce_mean_89_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_89_keep_dims_0"), val = tensor<bool, []>(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = tensor<string, []>("reduce_mean_89")]; |
| tensor<fp32, []> add_58_y_0 = const()[name = tensor<string, []>("add_58_y_0"), val = tensor<fp32, []>(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = tensor<string, []>("add_58")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_29 = sqrt(x = add_58)[name = tensor<string, []>("sqrt_29")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = tensor<string, []>("real_div_29")]; |
| tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = tensor<string, []>("reshape_117")]; |
| tensor<fp32, [128]> add_59_gamma_0 = const()[name = tensor<string, []>("add_59_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197976064)))]; |
| tensor<fp32, [128]> add_59_beta_0 = const()[name = tensor<string, []>("add_59_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197976640)))]; |
| tensor<fp32, []> add_59_epsilon_0 = const()[name = tensor<string, []>("add_59_epsilon_0"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_117)[name = tensor<string, []>("add_59")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input = silu(x = add_59)[name = tensor<string, []>("input")]; |
| tensor<string, []> var_627_pad_type_0 = const()[name = tensor<string, []>("op_627_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [4]> var_627_pad_0 = const()[name = tensor<string, []>("op_627_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> var_627_strides_0 = const()[name = tensor<string, []>("op_627_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> var_627_dilations_0 = const()[name = tensor<string, []>("op_627_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> var_627_groups_0 = const()[name = tensor<string, []>("op_627_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 3, 1024, 1024]> image = conv(bias = decoder_conv_out_bias, dilations = var_627_dilations_0, groups = var_627_groups_0, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_627_strides_0, weight = decoder_conv_out_weight, x = input)[name = tensor<string, []>("op_627")]; |
| } -> (image); |
| } |
