| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios18>(tensor<fp16, [1, 4, 64, 64]> z) { |
| string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)]; |
| tensor<fp16, [4, 4, 1, 1]> post_quant_conv_weight_to_fp16 = const()[name = string("post_quant_conv_weight_to_fp16"), val = tensor<fp16, [4, 4, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| tensor<fp16, [4]> post_quant_conv_bias_to_fp16 = const()[name = string("post_quant_conv_bias_to_fp16"), val = tensor<fp16, [4]>([-0x1.a6p-6, -0x1.9f4p-4, -0x1.b58p-3, 0x1.7fp-3])]; |
| tensor<fp16, [1, 4, 64, 64]> input_1_cast_fp16 = conv(bias = post_quant_conv_bias_to_fp16, 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_to_fp16, x = z)[name = string("input_1_cast_fp16")]; |
| string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 4, 3, 3]> decoder_conv_in_weight_to_fp16 = const()[name = string("decoder_conv_in_weight_to_fp16"), val = tensor<fp16, [512, 4, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192)))]; |
| tensor<fp16, [512]> decoder_conv_in_bias_to_fp16 = const()[name = string("decoder_conv_in_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37120)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_3_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, 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_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; |
| tensor<int32, [5]> reshape_0_shape_0 = const()[name = string("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_3_cast_fp16)[name = string("reshape_0_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = string("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_0_keep_dims_0 = const()[name = string("reduce_mean_0_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = string("reduce_mean_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = string("sub_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = string("square_0_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = string("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_2_keep_dims_0 = const()[name = string("reduce_mean_2_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = string("reduce_mean_2_cast_fp16")]; |
| fp16 add_0_y_0_to_fp16 = const()[name = string("add_0_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = string("add_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = string("sqrt_0_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = string("real_div_0_cast_fp16")]; |
| tensor<int32, [4]> reshape_1_shape_0 = const()[name = string("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = string("reshape_1_cast_fp16")]; |
| tensor<fp16, [512]> add_1_mean_0_to_fp16 = const()[name = string("add_1_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38208)))]; |
| tensor<fp16, [512]> add_1_variance_0_to_fp16 = const()[name = string("add_1_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39296)))]; |
| tensor<fp16, [512]> add_1_gamma_0_to_fp16 = const()[name = string("add_1_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40384)))]; |
| tensor<fp16, [512]> add_1_beta_0_to_fp16 = const()[name = string("add_1_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41472)))]; |
| fp16 add_1_epsilon_0_to_fp16 = const()[name = string("add_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = string("add_1_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_7_cast_fp16 = silu(x = add_1_cast_fp16)[name = string("input_7_cast_fp16")]; |
| string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = string("decoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42560)))]; |
| tensor<fp16, [512]> decoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = string("decoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4761216)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_9_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")]; |
| tensor<int32, [5]> reshape_4_shape_0 = const()[name = string("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_9_cast_fp16)[name = string("reshape_4_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = string("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_3_keep_dims_0 = const()[name = string("reduce_mean_3_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = string("reduce_mean_3_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = string("sub_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = string("square_1_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = string("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_5_keep_dims_0 = const()[name = string("reduce_mean_5_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = string("reduce_mean_5_cast_fp16")]; |
| fp16 add_2_y_0_to_fp16 = const()[name = string("add_2_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = string("add_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = string("sqrt_1_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = string("real_div_1_cast_fp16")]; |
| tensor<int32, [4]> reshape_5_shape_0 = const()[name = string("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = string("reshape_5_cast_fp16")]; |
| tensor<fp16, [512]> add_3_gamma_0_to_fp16 = const()[name = string("add_3_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4762304)))]; |
| tensor<fp16, [512]> add_3_beta_0_to_fp16 = const()[name = string("add_3_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4763392)))]; |
| fp16 add_3_epsilon_0_to_fp16 = const()[name = string("add_3_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = string("add_3_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_13_cast_fp16 = silu(x = add_3_cast_fp16)[name = string("input_13_cast_fp16")]; |
| string hidden_states_1_pad_type_0 = const()[name = string("hidden_states_1_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = string("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_1_strides_0 = const()[name = string("hidden_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_1_dilations_0 = const()[name = string("hidden_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_1_groups_0 = const()[name = string("hidden_states_1_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = string("decoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4764480)))]; |
| tensor<fp16, [512]> decoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = string("decoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9483136)))]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_1_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_1_dilations_0, groups = hidden_states_1_groups_0, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = hidden_states_1_strides_0, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_13_cast_fp16)[name = string("hidden_states_1_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> var_82_cast_fp16 = add(x = input_3_cast_fp16, y = hidden_states_1_cast_fp16)[name = string("op_82_cast_fp16")]; |
| tensor<int32, [4]> reshape_8_shape_0 = const()[name = string("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 4096])]; |
| tensor<fp16, [1, 32, 16, 4096]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_82_cast_fp16)[name = string("reshape_8_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_6_axes_0 = const()[name = string("reduce_mean_6_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| bool reduce_mean_6_keep_dims_0 = const()[name = string("reduce_mean_6_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = string("reduce_mean_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 4096]> sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = string("sub_4_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 4096]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = string("square_2_cast_fp16")]; |
| tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = string("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| bool reduce_mean_8_keep_dims_0 = const()[name = string("reduce_mean_8_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1]> reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = string("reduce_mean_8_cast_fp16")]; |
| fp16 add_4_y_0_to_fp16 = const()[name = string("add_4_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = string("add_4_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = string("sqrt_2_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 4096]> real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = string("real_div_2_cast_fp16")]; |
| tensor<int32, [3]> reshape_9_shape_0 = const()[name = string("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 512, 4096])]; |
| tensor<fp16, [1, 512, 4096]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = string("reshape_9_cast_fp16")]; |
| tensor<fp16, [1, 512, 1]> reshape_10_to_fp16 = const()[name = string("reshape_10_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9484224)))]; |
| tensor<fp16, [1, 512, 4096]> mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = string("mul_2_cast_fp16")]; |
| tensor<fp16, [1, 512, 1]> reshape_11_to_fp16 = const()[name = string("reshape_11_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9485312)))]; |
| tensor<fp16, [1, 512, 4096]> add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = string("add_5_cast_fp16")]; |
| tensor<int32, [3]> input_19_perm_0 = const()[name = string("input_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9486400)))]; |
| tensor<fp16, [512]> decoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10010752)))]; |
| tensor<fp16, [1, 4096, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = add_5_cast_fp16)[name = string("transpose_14")]; |
| tensor<fp16, [1, 4096, 512]> linear_0_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_q_weight_to_fp16, x = input_19_cast_fp16)[name = string("linear_0_cast_fp16")]; |
| tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10011840)))]; |
| tensor<fp16, [512]> decoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10536192)))]; |
| tensor<fp16, [1, 4096, 512]> linear_1_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_k_weight_to_fp16, x = input_19_cast_fp16)[name = string("linear_1_cast_fp16")]; |
| tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10537280)))]; |
| tensor<fp16, [512]> decoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11061632)))]; |
| tensor<fp16, [1, 4096, 512]> linear_2_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_v_weight_to_fp16, x = input_19_cast_fp16)[name = string("linear_2_cast_fp16")]; |
| tensor<int32, [4]> var_123 = const()[name = string("op_123"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp16, [1, 4096, 1, 512]> var_124_cast_fp16 = reshape(shape = var_123, x = linear_0_cast_fp16)[name = string("op_124_cast_fp16")]; |
| tensor<int32, [4]> var_126 = const()[name = string("op_126"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp16, [1, 4096, 1, 512]> var_127_cast_fp16 = reshape(shape = var_126, x = linear_1_cast_fp16)[name = string("op_127_cast_fp16")]; |
| tensor<int32, [4]> var_129 = const()[name = string("op_129"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp16, [1, 4096, 1, 512]> var_130_cast_fp16 = reshape(shape = var_129, x = linear_2_cast_fp16)[name = string("op_130_cast_fp16")]; |
| tensor<int32, [4]> transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [4]> transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [4]> transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1, 1, 4096, 512]> transpose_8 = transpose(perm = transpose_8_perm_0, x = var_130_cast_fp16)[name = string("transpose_11")]; |
| tensor<fp16, [1, 1, 4096, 512]> transpose_7 = transpose(perm = transpose_7_perm_0, x = var_127_cast_fp16)[name = string("transpose_12")]; |
| tensor<fp16, [1, 1, 4096, 512]> transpose_6 = transpose(perm = transpose_6_perm_0, x = var_124_cast_fp16)[name = string("transpose_13")]; |
| tensor<fp16, [1, 1, 4096, 512]> hidden_states_7_cast_fp16 = scaled_dot_product_attention(key = transpose_7, query = transpose_6, value = transpose_8)[name = string("hidden_states_7_cast_fp16")]; |
| tensor<int32, [4]> var_133_perm_0 = const()[name = string("op_133_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_137 = const()[name = string("op_137"), val = tensor<int32, [3]>([1, -1, 512])]; |
| tensor<fp16, [1, 4096, 1, 512]> var_133_cast_fp16 = transpose(perm = var_133_perm_0, x = hidden_states_7_cast_fp16)[name = string("transpose_10")]; |
| tensor<fp16, [1, 4096, 512]> hidden_states_9_cast_fp16 = reshape(shape = var_137, x = var_133_cast_fp16)[name = string("hidden_states_9_cast_fp16")]; |
| tensor<fp16, [512, 512]> decoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11062720)))]; |
| tensor<fp16, [512]> decoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = string("decoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11587072)))]; |
| tensor<fp16, [1, 4096, 512]> linear_3_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_3_cast_fp16")]; |
| tensor<int32, [3]> var_144_perm_0 = const()[name = string("op_144_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, [4]> var_145 = const()[name = string("op_145"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 4096]> var_144_cast_fp16 = transpose(perm = var_144_perm_0, x = linear_3_cast_fp16)[name = string("transpose_9")]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_13_cast_fp16 = reshape(shape = var_145, x = var_144_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_15_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_82_cast_fp16)[name = string("hidden_states_15_cast_fp16")]; |
| tensor<int32, [5]> reshape_12_shape_0 = const()[name = string("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = hidden_states_15_cast_fp16)[name = string("reshape_12_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = string("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_9_keep_dims_0 = const()[name = string("reduce_mean_9_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = string("reduce_mean_9_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = string("sub_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = string("square_3_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = string("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_11_keep_dims_0 = const()[name = string("reduce_mean_11_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = string("reduce_mean_11_cast_fp16")]; |
| fp16 add_6_y_0_to_fp16 = const()[name = string("add_6_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = string("add_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = string("sqrt_3_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = string("real_div_3_cast_fp16")]; |
| tensor<int32, [4]> reshape_13_shape_0 = const()[name = string("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = string("reshape_13_cast_fp16")]; |
| tensor<fp16, [512]> add_7_gamma_0_to_fp16 = const()[name = string("add_7_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11588160)))]; |
| tensor<fp16, [512]> add_7_beta_0_to_fp16 = const()[name = string("add_7_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11589248)))]; |
| fp16 add_7_epsilon_0_to_fp16 = const()[name = string("add_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = string("add_7_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_29_cast_fp16 = silu(x = add_7_cast_fp16)[name = string("input_29_cast_fp16")]; |
| string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = string("decoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11590336)))]; |
| tensor<fp16, [512]> decoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = string("decoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16308992)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_31_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; |
| tensor<int32, [5]> reshape_16_shape_0 = const()[name = string("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_31_cast_fp16)[name = string("reshape_16_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = string("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_12_keep_dims_0 = const()[name = string("reduce_mean_12_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = string("reduce_mean_12_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = string("sub_8_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = string("square_4_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = string("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_14_keep_dims_0 = const()[name = string("reduce_mean_14_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = string("reduce_mean_14_cast_fp16")]; |
| fp16 add_8_y_0_to_fp16 = const()[name = string("add_8_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = string("add_8_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = string("sqrt_4_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = string("real_div_4_cast_fp16")]; |
| tensor<int32, [4]> reshape_17_shape_0 = const()[name = string("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = string("reshape_17_cast_fp16")]; |
| tensor<fp16, [512]> add_9_gamma_0_to_fp16 = const()[name = string("add_9_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16310080)))]; |
| tensor<fp16, [512]> add_9_beta_0_to_fp16 = const()[name = string("add_9_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16311168)))]; |
| fp16 add_9_epsilon_0_to_fp16 = const()[name = string("add_9_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = string("add_9_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_35_cast_fp16 = silu(x = add_9_cast_fp16)[name = string("input_35_cast_fp16")]; |
| string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = string("decoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16312256)))]; |
| tensor<fp16, [512]> decoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = string("decoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21030912)))]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_17_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_35_cast_fp16)[name = string("hidden_states_17_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> var_177_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("op_177_cast_fp16")]; |
| tensor<int32, [5]> reshape_20_shape_0 = const()[name = string("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_177_cast_fp16)[name = string("reshape_20_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = string("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_15_keep_dims_0 = const()[name = string("reduce_mean_15_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = string("reduce_mean_15_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = string("sub_10_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = string("square_5_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = string("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_17_keep_dims_0 = const()[name = string("reduce_mean_17_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = string("reduce_mean_17_cast_fp16")]; |
| fp16 add_10_y_0_to_fp16 = const()[name = string("add_10_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = string("add_10_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = string("sqrt_5_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = string("real_div_5_cast_fp16")]; |
| tensor<int32, [4]> reshape_21_shape_0 = const()[name = string("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = string("reshape_21_cast_fp16")]; |
| tensor<fp16, [512]> add_11_gamma_0_to_fp16 = const()[name = string("add_11_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21032000)))]; |
| tensor<fp16, [512]> add_11_beta_0_to_fp16 = const()[name = string("add_11_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21033088)))]; |
| fp16 add_11_epsilon_0_to_fp16 = const()[name = string("add_11_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = string("add_11_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_43_cast_fp16 = silu(x = add_11_cast_fp16)[name = string("input_43_cast_fp16")]; |
| string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21034176)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25752832)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_45_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, 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_0_conv1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; |
| tensor<int32, [5]> reshape_24_shape_0 = const()[name = string("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_45_cast_fp16)[name = string("reshape_24_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = string("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_18_keep_dims_0 = const()[name = string("reduce_mean_18_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = string("reduce_mean_18_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = string("sub_12_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = string("square_6_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = string("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_20_keep_dims_0 = const()[name = string("reduce_mean_20_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = string("reduce_mean_20_cast_fp16")]; |
| fp16 add_12_y_0_to_fp16 = const()[name = string("add_12_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = string("add_12_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = string("sqrt_6_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = string("real_div_6_cast_fp16")]; |
| tensor<int32, [4]> reshape_25_shape_0 = const()[name = string("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = string("reshape_25_cast_fp16")]; |
| tensor<fp16, [512]> add_13_gamma_0_to_fp16 = const()[name = string("add_13_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25753920)))]; |
| tensor<fp16, [512]> add_13_beta_0_to_fp16 = const()[name = string("add_13_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25755008)))]; |
| fp16 add_13_epsilon_0_to_fp16 = const()[name = string("add_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = string("add_13_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_49_cast_fp16 = silu(x = add_13_cast_fp16)[name = string("input_49_cast_fp16")]; |
| string hidden_states_19_pad_type_0 = const()[name = string("hidden_states_19_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = string("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = string("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_19_dilations_0 = const()[name = string("hidden_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_19_groups_0 = const()[name = string("hidden_states_19_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25756096)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30474752)))]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_19_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_49_cast_fp16)[name = string("hidden_states_19_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> var_216_cast_fp16 = add(x = var_177_cast_fp16, y = hidden_states_19_cast_fp16)[name = string("op_216_cast_fp16")]; |
| tensor<int32, [5]> reshape_28_shape_0 = const()[name = string("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_216_cast_fp16)[name = string("reshape_28_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = string("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_21_keep_dims_0 = const()[name = string("reduce_mean_21_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = string("reduce_mean_21_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = string("sub_14_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = string("square_7_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = string("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_23_keep_dims_0 = const()[name = string("reduce_mean_23_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = string("reduce_mean_23_cast_fp16")]; |
| fp16 add_14_y_0_to_fp16 = const()[name = string("add_14_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = string("add_14_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = string("sqrt_7_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = string("real_div_7_cast_fp16")]; |
| tensor<int32, [4]> reshape_29_shape_0 = const()[name = string("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = string("reshape_29_cast_fp16")]; |
| tensor<fp16, [512]> add_15_gamma_0_to_fp16 = const()[name = string("add_15_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30475840)))]; |
| tensor<fp16, [512]> add_15_beta_0_to_fp16 = const()[name = string("add_15_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30476928)))]; |
| fp16 add_15_epsilon_0_to_fp16 = const()[name = string("add_15_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = string("add_15_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_57_cast_fp16 = silu(x = add_15_cast_fp16)[name = string("input_57_cast_fp16")]; |
| string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30478016)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35196672)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_59_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; |
| tensor<int32, [5]> reshape_32_shape_0 = const()[name = string("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_59_cast_fp16)[name = string("reshape_32_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = string("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_24_keep_dims_0 = const()[name = string("reduce_mean_24_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = string("reduce_mean_24_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = string("sub_16_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = string("square_8_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = string("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_26_keep_dims_0 = const()[name = string("reduce_mean_26_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = string("reduce_mean_26_cast_fp16")]; |
| fp16 add_16_y_0_to_fp16 = const()[name = string("add_16_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = string("add_16_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = string("sqrt_8_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = string("real_div_8_cast_fp16")]; |
| tensor<int32, [4]> reshape_33_shape_0 = const()[name = string("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = string("reshape_33_cast_fp16")]; |
| tensor<fp16, [512]> add_17_gamma_0_to_fp16 = const()[name = string("add_17_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35197760)))]; |
| tensor<fp16, [512]> add_17_beta_0_to_fp16 = const()[name = string("add_17_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35198848)))]; |
| fp16 add_17_epsilon_0_to_fp16 = const()[name = string("add_17_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = string("add_17_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_63_cast_fp16 = silu(x = add_17_cast_fp16)[name = string("input_63_cast_fp16")]; |
| string hidden_states_21_pad_type_0 = const()[name = string("hidden_states_21_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = string("hidden_states_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_21_strides_0 = const()[name = string("hidden_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_21_dilations_0 = const()[name = string("hidden_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_21_groups_0 = const()[name = string("hidden_states_21_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35199936)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39918592)))]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_21_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_21_dilations_0, groups = hidden_states_21_groups_0, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = hidden_states_21_strides_0, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_63_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> var_246_cast_fp16 = add(x = var_216_cast_fp16, y = hidden_states_21_cast_fp16)[name = string("op_246_cast_fp16")]; |
| tensor<int32, [5]> reshape_36_shape_0 = const()[name = string("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_246_cast_fp16)[name = string("reshape_36_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = string("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_27_keep_dims_0 = const()[name = string("reduce_mean_27_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = string("reduce_mean_27_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = string("sub_18_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = string("square_9_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = string("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_29_keep_dims_0 = const()[name = string("reduce_mean_29_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = string("reduce_mean_29_cast_fp16")]; |
| fp16 add_18_y_0_to_fp16 = const()[name = string("add_18_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = string("add_18_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = string("sqrt_9_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = string("real_div_9_cast_fp16")]; |
| tensor<int32, [4]> reshape_37_shape_0 = const()[name = string("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = string("reshape_37_cast_fp16")]; |
| tensor<fp16, [512]> add_19_gamma_0_to_fp16 = const()[name = string("add_19_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39919680)))]; |
| tensor<fp16, [512]> add_19_beta_0_to_fp16 = const()[name = string("add_19_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39920768)))]; |
| fp16 add_19_epsilon_0_to_fp16 = const()[name = string("add_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = string("add_19_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_71_cast_fp16 = silu(x = add_19_cast_fp16)[name = string("input_71_cast_fp16")]; |
| string input_73_pad_type_0 = const()[name = string("input_73_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_73_pad_0 = const()[name = string("input_73_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_73_strides_0 = const()[name = string("input_73_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_73_dilations_0 = const()[name = string("input_73_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_73_groups_0 = const()[name = string("input_73_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39921856)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44640512)))]; |
| tensor<fp16, [1, 512, 64, 64]> input_73_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; |
| tensor<int32, [5]> reshape_40_shape_0 = const()[name = string("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])]; |
| tensor<fp16, [1, 32, 16, 64, 64]> reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_73_cast_fp16)[name = string("reshape_40_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = string("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_30_keep_dims_0 = const()[name = string("reduce_mean_30_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = string("reduce_mean_30_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = string("sub_20_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = string("square_10_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = string("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_32_keep_dims_0 = const()[name = string("reduce_mean_32_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = string("reduce_mean_32_cast_fp16")]; |
| fp16 add_20_y_0_to_fp16 = const()[name = string("add_20_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = string("add_20_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = string("sqrt_10_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 64, 64]> real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = string("real_div_10_cast_fp16")]; |
| tensor<int32, [4]> reshape_41_shape_0 = const()[name = string("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])]; |
| tensor<fp16, [1, 512, 64, 64]> reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = string("reshape_41_cast_fp16")]; |
| tensor<fp16, [512]> add_21_gamma_0_to_fp16 = const()[name = string("add_21_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44641600)))]; |
| tensor<fp16, [512]> add_21_beta_0_to_fp16 = const()[name = string("add_21_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44642688)))]; |
| fp16 add_21_epsilon_0_to_fp16 = const()[name = string("add_21_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 64, 64]> add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = string("add_21_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> input_77_cast_fp16 = silu(x = add_21_cast_fp16)[name = string("input_77_cast_fp16")]; |
| string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44643776)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49362432)))]; |
| tensor<fp16, [1, 512, 64, 64]> hidden_states_23_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_77_cast_fp16)[name = string("hidden_states_23_cast_fp16")]; |
| tensor<fp16, [1, 512, 64, 64]> var_276_cast_fp16 = add(x = var_246_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("op_276_cast_fp16")]; |
| fp32 input_81_scale_factor_height_0 = const()[name = string("input_81_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_81_scale_factor_width_0 = const()[name = string("input_81_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp16, [1, 512, 128, 128]> input_81_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276_cast_fp16)[name = string("input_81_cast_fp16")]; |
| string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = string("decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49363520)))]; |
| tensor<fp16, [512]> decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = string("decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54082176)))]; |
| tensor<fp16, [1, 512, 128, 128]> input_83_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; |
| tensor<int32, [5]> reshape_44_shape_0 = const()[name = string("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_83_cast_fp16)[name = string("reshape_44_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = string("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_33_keep_dims_0 = const()[name = string("reduce_mean_33_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = string("reduce_mean_33_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = string("sub_22_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = string("square_11_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = string("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_35_keep_dims_0 = const()[name = string("reduce_mean_35_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = string("reduce_mean_35_cast_fp16")]; |
| fp16 add_22_y_0_to_fp16 = const()[name = string("add_22_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = string("add_22_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = string("sqrt_11_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = string("real_div_11_cast_fp16")]; |
| tensor<int32, [4]> reshape_45_shape_0 = const()[name = string("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = string("reshape_45_cast_fp16")]; |
| tensor<fp16, [512]> add_23_gamma_0_to_fp16 = const()[name = string("add_23_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54083264)))]; |
| tensor<fp16, [512]> add_23_beta_0_to_fp16 = const()[name = string("add_23_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54084352)))]; |
| fp16 add_23_epsilon_0_to_fp16 = const()[name = string("add_23_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = string("add_23_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_87_cast_fp16 = silu(x = add_23_cast_fp16)[name = string("input_87_cast_fp16")]; |
| string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54085440)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58804096)))]; |
| tensor<fp16, [1, 512, 128, 128]> input_89_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_87_cast_fp16)[name = string("input_89_cast_fp16")]; |
| tensor<int32, [5]> reshape_48_shape_0 = const()[name = string("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_89_cast_fp16)[name = string("reshape_48_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = string("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_36_keep_dims_0 = const()[name = string("reduce_mean_36_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = string("reduce_mean_36_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = string("sub_24_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = string("square_12_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = string("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_38_keep_dims_0 = const()[name = string("reduce_mean_38_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = string("reduce_mean_38_cast_fp16")]; |
| fp16 add_24_y_0_to_fp16 = const()[name = string("add_24_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = string("add_24_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = string("sqrt_12_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = string("real_div_12_cast_fp16")]; |
| tensor<int32, [4]> reshape_49_shape_0 = const()[name = string("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = string("reshape_49_cast_fp16")]; |
| tensor<fp16, [512]> add_25_gamma_0_to_fp16 = const()[name = string("add_25_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58805184)))]; |
| tensor<fp16, [512]> add_25_beta_0_to_fp16 = const()[name = string("add_25_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58806272)))]; |
| fp16 add_25_epsilon_0_to_fp16 = const()[name = string("add_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = string("add_25_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_93_cast_fp16 = silu(x = add_25_cast_fp16)[name = string("input_93_cast_fp16")]; |
| string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58807360)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63526016)))]; |
| tensor<fp16, [1, 512, 128, 128]> hidden_states_27_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_93_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> var_324_cast_fp16 = add(x = input_83_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("op_324_cast_fp16")]; |
| tensor<int32, [5]> reshape_52_shape_0 = const()[name = string("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_324_cast_fp16)[name = string("reshape_52_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = string("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_39_keep_dims_0 = const()[name = string("reduce_mean_39_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = string("reduce_mean_39_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = string("sub_26_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = string("square_13_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = string("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_41_keep_dims_0 = const()[name = string("reduce_mean_41_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = string("reduce_mean_41_cast_fp16")]; |
| fp16 add_26_y_0_to_fp16 = const()[name = string("add_26_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = string("add_26_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = string("sqrt_13_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = string("real_div_13_cast_fp16")]; |
| tensor<int32, [4]> reshape_53_shape_0 = const()[name = string("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = string("reshape_53_cast_fp16")]; |
| tensor<fp16, [512]> add_27_gamma_0_to_fp16 = const()[name = string("add_27_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63527104)))]; |
| tensor<fp16, [512]> add_27_beta_0_to_fp16 = const()[name = string("add_27_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63528192)))]; |
| fp16 add_27_epsilon_0_to_fp16 = const()[name = string("add_27_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = string("add_27_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_101_cast_fp16 = silu(x = add_27_cast_fp16)[name = string("input_101_cast_fp16")]; |
| string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63529280)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68247936)))]; |
| tensor<fp16, [1, 512, 128, 128]> input_103_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; |
| tensor<int32, [5]> reshape_56_shape_0 = const()[name = string("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_103_cast_fp16)[name = string("reshape_56_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = string("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_42_keep_dims_0 = const()[name = string("reduce_mean_42_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = string("reduce_mean_42_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = string("sub_28_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = string("square_14_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = string("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_44_keep_dims_0 = const()[name = string("reduce_mean_44_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = string("reduce_mean_44_cast_fp16")]; |
| fp16 add_28_y_0_to_fp16 = const()[name = string("add_28_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = string("add_28_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = string("sqrt_14_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = string("real_div_14_cast_fp16")]; |
| tensor<int32, [4]> reshape_57_shape_0 = const()[name = string("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = string("reshape_57_cast_fp16")]; |
| tensor<fp16, [512]> add_29_gamma_0_to_fp16 = const()[name = string("add_29_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68249024)))]; |
| tensor<fp16, [512]> add_29_beta_0_to_fp16 = const()[name = string("add_29_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68250112)))]; |
| fp16 add_29_epsilon_0_to_fp16 = const()[name = string("add_29_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = string("add_29_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_107_cast_fp16 = silu(x = add_29_cast_fp16)[name = string("input_107_cast_fp16")]; |
| string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68251200)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72969856)))]; |
| tensor<fp16, [1, 512, 128, 128]> hidden_states_29_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_107_cast_fp16)[name = string("hidden_states_29_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> var_354_cast_fp16 = add(x = var_324_cast_fp16, y = hidden_states_29_cast_fp16)[name = string("op_354_cast_fp16")]; |
| tensor<int32, [5]> reshape_60_shape_0 = const()[name = string("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_354_cast_fp16)[name = string("reshape_60_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = string("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_45_keep_dims_0 = const()[name = string("reduce_mean_45_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = string("reduce_mean_45_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = string("sub_30_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = string("square_15_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = string("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_47_keep_dims_0 = const()[name = string("reduce_mean_47_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = string("reduce_mean_47_cast_fp16")]; |
| fp16 add_30_y_0_to_fp16 = const()[name = string("add_30_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = string("add_30_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = string("sqrt_15_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = string("real_div_15_cast_fp16")]; |
| tensor<int32, [4]> reshape_61_shape_0 = const()[name = string("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = string("reshape_61_cast_fp16")]; |
| tensor<fp16, [512]> add_31_gamma_0_to_fp16 = const()[name = string("add_31_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72970944)))]; |
| tensor<fp16, [512]> add_31_beta_0_to_fp16 = const()[name = string("add_31_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72972032)))]; |
| fp16 add_31_epsilon_0_to_fp16 = const()[name = string("add_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = string("add_31_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_115_cast_fp16 = silu(x = add_31_cast_fp16)[name = string("input_115_cast_fp16")]; |
| string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72973120)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77691776)))]; |
| tensor<fp16, [1, 512, 128, 128]> input_117_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; |
| tensor<int32, [5]> reshape_64_shape_0 = const()[name = string("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp16, [1, 32, 16, 128, 128]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_117_cast_fp16)[name = string("reshape_64_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = string("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_48_keep_dims_0 = const()[name = string("reduce_mean_48_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = string("reduce_mean_48_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = string("sub_32_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = string("square_16_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = string("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_50_keep_dims_0 = const()[name = string("reduce_mean_50_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = string("reduce_mean_50_cast_fp16")]; |
| fp16 add_32_y_0_to_fp16 = const()[name = string("add_32_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = string("add_32_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = string("sqrt_16_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 128, 128]> real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = string("real_div_16_cast_fp16")]; |
| tensor<int32, [4]> reshape_65_shape_0 = const()[name = string("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp16, [1, 512, 128, 128]> reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = string("reshape_65_cast_fp16")]; |
| tensor<fp16, [512]> add_33_gamma_0_to_fp16 = const()[name = string("add_33_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77692864)))]; |
| tensor<fp16, [512]> add_33_beta_0_to_fp16 = const()[name = string("add_33_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77693952)))]; |
| fp16 add_33_epsilon_0_to_fp16 = const()[name = string("add_33_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 128, 128]> add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = string("add_33_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> input_121_cast_fp16 = silu(x = add_33_cast_fp16)[name = string("input_121_cast_fp16")]; |
| string hidden_states_31_pad_type_0 = const()[name = string("hidden_states_31_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = string("hidden_states_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_31_strides_0 = const()[name = string("hidden_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_31_dilations_0 = const()[name = string("hidden_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_31_groups_0 = const()[name = string("hidden_states_31_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77695040)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82413696)))]; |
| tensor<fp16, [1, 512, 128, 128]> hidden_states_31_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, 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_1_resnets_2_conv2_weight_to_fp16, x = input_121_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; |
| tensor<fp16, [1, 512, 128, 128]> var_384_cast_fp16 = add(x = var_354_cast_fp16, y = hidden_states_31_cast_fp16)[name = string("op_384_cast_fp16")]; |
| fp32 input_125_scale_factor_height_0 = const()[name = string("input_125_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_125_scale_factor_width_0 = const()[name = string("input_125_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp16, [1, 512, 256, 256]> input_125_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_384_cast_fp16)[name = string("input_125_cast_fp16")]; |
| string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 512, 3, 3]> decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = string("decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82414784)))]; |
| tensor<fp16, [512]> decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = string("decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87133440)))]; |
| tensor<fp16, [1, 512, 256, 256]> input_127_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; |
| tensor<int32, [5]> reshape_68_shape_0 = const()[name = string("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp16, [1, 32, 16, 256, 256]> reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_127_cast_fp16)[name = string("reshape_68_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = string("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_51_keep_dims_0 = const()[name = string("reduce_mean_51_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = string("reduce_mean_51_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 256, 256]> sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = string("sub_34_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 256, 256]> square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = string("square_17_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = string("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_53_keep_dims_0 = const()[name = string("reduce_mean_53_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = string("reduce_mean_53_cast_fp16")]; |
| fp16 add_34_y_0_to_fp16 = const()[name = string("add_34_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = string("add_34_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = string("sqrt_17_cast_fp16")]; |
| tensor<fp16, [1, 32, 16, 256, 256]> real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = string("real_div_17_cast_fp16")]; |
| tensor<int32, [4]> reshape_69_shape_0 = const()[name = string("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp16, [1, 512, 256, 256]> reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = string("reshape_69_cast_fp16")]; |
| tensor<fp16, [512]> add_35_gamma_0_to_fp16 = const()[name = string("add_35_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87134528)))]; |
| tensor<fp16, [512]> add_35_beta_0_to_fp16 = const()[name = string("add_35_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87135616)))]; |
| fp16 add_35_epsilon_0_to_fp16 = const()[name = string("add_35_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 512, 256, 256]> add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = string("add_35_cast_fp16")]; |
| tensor<fp16, [1, 512, 256, 256]> input_131_cast_fp16 = silu(x = add_35_cast_fp16)[name = string("input_131_cast_fp16")]; |
| string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 512, 3, 3]> decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87136704)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89496064)))]; |
| tensor<fp16, [1, 256, 256, 256]> input_133_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")]; |
| tensor<int32, [5]> reshape_72_shape_0 = const()[name = string("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; |
| tensor<fp16, [1, 32, 8, 256, 256]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_133_cast_fp16)[name = string("reshape_72_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = string("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_54_keep_dims_0 = const()[name = string("reduce_mean_54_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = string("reduce_mean_54_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = string("sub_36_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = string("square_18_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = string("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_56_keep_dims_0 = const()[name = string("reduce_mean_56_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = string("reduce_mean_56_cast_fp16")]; |
| fp16 add_36_y_0_to_fp16 = const()[name = string("add_36_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = string("add_36_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = string("sqrt_18_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = string("real_div_18_cast_fp16")]; |
| tensor<int32, [4]> reshape_73_shape_0 = const()[name = string("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; |
| tensor<fp16, [1, 256, 256, 256]> reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = string("reshape_73_cast_fp16")]; |
| tensor<fp16, [256]> add_37_mean_0_to_fp16 = const()[name = string("add_37_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89496640)))]; |
| tensor<fp16, [256]> add_37_variance_0_to_fp16 = const()[name = string("add_37_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89497216)))]; |
| tensor<fp16, [256]> add_37_gamma_0_to_fp16 = const()[name = string("add_37_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89497792)))]; |
| tensor<fp16, [256]> add_37_beta_0_to_fp16 = const()[name = string("add_37_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89498368)))]; |
| fp16 add_37_epsilon_0_to_fp16 = const()[name = string("add_37_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 256, 256]> add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = string("add_37_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> input_137_cast_fp16 = silu(x = add_37_cast_fp16)[name = string("input_137_cast_fp16")]; |
| string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89498944)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90678656)))]; |
| tensor<fp16, [1, 256, 256, 256]> hidden_states_35_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_137_cast_fp16)[name = string("hidden_states_35_cast_fp16")]; |
| string input_tensor_1_pad_type_0 = const()[name = string("input_tensor_1_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_tensor_1_strides_0 = const()[name = string("input_tensor_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = string("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_tensor_1_dilations_0 = const()[name = string("input_tensor_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_tensor_1_groups_0 = const()[name = string("input_tensor_1_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 512, 1, 1]> decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [256, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90679232)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90941440)))]; |
| tensor<fp16, [1, 256, 256, 256]> input_tensor_1_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, 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_to_fp16, x = input_127_cast_fp16)[name = string("input_tensor_1_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> var_440_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_35_cast_fp16)[name = string("op_440_cast_fp16")]; |
| tensor<int32, [5]> reshape_76_shape_0 = const()[name = string("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; |
| tensor<fp16, [1, 32, 8, 256, 256]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_440_cast_fp16)[name = string("reshape_76_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = string("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_57_keep_dims_0 = const()[name = string("reduce_mean_57_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = string("reduce_mean_57_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = string("sub_38_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = string("square_19_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = string("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_59_keep_dims_0 = const()[name = string("reduce_mean_59_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = string("reduce_mean_59_cast_fp16")]; |
| fp16 add_38_y_0_to_fp16 = const()[name = string("add_38_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = string("add_38_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = string("sqrt_19_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = string("real_div_19_cast_fp16")]; |
| tensor<int32, [4]> reshape_77_shape_0 = const()[name = string("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; |
| tensor<fp16, [1, 256, 256, 256]> reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = string("reshape_77_cast_fp16")]; |
| tensor<fp16, [256]> add_39_gamma_0_to_fp16 = const()[name = string("add_39_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90942016)))]; |
| tensor<fp16, [256]> add_39_beta_0_to_fp16 = const()[name = string("add_39_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90942592)))]; |
| fp16 add_39_epsilon_0_to_fp16 = const()[name = string("add_39_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 256, 256]> add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = string("add_39_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> input_145_cast_fp16 = silu(x = add_39_cast_fp16)[name = string("input_145_cast_fp16")]; |
| string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90943168)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92122880)))]; |
| tensor<fp16, [1, 256, 256, 256]> input_147_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")]; |
| tensor<int32, [5]> reshape_80_shape_0 = const()[name = string("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; |
| tensor<fp16, [1, 32, 8, 256, 256]> reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_147_cast_fp16)[name = string("reshape_80_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = string("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_60_keep_dims_0 = const()[name = string("reduce_mean_60_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = string("reduce_mean_60_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = string("sub_40_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = string("square_20_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = string("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_62_keep_dims_0 = const()[name = string("reduce_mean_62_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = string("reduce_mean_62_cast_fp16")]; |
| fp16 add_40_y_0_to_fp16 = const()[name = string("add_40_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = string("add_40_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = string("sqrt_20_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = string("real_div_20_cast_fp16")]; |
| tensor<int32, [4]> reshape_81_shape_0 = const()[name = string("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; |
| tensor<fp16, [1, 256, 256, 256]> reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = string("reshape_81_cast_fp16")]; |
| tensor<fp16, [256]> add_41_gamma_0_to_fp16 = const()[name = string("add_41_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92123456)))]; |
| tensor<fp16, [256]> add_41_beta_0_to_fp16 = const()[name = string("add_41_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92124032)))]; |
| fp16 add_41_epsilon_0_to_fp16 = const()[name = string("add_41_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 256, 256]> add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = string("add_41_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> input_151_cast_fp16 = silu(x = add_41_cast_fp16)[name = string("input_151_cast_fp16")]; |
| string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92124608)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93304320)))]; |
| tensor<fp16, [1, 256, 256, 256]> hidden_states_37_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, 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_2_resnets_1_conv2_weight_to_fp16, x = input_151_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> var_470_cast_fp16 = add(x = var_440_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("op_470_cast_fp16")]; |
| tensor<int32, [5]> reshape_84_shape_0 = const()[name = string("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; |
| tensor<fp16, [1, 32, 8, 256, 256]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_470_cast_fp16)[name = string("reshape_84_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = string("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_63_keep_dims_0 = const()[name = string("reduce_mean_63_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = string("reduce_mean_63_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = string("sub_42_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = string("square_21_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = string("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_65_keep_dims_0 = const()[name = string("reduce_mean_65_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = string("reduce_mean_65_cast_fp16")]; |
| fp16 add_42_y_0_to_fp16 = const()[name = string("add_42_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = string("add_42_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = string("sqrt_21_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = string("real_div_21_cast_fp16")]; |
| tensor<int32, [4]> reshape_85_shape_0 = const()[name = string("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; |
| tensor<fp16, [1, 256, 256, 256]> reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = string("reshape_85_cast_fp16")]; |
| tensor<fp16, [256]> add_43_gamma_0_to_fp16 = const()[name = string("add_43_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93304896)))]; |
| tensor<fp16, [256]> add_43_beta_0_to_fp16 = const()[name = string("add_43_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93305472)))]; |
| fp16 add_43_epsilon_0_to_fp16 = const()[name = string("add_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 256, 256]> add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = string("add_43_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> input_159_cast_fp16 = silu(x = add_43_cast_fp16)[name = string("input_159_cast_fp16")]; |
| string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93306048)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94485760)))]; |
| tensor<fp16, [1, 256, 256, 256]> input_161_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_159_cast_fp16)[name = string("input_161_cast_fp16")]; |
| tensor<int32, [5]> reshape_88_shape_0 = const()[name = string("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])]; |
| tensor<fp16, [1, 32, 8, 256, 256]> reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_161_cast_fp16)[name = string("reshape_88_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = string("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_66_keep_dims_0 = const()[name = string("reduce_mean_66_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = string("reduce_mean_66_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = string("sub_44_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = string("square_22_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = string("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_68_keep_dims_0 = const()[name = string("reduce_mean_68_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = string("reduce_mean_68_cast_fp16")]; |
| fp16 add_44_y_0_to_fp16 = const()[name = string("add_44_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = string("add_44_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = string("sqrt_22_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 256, 256]> real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = string("real_div_22_cast_fp16")]; |
| tensor<int32, [4]> reshape_89_shape_0 = const()[name = string("reshape_89_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])]; |
| tensor<fp16, [1, 256, 256, 256]> reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = string("reshape_89_cast_fp16")]; |
| tensor<fp16, [256]> add_45_gamma_0_to_fp16 = const()[name = string("add_45_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94486336)))]; |
| tensor<fp16, [256]> add_45_beta_0_to_fp16 = const()[name = string("add_45_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94486912)))]; |
| fp16 add_45_epsilon_0_to_fp16 = const()[name = string("add_45_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 256, 256]> add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = string("add_45_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> input_165_cast_fp16 = silu(x = add_45_cast_fp16)[name = string("input_165_cast_fp16")]; |
| string hidden_states_39_pad_type_0 = const()[name = string("hidden_states_39_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = string("hidden_states_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_39_strides_0 = const()[name = string("hidden_states_39_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_39_dilations_0 = const()[name = string("hidden_states_39_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_39_groups_0 = const()[name = string("hidden_states_39_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94487488)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95667200)))]; |
| tensor<fp16, [1, 256, 256, 256]> hidden_states_39_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = hidden_states_39_dilations_0, groups = hidden_states_39_groups_0, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = hidden_states_39_strides_0, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_165_cast_fp16)[name = string("hidden_states_39_cast_fp16")]; |
| tensor<fp16, [1, 256, 256, 256]> var_500_cast_fp16 = add(x = var_470_cast_fp16, y = hidden_states_39_cast_fp16)[name = string("op_500_cast_fp16")]; |
| fp32 input_169_scale_factor_height_0 = const()[name = string("input_169_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_169_scale_factor_width_0 = const()[name = string("input_169_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp16, [1, 256, 512, 512]> input_169_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500_cast_fp16)[name = string("input_169_cast_fp16")]; |
| string input_171_pad_type_0 = const()[name = string("input_171_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_171_pad_0 = const()[name = string("input_171_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_171_strides_0 = const()[name = string("input_171_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_171_dilations_0 = const()[name = string("input_171_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_171_groups_0 = const()[name = string("input_171_groups_0"), val = int32(1)]; |
| tensor<fp16, [256, 256, 3, 3]> decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = string("decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95667776)))]; |
| tensor<fp16, [256]> decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = string("decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96847488)))]; |
| tensor<fp16, [1, 256, 512, 512]> input_171_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = input_171_dilations_0, groups = input_171_groups_0, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = input_171_strides_0, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")]; |
| tensor<int32, [5]> reshape_92_shape_0 = const()[name = string("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp16, [1, 32, 8, 512, 512]> reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_171_cast_fp16)[name = string("reshape_92_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = string("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_69_keep_dims_0 = const()[name = string("reduce_mean_69_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = string("reduce_mean_69_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 512, 512]> sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = string("sub_46_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 512, 512]> square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = string("square_23_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = string("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_71_keep_dims_0 = const()[name = string("reduce_mean_71_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = string("reduce_mean_71_cast_fp16")]; |
| fp16 add_46_y_0_to_fp16 = const()[name = string("add_46_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = string("add_46_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = string("sqrt_23_cast_fp16")]; |
| tensor<fp16, [1, 32, 8, 512, 512]> real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = string("real_div_23_cast_fp16")]; |
| tensor<int32, [4]> reshape_93_shape_0 = const()[name = string("reshape_93_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp16, [1, 256, 512, 512]> reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = string("reshape_93_cast_fp16")]; |
| tensor<fp16, [256]> add_47_gamma_0_to_fp16 = const()[name = string("add_47_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96848064)))]; |
| tensor<fp16, [256]> add_47_beta_0_to_fp16 = const()[name = string("add_47_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96848640)))]; |
| fp16 add_47_epsilon_0_to_fp16 = const()[name = string("add_47_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 256, 512, 512]> add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = string("add_47_cast_fp16")]; |
| tensor<fp16, [1, 256, 512, 512]> input_175_cast_fp16 = silu(x = add_47_cast_fp16)[name = string("input_175_cast_fp16")]; |
| string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 256, 3, 3]> decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [128, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96849216)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97439104)))]; |
| tensor<fp16, [1, 128, 512, 512]> input_177_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")]; |
| tensor<int32, [5]> reshape_96_shape_0 = const()[name = string("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_177_cast_fp16)[name = string("reshape_96_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = string("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_72_keep_dims_0 = const()[name = string("reduce_mean_72_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = string("reduce_mean_72_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = string("sub_48_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = string("square_24_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = string("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_74_keep_dims_0 = const()[name = string("reduce_mean_74_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = string("reduce_mean_74_cast_fp16")]; |
| fp16 add_48_y_0_to_fp16 = const()[name = string("add_48_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = string("add_48_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = string("sqrt_24_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = string("real_div_24_cast_fp16")]; |
| tensor<int32, [4]> reshape_97_shape_0 = const()[name = string("reshape_97_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = string("reshape_97_cast_fp16")]; |
| tensor<fp16, [128]> add_49_mean_0_to_fp16 = const()[name = string("add_49_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97439424)))]; |
| tensor<fp16, [128]> add_49_variance_0_to_fp16 = const()[name = string("add_49_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97439744)))]; |
| tensor<fp16, [128]> add_49_gamma_0_to_fp16 = const()[name = string("add_49_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97440064)))]; |
| tensor<fp16, [128]> add_49_beta_0_to_fp16 = const()[name = string("add_49_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97440384)))]; |
| fp16 add_49_epsilon_0_to_fp16 = const()[name = string("add_49_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = string("add_49_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_181_cast_fp16 = silu(x = add_49_cast_fp16)[name = string("input_181_cast_fp16")]; |
| string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97440704)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97735680)))]; |
| tensor<fp16, [1, 128, 512, 512]> hidden_states_43_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, 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_3_resnets_0_conv2_weight_to_fp16, x = input_181_cast_fp16)[name = string("hidden_states_43_cast_fp16")]; |
| string input_tensor_pad_type_0 = const()[name = string("input_tensor_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_tensor_strides_0 = const()[name = string("input_tensor_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_tensor_pad_0 = const()[name = string("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_tensor_dilations_0 = const()[name = string("input_tensor_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_tensor_groups_0 = const()[name = string("input_tensor_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 256, 1, 1]> decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [128, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97736000)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97801600)))]; |
| tensor<fp16, [1, 128, 512, 512]> input_tensor_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, 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_to_fp16, x = input_171_cast_fp16)[name = string("input_tensor_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> var_554_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("op_554_cast_fp16")]; |
| tensor<int32, [5]> reshape_100_shape_0 = const()[name = string("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_554_cast_fp16)[name = string("reshape_100_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = string("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_75_keep_dims_0 = const()[name = string("reduce_mean_75_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = string("reduce_mean_75_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = string("sub_50_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = string("square_25_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = string("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_77_keep_dims_0 = const()[name = string("reduce_mean_77_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = string("reduce_mean_77_cast_fp16")]; |
| fp16 add_50_y_0_to_fp16 = const()[name = string("add_50_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = string("add_50_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = string("sqrt_25_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = string("real_div_25_cast_fp16")]; |
| tensor<int32, [4]> reshape_101_shape_0 = const()[name = string("reshape_101_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = string("reshape_101_cast_fp16")]; |
| tensor<fp16, [128]> add_51_gamma_0_to_fp16 = const()[name = string("add_51_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97801920)))]; |
| tensor<fp16, [128]> add_51_beta_0_to_fp16 = const()[name = string("add_51_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97802240)))]; |
| fp16 add_51_epsilon_0_to_fp16 = const()[name = string("add_51_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = string("add_51_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_189_cast_fp16 = silu(x = add_51_cast_fp16)[name = string("input_189_cast_fp16")]; |
| string input_191_pad_type_0 = const()[name = string("input_191_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_191_pad_0 = const()[name = string("input_191_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_191_strides_0 = const()[name = string("input_191_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_191_dilations_0 = const()[name = string("input_191_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_191_groups_0 = const()[name = string("input_191_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97802560)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98097536)))]; |
| tensor<fp16, [1, 128, 512, 512]> input_191_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = input_191_dilations_0, groups = input_191_groups_0, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = input_191_strides_0, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_cast_fp16)[name = string("input_191_cast_fp16")]; |
| tensor<int32, [5]> reshape_104_shape_0 = const()[name = string("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_191_cast_fp16)[name = string("reshape_104_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = string("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_78_keep_dims_0 = const()[name = string("reduce_mean_78_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = string("reduce_mean_78_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = string("sub_52_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = string("square_26_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = string("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_80_keep_dims_0 = const()[name = string("reduce_mean_80_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = string("reduce_mean_80_cast_fp16")]; |
| fp16 add_52_y_0_to_fp16 = const()[name = string("add_52_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = string("add_52_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = string("sqrt_26_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = string("real_div_26_cast_fp16")]; |
| tensor<int32, [4]> reshape_105_shape_0 = const()[name = string("reshape_105_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = string("reshape_105_cast_fp16")]; |
| tensor<fp16, [128]> add_53_gamma_0_to_fp16 = const()[name = string("add_53_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98097856)))]; |
| tensor<fp16, [128]> add_53_beta_0_to_fp16 = const()[name = string("add_53_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98098176)))]; |
| fp16 add_53_epsilon_0_to_fp16 = const()[name = string("add_53_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = string("add_53_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_195_cast_fp16 = silu(x = add_53_cast_fp16)[name = string("input_195_cast_fp16")]; |
| string hidden_states_45_pad_type_0 = const()[name = string("hidden_states_45_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = string("hidden_states_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_45_strides_0 = const()[name = string("hidden_states_45_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_45_dilations_0 = const()[name = string("hidden_states_45_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_45_groups_0 = const()[name = string("hidden_states_45_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98098496)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98393472)))]; |
| tensor<fp16, [1, 128, 512, 512]> hidden_states_45_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_45_dilations_0, groups = hidden_states_45_groups_0, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = hidden_states_45_strides_0, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_195_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> var_584_cast_fp16 = add(x = var_554_cast_fp16, y = hidden_states_45_cast_fp16)[name = string("op_584_cast_fp16")]; |
| tensor<int32, [5]> reshape_108_shape_0 = const()[name = string("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_584_cast_fp16)[name = string("reshape_108_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = string("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_81_keep_dims_0 = const()[name = string("reduce_mean_81_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = string("reduce_mean_81_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = string("sub_54_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = string("square_27_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = string("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_83_keep_dims_0 = const()[name = string("reduce_mean_83_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = string("reduce_mean_83_cast_fp16")]; |
| fp16 add_54_y_0_to_fp16 = const()[name = string("add_54_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = string("add_54_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = string("sqrt_27_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = string("real_div_27_cast_fp16")]; |
| tensor<int32, [4]> reshape_109_shape_0 = const()[name = string("reshape_109_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = string("reshape_109_cast_fp16")]; |
| tensor<fp16, [128]> add_55_gamma_0_to_fp16 = const()[name = string("add_55_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98393792)))]; |
| tensor<fp16, [128]> add_55_beta_0_to_fp16 = const()[name = string("add_55_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98394112)))]; |
| fp16 add_55_epsilon_0_to_fp16 = const()[name = string("add_55_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = string("add_55_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_203_cast_fp16 = silu(x = add_55_cast_fp16)[name = string("input_203_cast_fp16")]; |
| string input_205_pad_type_0 = const()[name = string("input_205_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_205_pad_0 = const()[name = string("input_205_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_205_strides_0 = const()[name = string("input_205_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_205_dilations_0 = const()[name = string("input_205_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_205_groups_0 = const()[name = string("input_205_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98394432)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98689408)))]; |
| tensor<fp16, [1, 128, 512, 512]> input_205_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_203_cast_fp16)[name = string("input_205_cast_fp16")]; |
| tensor<int32, [5]> reshape_112_shape_0 = const()[name = string("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_205_cast_fp16)[name = string("reshape_112_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = string("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_84_keep_dims_0 = const()[name = string("reduce_mean_84_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = string("reduce_mean_84_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = string("sub_56_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = string("square_28_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = string("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_86_keep_dims_0 = const()[name = string("reduce_mean_86_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = string("reduce_mean_86_cast_fp16")]; |
| fp16 add_56_y_0_to_fp16 = const()[name = string("add_56_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = string("add_56_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = string("sqrt_28_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = string("real_div_28_cast_fp16")]; |
| tensor<int32, [4]> reshape_113_shape_0 = const()[name = string("reshape_113_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = string("reshape_113_cast_fp16")]; |
| tensor<fp16, [128]> add_57_gamma_0_to_fp16 = const()[name = string("add_57_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98689728)))]; |
| tensor<fp16, [128]> add_57_beta_0_to_fp16 = const()[name = string("add_57_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98690048)))]; |
| fp16 add_57_epsilon_0_to_fp16 = const()[name = string("add_57_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = string("add_57_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_209_cast_fp16 = silu(x = add_57_cast_fp16)[name = string("input_209_cast_fp16")]; |
| string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 128, 3, 3]> decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98690368)))]; |
| tensor<fp16, [128]> decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = string("decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98985344)))]; |
| tensor<fp16, [1, 128, 512, 512]> hidden_states_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, 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_to_fp16, x = input_209_cast_fp16)[name = string("hidden_states_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> var_614_cast_fp16 = add(x = var_584_cast_fp16, y = hidden_states_cast_fp16)[name = string("op_614_cast_fp16")]; |
| tensor<int32, [5]> reshape_116_shape_0 = const()[name = string("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])]; |
| tensor<fp16, [1, 32, 4, 512, 512]> reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_614_cast_fp16)[name = string("reshape_116_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = string("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_87_keep_dims_0 = const()[name = string("reduce_mean_87_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = string("reduce_mean_87_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = string("sub_58_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = string("square_29_cast_fp16")]; |
| tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = string("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_89_keep_dims_0 = const()[name = string("reduce_mean_89_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = string("reduce_mean_89_cast_fp16")]; |
| fp16 add_58_y_0_to_fp16 = const()[name = string("add_58_y_0_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 32, 1, 1, 1]> add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = string("add_58_cast_fp16")]; |
| tensor<fp16, [1, 32, 1, 1, 1]> sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = string("sqrt_29_cast_fp16")]; |
| tensor<fp16, [1, 32, 4, 512, 512]> real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = string("real_div_29_cast_fp16")]; |
| tensor<int32, [4]> reshape_117_shape_0 = const()[name = string("reshape_117_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])]; |
| tensor<fp16, [1, 128, 512, 512]> reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = string("reshape_117_cast_fp16")]; |
| tensor<fp16, [128]> add_59_gamma_0_to_fp16 = const()[name = string("add_59_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98985664)))]; |
| tensor<fp16, [128]> add_59_beta_0_to_fp16 = const()[name = string("add_59_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98985984)))]; |
| fp16 add_59_epsilon_0_to_fp16 = const()[name = string("add_59_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 512, 512]> add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = string("add_59_cast_fp16")]; |
| tensor<fp16, [1, 128, 512, 512]> input_cast_fp16 = silu(x = add_59_cast_fp16)[name = string("input_cast_fp16")]; |
| string var_627_pad_type_0 = const()[name = string("op_627_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> var_627_pad_0 = const()[name = string("op_627_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> var_627_strides_0 = const()[name = string("op_627_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> var_627_dilations_0 = const()[name = string("op_627_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 var_627_groups_0 = const()[name = string("op_627_groups_0"), val = int32(1)]; |
| tensor<fp16, [3, 128, 3, 3]> decoder_conv_out_weight_to_fp16 = const()[name = string("decoder_conv_out_weight_to_fp16"), val = tensor<fp16, [3, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98986304)))]; |
| tensor<fp16, [3]> decoder_conv_out_bias_to_fp16 = const()[name = string("decoder_conv_out_bias_to_fp16"), val = tensor<fp16, [3]>([0x1.514p-8, -0x1.c4cp-6, -0x1.67p-5])]; |
| tensor<fp16, [1, 3, 512, 512]> var_627_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, 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_to_fp16, x = input_cast_fp16)[name = string("op_627_cast_fp16")]; |
| string var_627_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_627_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; |
| tensor<fp32, [1, 3, 512, 512]> image = cast(dtype = var_627_cast_fp16_to_fp32_dtype_0, x = var_627_cast_fp16)[name = string("cast_37")]; |
| } -> (image); |
| } |
