import os import numpy as np import tempfile, zipfile import torch import torch.nn as nn import torch.nn.functional as F try: import torchvision except: pass class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv_first = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=3, kernel_size=(8,8), out_channels=192, padding=(0,0), padding_mode='zeros', stride=(8,8)) self.pnnx_unique_0 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=3, kernel_size=(8,8), out_channels=192, padding=(0,0), padding_mode='zeros', stride=(8,8)) self.patch_embed_dfe_norm = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_0_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_0_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_0_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_0_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_0_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_0_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_0_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_dfe_1_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_1_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_1_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_1_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_1_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_1_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_1_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_1_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_dfe_2_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_2_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_2_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_2_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_2_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_2_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_2_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_2_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_dfe_3_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_3_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_3_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_3_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_3_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_3_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_3_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_3_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_dfe_4_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_4_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_4_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_4_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_4_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_4_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_4_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_4_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_dfe_5_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_dfe_5_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_dfe_5_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_dfe_5_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_dfe_5_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_dfe_5_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_dfe_5_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_dfe_5_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.norm_dfe = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.conv_after_body_dfe = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_73 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_75 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_78 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_79 = nn.Softmax(dim=-1) self.pnnx_unique_81 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_83 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_84 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_85 = nn.GELU() self.pnnx_unique_87 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_90 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_93 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_94 = nn.Softmax(dim=-1) self.pnnx_unique_96 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_98 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_99 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_100 = nn.GELU() self.pnnx_unique_102 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_104 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_107 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_108 = nn.Softmax(dim=-1) self.pnnx_unique_110 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_112 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_113 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_114 = nn.GELU() self.pnnx_unique_116 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_119 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_122 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_123 = nn.Softmax(dim=-1) self.pnnx_unique_125 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_127 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_128 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_129 = nn.GELU() self.pnnx_unique_131 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_133 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_136 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_137 = nn.Softmax(dim=-1) self.pnnx_unique_139 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_141 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_142 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_143 = nn.GELU() self.pnnx_unique_145 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_148 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_151 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_152 = nn.Softmax(dim=-1) self.pnnx_unique_154 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_156 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_157 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_158 = nn.GELU() self.pnnx_unique_160 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_162 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_163 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_166 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_167 = nn.Softmax(dim=-1) self.pnnx_unique_169 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_171 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_172 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_173 = nn.GELU() self.pnnx_unique_175 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_178 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_181 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_182 = nn.Softmax(dim=-1) self.pnnx_unique_184 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_186 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_187 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_188 = nn.GELU() self.pnnx_unique_190 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_192 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_195 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_196 = nn.Softmax(dim=-1) self.pnnx_unique_198 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_200 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_201 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_202 = nn.GELU() self.pnnx_unique_204 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_207 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_210 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_211 = nn.Softmax(dim=-1) self.pnnx_unique_213 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_215 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_216 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_217 = nn.GELU() self.pnnx_unique_219 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_221 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_224 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_225 = nn.Softmax(dim=-1) self.pnnx_unique_227 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_229 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_230 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_231 = nn.GELU() self.pnnx_unique_233 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_236 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_239 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_240 = nn.Softmax(dim=-1) self.pnnx_unique_242 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_244 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_245 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_246 = nn.GELU() self.pnnx_unique_248 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_250 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_251 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_254 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_255 = nn.Softmax(dim=-1) self.pnnx_unique_257 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_259 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_260 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_261 = nn.GELU() self.pnnx_unique_263 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_266 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_269 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_270 = nn.Softmax(dim=-1) self.pnnx_unique_272 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_274 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_275 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_276 = nn.GELU() self.pnnx_unique_278 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_280 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_283 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_284 = nn.Softmax(dim=-1) self.pnnx_unique_286 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_288 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_289 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_290 = nn.GELU() self.pnnx_unique_292 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_295 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_298 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_299 = nn.Softmax(dim=-1) self.pnnx_unique_301 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_303 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_304 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_305 = nn.GELU() self.pnnx_unique_307 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_309 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_312 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_313 = nn.Softmax(dim=-1) self.pnnx_unique_315 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_317 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_318 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_319 = nn.GELU() self.pnnx_unique_321 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_324 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_327 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_328 = nn.Softmax(dim=-1) self.pnnx_unique_330 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_332 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_333 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_334 = nn.GELU() self.pnnx_unique_336 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_338 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_339 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_342 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_343 = nn.Softmax(dim=-1) self.pnnx_unique_345 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_347 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_348 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_349 = nn.GELU() self.pnnx_unique_351 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_354 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_357 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_358 = nn.Softmax(dim=-1) self.pnnx_unique_360 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_362 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_363 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_364 = nn.GELU() self.pnnx_unique_366 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_368 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_371 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_372 = nn.Softmax(dim=-1) self.pnnx_unique_374 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_376 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_377 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_378 = nn.GELU() self.pnnx_unique_380 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_383 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_386 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_387 = nn.Softmax(dim=-1) self.pnnx_unique_389 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_391 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_392 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_393 = nn.GELU() self.pnnx_unique_395 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_397 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_400 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_401 = nn.Softmax(dim=-1) self.pnnx_unique_403 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_405 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_406 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_407 = nn.GELU() self.pnnx_unique_409 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_412 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_415 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_416 = nn.Softmax(dim=-1) self.pnnx_unique_418 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_420 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_421 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_422 = nn.GELU() self.pnnx_unique_424 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_426 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_427 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_430 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_431 = nn.Softmax(dim=-1) self.pnnx_unique_433 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_435 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_436 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_437 = nn.GELU() self.pnnx_unique_439 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_442 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_445 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_446 = nn.Softmax(dim=-1) self.pnnx_unique_448 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_450 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_451 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_452 = nn.GELU() self.pnnx_unique_454 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_456 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_459 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_460 = nn.Softmax(dim=-1) self.pnnx_unique_462 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_464 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_465 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_466 = nn.GELU() self.pnnx_unique_468 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_471 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_474 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_475 = nn.Softmax(dim=-1) self.pnnx_unique_477 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_479 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_480 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_481 = nn.GELU() self.pnnx_unique_483 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_485 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_488 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_489 = nn.Softmax(dim=-1) self.pnnx_unique_491 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_493 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_494 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_495 = nn.GELU() self.pnnx_unique_497 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_500 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_503 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_504 = nn.Softmax(dim=-1) self.pnnx_unique_506 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_508 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_509 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_510 = nn.GELU() self.pnnx_unique_512 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_514 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_515 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_518 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_519 = nn.Softmax(dim=-1) self.pnnx_unique_521 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_523 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_524 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_525 = nn.GELU() self.pnnx_unique_527 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_530 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_533 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_534 = nn.Softmax(dim=-1) self.pnnx_unique_536 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_538 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_539 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_540 = nn.GELU() self.pnnx_unique_542 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_544 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_547 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_548 = nn.Softmax(dim=-1) self.pnnx_unique_550 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_552 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_553 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_554 = nn.GELU() self.pnnx_unique_556 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_559 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_562 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_563 = nn.Softmax(dim=-1) self.pnnx_unique_565 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_567 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_568 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_569 = nn.GELU() self.pnnx_unique_571 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_573 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_576 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_577 = nn.Softmax(dim=-1) self.pnnx_unique_579 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_581 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_582 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_583 = nn.GELU() self.pnnx_unique_585 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_588 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_591 = nn.Linear(bias=True, in_features=192, out_features=576) self.pnnx_unique_592 = nn.Softmax(dim=-1) self.pnnx_unique_594 = nn.Linear(bias=True, in_features=192, out_features=192) self.pnnx_unique_596 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_597 = nn.Linear(bias=True, in_features=192, out_features=384) self.pnnx_unique_598 = nn.GELU() self.pnnx_unique_600 = nn.Linear(bias=True, in_features=384, out_features=192) self.pnnx_unique_602 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.pnnx_unique_603 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.pnnx_unique_604 = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.conv2d_0 = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=192, kernel_size=(7,7), out_channels=192, padding=(3,3), padding_mode='reflect', stride=(1,1)) self.manipulator_convblks_0_relu = nn.ReLU() self.conv2d_1 = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='reflect', stride=(1,1)) self.conv2d_2 = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='reflect', stride=(1,1)) self.manipulator_resblks_0_relu = nn.ReLU() self.conv2d_3 = nn.Conv2d(bias=False, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='reflect', stride=(1,1)) self.patch_embed_mmsa_norm = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_0_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_0_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_0_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_0_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_0_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_0_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_0_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_mmsa_1_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_1_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_1_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_1_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_1_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_1_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_1_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_1_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_mmsa_2_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_2_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_2_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_2_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_2_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_2_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_2_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_2_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_mmsa_3_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_3_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_3_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_3_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_3_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_3_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_3_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_3_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_mmsa_4_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_4_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_4_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_4_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_4_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_4_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_4_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_4_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.layers_mmsa_5_residual_group_blocks_0_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_0_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_0_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_0_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_0_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_0_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_0_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_0_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_residual_group_blocks_1_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_1_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_1_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_1_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_1_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_1_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_1_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_1_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_residual_group_blocks_2_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_2_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_2_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_2_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_2_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_2_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_2_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_2_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_residual_group_blocks_3_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_3_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_3_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_3_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_3_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_3_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_3_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_3_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_residual_group_blocks_4_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_4_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_4_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_4_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_4_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_4_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_4_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_4_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_residual_group_blocks_5_norm1 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_5_attn_qkv = nn.Linear(bias=True, in_features=192, out_features=576) self.layers_mmsa_5_residual_group_blocks_5_attn_softmax = nn.Softmax(dim=-1) self.layers_mmsa_5_residual_group_blocks_5_attn_proj = nn.Linear(bias=True, in_features=192, out_features=192) self.layers_mmsa_5_residual_group_blocks_5_norm2 = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.layers_mmsa_5_residual_group_blocks_5_mlp_fc1 = nn.Linear(bias=True, in_features=192, out_features=384) self.layers_mmsa_5_residual_group_blocks_5_mlp_act = nn.GELU() self.layers_mmsa_5_residual_group_blocks_5_mlp_fc2 = nn.Linear(bias=True, in_features=384, out_features=192) self.layers_mmsa_5_conv = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.norm_mmsa = nn.LayerNorm(elementwise_affine=True, eps=0.000010, normalized_shape=(192,)) self.conv_after_body_mmsa = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=192, padding=(1,1), padding_mode='zeros', stride=(1,1)) self.upsample_conv = nn.ConvTranspose2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(8,8), out_channels=192, output_padding=(0,0), padding=(0,0), stride=(8,8)) self.conv_last = nn.Conv2d(bias=True, dilation=(1,1), groups=1, in_channels=192, kernel_size=(3,3), out_channels=3, padding=(1,1), padding_mode='zeros', stride=(1,1)) archive = zipfile.ZipFile('/Users/raoulritter/STB-VMM/20x/modelpnnx20x.pnnx.bin', 'r') self.conv_first.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_first.bias', (192), 'float32') self.conv_first.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_first.weight', (192,3,8,8), 'float32') self.pnnx_unique_0.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_0.bias', (192), 'float32') self.pnnx_unique_0.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_0.weight', (192,3,8,8), 'float32') self.patch_embed_dfe_norm.bias = self.load_pnnx_bin_as_parameter(archive, 'patch_embed_dfe.norm.bias', (192), 'float32') self.patch_embed_dfe_norm.weight = self.load_pnnx_bin_as_parameter(archive, 'patch_embed_dfe.norm.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_0_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_0_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_0_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_0_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_0_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_0_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_0_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.conv.bias', (192), 'float32') self.layers_dfe_0_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.0.conv.weight', (192,192,3,3), 'float32') self.layers_dfe_1_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_1_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_1_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_1_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_1_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_1_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_1_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_1_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.conv.bias', (192), 'float32') self.layers_dfe_1_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.1.conv.weight', (192,192,3,3), 'float32') self.layers_dfe_2_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_2_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_2_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_2_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_2_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_2_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_2_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_2_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.conv.bias', (192), 'float32') self.layers_dfe_2_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.2.conv.weight', (192,192,3,3), 'float32') self.layers_dfe_3_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_3_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_3_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_3_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_3_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_3_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_3_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_3_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.conv.bias', (192), 'float32') self.layers_dfe_3_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.3.conv.weight', (192,192,3,3), 'float32') self.layers_dfe_4_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_4_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_4_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_4_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_4_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_4_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_4_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_4_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.conv.bias', (192), 'float32') self.layers_dfe_4_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.4.conv.weight', (192,192,3,3), 'float32') self.layers_dfe_5_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_dfe_5_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_dfe_5_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_dfe_5_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_dfe_5_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_dfe_5_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_dfe_5_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_dfe_5_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.conv.bias', (192), 'float32') self.layers_dfe_5_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_dfe.5.conv.weight', (192,192,3,3), 'float32') self.norm_dfe.bias = self.load_pnnx_bin_as_parameter(archive, 'norm_dfe.bias', (192), 'float32') self.norm_dfe.weight = self.load_pnnx_bin_as_parameter(archive, 'norm_dfe.weight', (192), 'float32') self.conv_after_body_dfe.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_after_body_dfe.bias', (192), 'float32') self.conv_after_body_dfe.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_after_body_dfe.weight', (192,192,3,3), 'float32') self.pnnx_unique_73.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_73.bias', (192), 'float32') self.pnnx_unique_73.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_73.weight', (192), 'float32') self.pnnx_unique_75.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_75.bias', (192), 'float32') self.pnnx_unique_75.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_75.weight', (192), 'float32') self.pnnx_unique_78.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_78.bias', (576), 'float32') self.pnnx_unique_78.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_78.weight', (576,192), 'float32') self.pnnx_unique_81.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_81.bias', (192), 'float32') self.pnnx_unique_81.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_81.weight', (192,192), 'float32') self.pnnx_unique_83.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_83.bias', (192), 'float32') self.pnnx_unique_83.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_83.weight', (192), 'float32') self.pnnx_unique_84.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_84.bias', (384), 'float32') self.pnnx_unique_84.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_84.weight', (384,192), 'float32') self.pnnx_unique_87.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_87.bias', (192), 'float32') self.pnnx_unique_87.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_87.weight', (192,384), 'float32') self.pnnx_unique_90.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_90.bias', (192), 'float32') self.pnnx_unique_90.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_90.weight', (192), 'float32') self.pnnx_unique_93.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_93.bias', (576), 'float32') self.pnnx_unique_93.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_93.weight', (576,192), 'float32') self.pnnx_unique_96.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_96.bias', (192), 'float32') self.pnnx_unique_96.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_96.weight', (192,192), 'float32') self.pnnx_unique_98.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_98.bias', (192), 'float32') self.pnnx_unique_98.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_98.weight', (192), 'float32') self.pnnx_unique_99.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_99.bias', (384), 'float32') self.pnnx_unique_99.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_99.weight', (384,192), 'float32') self.pnnx_unique_102.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_102.bias', (192), 'float32') self.pnnx_unique_102.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_102.weight', (192,384), 'float32') self.pnnx_unique_104.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_104.bias', (192), 'float32') self.pnnx_unique_104.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_104.weight', (192), 'float32') self.pnnx_unique_107.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_107.bias', (576), 'float32') self.pnnx_unique_107.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_107.weight', (576,192), 'float32') self.pnnx_unique_110.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_110.bias', (192), 'float32') self.pnnx_unique_110.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_110.weight', (192,192), 'float32') self.pnnx_unique_112.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_112.bias', (192), 'float32') self.pnnx_unique_112.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_112.weight', (192), 'float32') self.pnnx_unique_113.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_113.bias', (384), 'float32') self.pnnx_unique_113.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_113.weight', (384,192), 'float32') self.pnnx_unique_116.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_116.bias', (192), 'float32') self.pnnx_unique_116.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_116.weight', (192,384), 'float32') self.pnnx_unique_119.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_119.bias', (192), 'float32') self.pnnx_unique_119.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_119.weight', (192), 'float32') self.pnnx_unique_122.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_122.bias', (576), 'float32') self.pnnx_unique_122.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_122.weight', (576,192), 'float32') self.pnnx_unique_125.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_125.bias', (192), 'float32') self.pnnx_unique_125.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_125.weight', (192,192), 'float32') self.pnnx_unique_127.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_127.bias', (192), 'float32') self.pnnx_unique_127.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_127.weight', (192), 'float32') self.pnnx_unique_128.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_128.bias', (384), 'float32') self.pnnx_unique_128.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_128.weight', (384,192), 'float32') self.pnnx_unique_131.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_131.bias', (192), 'float32') self.pnnx_unique_131.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_131.weight', (192,384), 'float32') self.pnnx_unique_133.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_133.bias', (192), 'float32') self.pnnx_unique_133.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_133.weight', (192), 'float32') self.pnnx_unique_136.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_136.bias', (576), 'float32') self.pnnx_unique_136.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_136.weight', (576,192), 'float32') self.pnnx_unique_139.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_139.bias', (192), 'float32') self.pnnx_unique_139.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_139.weight', (192,192), 'float32') self.pnnx_unique_141.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_141.bias', (192), 'float32') self.pnnx_unique_141.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_141.weight', (192), 'float32') self.pnnx_unique_142.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_142.bias', (384), 'float32') self.pnnx_unique_142.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_142.weight', (384,192), 'float32') self.pnnx_unique_145.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_145.bias', (192), 'float32') self.pnnx_unique_145.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_145.weight', (192,384), 'float32') self.pnnx_unique_148.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_148.bias', (192), 'float32') self.pnnx_unique_148.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_148.weight', (192), 'float32') self.pnnx_unique_151.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_151.bias', (576), 'float32') self.pnnx_unique_151.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_151.weight', (576,192), 'float32') self.pnnx_unique_154.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_154.bias', (192), 'float32') self.pnnx_unique_154.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_154.weight', (192,192), 'float32') self.pnnx_unique_156.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_156.bias', (192), 'float32') self.pnnx_unique_156.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_156.weight', (192), 'float32') self.pnnx_unique_157.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_157.bias', (384), 'float32') self.pnnx_unique_157.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_157.weight', (384,192), 'float32') self.pnnx_unique_160.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_160.bias', (192), 'float32') self.pnnx_unique_160.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_160.weight', (192,384), 'float32') self.pnnx_unique_162.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_162.bias', (192), 'float32') self.pnnx_unique_162.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_162.weight', (192,192,3,3), 'float32') self.pnnx_unique_163.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_163.bias', (192), 'float32') self.pnnx_unique_163.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_163.weight', (192), 'float32') self.pnnx_unique_166.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_166.bias', (576), 'float32') self.pnnx_unique_166.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_166.weight', (576,192), 'float32') self.pnnx_unique_169.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_169.bias', (192), 'float32') self.pnnx_unique_169.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_169.weight', (192,192), 'float32') self.pnnx_unique_171.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_171.bias', (192), 'float32') self.pnnx_unique_171.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_171.weight', (192), 'float32') self.pnnx_unique_172.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_172.bias', (384), 'float32') self.pnnx_unique_172.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_172.weight', (384,192), 'float32') self.pnnx_unique_175.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_175.bias', (192), 'float32') self.pnnx_unique_175.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_175.weight', (192,384), 'float32') self.pnnx_unique_178.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_178.bias', (192), 'float32') self.pnnx_unique_178.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_178.weight', (192), 'float32') self.pnnx_unique_181.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_181.bias', (576), 'float32') self.pnnx_unique_181.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_181.weight', (576,192), 'float32') self.pnnx_unique_184.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_184.bias', (192), 'float32') self.pnnx_unique_184.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_184.weight', (192,192), 'float32') self.pnnx_unique_186.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_186.bias', (192), 'float32') self.pnnx_unique_186.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_186.weight', (192), 'float32') self.pnnx_unique_187.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_187.bias', (384), 'float32') self.pnnx_unique_187.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_187.weight', (384,192), 'float32') self.pnnx_unique_190.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_190.bias', (192), 'float32') self.pnnx_unique_190.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_190.weight', (192,384), 'float32') self.pnnx_unique_192.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_192.bias', (192), 'float32') self.pnnx_unique_192.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_192.weight', (192), 'float32') self.pnnx_unique_195.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_195.bias', (576), 'float32') self.pnnx_unique_195.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_195.weight', (576,192), 'float32') self.pnnx_unique_198.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_198.bias', (192), 'float32') self.pnnx_unique_198.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_198.weight', (192,192), 'float32') self.pnnx_unique_200.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_200.bias', (192), 'float32') self.pnnx_unique_200.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_200.weight', (192), 'float32') self.pnnx_unique_201.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_201.bias', (384), 'float32') self.pnnx_unique_201.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_201.weight', (384,192), 'float32') self.pnnx_unique_204.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_204.bias', (192), 'float32') self.pnnx_unique_204.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_204.weight', (192,384), 'float32') self.pnnx_unique_207.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_207.bias', (192), 'float32') self.pnnx_unique_207.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_207.weight', (192), 'float32') self.pnnx_unique_210.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_210.bias', (576), 'float32') self.pnnx_unique_210.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_210.weight', (576,192), 'float32') self.pnnx_unique_213.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_213.bias', (192), 'float32') self.pnnx_unique_213.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_213.weight', (192,192), 'float32') self.pnnx_unique_215.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_215.bias', (192), 'float32') self.pnnx_unique_215.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_215.weight', (192), 'float32') self.pnnx_unique_216.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_216.bias', (384), 'float32') self.pnnx_unique_216.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_216.weight', (384,192), 'float32') self.pnnx_unique_219.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_219.bias', (192), 'float32') self.pnnx_unique_219.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_219.weight', (192,384), 'float32') self.pnnx_unique_221.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_221.bias', (192), 'float32') self.pnnx_unique_221.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_221.weight', (192), 'float32') self.pnnx_unique_224.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_224.bias', (576), 'float32') self.pnnx_unique_224.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_224.weight', (576,192), 'float32') self.pnnx_unique_227.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_227.bias', (192), 'float32') self.pnnx_unique_227.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_227.weight', (192,192), 'float32') self.pnnx_unique_229.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_229.bias', (192), 'float32') self.pnnx_unique_229.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_229.weight', (192), 'float32') self.pnnx_unique_230.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_230.bias', (384), 'float32') self.pnnx_unique_230.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_230.weight', (384,192), 'float32') self.pnnx_unique_233.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_233.bias', (192), 'float32') self.pnnx_unique_233.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_233.weight', (192,384), 'float32') self.pnnx_unique_236.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_236.bias', (192), 'float32') self.pnnx_unique_236.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_236.weight', (192), 'float32') self.pnnx_unique_239.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_239.bias', (576), 'float32') self.pnnx_unique_239.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_239.weight', (576,192), 'float32') self.pnnx_unique_242.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_242.bias', (192), 'float32') self.pnnx_unique_242.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_242.weight', (192,192), 'float32') self.pnnx_unique_244.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_244.bias', (192), 'float32') self.pnnx_unique_244.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_244.weight', (192), 'float32') self.pnnx_unique_245.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_245.bias', (384), 'float32') self.pnnx_unique_245.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_245.weight', (384,192), 'float32') self.pnnx_unique_248.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_248.bias', (192), 'float32') self.pnnx_unique_248.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_248.weight', (192,384), 'float32') self.pnnx_unique_250.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_250.bias', (192), 'float32') self.pnnx_unique_250.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_250.weight', (192,192,3,3), 'float32') self.pnnx_unique_251.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_251.bias', (192), 'float32') self.pnnx_unique_251.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_251.weight', (192), 'float32') self.pnnx_unique_254.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_254.bias', (576), 'float32') self.pnnx_unique_254.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_254.weight', (576,192), 'float32') self.pnnx_unique_257.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_257.bias', (192), 'float32') self.pnnx_unique_257.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_257.weight', (192,192), 'float32') self.pnnx_unique_259.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_259.bias', (192), 'float32') self.pnnx_unique_259.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_259.weight', (192), 'float32') self.pnnx_unique_260.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_260.bias', (384), 'float32') self.pnnx_unique_260.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_260.weight', (384,192), 'float32') self.pnnx_unique_263.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_263.bias', (192), 'float32') self.pnnx_unique_263.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_263.weight', (192,384), 'float32') self.pnnx_unique_266.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_266.bias', (192), 'float32') self.pnnx_unique_266.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_266.weight', (192), 'float32') self.pnnx_unique_269.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_269.bias', (576), 'float32') self.pnnx_unique_269.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_269.weight', (576,192), 'float32') self.pnnx_unique_272.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_272.bias', (192), 'float32') self.pnnx_unique_272.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_272.weight', (192,192), 'float32') self.pnnx_unique_274.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_274.bias', (192), 'float32') self.pnnx_unique_274.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_274.weight', (192), 'float32') self.pnnx_unique_275.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_275.bias', (384), 'float32') self.pnnx_unique_275.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_275.weight', (384,192), 'float32') self.pnnx_unique_278.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_278.bias', (192), 'float32') self.pnnx_unique_278.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_278.weight', (192,384), 'float32') self.pnnx_unique_280.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_280.bias', (192), 'float32') self.pnnx_unique_280.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_280.weight', (192), 'float32') self.pnnx_unique_283.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_283.bias', (576), 'float32') self.pnnx_unique_283.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_283.weight', (576,192), 'float32') self.pnnx_unique_286.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_286.bias', (192), 'float32') self.pnnx_unique_286.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_286.weight', (192,192), 'float32') self.pnnx_unique_288.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_288.bias', (192), 'float32') self.pnnx_unique_288.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_288.weight', (192), 'float32') self.pnnx_unique_289.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_289.bias', (384), 'float32') self.pnnx_unique_289.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_289.weight', (384,192), 'float32') self.pnnx_unique_292.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_292.bias', (192), 'float32') self.pnnx_unique_292.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_292.weight', (192,384), 'float32') self.pnnx_unique_295.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_295.bias', (192), 'float32') self.pnnx_unique_295.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_295.weight', (192), 'float32') self.pnnx_unique_298.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_298.bias', (576), 'float32') self.pnnx_unique_298.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_298.weight', (576,192), 'float32') self.pnnx_unique_301.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_301.bias', (192), 'float32') self.pnnx_unique_301.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_301.weight', (192,192), 'float32') self.pnnx_unique_303.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_303.bias', (192), 'float32') self.pnnx_unique_303.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_303.weight', (192), 'float32') self.pnnx_unique_304.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_304.bias', (384), 'float32') self.pnnx_unique_304.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_304.weight', (384,192), 'float32') self.pnnx_unique_307.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_307.bias', (192), 'float32') self.pnnx_unique_307.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_307.weight', (192,384), 'float32') self.pnnx_unique_309.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_309.bias', (192), 'float32') self.pnnx_unique_309.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_309.weight', (192), 'float32') self.pnnx_unique_312.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_312.bias', (576), 'float32') self.pnnx_unique_312.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_312.weight', (576,192), 'float32') self.pnnx_unique_315.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_315.bias', (192), 'float32') self.pnnx_unique_315.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_315.weight', (192,192), 'float32') self.pnnx_unique_317.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_317.bias', (192), 'float32') self.pnnx_unique_317.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_317.weight', (192), 'float32') self.pnnx_unique_318.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_318.bias', (384), 'float32') self.pnnx_unique_318.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_318.weight', (384,192), 'float32') self.pnnx_unique_321.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_321.bias', (192), 'float32') self.pnnx_unique_321.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_321.weight', (192,384), 'float32') self.pnnx_unique_324.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_324.bias', (192), 'float32') self.pnnx_unique_324.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_324.weight', (192), 'float32') self.pnnx_unique_327.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_327.bias', (576), 'float32') self.pnnx_unique_327.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_327.weight', (576,192), 'float32') self.pnnx_unique_330.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_330.bias', (192), 'float32') self.pnnx_unique_330.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_330.weight', (192,192), 'float32') self.pnnx_unique_332.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_332.bias', (192), 'float32') self.pnnx_unique_332.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_332.weight', (192), 'float32') self.pnnx_unique_333.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_333.bias', (384), 'float32') self.pnnx_unique_333.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_333.weight', (384,192), 'float32') self.pnnx_unique_336.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_336.bias', (192), 'float32') self.pnnx_unique_336.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_336.weight', (192,384), 'float32') self.pnnx_unique_338.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_338.bias', (192), 'float32') self.pnnx_unique_338.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_338.weight', (192,192,3,3), 'float32') self.pnnx_unique_339.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_339.bias', (192), 'float32') self.pnnx_unique_339.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_339.weight', (192), 'float32') self.pnnx_unique_342.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_342.bias', (576), 'float32') self.pnnx_unique_342.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_342.weight', (576,192), 'float32') self.pnnx_unique_345.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_345.bias', (192), 'float32') self.pnnx_unique_345.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_345.weight', (192,192), 'float32') self.pnnx_unique_347.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_347.bias', (192), 'float32') self.pnnx_unique_347.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_347.weight', (192), 'float32') self.pnnx_unique_348.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_348.bias', (384), 'float32') self.pnnx_unique_348.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_348.weight', (384,192), 'float32') self.pnnx_unique_351.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_351.bias', (192), 'float32') self.pnnx_unique_351.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_351.weight', (192,384), 'float32') self.pnnx_unique_354.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_354.bias', (192), 'float32') self.pnnx_unique_354.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_354.weight', (192), 'float32') self.pnnx_unique_357.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_357.bias', (576), 'float32') self.pnnx_unique_357.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_357.weight', (576,192), 'float32') self.pnnx_unique_360.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_360.bias', (192), 'float32') self.pnnx_unique_360.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_360.weight', (192,192), 'float32') self.pnnx_unique_362.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_362.bias', (192), 'float32') self.pnnx_unique_362.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_362.weight', (192), 'float32') self.pnnx_unique_363.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_363.bias', (384), 'float32') self.pnnx_unique_363.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_363.weight', (384,192), 'float32') self.pnnx_unique_366.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_366.bias', (192), 'float32') self.pnnx_unique_366.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_366.weight', (192,384), 'float32') self.pnnx_unique_368.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_368.bias', (192), 'float32') self.pnnx_unique_368.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_368.weight', (192), 'float32') self.pnnx_unique_371.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_371.bias', (576), 'float32') self.pnnx_unique_371.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_371.weight', (576,192), 'float32') self.pnnx_unique_374.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_374.bias', (192), 'float32') self.pnnx_unique_374.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_374.weight', (192,192), 'float32') self.pnnx_unique_376.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_376.bias', (192), 'float32') self.pnnx_unique_376.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_376.weight', (192), 'float32') self.pnnx_unique_377.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_377.bias', (384), 'float32') self.pnnx_unique_377.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_377.weight', (384,192), 'float32') self.pnnx_unique_380.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_380.bias', (192), 'float32') self.pnnx_unique_380.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_380.weight', (192,384), 'float32') self.pnnx_unique_383.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_383.bias', (192), 'float32') self.pnnx_unique_383.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_383.weight', (192), 'float32') self.pnnx_unique_386.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_386.bias', (576), 'float32') self.pnnx_unique_386.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_386.weight', (576,192), 'float32') self.pnnx_unique_389.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_389.bias', (192), 'float32') self.pnnx_unique_389.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_389.weight', (192,192), 'float32') self.pnnx_unique_391.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_391.bias', (192), 'float32') self.pnnx_unique_391.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_391.weight', (192), 'float32') self.pnnx_unique_392.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_392.bias', (384), 'float32') self.pnnx_unique_392.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_392.weight', (384,192), 'float32') self.pnnx_unique_395.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_395.bias', (192), 'float32') self.pnnx_unique_395.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_395.weight', (192,384), 'float32') self.pnnx_unique_397.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_397.bias', (192), 'float32') self.pnnx_unique_397.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_397.weight', (192), 'float32') self.pnnx_unique_400.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_400.bias', (576), 'float32') self.pnnx_unique_400.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_400.weight', (576,192), 'float32') self.pnnx_unique_403.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_403.bias', (192), 'float32') self.pnnx_unique_403.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_403.weight', (192,192), 'float32') self.pnnx_unique_405.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_405.bias', (192), 'float32') self.pnnx_unique_405.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_405.weight', (192), 'float32') self.pnnx_unique_406.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_406.bias', (384), 'float32') self.pnnx_unique_406.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_406.weight', (384,192), 'float32') self.pnnx_unique_409.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_409.bias', (192), 'float32') self.pnnx_unique_409.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_409.weight', (192,384), 'float32') self.pnnx_unique_412.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_412.bias', (192), 'float32') self.pnnx_unique_412.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_412.weight', (192), 'float32') self.pnnx_unique_415.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_415.bias', (576), 'float32') self.pnnx_unique_415.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_415.weight', (576,192), 'float32') self.pnnx_unique_418.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_418.bias', (192), 'float32') self.pnnx_unique_418.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_418.weight', (192,192), 'float32') self.pnnx_unique_420.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_420.bias', (192), 'float32') self.pnnx_unique_420.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_420.weight', (192), 'float32') self.pnnx_unique_421.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_421.bias', (384), 'float32') self.pnnx_unique_421.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_421.weight', (384,192), 'float32') self.pnnx_unique_424.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_424.bias', (192), 'float32') self.pnnx_unique_424.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_424.weight', (192,384), 'float32') self.pnnx_unique_426.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_426.bias', (192), 'float32') self.pnnx_unique_426.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_426.weight', (192,192,3,3), 'float32') self.pnnx_unique_427.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_427.bias', (192), 'float32') self.pnnx_unique_427.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_427.weight', (192), 'float32') self.pnnx_unique_430.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_430.bias', (576), 'float32') self.pnnx_unique_430.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_430.weight', (576,192), 'float32') self.pnnx_unique_433.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_433.bias', (192), 'float32') self.pnnx_unique_433.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_433.weight', (192,192), 'float32') self.pnnx_unique_435.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_435.bias', (192), 'float32') self.pnnx_unique_435.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_435.weight', (192), 'float32') self.pnnx_unique_436.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_436.bias', (384), 'float32') self.pnnx_unique_436.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_436.weight', (384,192), 'float32') self.pnnx_unique_439.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_439.bias', (192), 'float32') self.pnnx_unique_439.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_439.weight', (192,384), 'float32') self.pnnx_unique_442.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_442.bias', (192), 'float32') self.pnnx_unique_442.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_442.weight', (192), 'float32') self.pnnx_unique_445.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_445.bias', (576), 'float32') self.pnnx_unique_445.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_445.weight', (576,192), 'float32') self.pnnx_unique_448.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_448.bias', (192), 'float32') self.pnnx_unique_448.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_448.weight', (192,192), 'float32') self.pnnx_unique_450.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_450.bias', (192), 'float32') self.pnnx_unique_450.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_450.weight', (192), 'float32') self.pnnx_unique_451.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_451.bias', (384), 'float32') self.pnnx_unique_451.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_451.weight', (384,192), 'float32') self.pnnx_unique_454.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_454.bias', (192), 'float32') self.pnnx_unique_454.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_454.weight', (192,384), 'float32') self.pnnx_unique_456.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_456.bias', (192), 'float32') self.pnnx_unique_456.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_456.weight', (192), 'float32') self.pnnx_unique_459.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_459.bias', (576), 'float32') self.pnnx_unique_459.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_459.weight', (576,192), 'float32') self.pnnx_unique_462.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_462.bias', (192), 'float32') self.pnnx_unique_462.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_462.weight', (192,192), 'float32') self.pnnx_unique_464.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_464.bias', (192), 'float32') self.pnnx_unique_464.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_464.weight', (192), 'float32') self.pnnx_unique_465.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_465.bias', (384), 'float32') self.pnnx_unique_465.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_465.weight', (384,192), 'float32') self.pnnx_unique_468.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_468.bias', (192), 'float32') self.pnnx_unique_468.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_468.weight', (192,384), 'float32') self.pnnx_unique_471.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_471.bias', (192), 'float32') self.pnnx_unique_471.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_471.weight', (192), 'float32') self.pnnx_unique_474.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_474.bias', (576), 'float32') self.pnnx_unique_474.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_474.weight', (576,192), 'float32') self.pnnx_unique_477.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_477.bias', (192), 'float32') self.pnnx_unique_477.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_477.weight', (192,192), 'float32') self.pnnx_unique_479.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_479.bias', (192), 'float32') self.pnnx_unique_479.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_479.weight', (192), 'float32') self.pnnx_unique_480.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_480.bias', (384), 'float32') self.pnnx_unique_480.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_480.weight', (384,192), 'float32') self.pnnx_unique_483.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_483.bias', (192), 'float32') self.pnnx_unique_483.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_483.weight', (192,384), 'float32') self.pnnx_unique_485.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_485.bias', (192), 'float32') self.pnnx_unique_485.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_485.weight', (192), 'float32') self.pnnx_unique_488.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_488.bias', (576), 'float32') self.pnnx_unique_488.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_488.weight', (576,192), 'float32') self.pnnx_unique_491.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_491.bias', (192), 'float32') self.pnnx_unique_491.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_491.weight', (192,192), 'float32') self.pnnx_unique_493.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_493.bias', (192), 'float32') self.pnnx_unique_493.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_493.weight', (192), 'float32') self.pnnx_unique_494.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_494.bias', (384), 'float32') self.pnnx_unique_494.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_494.weight', (384,192), 'float32') self.pnnx_unique_497.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_497.bias', (192), 'float32') self.pnnx_unique_497.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_497.weight', (192,384), 'float32') self.pnnx_unique_500.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_500.bias', (192), 'float32') self.pnnx_unique_500.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_500.weight', (192), 'float32') self.pnnx_unique_503.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_503.bias', (576), 'float32') self.pnnx_unique_503.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_503.weight', (576,192), 'float32') self.pnnx_unique_506.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_506.bias', (192), 'float32') self.pnnx_unique_506.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_506.weight', (192,192), 'float32') self.pnnx_unique_508.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_508.bias', (192), 'float32') self.pnnx_unique_508.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_508.weight', (192), 'float32') self.pnnx_unique_509.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_509.bias', (384), 'float32') self.pnnx_unique_509.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_509.weight', (384,192), 'float32') self.pnnx_unique_512.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_512.bias', (192), 'float32') self.pnnx_unique_512.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_512.weight', (192,384), 'float32') self.pnnx_unique_514.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_514.bias', (192), 'float32') self.pnnx_unique_514.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_514.weight', (192,192,3,3), 'float32') self.pnnx_unique_515.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_515.bias', (192), 'float32') self.pnnx_unique_515.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_515.weight', (192), 'float32') self.pnnx_unique_518.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_518.bias', (576), 'float32') self.pnnx_unique_518.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_518.weight', (576,192), 'float32') self.pnnx_unique_521.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_521.bias', (192), 'float32') self.pnnx_unique_521.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_521.weight', (192,192), 'float32') self.pnnx_unique_523.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_523.bias', (192), 'float32') self.pnnx_unique_523.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_523.weight', (192), 'float32') self.pnnx_unique_524.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_524.bias', (384), 'float32') self.pnnx_unique_524.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_524.weight', (384,192), 'float32') self.pnnx_unique_527.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_527.bias', (192), 'float32') self.pnnx_unique_527.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_527.weight', (192,384), 'float32') self.pnnx_unique_530.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_530.bias', (192), 'float32') self.pnnx_unique_530.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_530.weight', (192), 'float32') self.pnnx_unique_533.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_533.bias', (576), 'float32') self.pnnx_unique_533.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_533.weight', (576,192), 'float32') self.pnnx_unique_536.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_536.bias', (192), 'float32') self.pnnx_unique_536.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_536.weight', (192,192), 'float32') self.pnnx_unique_538.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_538.bias', (192), 'float32') self.pnnx_unique_538.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_538.weight', (192), 'float32') self.pnnx_unique_539.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_539.bias', (384), 'float32') self.pnnx_unique_539.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_539.weight', (384,192), 'float32') self.pnnx_unique_542.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_542.bias', (192), 'float32') self.pnnx_unique_542.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_542.weight', (192,384), 'float32') self.pnnx_unique_544.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_544.bias', (192), 'float32') self.pnnx_unique_544.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_544.weight', (192), 'float32') self.pnnx_unique_547.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_547.bias', (576), 'float32') self.pnnx_unique_547.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_547.weight', (576,192), 'float32') self.pnnx_unique_550.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_550.bias', (192), 'float32') self.pnnx_unique_550.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_550.weight', (192,192), 'float32') self.pnnx_unique_552.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_552.bias', (192), 'float32') self.pnnx_unique_552.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_552.weight', (192), 'float32') self.pnnx_unique_553.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_553.bias', (384), 'float32') self.pnnx_unique_553.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_553.weight', (384,192), 'float32') self.pnnx_unique_556.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_556.bias', (192), 'float32') self.pnnx_unique_556.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_556.weight', (192,384), 'float32') self.pnnx_unique_559.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_559.bias', (192), 'float32') self.pnnx_unique_559.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_559.weight', (192), 'float32') self.pnnx_unique_562.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_562.bias', (576), 'float32') self.pnnx_unique_562.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_562.weight', (576,192), 'float32') self.pnnx_unique_565.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_565.bias', (192), 'float32') self.pnnx_unique_565.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_565.weight', (192,192), 'float32') self.pnnx_unique_567.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_567.bias', (192), 'float32') self.pnnx_unique_567.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_567.weight', (192), 'float32') self.pnnx_unique_568.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_568.bias', (384), 'float32') self.pnnx_unique_568.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_568.weight', (384,192), 'float32') self.pnnx_unique_571.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_571.bias', (192), 'float32') self.pnnx_unique_571.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_571.weight', (192,384), 'float32') self.pnnx_unique_573.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_573.bias', (192), 'float32') self.pnnx_unique_573.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_573.weight', (192), 'float32') self.pnnx_unique_576.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_576.bias', (576), 'float32') self.pnnx_unique_576.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_576.weight', (576,192), 'float32') self.pnnx_unique_579.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_579.bias', (192), 'float32') self.pnnx_unique_579.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_579.weight', (192,192), 'float32') self.pnnx_unique_581.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_581.bias', (192), 'float32') self.pnnx_unique_581.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_581.weight', (192), 'float32') self.pnnx_unique_582.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_582.bias', (384), 'float32') self.pnnx_unique_582.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_582.weight', (384,192), 'float32') self.pnnx_unique_585.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_585.bias', (192), 'float32') self.pnnx_unique_585.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_585.weight', (192,384), 'float32') self.pnnx_unique_588.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_588.bias', (192), 'float32') self.pnnx_unique_588.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_588.weight', (192), 'float32') self.pnnx_unique_591.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_591.bias', (576), 'float32') self.pnnx_unique_591.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_591.weight', (576,192), 'float32') self.pnnx_unique_594.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_594.bias', (192), 'float32') self.pnnx_unique_594.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_594.weight', (192,192), 'float32') self.pnnx_unique_596.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_596.bias', (192), 'float32') self.pnnx_unique_596.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_596.weight', (192), 'float32') self.pnnx_unique_597.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_597.bias', (384), 'float32') self.pnnx_unique_597.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_597.weight', (384,192), 'float32') self.pnnx_unique_600.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_600.bias', (192), 'float32') self.pnnx_unique_600.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_600.weight', (192,384), 'float32') self.pnnx_unique_602.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_602.bias', (192), 'float32') self.pnnx_unique_602.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_602.weight', (192,192,3,3), 'float32') self.pnnx_unique_603.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_603.bias', (192), 'float32') self.pnnx_unique_603.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_603.weight', (192), 'float32') self.pnnx_unique_604.bias = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_604.bias', (192), 'float32') self.pnnx_unique_604.weight = self.load_pnnx_bin_as_parameter(archive, 'pnnx_unique_604.weight', (192,192,3,3), 'float32') self.conv2d_0.weight = self.load_pnnx_bin_as_parameter(archive, 'conv2d_0.weight', (192,192,7,7), 'float32') self.conv2d_1.weight = self.load_pnnx_bin_as_parameter(archive, 'conv2d_1.weight', (192,192,3,3), 'float32') self.conv2d_2.weight = self.load_pnnx_bin_as_parameter(archive, 'conv2d_2.weight', (192,192,3,3), 'float32') self.conv2d_3.weight = self.load_pnnx_bin_as_parameter(archive, 'conv2d_3.weight', (192,192,3,3), 'float32') self.patch_embed_mmsa_norm.bias = self.load_pnnx_bin_as_parameter(archive, 'patch_embed_mmsa.norm.bias', (192), 'float32') self.patch_embed_mmsa_norm.weight = self.load_pnnx_bin_as_parameter(archive, 'patch_embed_mmsa.norm.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_0_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_0_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_0_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_0_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.conv.bias', (192), 'float32') self.layers_mmsa_0_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.0.conv.weight', (192,192,3,3), 'float32') self.layers_mmsa_1_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_1_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_1_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_1_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_1_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.conv.bias', (192), 'float32') self.layers_mmsa_1_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.1.conv.weight', (192,192,3,3), 'float32') self.layers_mmsa_2_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_2_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_2_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_2_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_2_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.conv.bias', (192), 'float32') self.layers_mmsa_2_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.2.conv.weight', (192,192,3,3), 'float32') self.layers_mmsa_3_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_3_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_3_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_3_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_3_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.conv.bias', (192), 'float32') self.layers_mmsa_3_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.3.conv.weight', (192,192,3,3), 'float32') self.layers_mmsa_4_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_4_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_4_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_4_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_4_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.conv.bias', (192), 'float32') self.layers_mmsa_4_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.4.conv.weight', (192,192,3,3), 'float32') self.layers_mmsa_5_residual_group_blocks_0_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_0_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_0_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_0_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.0.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_residual_group_blocks_1_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_1_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_1_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_1_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.1.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_residual_group_blocks_2_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_2_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_2_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_2_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.2.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_residual_group_blocks_3_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_3_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_3_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_3_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.3.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_residual_group_blocks_4_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_4_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_4_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_4_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.4.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_residual_group_blocks_5_norm1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.norm1.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_norm1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.norm1.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_attn_qkv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.attn.qkv.bias', (576), 'float32') self.layers_mmsa_5_residual_group_blocks_5_attn_qkv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.attn.qkv.weight', (576,192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_attn_proj.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.attn.proj.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_attn_proj.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.attn.proj.weight', (192,192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_norm2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.norm2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_norm2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.norm2.weight', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_mlp_fc1.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.mlp.fc1.bias', (384), 'float32') self.layers_mmsa_5_residual_group_blocks_5_mlp_fc1.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.mlp.fc1.weight', (384,192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_mlp_fc2.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.mlp.fc2.bias', (192), 'float32') self.layers_mmsa_5_residual_group_blocks_5_mlp_fc2.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.residual_group.blocks.5.mlp.fc2.weight', (192,384), 'float32') self.layers_mmsa_5_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.conv.bias', (192), 'float32') self.layers_mmsa_5_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'layers_mmsa.5.conv.weight', (192,192,3,3), 'float32') self.norm_mmsa.bias = self.load_pnnx_bin_as_parameter(archive, 'norm_mmsa.bias', (192), 'float32') self.norm_mmsa.weight = self.load_pnnx_bin_as_parameter(archive, 'norm_mmsa.weight', (192), 'float32') self.conv_after_body_mmsa.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_after_body_mmsa.bias', (192), 'float32') self.conv_after_body_mmsa.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_after_body_mmsa.weight', (192,192,3,3), 'float32') self.upsample_conv.bias = self.load_pnnx_bin_as_parameter(archive, 'upsample_conv.bias', (192), 'float32') self.upsample_conv.weight = self.load_pnnx_bin_as_parameter(archive, 'upsample_conv.weight', (192,192,8,8), 'float32') self.conv_last.bias = self.load_pnnx_bin_as_parameter(archive, 'conv_last.bias', (3), 'float32') self.conv_last.weight = self.load_pnnx_bin_as_parameter(archive, 'conv_last.weight', (3,192,3,3), 'float32') self.pnnx_3_pnnx_3 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_3.pnnx_3', (1,3,1,1), 'float32') self.pnnx_fold_2377_pnnx_fold_2377 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2377.pnnx_fold_2377', (1,6,64,64), 'float32') self.pnnx_fold_2530_pnnx_fold_2530 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2530.pnnx_fold_2530', (1,6,64,64), 'float32') self.pnnx_fold_2540_pnnx_fold_2540 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2540.pnnx_fold_2540', (1,36,1,64,64), 'float32') self.pnnx_fold_2689_pnnx_fold_2689 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2689.pnnx_fold_2689', (1,6,64,64), 'float32') self.pnnx_fold_2842_pnnx_fold_2842 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2842.pnnx_fold_2842', (1,6,64,64), 'float32') self.pnnx_fold_2852_pnnx_fold_2852 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2852.pnnx_fold_2852', (1,36,1,64,64), 'float32') self.pnnx_fold_3001_pnnx_fold_3001 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3001.pnnx_fold_3001', (1,6,64,64), 'float32') self.pnnx_fold_3154_pnnx_fold_3154 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3154.pnnx_fold_3154', (1,6,64,64), 'float32') self.pnnx_fold_3164_pnnx_fold_3164 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3164.pnnx_fold_3164', (1,36,1,64,64), 'float32') self.pnnx_fold_3347_pnnx_fold_3347 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3347.pnnx_fold_3347', (1,6,64,64), 'float32') self.pnnx_fold_3500_pnnx_fold_3500 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3500.pnnx_fold_3500', (1,6,64,64), 'float32') self.pnnx_fold_3510_pnnx_fold_3510 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3510.pnnx_fold_3510', (1,36,1,64,64), 'float32') self.pnnx_fold_3659_pnnx_fold_3659 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3659.pnnx_fold_3659', (1,6,64,64), 'float32') self.pnnx_fold_3812_pnnx_fold_3812 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3812.pnnx_fold_3812', (1,6,64,64), 'float32') self.pnnx_fold_3822_pnnx_fold_3822 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3822.pnnx_fold_3822', (1,36,1,64,64), 'float32') self.pnnx_fold_3971_pnnx_fold_3971 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_3971.pnnx_fold_3971', (1,6,64,64), 'float32') self.pnnx_fold_4124_pnnx_fold_4124 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4124.pnnx_fold_4124', (1,6,64,64), 'float32') self.pnnx_fold_4134_pnnx_fold_4134 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4134.pnnx_fold_4134', (1,36,1,64,64), 'float32') self.pnnx_fold_4317_pnnx_fold_4317 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4317.pnnx_fold_4317', (1,6,64,64), 'float32') self.pnnx_fold_4470_pnnx_fold_4470 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4470.pnnx_fold_4470', (1,6,64,64), 'float32') self.pnnx_fold_4480_pnnx_fold_4480 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4480.pnnx_fold_4480', (1,36,1,64,64), 'float32') self.pnnx_fold_4629_pnnx_fold_4629 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4629.pnnx_fold_4629', (1,6,64,64), 'float32') self.pnnx_fold_4782_pnnx_fold_4782 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4782.pnnx_fold_4782', (1,6,64,64), 'float32') self.pnnx_fold_4792_pnnx_fold_4792 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4792.pnnx_fold_4792', (1,36,1,64,64), 'float32') self.pnnx_fold_4941_pnnx_fold_4941 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_4941.pnnx_fold_4941', (1,6,64,64), 'float32') self.pnnx_fold_5094_pnnx_fold_5094 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5094.pnnx_fold_5094', (1,6,64,64), 'float32') self.pnnx_fold_5104_pnnx_fold_5104 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5104.pnnx_fold_5104', (1,36,1,64,64), 'float32') self.pnnx_fold_5287_pnnx_fold_5287 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5287.pnnx_fold_5287', (1,6,64,64), 'float32') self.pnnx_fold_5440_pnnx_fold_5440 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5440.pnnx_fold_5440', (1,6,64,64), 'float32') self.pnnx_fold_5450_pnnx_fold_5450 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5450.pnnx_fold_5450', (1,36,1,64,64), 'float32') self.pnnx_fold_5599_pnnx_fold_5599 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5599.pnnx_fold_5599', (1,6,64,64), 'float32') self.pnnx_fold_5752_pnnx_fold_5752 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5752.pnnx_fold_5752', (1,6,64,64), 'float32') self.pnnx_fold_5762_pnnx_fold_5762 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5762.pnnx_fold_5762', (1,36,1,64,64), 'float32') self.pnnx_fold_5911_pnnx_fold_5911 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_5911.pnnx_fold_5911', (1,6,64,64), 'float32') self.pnnx_fold_6064_pnnx_fold_6064 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6064.pnnx_fold_6064', (1,6,64,64), 'float32') self.pnnx_fold_6074_pnnx_fold_6074 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6074.pnnx_fold_6074', (1,36,1,64,64), 'float32') self.pnnx_fold_6257_pnnx_fold_6257 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6257.pnnx_fold_6257', (1,6,64,64), 'float32') self.pnnx_fold_6410_pnnx_fold_6410 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6410.pnnx_fold_6410', (1,6,64,64), 'float32') self.pnnx_fold_6420_pnnx_fold_6420 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6420.pnnx_fold_6420', (1,36,1,64,64), 'float32') self.pnnx_fold_6569_pnnx_fold_6569 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6569.pnnx_fold_6569', (1,6,64,64), 'float32') self.pnnx_fold_6722_pnnx_fold_6722 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6722.pnnx_fold_6722', (1,6,64,64), 'float32') self.pnnx_fold_6732_pnnx_fold_6732 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6732.pnnx_fold_6732', (1,36,1,64,64), 'float32') self.pnnx_fold_6881_pnnx_fold_6881 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_6881.pnnx_fold_6881', (1,6,64,64), 'float32') self.pnnx_fold_7034_pnnx_fold_7034 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7034.pnnx_fold_7034', (1,6,64,64), 'float32') self.pnnx_fold_7044_pnnx_fold_7044 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7044.pnnx_fold_7044', (1,36,1,64,64), 'float32') self.pnnx_fold_7227_pnnx_fold_7227 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7227.pnnx_fold_7227', (1,6,64,64), 'float32') self.pnnx_fold_7380_pnnx_fold_7380 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7380.pnnx_fold_7380', (1,6,64,64), 'float32') self.pnnx_fold_7390_pnnx_fold_7390 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7390.pnnx_fold_7390', (1,36,1,64,64), 'float32') self.pnnx_fold_7539_pnnx_fold_7539 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7539.pnnx_fold_7539', (1,6,64,64), 'float32') self.pnnx_fold_7692_pnnx_fold_7692 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7692.pnnx_fold_7692', (1,6,64,64), 'float32') self.pnnx_fold_7702_pnnx_fold_7702 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7702.pnnx_fold_7702', (1,36,1,64,64), 'float32') self.pnnx_fold_7851_pnnx_fold_7851 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_7851.pnnx_fold_7851', (1,6,64,64), 'float32') self.pnnx_fold_8004_pnnx_fold_8004 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8004.pnnx_fold_8004', (1,6,64,64), 'float32') self.pnnx_fold_8014_pnnx_fold_8014 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8014.pnnx_fold_8014', (1,36,1,64,64), 'float32') self.pnnx_fold_8214_pnnx_fold_8214 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8214.pnnx_fold_8214', (1,6,64,64), 'float32') self.pnnx_fold_8367_pnnx_fold_8367 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8367.pnnx_fold_8367', (1,6,64,64), 'float32') self.pnnx_fold_8377_pnnx_fold_8377 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8377.pnnx_fold_8377', (1,36,1,64,64), 'float32') self.pnnx_fold_8526_pnnx_fold_8526 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8526.pnnx_fold_8526', (1,6,64,64), 'float32') self.pnnx_fold_8679_pnnx_fold_8679 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8679.pnnx_fold_8679', (1,6,64,64), 'float32') self.pnnx_fold_8689_pnnx_fold_8689 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8689.pnnx_fold_8689', (1,36,1,64,64), 'float32') self.pnnx_fold_8838_pnnx_fold_8838 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8838.pnnx_fold_8838', (1,6,64,64), 'float32') self.pnnx_fold_8991_pnnx_fold_8991 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_8991.pnnx_fold_8991', (1,6,64,64), 'float32') self.pnnx_fold_9001_pnnx_fold_9001 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9001.pnnx_fold_9001', (1,36,1,64,64), 'float32') self.pnnx_fold_9184_pnnx_fold_9184 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9184.pnnx_fold_9184', (1,6,64,64), 'float32') self.pnnx_fold_9337_pnnx_fold_9337 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9337.pnnx_fold_9337', (1,6,64,64), 'float32') self.pnnx_fold_9347_pnnx_fold_9347 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9347.pnnx_fold_9347', (1,36,1,64,64), 'float32') self.pnnx_fold_9496_pnnx_fold_9496 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9496.pnnx_fold_9496', (1,6,64,64), 'float32') self.pnnx_fold_9649_pnnx_fold_9649 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9649.pnnx_fold_9649', (1,6,64,64), 'float32') self.pnnx_fold_9659_pnnx_fold_9659 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9659.pnnx_fold_9659', (1,36,1,64,64), 'float32') self.pnnx_fold_9808_pnnx_fold_9808 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9808.pnnx_fold_9808', (1,6,64,64), 'float32') self.pnnx_fold_9961_pnnx_fold_9961 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9961.pnnx_fold_9961', (1,6,64,64), 'float32') self.pnnx_fold_9971_pnnx_fold_9971 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_9971.pnnx_fold_9971', (1,36,1,64,64), 'float32') self.pnnx_fold_10154_pnnx_fold_10154 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10154.pnnx_fold_10154', (1,6,64,64), 'float32') self.pnnx_fold_10307_pnnx_fold_10307 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10307.pnnx_fold_10307', (1,6,64,64), 'float32') self.pnnx_fold_10317_pnnx_fold_10317 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10317.pnnx_fold_10317', (1,36,1,64,64), 'float32') self.pnnx_fold_10466_pnnx_fold_10466 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10466.pnnx_fold_10466', (1,6,64,64), 'float32') self.pnnx_fold_10619_pnnx_fold_10619 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10619.pnnx_fold_10619', (1,6,64,64), 'float32') self.pnnx_fold_10629_pnnx_fold_10629 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10629.pnnx_fold_10629', (1,36,1,64,64), 'float32') self.pnnx_fold_10778_pnnx_fold_10778 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10778.pnnx_fold_10778', (1,6,64,64), 'float32') self.pnnx_fold_10931_pnnx_fold_10931 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10931.pnnx_fold_10931', (1,6,64,64), 'float32') self.pnnx_fold_10941_pnnx_fold_10941 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_10941.pnnx_fold_10941', (1,36,1,64,64), 'float32') self.pnnx_fold_11124_pnnx_fold_11124 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11124.pnnx_fold_11124', (1,6,64,64), 'float32') self.pnnx_fold_11277_pnnx_fold_11277 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11277.pnnx_fold_11277', (1,6,64,64), 'float32') self.pnnx_fold_11287_pnnx_fold_11287 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11287.pnnx_fold_11287', (1,36,1,64,64), 'float32') self.pnnx_fold_11436_pnnx_fold_11436 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11436.pnnx_fold_11436', (1,6,64,64), 'float32') self.pnnx_fold_11589_pnnx_fold_11589 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11589.pnnx_fold_11589', (1,6,64,64), 'float32') self.pnnx_fold_11599_pnnx_fold_11599 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11599.pnnx_fold_11599', (1,36,1,64,64), 'float32') self.pnnx_fold_11748_pnnx_fold_11748 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11748.pnnx_fold_11748', (1,6,64,64), 'float32') self.pnnx_fold_11901_pnnx_fold_11901 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11901.pnnx_fold_11901', (1,6,64,64), 'float32') self.pnnx_fold_11911_pnnx_fold_11911 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_11911.pnnx_fold_11911', (1,36,1,64,64), 'float32') self.pnnx_fold_12094_pnnx_fold_12094 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12094.pnnx_fold_12094', (1,6,64,64), 'float32') self.pnnx_fold_12247_pnnx_fold_12247 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12247.pnnx_fold_12247', (1,6,64,64), 'float32') self.pnnx_fold_12257_pnnx_fold_12257 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12257.pnnx_fold_12257', (1,36,1,64,64), 'float32') self.pnnx_fold_12406_pnnx_fold_12406 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12406.pnnx_fold_12406', (1,6,64,64), 'float32') self.pnnx_fold_12559_pnnx_fold_12559 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12559.pnnx_fold_12559', (1,6,64,64), 'float32') self.pnnx_fold_12569_pnnx_fold_12569 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12569.pnnx_fold_12569', (1,36,1,64,64), 'float32') self.pnnx_fold_12718_pnnx_fold_12718 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12718.pnnx_fold_12718', (1,6,64,64), 'float32') self.pnnx_fold_12871_pnnx_fold_12871 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12871.pnnx_fold_12871', (1,6,64,64), 'float32') self.pnnx_fold_12881_pnnx_fold_12881 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_12881.pnnx_fold_12881', (1,36,1,64,64), 'float32') self.pnnx_fold_13064_pnnx_fold_13064 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13064.pnnx_fold_13064', (1,6,64,64), 'float32') self.pnnx_fold_13217_pnnx_fold_13217 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13217.pnnx_fold_13217', (1,6,64,64), 'float32') self.pnnx_fold_13227_pnnx_fold_13227 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13227.pnnx_fold_13227', (1,36,1,64,64), 'float32') self.pnnx_fold_13376_pnnx_fold_13376 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13376.pnnx_fold_13376', (1,6,64,64), 'float32') self.pnnx_fold_13529_pnnx_fold_13529 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13529.pnnx_fold_13529', (1,6,64,64), 'float32') self.pnnx_fold_13539_pnnx_fold_13539 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13539.pnnx_fold_13539', (1,36,1,64,64), 'float32') self.pnnx_fold_13688_pnnx_fold_13688 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13688.pnnx_fold_13688', (1,6,64,64), 'float32') self.pnnx_fold_13841_pnnx_fold_13841 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13841.pnnx_fold_13841', (1,6,64,64), 'float32') self.pnnx_fold_13851_pnnx_fold_13851 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_13851.pnnx_fold_13851', (1,36,1,64,64), 'float32') self.pnnx_fold_14084_pnnx_fold_14084 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14084.pnnx_fold_14084', (1,6,64,64), 'float32') self.pnnx_fold_14237_pnnx_fold_14237 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14237.pnnx_fold_14237', (1,6,64,64), 'float32') self.pnnx_fold_14247_pnnx_fold_14247 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14247.pnnx_fold_14247', (1,36,1,64,64), 'float32') self.pnnx_fold_14396_pnnx_fold_14396 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14396.pnnx_fold_14396', (1,6,64,64), 'float32') self.pnnx_fold_14549_pnnx_fold_14549 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14549.pnnx_fold_14549', (1,6,64,64), 'float32') self.pnnx_fold_14559_pnnx_fold_14559 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14559.pnnx_fold_14559', (1,36,1,64,64), 'float32') self.pnnx_fold_14708_pnnx_fold_14708 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14708.pnnx_fold_14708', (1,6,64,64), 'float32') self.pnnx_fold_14861_pnnx_fold_14861 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14861.pnnx_fold_14861', (1,6,64,64), 'float32') self.pnnx_fold_14871_pnnx_fold_14871 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_14871.pnnx_fold_14871', (1,36,1,64,64), 'float32') self.pnnx_fold_15054_pnnx_fold_15054 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15054.pnnx_fold_15054', (1,6,64,64), 'float32') self.pnnx_fold_15207_pnnx_fold_15207 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15207.pnnx_fold_15207', (1,6,64,64), 'float32') self.pnnx_fold_15217_pnnx_fold_15217 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15217.pnnx_fold_15217', (1,36,1,64,64), 'float32') self.pnnx_fold_15366_pnnx_fold_15366 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15366.pnnx_fold_15366', (1,6,64,64), 'float32') self.pnnx_fold_15519_pnnx_fold_15519 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15519.pnnx_fold_15519', (1,6,64,64), 'float32') self.pnnx_fold_15529_pnnx_fold_15529 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15529.pnnx_fold_15529', (1,36,1,64,64), 'float32') self.pnnx_fold_15678_pnnx_fold_15678 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15678.pnnx_fold_15678', (1,6,64,64), 'float32') self.pnnx_fold_15831_pnnx_fold_15831 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15831.pnnx_fold_15831', (1,6,64,64), 'float32') self.pnnx_fold_15841_pnnx_fold_15841 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_15841.pnnx_fold_15841', (1,36,1,64,64), 'float32') self.pnnx_fold_16024_pnnx_fold_16024 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16024.pnnx_fold_16024', (1,6,64,64), 'float32') self.pnnx_fold_16177_pnnx_fold_16177 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16177.pnnx_fold_16177', (1,6,64,64), 'float32') self.pnnx_fold_16187_pnnx_fold_16187 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16187.pnnx_fold_16187', (1,36,1,64,64), 'float32') self.pnnx_fold_16336_pnnx_fold_16336 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16336.pnnx_fold_16336', (1,6,64,64), 'float32') self.pnnx_fold_16489_pnnx_fold_16489 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16489.pnnx_fold_16489', (1,6,64,64), 'float32') self.pnnx_fold_16499_pnnx_fold_16499 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16499.pnnx_fold_16499', (1,36,1,64,64), 'float32') self.pnnx_fold_16648_pnnx_fold_16648 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16648.pnnx_fold_16648', (1,6,64,64), 'float32') self.pnnx_fold_16801_pnnx_fold_16801 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16801.pnnx_fold_16801', (1,6,64,64), 'float32') self.pnnx_fold_16811_pnnx_fold_16811 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16811.pnnx_fold_16811', (1,36,1,64,64), 'float32') self.pnnx_fold_16994_pnnx_fold_16994 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_16994.pnnx_fold_16994', (1,6,64,64), 'float32') self.pnnx_fold_17147_pnnx_fold_17147 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17147.pnnx_fold_17147', (1,6,64,64), 'float32') self.pnnx_fold_17157_pnnx_fold_17157 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17157.pnnx_fold_17157', (1,36,1,64,64), 'float32') self.pnnx_fold_17306_pnnx_fold_17306 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17306.pnnx_fold_17306', (1,6,64,64), 'float32') self.pnnx_fold_17459_pnnx_fold_17459 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17459.pnnx_fold_17459', (1,6,64,64), 'float32') self.pnnx_fold_17469_pnnx_fold_17469 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17469.pnnx_fold_17469', (1,36,1,64,64), 'float32') self.pnnx_fold_17618_pnnx_fold_17618 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17618.pnnx_fold_17618', (1,6,64,64), 'float32') self.pnnx_fold_17771_pnnx_fold_17771 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17771.pnnx_fold_17771', (1,6,64,64), 'float32') self.pnnx_fold_17781_pnnx_fold_17781 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17781.pnnx_fold_17781', (1,36,1,64,64), 'float32') self.pnnx_fold_17964_pnnx_fold_17964 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_17964.pnnx_fold_17964', (1,6,64,64), 'float32') self.pnnx_fold_18117_pnnx_fold_18117 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18117.pnnx_fold_18117', (1,6,64,64), 'float32') self.pnnx_fold_18127_pnnx_fold_18127 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18127.pnnx_fold_18127', (1,36,1,64,64), 'float32') self.pnnx_fold_18276_pnnx_fold_18276 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18276.pnnx_fold_18276', (1,6,64,64), 'float32') self.pnnx_fold_18429_pnnx_fold_18429 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18429.pnnx_fold_18429', (1,6,64,64), 'float32') self.pnnx_fold_18439_pnnx_fold_18439 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18439.pnnx_fold_18439', (1,36,1,64,64), 'float32') self.pnnx_fold_18588_pnnx_fold_18588 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18588.pnnx_fold_18588', (1,6,64,64), 'float32') self.pnnx_fold_18741_pnnx_fold_18741 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18741.pnnx_fold_18741', (1,6,64,64), 'float32') self.pnnx_fold_18751_pnnx_fold_18751 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18751.pnnx_fold_18751', (1,36,1,64,64), 'float32') self.pnnx_fold_18934_pnnx_fold_18934 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_18934.pnnx_fold_18934', (1,6,64,64), 'float32') self.pnnx_fold_19087_pnnx_fold_19087 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19087.pnnx_fold_19087', (1,6,64,64), 'float32') self.pnnx_fold_19097_pnnx_fold_19097 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19097.pnnx_fold_19097', (1,36,1,64,64), 'float32') self.pnnx_fold_19246_pnnx_fold_19246 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19246.pnnx_fold_19246', (1,6,64,64), 'float32') self.pnnx_fold_19399_pnnx_fold_19399 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19399.pnnx_fold_19399', (1,6,64,64), 'float32') self.pnnx_fold_19409_pnnx_fold_19409 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19409.pnnx_fold_19409', (1,36,1,64,64), 'float32') self.pnnx_fold_19558_pnnx_fold_19558 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19558.pnnx_fold_19558', (1,6,64,64), 'float32') self.pnnx_fold_19711_pnnx_fold_19711 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19711.pnnx_fold_19711', (1,6,64,64), 'float32') self.pnnx_fold_19721_pnnx_fold_19721 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_19721.pnnx_fold_19721', (1,36,1,64,64), 'float32') self.pnnx_fold_2234_pnnx_fold_2234 = self.load_pnnx_bin_as_parameter(archive, 'pnnx_fold_2234.pnnx_fold_2234', (1,192,48,48), 'float32') archive.close() def load_pnnx_bin_as_parameter(self, archive, key, shape, dtype, requires_grad=True): return nn.Parameter(self.load_pnnx_bin_as_tensor(archive, key, shape, dtype), requires_grad) def load_pnnx_bin_as_tensor(self, archive, key, shape, dtype): _, tmppath = tempfile.mkstemp() tmpf = open(tmppath, 'wb') with archive.open(key) as keyfile: tmpf.write(keyfile.read()) tmpf.close() m = np.memmap(tmppath, dtype=dtype, mode='r', shape=shape).copy() os.remove(tmppath) return torch.from_numpy(m) def forward(self, v_0, v_1): v_2 = self.pnnx_3_pnnx_3 v_3 = aten::type_as(v_2, v_0) v_4 = (v_0 - v_3) v_5 = (v_1 - v_3) v_6 = self.conv_first(v_4) v_7 = self.pnnx_unique_0(v_5) v_8 = torch.flatten(input=v_6, end_dim=-1, start_dim=2) v_9 = torch.transpose(input=v_8, dim0=1, dim1=2) v_10 = self.patch_embed_dfe_norm(v_9) v_11 = self.layers_dfe_0_residual_group_blocks_0_norm1(v_10) v_12 = v_11.reshape(1, 6, 8, 6, 8, 192) v_13 = torch.permute(input=v_12, dims=(0,1,3,2,4,5)) v_14 = v_13.reshape(36, 64, 192) v_15 = self.layers_dfe_0_residual_group_blocks_0_attn_qkv(v_14) v_16 = v_15.reshape(36, 64, 3, 6, 32) v_17 = torch.permute(input=v_16, dims=(2,0,3,1,4)) v_18, v_19, v_20 = torch.unbind(v_17, dim=0) v_21 = (v_18 * 1.767767e-01) v_22 = torch.transpose(input=v_19, dim0=-2, dim1=-1) v_23 = torch.matmul(input=v_21, other=v_22) v_24 = self.pnnx_fold_2377_pnnx_fold_2377 v_25 = (v_23 + v_24) v_26 = self.layers_dfe_0_residual_group_blocks_0_attn_softmax(v_25) v_27 = torch.matmul(input=v_26, other=v_20) v_28 = torch.transpose(input=v_27, dim0=1, dim1=2) v_29 = v_28.reshape(36, 64, 192) v_30 = self.layers_dfe_0_residual_group_blocks_0_attn_proj(v_29) v_31 = v_30.reshape(1, 6, 6, 8, 8, 192) v_32 = torch.permute(input=v_31, dims=(0,1,3,2,4,5)) v_33 = v_32.reshape(1, 2304, 192) v_34 = (v_10 + v_33) v_35 = self.layers_dfe_0_residual_group_blocks_0_norm2(v_34) v_36 = self.layers_dfe_0_residual_group_blocks_0_mlp_fc1(v_35) v_37 = self.layers_dfe_0_residual_group_blocks_0_mlp_act(v_36) v_38 = self.layers_dfe_0_residual_group_blocks_0_mlp_fc2(v_37) v_39 = (v_34 + v_38) v_40 = self.layers_dfe_0_residual_group_blocks_1_norm1(v_39) v_41 = v_40.view(1, 48, 48, 192) v_42 = torch.roll(input=v_41, dims=(1,2), shifts=(-4,-4)) v_43 = v_42.view(1, 6, 8, 6, 8, 192) v_44 = torch.permute(input=v_43, dims=(0,1,3,2,4,5)) v_45 = v_44.reshape(36, 64, 192) v_46 = self.layers_dfe_0_residual_group_blocks_1_attn_qkv(v_45) v_47 = v_46.reshape(36, 64, 3, 6, 32) v_48 = torch.permute(input=v_47, dims=(2,0,3,1,4)) v_49, v_50, v_51 = torch.unbind(v_48, dim=0) v_52 = (v_49 * 1.767767e-01) v_53 = torch.transpose(input=v_50, dim0=-2, dim1=-1) v_54 = torch.matmul(input=v_52, other=v_53) v_55 = self.pnnx_fold_2530_pnnx_fold_2530 v_56 = (v_54 + v_55) v_57 = v_56.view(1, 36, 6, 64, 64) v_58 = self.pnnx_fold_2540_pnnx_fold_2540 v_59 = (v_57 + v_58) v_60 = v_59.view(-1, 6, 64, 64) v_61 = self.layers_dfe_0_residual_group_blocks_1_attn_softmax(v_60) v_62 = torch.matmul(input=v_61, other=v_51) v_63 = torch.transpose(input=v_62, dim0=1, dim1=2) v_64 = v_63.reshape(36, 64, 192) v_65 = self.layers_dfe_0_residual_group_blocks_1_attn_proj(v_64) v_66 = v_65.reshape(1, 6, 6, 8, 8, 192) v_67 = torch.permute(input=v_66, dims=(0,1,3,2,4,5)) v_68 = v_67.reshape(1, 48, 48, -1) v_69 = torch.roll(input=v_68, dims=(1,2), shifts=(4,4)) v_70 = v_69.view(1, 2304, 192) v_71 = (v_39 + v_70) v_72 = self.layers_dfe_0_residual_group_blocks_1_norm2(v_71) v_73 = self.layers_dfe_0_residual_group_blocks_1_mlp_fc1(v_72) v_74 = self.layers_dfe_0_residual_group_blocks_1_mlp_act(v_73) v_75 = self.layers_dfe_0_residual_group_blocks_1_mlp_fc2(v_74) v_76 = (v_71 + v_75) v_77 = self.layers_dfe_0_residual_group_blocks_2_norm1(v_76) v_78 = v_77.reshape(1, 6, 8, 6, 8, 192) v_79 = torch.permute(input=v_78, dims=(0,1,3,2,4,5)) v_80 = v_79.reshape(36, 64, 192) v_81 = self.layers_dfe_0_residual_group_blocks_2_attn_qkv(v_80) v_82 = v_81.reshape(36, 64, 3, 6, 32) v_83 = torch.permute(input=v_82, dims=(2,0,3,1,4)) v_84, v_85, v_86 = torch.unbind(v_83, dim=0) v_87 = (v_84 * 1.767767e-01) v_88 = torch.transpose(input=v_85, dim0=-2, dim1=-1) v_89 = torch.matmul(input=v_87, other=v_88) v_90 = self.pnnx_fold_2689_pnnx_fold_2689 v_91 = (v_89 + v_90) v_92 = self.layers_dfe_0_residual_group_blocks_2_attn_softmax(v_91) v_93 = torch.matmul(input=v_92, other=v_86) v_94 = torch.transpose(input=v_93, dim0=1, dim1=2) v_95 = v_94.reshape(36, 64, 192) v_96 = self.layers_dfe_0_residual_group_blocks_2_attn_proj(v_95) v_97 = v_96.reshape(1, 6, 6, 8, 8, 192) v_98 = torch.permute(input=v_97, dims=(0,1,3,2,4,5)) v_99 = v_98.reshape(1, 2304, 192) v_100 = (v_76 + v_99) v_101 = self.layers_dfe_0_residual_group_blocks_2_norm2(v_100) v_102 = self.layers_dfe_0_residual_group_blocks_2_mlp_fc1(v_101) v_103 = self.layers_dfe_0_residual_group_blocks_2_mlp_act(v_102) v_104 = self.layers_dfe_0_residual_group_blocks_2_mlp_fc2(v_103) v_105 = (v_100 + v_104) v_106 = self.layers_dfe_0_residual_group_blocks_3_norm1(v_105) v_107 = v_106.view(1, 48, 48, 192) v_108 = torch.roll(input=v_107, dims=(1,2), shifts=(-4,-4)) v_109 = v_108.view(1, 6, 8, 6, 8, 192) v_110 = torch.permute(input=v_109, dims=(0,1,3,2,4,5)) v_111 = v_110.reshape(36, 64, 192) v_112 = self.layers_dfe_0_residual_group_blocks_3_attn_qkv(v_111) v_113 = v_112.reshape(36, 64, 3, 6, 32) v_114 = torch.permute(input=v_113, dims=(2,0,3,1,4)) v_115, v_116, v_117 = torch.unbind(v_114, dim=0) v_118 = (v_115 * 1.767767e-01) v_119 = torch.transpose(input=v_116, dim0=-2, dim1=-1) v_120 = torch.matmul(input=v_118, other=v_119) v_121 = self.pnnx_fold_2842_pnnx_fold_2842 v_122 = (v_120 + v_121) v_123 = v_122.view(1, 36, 6, 64, 64) v_124 = self.pnnx_fold_2852_pnnx_fold_2852 v_125 = (v_123 + v_124) v_126 = v_125.view(-1, 6, 64, 64) v_127 = self.layers_dfe_0_residual_group_blocks_3_attn_softmax(v_126) v_128 = torch.matmul(input=v_127, other=v_117) v_129 = torch.transpose(input=v_128, dim0=1, dim1=2) v_130 = v_129.reshape(36, 64, 192) v_131 = self.layers_dfe_0_residual_group_blocks_3_attn_proj(v_130) v_132 = v_131.reshape(1, 6, 6, 8, 8, 192) v_133 = torch.permute(input=v_132, dims=(0,1,3,2,4,5)) v_134 = v_133.reshape(1, 48, 48, -1) v_135 = torch.roll(input=v_134, dims=(1,2), shifts=(4,4)) v_136 = v_135.view(1, 2304, 192) v_137 = (v_105 + v_136) v_138 = self.layers_dfe_0_residual_group_blocks_3_norm2(v_137) v_139 = self.layers_dfe_0_residual_group_blocks_3_mlp_fc1(v_138) v_140 = self.layers_dfe_0_residual_group_blocks_3_mlp_act(v_139) v_141 = self.layers_dfe_0_residual_group_blocks_3_mlp_fc2(v_140) v_142 = (v_137 + v_141) v_143 = self.layers_dfe_0_residual_group_blocks_4_norm1(v_142) v_144 = v_143.reshape(1, 6, 8, 6, 8, 192) v_145 = torch.permute(input=v_144, dims=(0,1,3,2,4,5)) v_146 = v_145.reshape(36, 64, 192) v_147 = self.layers_dfe_0_residual_group_blocks_4_attn_qkv(v_146) v_148 = v_147.reshape(36, 64, 3, 6, 32) v_149 = torch.permute(input=v_148, dims=(2,0,3,1,4)) v_150, v_151, v_152 = torch.unbind(v_149, dim=0) v_153 = (v_150 * 1.767767e-01) v_154 = torch.transpose(input=v_151, dim0=-2, dim1=-1) v_155 = torch.matmul(input=v_153, other=v_154) v_156 = self.pnnx_fold_3001_pnnx_fold_3001 v_157 = (v_155 + v_156) v_158 = self.layers_dfe_0_residual_group_blocks_4_attn_softmax(v_157) v_159 = torch.matmul(input=v_158, other=v_152) v_160 = torch.transpose(input=v_159, dim0=1, dim1=2) v_161 = v_160.reshape(36, 64, 192) v_162 = self.layers_dfe_0_residual_group_blocks_4_attn_proj(v_161) v_163 = v_162.reshape(1, 6, 6, 8, 8, 192) v_164 = torch.permute(input=v_163, dims=(0,1,3,2,4,5)) v_165 = v_164.reshape(1, 2304, 192) v_166 = (v_142 + v_165) v_167 = self.layers_dfe_0_residual_group_blocks_4_norm2(v_166) v_168 = self.layers_dfe_0_residual_group_blocks_4_mlp_fc1(v_167) v_169 = self.layers_dfe_0_residual_group_blocks_4_mlp_act(v_168) v_170 = self.layers_dfe_0_residual_group_blocks_4_mlp_fc2(v_169) v_171 = (v_166 + v_170) v_172 = self.layers_dfe_0_residual_group_blocks_5_norm1(v_171) v_173 = v_172.view(1, 48, 48, 192) v_174 = torch.roll(input=v_173, dims=(1,2), shifts=(-4,-4)) v_175 = v_174.view(1, 6, 8, 6, 8, 192) v_176 = torch.permute(input=v_175, dims=(0,1,3,2,4,5)) v_177 = v_176.reshape(36, 64, 192) v_178 = self.layers_dfe_0_residual_group_blocks_5_attn_qkv(v_177) v_179 = v_178.reshape(36, 64, 3, 6, 32) v_180 = torch.permute(input=v_179, dims=(2,0,3,1,4)) v_181, v_182, v_183 = torch.unbind(v_180, dim=0) v_184 = (v_181 * 1.767767e-01) v_185 = torch.transpose(input=v_182, dim0=-2, dim1=-1) v_186 = torch.matmul(input=v_184, other=v_185) v_187 = self.pnnx_fold_3154_pnnx_fold_3154 v_188 = (v_186 + v_187) v_189 = v_188.view(1, 36, 6, 64, 64) v_190 = self.pnnx_fold_3164_pnnx_fold_3164 v_191 = (v_189 + v_190) v_192 = v_191.view(-1, 6, 64, 64) v_193 = self.layers_dfe_0_residual_group_blocks_5_attn_softmax(v_192) v_194 = torch.matmul(input=v_193, other=v_183) v_195 = torch.transpose(input=v_194, dim0=1, dim1=2) v_196 = v_195.reshape(36, 64, 192) v_197 = self.layers_dfe_0_residual_group_blocks_5_attn_proj(v_196) v_198 = v_197.reshape(1, 6, 6, 8, 8, 192) v_199 = torch.permute(input=v_198, dims=(0,1,3,2,4,5)) v_200 = v_199.reshape(1, 48, 48, -1) v_201 = torch.roll(input=v_200, dims=(1,2), shifts=(4,4)) v_202 = v_201.view(1, 2304, 192) v_203 = (v_171 + v_202) v_204 = self.layers_dfe_0_residual_group_blocks_5_norm2(v_203) v_205 = self.layers_dfe_0_residual_group_blocks_5_mlp_fc1(v_204) v_206 = self.layers_dfe_0_residual_group_blocks_5_mlp_act(v_205) v_207 = self.layers_dfe_0_residual_group_blocks_5_mlp_fc2(v_206) v_208 = (v_203 + v_207) v_209 = torch.transpose(input=v_208, dim0=1, dim1=2) v_210 = v_209.view(1, 192, 48, 48) v_211 = self.layers_dfe_0_conv(v_210) v_212 = torch.flatten(input=v_211, end_dim=-1, start_dim=2) v_213 = torch.transpose(input=v_212, dim0=1, dim1=2) v_214 = (v_213 + v_10) v_215 = self.layers_dfe_1_residual_group_blocks_0_norm1(v_214) v_216 = v_215.reshape(1, 6, 8, 6, 8, 192) v_217 = torch.permute(input=v_216, dims=(0,1,3,2,4,5)) v_218 = v_217.reshape(36, 64, 192) v_219 = self.layers_dfe_1_residual_group_blocks_0_attn_qkv(v_218) v_220 = v_219.reshape(36, 64, 3, 6, 32) v_221 = torch.permute(input=v_220, dims=(2,0,3,1,4)) v_222, v_223, v_224 = torch.unbind(v_221, dim=0) v_225 = (v_222 * 1.767767e-01) v_226 = torch.transpose(input=v_223, dim0=-2, dim1=-1) v_227 = torch.matmul(input=v_225, other=v_226) v_228 = self.pnnx_fold_3347_pnnx_fold_3347 v_229 = (v_227 + v_228) v_230 = self.layers_dfe_1_residual_group_blocks_0_attn_softmax(v_229) v_231 = torch.matmul(input=v_230, other=v_224) v_232 = torch.transpose(input=v_231, dim0=1, dim1=2) v_233 = v_232.reshape(36, 64, 192) v_234 = self.layers_dfe_1_residual_group_blocks_0_attn_proj(v_233) v_235 = v_234.reshape(1, 6, 6, 8, 8, 192) v_236 = torch.permute(input=v_235, dims=(0,1,3,2,4,5)) v_237 = v_236.reshape(1, 2304, 192) v_238 = (v_214 + v_237) v_239 = self.layers_dfe_1_residual_group_blocks_0_norm2(v_238) v_240 = self.layers_dfe_1_residual_group_blocks_0_mlp_fc1(v_239) v_241 = self.layers_dfe_1_residual_group_blocks_0_mlp_act(v_240) v_242 = self.layers_dfe_1_residual_group_blocks_0_mlp_fc2(v_241) v_243 = (v_238 + v_242) v_244 = self.layers_dfe_1_residual_group_blocks_1_norm1(v_243) v_245 = v_244.view(1, 48, 48, 192) v_246 = torch.roll(input=v_245, dims=(1,2), shifts=(-4,-4)) v_247 = v_246.view(1, 6, 8, 6, 8, 192) v_248 = torch.permute(input=v_247, dims=(0,1,3,2,4,5)) v_249 = v_248.reshape(36, 64, 192) v_250 = self.layers_dfe_1_residual_group_blocks_1_attn_qkv(v_249) v_251 = v_250.reshape(36, 64, 3, 6, 32) v_252 = torch.permute(input=v_251, dims=(2,0,3,1,4)) v_253, v_254, v_255 = torch.unbind(v_252, dim=0) v_256 = (v_253 * 1.767767e-01) v_257 = torch.transpose(input=v_254, dim0=-2, dim1=-1) v_258 = torch.matmul(input=v_256, other=v_257) v_259 = self.pnnx_fold_3500_pnnx_fold_3500 v_260 = (v_258 + v_259) v_261 = v_260.view(1, 36, 6, 64, 64) v_262 = self.pnnx_fold_3510_pnnx_fold_3510 v_263 = (v_261 + v_262) v_264 = v_263.view(-1, 6, 64, 64) v_265 = self.layers_dfe_1_residual_group_blocks_1_attn_softmax(v_264) v_266 = torch.matmul(input=v_265, other=v_255) v_267 = torch.transpose(input=v_266, dim0=1, dim1=2) v_268 = v_267.reshape(36, 64, 192) v_269 = self.layers_dfe_1_residual_group_blocks_1_attn_proj(v_268) v_270 = v_269.reshape(1, 6, 6, 8, 8, 192) v_271 = torch.permute(input=v_270, dims=(0,1,3,2,4,5)) v_272 = v_271.reshape(1, 48, 48, -1) v_273 = torch.roll(input=v_272, dims=(1,2), shifts=(4,4)) v_274 = v_273.view(1, 2304, 192) v_275 = (v_243 + v_274) v_276 = self.layers_dfe_1_residual_group_blocks_1_norm2(v_275) v_277 = self.layers_dfe_1_residual_group_blocks_1_mlp_fc1(v_276) v_278 = self.layers_dfe_1_residual_group_blocks_1_mlp_act(v_277) v_279 = self.layers_dfe_1_residual_group_blocks_1_mlp_fc2(v_278) v_280 = (v_275 + v_279) v_281 = self.layers_dfe_1_residual_group_blocks_2_norm1(v_280) v_282 = v_281.reshape(1, 6, 8, 6, 8, 192) v_283 = torch.permute(input=v_282, dims=(0,1,3,2,4,5)) v_284 = v_283.reshape(36, 64, 192) v_285 = self.layers_dfe_1_residual_group_blocks_2_attn_qkv(v_284) v_286 = v_285.reshape(36, 64, 3, 6, 32) v_287 = torch.permute(input=v_286, dims=(2,0,3,1,4)) v_288, v_289, v_290 = torch.unbind(v_287, dim=0) v_291 = (v_288 * 1.767767e-01) v_292 = torch.transpose(input=v_289, dim0=-2, dim1=-1) v_293 = torch.matmul(input=v_291, other=v_292) v_294 = self.pnnx_fold_3659_pnnx_fold_3659 v_295 = (v_293 + v_294) v_296 = self.layers_dfe_1_residual_group_blocks_2_attn_softmax(v_295) v_297 = torch.matmul(input=v_296, other=v_290) v_298 = torch.transpose(input=v_297, dim0=1, dim1=2) v_299 = v_298.reshape(36, 64, 192) v_300 = self.layers_dfe_1_residual_group_blocks_2_attn_proj(v_299) v_301 = v_300.reshape(1, 6, 6, 8, 8, 192) v_302 = torch.permute(input=v_301, dims=(0,1,3,2,4,5)) v_303 = v_302.reshape(1, 2304, 192) v_304 = (v_280 + v_303) v_305 = self.layers_dfe_1_residual_group_blocks_2_norm2(v_304) v_306 = self.layers_dfe_1_residual_group_blocks_2_mlp_fc1(v_305) v_307 = self.layers_dfe_1_residual_group_blocks_2_mlp_act(v_306) v_308 = self.layers_dfe_1_residual_group_blocks_2_mlp_fc2(v_307) v_309 = (v_304 + v_308) v_310 = self.layers_dfe_1_residual_group_blocks_3_norm1(v_309) v_311 = v_310.view(1, 48, 48, 192) v_312 = torch.roll(input=v_311, dims=(1,2), shifts=(-4,-4)) v_313 = v_312.view(1, 6, 8, 6, 8, 192) v_314 = torch.permute(input=v_313, dims=(0,1,3,2,4,5)) v_315 = v_314.reshape(36, 64, 192) v_316 = self.layers_dfe_1_residual_group_blocks_3_attn_qkv(v_315) v_317 = v_316.reshape(36, 64, 3, 6, 32) v_318 = torch.permute(input=v_317, dims=(2,0,3,1,4)) v_319, v_320, v_321 = torch.unbind(v_318, dim=0) v_322 = (v_319 * 1.767767e-01) v_323 = torch.transpose(input=v_320, dim0=-2, dim1=-1) v_324 = torch.matmul(input=v_322, other=v_323) v_325 = self.pnnx_fold_3812_pnnx_fold_3812 v_326 = (v_324 + v_325) v_327 = v_326.view(1, 36, 6, 64, 64) v_328 = self.pnnx_fold_3822_pnnx_fold_3822 v_329 = (v_327 + v_328) v_330 = v_329.view(-1, 6, 64, 64) v_331 = self.layers_dfe_1_residual_group_blocks_3_attn_softmax(v_330) v_332 = torch.matmul(input=v_331, other=v_321) v_333 = torch.transpose(input=v_332, dim0=1, dim1=2) v_334 = v_333.reshape(36, 64, 192) v_335 = self.layers_dfe_1_residual_group_blocks_3_attn_proj(v_334) v_336 = v_335.reshape(1, 6, 6, 8, 8, 192) v_337 = torch.permute(input=v_336, dims=(0,1,3,2,4,5)) v_338 = v_337.reshape(1, 48, 48, -1) v_339 = torch.roll(input=v_338, dims=(1,2), shifts=(4,4)) v_340 = v_339.view(1, 2304, 192) v_341 = (v_309 + v_340) v_342 = self.layers_dfe_1_residual_group_blocks_3_norm2(v_341) v_343 = self.layers_dfe_1_residual_group_blocks_3_mlp_fc1(v_342) v_344 = self.layers_dfe_1_residual_group_blocks_3_mlp_act(v_343) v_345 = self.layers_dfe_1_residual_group_blocks_3_mlp_fc2(v_344) v_346 = (v_341 + v_345) v_347 = self.layers_dfe_1_residual_group_blocks_4_norm1(v_346) v_348 = v_347.reshape(1, 6, 8, 6, 8, 192) v_349 = torch.permute(input=v_348, dims=(0,1,3,2,4,5)) v_350 = v_349.reshape(36, 64, 192) v_351 = self.layers_dfe_1_residual_group_blocks_4_attn_qkv(v_350) v_352 = v_351.reshape(36, 64, 3, 6, 32) v_353 = torch.permute(input=v_352, dims=(2,0,3,1,4)) v_354, v_355, v_356 = torch.unbind(v_353, dim=0) v_357 = (v_354 * 1.767767e-01) v_358 = torch.transpose(input=v_355, dim0=-2, dim1=-1) v_359 = torch.matmul(input=v_357, other=v_358) v_360 = self.pnnx_fold_3971_pnnx_fold_3971 v_361 = (v_359 + v_360) v_362 = self.layers_dfe_1_residual_group_blocks_4_attn_softmax(v_361) v_363 = torch.matmul(input=v_362, other=v_356) v_364 = torch.transpose(input=v_363, dim0=1, dim1=2) v_365 = v_364.reshape(36, 64, 192) v_366 = self.layers_dfe_1_residual_group_blocks_4_attn_proj(v_365) v_367 = v_366.reshape(1, 6, 6, 8, 8, 192) v_368 = torch.permute(input=v_367, dims=(0,1,3,2,4,5)) v_369 = v_368.reshape(1, 2304, 192) v_370 = (v_346 + v_369) v_371 = self.layers_dfe_1_residual_group_blocks_4_norm2(v_370) v_372 = self.layers_dfe_1_residual_group_blocks_4_mlp_fc1(v_371) v_373 = self.layers_dfe_1_residual_group_blocks_4_mlp_act(v_372) v_374 = self.layers_dfe_1_residual_group_blocks_4_mlp_fc2(v_373) v_375 = (v_370 + v_374) v_376 = self.layers_dfe_1_residual_group_blocks_5_norm1(v_375) v_377 = v_376.view(1, 48, 48, 192) v_378 = torch.roll(input=v_377, dims=(1,2), shifts=(-4,-4)) v_379 = v_378.view(1, 6, 8, 6, 8, 192) v_380 = torch.permute(input=v_379, dims=(0,1,3,2,4,5)) v_381 = v_380.reshape(36, 64, 192) v_382 = self.layers_dfe_1_residual_group_blocks_5_attn_qkv(v_381) v_383 = v_382.reshape(36, 64, 3, 6, 32) v_384 = torch.permute(input=v_383, dims=(2,0,3,1,4)) v_385, v_386, v_387 = torch.unbind(v_384, dim=0) v_388 = (v_385 * 1.767767e-01) v_389 = torch.transpose(input=v_386, dim0=-2, dim1=-1) v_390 = torch.matmul(input=v_388, other=v_389) v_391 = self.pnnx_fold_4124_pnnx_fold_4124 v_392 = (v_390 + v_391) v_393 = v_392.view(1, 36, 6, 64, 64) v_394 = self.pnnx_fold_4134_pnnx_fold_4134 v_395 = (v_393 + v_394) v_396 = v_395.view(-1, 6, 64, 64) v_397 = self.layers_dfe_1_residual_group_blocks_5_attn_softmax(v_396) v_398 = torch.matmul(input=v_397, other=v_387) v_399 = torch.transpose(input=v_398, dim0=1, dim1=2) v_400 = v_399.reshape(36, 64, 192) v_401 = self.layers_dfe_1_residual_group_blocks_5_attn_proj(v_400) v_402 = v_401.reshape(1, 6, 6, 8, 8, 192) v_403 = torch.permute(input=v_402, dims=(0,1,3,2,4,5)) v_404 = v_403.reshape(1, 48, 48, -1) v_405 = torch.roll(input=v_404, dims=(1,2), shifts=(4,4)) v_406 = v_405.view(1, 2304, 192) v_407 = (v_375 + v_406) v_408 = self.layers_dfe_1_residual_group_blocks_5_norm2(v_407) v_409 = self.layers_dfe_1_residual_group_blocks_5_mlp_fc1(v_408) v_410 = self.layers_dfe_1_residual_group_blocks_5_mlp_act(v_409) v_411 = self.layers_dfe_1_residual_group_blocks_5_mlp_fc2(v_410) v_412 = (v_407 + v_411) v_413 = torch.transpose(input=v_412, dim0=1, dim1=2) v_414 = v_413.view(1, 192, 48, 48) v_415 = self.layers_dfe_1_conv(v_414) v_416 = torch.flatten(input=v_415, end_dim=-1, start_dim=2) v_417 = torch.transpose(input=v_416, dim0=1, dim1=2) v_418 = (v_417 + v_214) v_419 = self.layers_dfe_2_residual_group_blocks_0_norm1(v_418) v_420 = v_419.reshape(1, 6, 8, 6, 8, 192) v_421 = torch.permute(input=v_420, dims=(0,1,3,2,4,5)) v_422 = v_421.reshape(36, 64, 192) v_423 = self.layers_dfe_2_residual_group_blocks_0_attn_qkv(v_422) v_424 = v_423.reshape(36, 64, 3, 6, 32) v_425 = torch.permute(input=v_424, dims=(2,0,3,1,4)) v_426, v_427, v_428 = torch.unbind(v_425, dim=0) v_429 = (v_426 * 1.767767e-01) v_430 = torch.transpose(input=v_427, dim0=-2, dim1=-1) v_431 = torch.matmul(input=v_429, other=v_430) v_432 = self.pnnx_fold_4317_pnnx_fold_4317 v_433 = (v_431 + v_432) v_434 = self.layers_dfe_2_residual_group_blocks_0_attn_softmax(v_433) v_435 = torch.matmul(input=v_434, other=v_428) v_436 = torch.transpose(input=v_435, dim0=1, dim1=2) v_437 = v_436.reshape(36, 64, 192) v_438 = self.layers_dfe_2_residual_group_blocks_0_attn_proj(v_437) v_439 = v_438.reshape(1, 6, 6, 8, 8, 192) v_440 = torch.permute(input=v_439, dims=(0,1,3,2,4,5)) v_441 = v_440.reshape(1, 2304, 192) v_442 = (v_418 + v_441) v_443 = self.layers_dfe_2_residual_group_blocks_0_norm2(v_442) v_444 = self.layers_dfe_2_residual_group_blocks_0_mlp_fc1(v_443) v_445 = self.layers_dfe_2_residual_group_blocks_0_mlp_act(v_444) v_446 = self.layers_dfe_2_residual_group_blocks_0_mlp_fc2(v_445) v_447 = (v_442 + v_446) v_448 = self.layers_dfe_2_residual_group_blocks_1_norm1(v_447) v_449 = v_448.view(1, 48, 48, 192) v_450 = torch.roll(input=v_449, dims=(1,2), shifts=(-4,-4)) v_451 = v_450.view(1, 6, 8, 6, 8, 192) v_452 = torch.permute(input=v_451, dims=(0,1,3,2,4,5)) v_453 = v_452.reshape(36, 64, 192) v_454 = self.layers_dfe_2_residual_group_blocks_1_attn_qkv(v_453) v_455 = v_454.reshape(36, 64, 3, 6, 32) v_456 = torch.permute(input=v_455, dims=(2,0,3,1,4)) v_457, v_458, v_459 = torch.unbind(v_456, dim=0) v_460 = (v_457 * 1.767767e-01) v_461 = torch.transpose(input=v_458, dim0=-2, dim1=-1) v_462 = torch.matmul(input=v_460, other=v_461) v_463 = self.pnnx_fold_4470_pnnx_fold_4470 v_464 = (v_462 + v_463) v_465 = v_464.view(1, 36, 6, 64, 64) v_466 = self.pnnx_fold_4480_pnnx_fold_4480 v_467 = (v_465 + v_466) v_468 = v_467.view(-1, 6, 64, 64) v_469 = self.layers_dfe_2_residual_group_blocks_1_attn_softmax(v_468) v_470 = torch.matmul(input=v_469, other=v_459) v_471 = torch.transpose(input=v_470, dim0=1, dim1=2) v_472 = v_471.reshape(36, 64, 192) v_473 = self.layers_dfe_2_residual_group_blocks_1_attn_proj(v_472) v_474 = v_473.reshape(1, 6, 6, 8, 8, 192) v_475 = torch.permute(input=v_474, dims=(0,1,3,2,4,5)) v_476 = v_475.reshape(1, 48, 48, -1) v_477 = torch.roll(input=v_476, dims=(1,2), shifts=(4,4)) v_478 = v_477.view(1, 2304, 192) v_479 = (v_447 + v_478) v_480 = self.layers_dfe_2_residual_group_blocks_1_norm2(v_479) v_481 = self.layers_dfe_2_residual_group_blocks_1_mlp_fc1(v_480) v_482 = self.layers_dfe_2_residual_group_blocks_1_mlp_act(v_481) v_483 = self.layers_dfe_2_residual_group_blocks_1_mlp_fc2(v_482) v_484 = (v_479 + v_483) v_485 = self.layers_dfe_2_residual_group_blocks_2_norm1(v_484) v_486 = v_485.reshape(1, 6, 8, 6, 8, 192) v_487 = torch.permute(input=v_486, dims=(0,1,3,2,4,5)) v_488 = v_487.reshape(36, 64, 192) v_489 = self.layers_dfe_2_residual_group_blocks_2_attn_qkv(v_488) v_490 = v_489.reshape(36, 64, 3, 6, 32) v_491 = torch.permute(input=v_490, dims=(2,0,3,1,4)) v_492, v_493, v_494 = torch.unbind(v_491, dim=0) v_495 = (v_492 * 1.767767e-01) v_496 = torch.transpose(input=v_493, dim0=-2, dim1=-1) v_497 = torch.matmul(input=v_495, other=v_496) v_498 = self.pnnx_fold_4629_pnnx_fold_4629 v_499 = (v_497 + v_498) v_500 = self.layers_dfe_2_residual_group_blocks_2_attn_softmax(v_499) v_501 = torch.matmul(input=v_500, other=v_494) v_502 = torch.transpose(input=v_501, dim0=1, dim1=2) v_503 = v_502.reshape(36, 64, 192) v_504 = self.layers_dfe_2_residual_group_blocks_2_attn_proj(v_503) v_505 = v_504.reshape(1, 6, 6, 8, 8, 192) v_506 = torch.permute(input=v_505, dims=(0,1,3,2,4,5)) v_507 = v_506.reshape(1, 2304, 192) v_508 = (v_484 + v_507) v_509 = self.layers_dfe_2_residual_group_blocks_2_norm2(v_508) v_510 = self.layers_dfe_2_residual_group_blocks_2_mlp_fc1(v_509) v_511 = self.layers_dfe_2_residual_group_blocks_2_mlp_act(v_510) v_512 = self.layers_dfe_2_residual_group_blocks_2_mlp_fc2(v_511) v_513 = (v_508 + v_512) v_514 = self.layers_dfe_2_residual_group_blocks_3_norm1(v_513) v_515 = v_514.view(1, 48, 48, 192) v_516 = torch.roll(input=v_515, dims=(1,2), shifts=(-4,-4)) v_517 = v_516.view(1, 6, 8, 6, 8, 192) v_518 = torch.permute(input=v_517, dims=(0,1,3,2,4,5)) v_519 = v_518.reshape(36, 64, 192) v_520 = self.layers_dfe_2_residual_group_blocks_3_attn_qkv(v_519) v_521 = v_520.reshape(36, 64, 3, 6, 32) v_522 = torch.permute(input=v_521, dims=(2,0,3,1,4)) v_523, v_524, v_525 = torch.unbind(v_522, dim=0) v_526 = (v_523 * 1.767767e-01) v_527 = torch.transpose(input=v_524, dim0=-2, dim1=-1) v_528 = torch.matmul(input=v_526, other=v_527) v_529 = self.pnnx_fold_4782_pnnx_fold_4782 v_530 = (v_528 + v_529) v_531 = v_530.view(1, 36, 6, 64, 64) v_532 = self.pnnx_fold_4792_pnnx_fold_4792 v_533 = (v_531 + v_532) v_534 = v_533.view(-1, 6, 64, 64) v_535 = self.layers_dfe_2_residual_group_blocks_3_attn_softmax(v_534) v_536 = torch.matmul(input=v_535, other=v_525) v_537 = torch.transpose(input=v_536, dim0=1, dim1=2) v_538 = v_537.reshape(36, 64, 192) v_539 = self.layers_dfe_2_residual_group_blocks_3_attn_proj(v_538) v_540 = v_539.reshape(1, 6, 6, 8, 8, 192) v_541 = torch.permute(input=v_540, dims=(0,1,3,2,4,5)) v_542 = v_541.reshape(1, 48, 48, -1) v_543 = torch.roll(input=v_542, dims=(1,2), shifts=(4,4)) v_544 = v_543.view(1, 2304, 192) v_545 = (v_513 + v_544) v_546 = self.layers_dfe_2_residual_group_blocks_3_norm2(v_545) v_547 = self.layers_dfe_2_residual_group_blocks_3_mlp_fc1(v_546) v_548 = self.layers_dfe_2_residual_group_blocks_3_mlp_act(v_547) v_549 = self.layers_dfe_2_residual_group_blocks_3_mlp_fc2(v_548) v_550 = (v_545 + v_549) v_551 = self.layers_dfe_2_residual_group_blocks_4_norm1(v_550) v_552 = v_551.reshape(1, 6, 8, 6, 8, 192) v_553 = torch.permute(input=v_552, dims=(0,1,3,2,4,5)) v_554 = v_553.reshape(36, 64, 192) v_555 = self.layers_dfe_2_residual_group_blocks_4_attn_qkv(v_554) v_556 = v_555.reshape(36, 64, 3, 6, 32) v_557 = torch.permute(input=v_556, dims=(2,0,3,1,4)) v_558, v_559, v_560 = torch.unbind(v_557, dim=0) v_561 = (v_558 * 1.767767e-01) v_562 = torch.transpose(input=v_559, dim0=-2, dim1=-1) v_563 = torch.matmul(input=v_561, other=v_562) v_564 = self.pnnx_fold_4941_pnnx_fold_4941 v_565 = (v_563 + v_564) v_566 = self.layers_dfe_2_residual_group_blocks_4_attn_softmax(v_565) v_567 = torch.matmul(input=v_566, other=v_560) v_568 = torch.transpose(input=v_567, dim0=1, dim1=2) v_569 = v_568.reshape(36, 64, 192) v_570 = self.layers_dfe_2_residual_group_blocks_4_attn_proj(v_569) v_571 = v_570.reshape(1, 6, 6, 8, 8, 192) v_572 = torch.permute(input=v_571, dims=(0,1,3,2,4,5)) v_573 = v_572.reshape(1, 2304, 192) v_574 = (v_550 + v_573) v_575 = self.layers_dfe_2_residual_group_blocks_4_norm2(v_574) v_576 = self.layers_dfe_2_residual_group_blocks_4_mlp_fc1(v_575) v_577 = self.layers_dfe_2_residual_group_blocks_4_mlp_act(v_576) v_578 = self.layers_dfe_2_residual_group_blocks_4_mlp_fc2(v_577) v_579 = (v_574 + v_578) v_580 = self.layers_dfe_2_residual_group_blocks_5_norm1(v_579) v_581 = v_580.view(1, 48, 48, 192) v_582 = torch.roll(input=v_581, dims=(1,2), shifts=(-4,-4)) v_583 = v_582.view(1, 6, 8, 6, 8, 192) v_584 = torch.permute(input=v_583, dims=(0,1,3,2,4,5)) v_585 = v_584.reshape(36, 64, 192) v_586 = self.layers_dfe_2_residual_group_blocks_5_attn_qkv(v_585) v_587 = v_586.reshape(36, 64, 3, 6, 32) v_588 = torch.permute(input=v_587, dims=(2,0,3,1,4)) v_589, v_590, v_591 = torch.unbind(v_588, dim=0) v_592 = (v_589 * 1.767767e-01) v_593 = torch.transpose(input=v_590, dim0=-2, dim1=-1) v_594 = torch.matmul(input=v_592, other=v_593) v_595 = self.pnnx_fold_5094_pnnx_fold_5094 v_596 = (v_594 + v_595) v_597 = v_596.view(1, 36, 6, 64, 64) v_598 = self.pnnx_fold_5104_pnnx_fold_5104 v_599 = (v_597 + v_598) v_600 = v_599.view(-1, 6, 64, 64) v_601 = self.layers_dfe_2_residual_group_blocks_5_attn_softmax(v_600) v_602 = torch.matmul(input=v_601, other=v_591) v_603 = torch.transpose(input=v_602, dim0=1, dim1=2) v_604 = v_603.reshape(36, 64, 192) v_605 = self.layers_dfe_2_residual_group_blocks_5_attn_proj(v_604) v_606 = v_605.reshape(1, 6, 6, 8, 8, 192) v_607 = torch.permute(input=v_606, dims=(0,1,3,2,4,5)) v_608 = v_607.reshape(1, 48, 48, -1) v_609 = torch.roll(input=v_608, dims=(1,2), shifts=(4,4)) v_610 = v_609.view(1, 2304, 192) v_611 = (v_579 + v_610) v_612 = self.layers_dfe_2_residual_group_blocks_5_norm2(v_611) v_613 = self.layers_dfe_2_residual_group_blocks_5_mlp_fc1(v_612) v_614 = self.layers_dfe_2_residual_group_blocks_5_mlp_act(v_613) v_615 = self.layers_dfe_2_residual_group_blocks_5_mlp_fc2(v_614) v_616 = (v_611 + v_615) v_617 = torch.transpose(input=v_616, dim0=1, dim1=2) v_618 = v_617.view(1, 192, 48, 48) v_619 = self.layers_dfe_2_conv(v_618) v_620 = torch.flatten(input=v_619, end_dim=-1, start_dim=2) v_621 = torch.transpose(input=v_620, dim0=1, dim1=2) v_622 = (v_621 + v_418) v_623 = self.layers_dfe_3_residual_group_blocks_0_norm1(v_622) v_624 = v_623.reshape(1, 6, 8, 6, 8, 192) v_625 = torch.permute(input=v_624, dims=(0,1,3,2,4,5)) v_626 = v_625.reshape(36, 64, 192) v_627 = self.layers_dfe_3_residual_group_blocks_0_attn_qkv(v_626) v_628 = v_627.reshape(36, 64, 3, 6, 32) v_629 = torch.permute(input=v_628, dims=(2,0,3,1,4)) v_630, v_631, v_632 = torch.unbind(v_629, dim=0) v_633 = (v_630 * 1.767767e-01) v_634 = torch.transpose(input=v_631, dim0=-2, dim1=-1) v_635 = torch.matmul(input=v_633, other=v_634) v_636 = self.pnnx_fold_5287_pnnx_fold_5287 v_637 = (v_635 + v_636) v_638 = self.layers_dfe_3_residual_group_blocks_0_attn_softmax(v_637) v_639 = torch.matmul(input=v_638, other=v_632) v_640 = torch.transpose(input=v_639, dim0=1, dim1=2) v_641 = v_640.reshape(36, 64, 192) v_642 = self.layers_dfe_3_residual_group_blocks_0_attn_proj(v_641) v_643 = v_642.reshape(1, 6, 6, 8, 8, 192) v_644 = torch.permute(input=v_643, dims=(0,1,3,2,4,5)) v_645 = v_644.reshape(1, 2304, 192) v_646 = (v_622 + v_645) v_647 = self.layers_dfe_3_residual_group_blocks_0_norm2(v_646) v_648 = self.layers_dfe_3_residual_group_blocks_0_mlp_fc1(v_647) v_649 = self.layers_dfe_3_residual_group_blocks_0_mlp_act(v_648) v_650 = self.layers_dfe_3_residual_group_blocks_0_mlp_fc2(v_649) v_651 = (v_646 + v_650) v_652 = self.layers_dfe_3_residual_group_blocks_1_norm1(v_651) v_653 = v_652.view(1, 48, 48, 192) v_654 = torch.roll(input=v_653, dims=(1,2), shifts=(-4,-4)) v_655 = v_654.view(1, 6, 8, 6, 8, 192) v_656 = torch.permute(input=v_655, dims=(0,1,3,2,4,5)) v_657 = v_656.reshape(36, 64, 192) v_658 = self.layers_dfe_3_residual_group_blocks_1_attn_qkv(v_657) v_659 = v_658.reshape(36, 64, 3, 6, 32) v_660 = torch.permute(input=v_659, dims=(2,0,3,1,4)) v_661, v_662, v_663 = torch.unbind(v_660, dim=0) v_664 = (v_661 * 1.767767e-01) v_665 = torch.transpose(input=v_662, dim0=-2, dim1=-1) v_666 = torch.matmul(input=v_664, other=v_665) v_667 = self.pnnx_fold_5440_pnnx_fold_5440 v_668 = (v_666 + v_667) v_669 = v_668.view(1, 36, 6, 64, 64) v_670 = self.pnnx_fold_5450_pnnx_fold_5450 v_671 = (v_669 + v_670) v_672 = v_671.view(-1, 6, 64, 64) v_673 = self.layers_dfe_3_residual_group_blocks_1_attn_softmax(v_672) v_674 = torch.matmul(input=v_673, other=v_663) v_675 = torch.transpose(input=v_674, dim0=1, dim1=2) v_676 = v_675.reshape(36, 64, 192) v_677 = self.layers_dfe_3_residual_group_blocks_1_attn_proj(v_676) v_678 = v_677.reshape(1, 6, 6, 8, 8, 192) v_679 = torch.permute(input=v_678, dims=(0,1,3,2,4,5)) v_680 = v_679.reshape(1, 48, 48, -1) v_681 = torch.roll(input=v_680, dims=(1,2), shifts=(4,4)) v_682 = v_681.view(1, 2304, 192) v_683 = (v_651 + v_682) v_684 = self.layers_dfe_3_residual_group_blocks_1_norm2(v_683) v_685 = self.layers_dfe_3_residual_group_blocks_1_mlp_fc1(v_684) v_686 = self.layers_dfe_3_residual_group_blocks_1_mlp_act(v_685) v_687 = self.layers_dfe_3_residual_group_blocks_1_mlp_fc2(v_686) v_688 = (v_683 + v_687) v_689 = self.layers_dfe_3_residual_group_blocks_2_norm1(v_688) v_690 = v_689.reshape(1, 6, 8, 6, 8, 192) v_691 = torch.permute(input=v_690, dims=(0,1,3,2,4,5)) v_692 = v_691.reshape(36, 64, 192) v_693 = self.layers_dfe_3_residual_group_blocks_2_attn_qkv(v_692) v_694 = v_693.reshape(36, 64, 3, 6, 32) v_695 = torch.permute(input=v_694, dims=(2,0,3,1,4)) v_696, v_697, v_698 = torch.unbind(v_695, dim=0) v_699 = (v_696 * 1.767767e-01) v_700 = torch.transpose(input=v_697, dim0=-2, dim1=-1) v_701 = torch.matmul(input=v_699, other=v_700) v_702 = self.pnnx_fold_5599_pnnx_fold_5599 v_703 = (v_701 + v_702) v_704 = self.layers_dfe_3_residual_group_blocks_2_attn_softmax(v_703) v_705 = torch.matmul(input=v_704, other=v_698) v_706 = torch.transpose(input=v_705, dim0=1, dim1=2) v_707 = v_706.reshape(36, 64, 192) v_708 = self.layers_dfe_3_residual_group_blocks_2_attn_proj(v_707) v_709 = v_708.reshape(1, 6, 6, 8, 8, 192) v_710 = torch.permute(input=v_709, dims=(0,1,3,2,4,5)) v_711 = v_710.reshape(1, 2304, 192) v_712 = (v_688 + v_711) v_713 = self.layers_dfe_3_residual_group_blocks_2_norm2(v_712) v_714 = self.layers_dfe_3_residual_group_blocks_2_mlp_fc1(v_713) v_715 = self.layers_dfe_3_residual_group_blocks_2_mlp_act(v_714) v_716 = self.layers_dfe_3_residual_group_blocks_2_mlp_fc2(v_715) v_717 = (v_712 + v_716) v_718 = self.layers_dfe_3_residual_group_blocks_3_norm1(v_717) v_719 = v_718.view(1, 48, 48, 192) v_720 = torch.roll(input=v_719, dims=(1,2), shifts=(-4,-4)) v_721 = v_720.view(1, 6, 8, 6, 8, 192) v_722 = torch.permute(input=v_721, dims=(0,1,3,2,4,5)) v_723 = v_722.reshape(36, 64, 192) v_724 = self.layers_dfe_3_residual_group_blocks_3_attn_qkv(v_723) v_725 = v_724.reshape(36, 64, 3, 6, 32) v_726 = torch.permute(input=v_725, dims=(2,0,3,1,4)) v_727, v_728, v_729 = torch.unbind(v_726, dim=0) v_730 = (v_727 * 1.767767e-01) v_731 = torch.transpose(input=v_728, dim0=-2, dim1=-1) v_732 = torch.matmul(input=v_730, other=v_731) v_733 = self.pnnx_fold_5752_pnnx_fold_5752 v_734 = (v_732 + v_733) v_735 = v_734.view(1, 36, 6, 64, 64) v_736 = self.pnnx_fold_5762_pnnx_fold_5762 v_737 = (v_735 + v_736) v_738 = v_737.view(-1, 6, 64, 64) v_739 = self.layers_dfe_3_residual_group_blocks_3_attn_softmax(v_738) v_740 = torch.matmul(input=v_739, other=v_729) v_741 = torch.transpose(input=v_740, dim0=1, dim1=2) v_742 = v_741.reshape(36, 64, 192) v_743 = self.layers_dfe_3_residual_group_blocks_3_attn_proj(v_742) v_744 = v_743.reshape(1, 6, 6, 8, 8, 192) v_745 = torch.permute(input=v_744, dims=(0,1,3,2,4,5)) v_746 = v_745.reshape(1, 48, 48, -1) v_747 = torch.roll(input=v_746, dims=(1,2), shifts=(4,4)) v_748 = v_747.view(1, 2304, 192) v_749 = (v_717 + v_748) v_750 = self.layers_dfe_3_residual_group_blocks_3_norm2(v_749) v_751 = self.layers_dfe_3_residual_group_blocks_3_mlp_fc1(v_750) v_752 = self.layers_dfe_3_residual_group_blocks_3_mlp_act(v_751) v_753 = self.layers_dfe_3_residual_group_blocks_3_mlp_fc2(v_752) v_754 = (v_749 + v_753) v_755 = self.layers_dfe_3_residual_group_blocks_4_norm1(v_754) v_756 = v_755.reshape(1, 6, 8, 6, 8, 192) v_757 = torch.permute(input=v_756, dims=(0,1,3,2,4,5)) v_758 = v_757.reshape(36, 64, 192) v_759 = self.layers_dfe_3_residual_group_blocks_4_attn_qkv(v_758) v_760 = v_759.reshape(36, 64, 3, 6, 32) v_761 = torch.permute(input=v_760, dims=(2,0,3,1,4)) v_762, v_763, v_764 = torch.unbind(v_761, dim=0) v_765 = (v_762 * 1.767767e-01) v_766 = torch.transpose(input=v_763, dim0=-2, dim1=-1) v_767 = torch.matmul(input=v_765, other=v_766) v_768 = self.pnnx_fold_5911_pnnx_fold_5911 v_769 = (v_767 + v_768) v_770 = self.layers_dfe_3_residual_group_blocks_4_attn_softmax(v_769) v_771 = torch.matmul(input=v_770, other=v_764) v_772 = torch.transpose(input=v_771, dim0=1, dim1=2) v_773 = v_772.reshape(36, 64, 192) v_774 = self.layers_dfe_3_residual_group_blocks_4_attn_proj(v_773) v_775 = v_774.reshape(1, 6, 6, 8, 8, 192) v_776 = torch.permute(input=v_775, dims=(0,1,3,2,4,5)) v_777 = v_776.reshape(1, 2304, 192) v_778 = (v_754 + v_777) v_779 = self.layers_dfe_3_residual_group_blocks_4_norm2(v_778) v_780 = self.layers_dfe_3_residual_group_blocks_4_mlp_fc1(v_779) v_781 = self.layers_dfe_3_residual_group_blocks_4_mlp_act(v_780) v_782 = self.layers_dfe_3_residual_group_blocks_4_mlp_fc2(v_781) v_783 = (v_778 + v_782) v_784 = self.layers_dfe_3_residual_group_blocks_5_norm1(v_783) v_785 = v_784.view(1, 48, 48, 192) v_786 = torch.roll(input=v_785, dims=(1,2), shifts=(-4,-4)) v_787 = v_786.view(1, 6, 8, 6, 8, 192) v_788 = torch.permute(input=v_787, dims=(0,1,3,2,4,5)) v_789 = v_788.reshape(36, 64, 192) v_790 = self.layers_dfe_3_residual_group_blocks_5_attn_qkv(v_789) v_791 = v_790.reshape(36, 64, 3, 6, 32) v_792 = torch.permute(input=v_791, dims=(2,0,3,1,4)) v_793, v_794, v_795 = torch.unbind(v_792, dim=0) v_796 = (v_793 * 1.767767e-01) v_797 = torch.transpose(input=v_794, dim0=-2, dim1=-1) v_798 = torch.matmul(input=v_796, other=v_797) v_799 = self.pnnx_fold_6064_pnnx_fold_6064 v_800 = (v_798 + v_799) v_801 = v_800.view(1, 36, 6, 64, 64) v_802 = self.pnnx_fold_6074_pnnx_fold_6074 v_803 = (v_801 + v_802) v_804 = v_803.view(-1, 6, 64, 64) v_805 = self.layers_dfe_3_residual_group_blocks_5_attn_softmax(v_804) v_806 = torch.matmul(input=v_805, other=v_795) v_807 = torch.transpose(input=v_806, dim0=1, dim1=2) v_808 = v_807.reshape(36, 64, 192) v_809 = self.layers_dfe_3_residual_group_blocks_5_attn_proj(v_808) v_810 = v_809.reshape(1, 6, 6, 8, 8, 192) v_811 = torch.permute(input=v_810, dims=(0,1,3,2,4,5)) v_812 = v_811.reshape(1, 48, 48, -1) v_813 = torch.roll(input=v_812, dims=(1,2), shifts=(4,4)) v_814 = v_813.view(1, 2304, 192) v_815 = (v_783 + v_814) v_816 = self.layers_dfe_3_residual_group_blocks_5_norm2(v_815) v_817 = self.layers_dfe_3_residual_group_blocks_5_mlp_fc1(v_816) v_818 = self.layers_dfe_3_residual_group_blocks_5_mlp_act(v_817) v_819 = self.layers_dfe_3_residual_group_blocks_5_mlp_fc2(v_818) v_820 = (v_815 + v_819) v_821 = torch.transpose(input=v_820, dim0=1, dim1=2) v_822 = v_821.view(1, 192, 48, 48) v_823 = self.layers_dfe_3_conv(v_822) v_824 = torch.flatten(input=v_823, end_dim=-1, start_dim=2) v_825 = torch.transpose(input=v_824, dim0=1, dim1=2) v_826 = (v_825 + v_622) v_827 = self.layers_dfe_4_residual_group_blocks_0_norm1(v_826) v_828 = v_827.reshape(1, 6, 8, 6, 8, 192) v_829 = torch.permute(input=v_828, dims=(0,1,3,2,4,5)) v_830 = v_829.reshape(36, 64, 192) v_831 = self.layers_dfe_4_residual_group_blocks_0_attn_qkv(v_830) v_832 = v_831.reshape(36, 64, 3, 6, 32) v_833 = torch.permute(input=v_832, dims=(2,0,3,1,4)) v_834, v_835, v_836 = torch.unbind(v_833, dim=0) v_837 = (v_834 * 1.767767e-01) v_838 = torch.transpose(input=v_835, dim0=-2, dim1=-1) v_839 = torch.matmul(input=v_837, other=v_838) v_840 = self.pnnx_fold_6257_pnnx_fold_6257 v_841 = (v_839 + v_840) v_842 = self.layers_dfe_4_residual_group_blocks_0_attn_softmax(v_841) v_843 = torch.matmul(input=v_842, other=v_836) v_844 = torch.transpose(input=v_843, dim0=1, dim1=2) v_845 = v_844.reshape(36, 64, 192) v_846 = self.layers_dfe_4_residual_group_blocks_0_attn_proj(v_845) v_847 = v_846.reshape(1, 6, 6, 8, 8, 192) v_848 = torch.permute(input=v_847, dims=(0,1,3,2,4,5)) v_849 = v_848.reshape(1, 2304, 192) v_850 = (v_826 + v_849) v_851 = self.layers_dfe_4_residual_group_blocks_0_norm2(v_850) v_852 = self.layers_dfe_4_residual_group_blocks_0_mlp_fc1(v_851) v_853 = self.layers_dfe_4_residual_group_blocks_0_mlp_act(v_852) v_854 = self.layers_dfe_4_residual_group_blocks_0_mlp_fc2(v_853) v_855 = (v_850 + v_854) v_856 = self.layers_dfe_4_residual_group_blocks_1_norm1(v_855) v_857 = v_856.view(1, 48, 48, 192) v_858 = torch.roll(input=v_857, dims=(1,2), shifts=(-4,-4)) v_859 = v_858.view(1, 6, 8, 6, 8, 192) v_860 = torch.permute(input=v_859, dims=(0,1,3,2,4,5)) v_861 = v_860.reshape(36, 64, 192) v_862 = self.layers_dfe_4_residual_group_blocks_1_attn_qkv(v_861) v_863 = v_862.reshape(36, 64, 3, 6, 32) v_864 = torch.permute(input=v_863, dims=(2,0,3,1,4)) v_865, v_866, v_867 = torch.unbind(v_864, dim=0) v_868 = (v_865 * 1.767767e-01) v_869 = torch.transpose(input=v_866, dim0=-2, dim1=-1) v_870 = torch.matmul(input=v_868, other=v_869) v_871 = self.pnnx_fold_6410_pnnx_fold_6410 v_872 = (v_870 + v_871) v_873 = v_872.view(1, 36, 6, 64, 64) v_874 = self.pnnx_fold_6420_pnnx_fold_6420 v_875 = (v_873 + v_874) v_876 = v_875.view(-1, 6, 64, 64) v_877 = self.layers_dfe_4_residual_group_blocks_1_attn_softmax(v_876) v_878 = torch.matmul(input=v_877, other=v_867) v_879 = torch.transpose(input=v_878, dim0=1, dim1=2) v_880 = v_879.reshape(36, 64, 192) v_881 = self.layers_dfe_4_residual_group_blocks_1_attn_proj(v_880) v_882 = v_881.reshape(1, 6, 6, 8, 8, 192) v_883 = torch.permute(input=v_882, dims=(0,1,3,2,4,5)) v_884 = v_883.reshape(1, 48, 48, -1) v_885 = torch.roll(input=v_884, dims=(1,2), shifts=(4,4)) v_886 = v_885.view(1, 2304, 192) v_887 = (v_855 + v_886) v_888 = self.layers_dfe_4_residual_group_blocks_1_norm2(v_887) v_889 = self.layers_dfe_4_residual_group_blocks_1_mlp_fc1(v_888) v_890 = self.layers_dfe_4_residual_group_blocks_1_mlp_act(v_889) v_891 = self.layers_dfe_4_residual_group_blocks_1_mlp_fc2(v_890) v_892 = (v_887 + v_891) v_893 = self.layers_dfe_4_residual_group_blocks_2_norm1(v_892) v_894 = v_893.reshape(1, 6, 8, 6, 8, 192) v_895 = torch.permute(input=v_894, dims=(0,1,3,2,4,5)) v_896 = v_895.reshape(36, 64, 192) v_897 = self.layers_dfe_4_residual_group_blocks_2_attn_qkv(v_896) v_898 = v_897.reshape(36, 64, 3, 6, 32) v_899 = torch.permute(input=v_898, dims=(2,0,3,1,4)) v_900, v_901, v_902 = torch.unbind(v_899, dim=0) v_903 = (v_900 * 1.767767e-01) v_904 = torch.transpose(input=v_901, dim0=-2, dim1=-1) v_905 = torch.matmul(input=v_903, other=v_904) v_906 = self.pnnx_fold_6569_pnnx_fold_6569 v_907 = (v_905 + v_906) v_908 = self.layers_dfe_4_residual_group_blocks_2_attn_softmax(v_907) v_909 = torch.matmul(input=v_908, other=v_902) v_910 = torch.transpose(input=v_909, dim0=1, dim1=2) v_911 = v_910.reshape(36, 64, 192) v_912 = self.layers_dfe_4_residual_group_blocks_2_attn_proj(v_911) v_913 = v_912.reshape(1, 6, 6, 8, 8, 192) v_914 = torch.permute(input=v_913, dims=(0,1,3,2,4,5)) v_915 = v_914.reshape(1, 2304, 192) v_916 = (v_892 + v_915) v_917 = self.layers_dfe_4_residual_group_blocks_2_norm2(v_916) v_918 = self.layers_dfe_4_residual_group_blocks_2_mlp_fc1(v_917) v_919 = self.layers_dfe_4_residual_group_blocks_2_mlp_act(v_918) v_920 = self.layers_dfe_4_residual_group_blocks_2_mlp_fc2(v_919) v_921 = (v_916 + v_920) v_922 = self.layers_dfe_4_residual_group_blocks_3_norm1(v_921) v_923 = v_922.view(1, 48, 48, 192) v_924 = torch.roll(input=v_923, dims=(1,2), shifts=(-4,-4)) v_925 = v_924.view(1, 6, 8, 6, 8, 192) v_926 = torch.permute(input=v_925, dims=(0,1,3,2,4,5)) v_927 = v_926.reshape(36, 64, 192) v_928 = self.layers_dfe_4_residual_group_blocks_3_attn_qkv(v_927) v_929 = v_928.reshape(36, 64, 3, 6, 32) v_930 = torch.permute(input=v_929, dims=(2,0,3,1,4)) v_931, v_932, v_933 = torch.unbind(v_930, dim=0) v_934 = (v_931 * 1.767767e-01) v_935 = torch.transpose(input=v_932, dim0=-2, dim1=-1) v_936 = torch.matmul(input=v_934, other=v_935) v_937 = self.pnnx_fold_6722_pnnx_fold_6722 v_938 = (v_936 + v_937) v_939 = v_938.view(1, 36, 6, 64, 64) v_940 = self.pnnx_fold_6732_pnnx_fold_6732 v_941 = (v_939 + v_940) v_942 = v_941.view(-1, 6, 64, 64) v_943 = self.layers_dfe_4_residual_group_blocks_3_attn_softmax(v_942) v_944 = torch.matmul(input=v_943, other=v_933) v_945 = torch.transpose(input=v_944, dim0=1, dim1=2) v_946 = v_945.reshape(36, 64, 192) v_947 = self.layers_dfe_4_residual_group_blocks_3_attn_proj(v_946) v_948 = v_947.reshape(1, 6, 6, 8, 8, 192) v_949 = torch.permute(input=v_948, dims=(0,1,3,2,4,5)) v_950 = v_949.reshape(1, 48, 48, -1) v_951 = torch.roll(input=v_950, dims=(1,2), shifts=(4,4)) v_952 = v_951.view(1, 2304, 192) v_953 = (v_921 + v_952) v_954 = self.layers_dfe_4_residual_group_blocks_3_norm2(v_953) v_955 = self.layers_dfe_4_residual_group_blocks_3_mlp_fc1(v_954) v_956 = self.layers_dfe_4_residual_group_blocks_3_mlp_act(v_955) v_957 = self.layers_dfe_4_residual_group_blocks_3_mlp_fc2(v_956) v_958 = (v_953 + v_957) v_959 = self.layers_dfe_4_residual_group_blocks_4_norm1(v_958) v_960 = v_959.reshape(1, 6, 8, 6, 8, 192) v_961 = torch.permute(input=v_960, dims=(0,1,3,2,4,5)) v_962 = v_961.reshape(36, 64, 192) v_963 = self.layers_dfe_4_residual_group_blocks_4_attn_qkv(v_962) v_964 = v_963.reshape(36, 64, 3, 6, 32) v_965 = torch.permute(input=v_964, dims=(2,0,3,1,4)) v_966, v_967, v_968 = torch.unbind(v_965, dim=0) v_969 = (v_966 * 1.767767e-01) v_970 = torch.transpose(input=v_967, dim0=-2, dim1=-1) v_971 = torch.matmul(input=v_969, other=v_970) v_972 = self.pnnx_fold_6881_pnnx_fold_6881 v_973 = (v_971 + v_972) v_974 = self.layers_dfe_4_residual_group_blocks_4_attn_softmax(v_973) v_975 = torch.matmul(input=v_974, other=v_968) v_976 = torch.transpose(input=v_975, dim0=1, dim1=2) v_977 = v_976.reshape(36, 64, 192) v_978 = self.layers_dfe_4_residual_group_blocks_4_attn_proj(v_977) v_979 = v_978.reshape(1, 6, 6, 8, 8, 192) v_980 = torch.permute(input=v_979, dims=(0,1,3,2,4,5)) v_981 = v_980.reshape(1, 2304, 192) v_982 = (v_958 + v_981) v_983 = self.layers_dfe_4_residual_group_blocks_4_norm2(v_982) v_984 = self.layers_dfe_4_residual_group_blocks_4_mlp_fc1(v_983) v_985 = self.layers_dfe_4_residual_group_blocks_4_mlp_act(v_984) v_986 = self.layers_dfe_4_residual_group_blocks_4_mlp_fc2(v_985) v_987 = (v_982 + v_986) v_988 = self.layers_dfe_4_residual_group_blocks_5_norm1(v_987) v_989 = v_988.view(1, 48, 48, 192) v_990 = torch.roll(input=v_989, dims=(1,2), shifts=(-4,-4)) v_991 = v_990.view(1, 6, 8, 6, 8, 192) v_992 = torch.permute(input=v_991, dims=(0,1,3,2,4,5)) v_993 = v_992.reshape(36, 64, 192) v_994 = self.layers_dfe_4_residual_group_blocks_5_attn_qkv(v_993) v_995 = v_994.reshape(36, 64, 3, 6, 32) v_996 = torch.permute(input=v_995, dims=(2,0,3,1,4)) v_997, v_998, v_999 = torch.unbind(v_996, dim=0) v_1000 = (v_997 * 1.767767e-01) v_1001 = torch.transpose(input=v_998, dim0=-2, dim1=-1) v_1002 = torch.matmul(input=v_1000, other=v_1001) v_1003 = self.pnnx_fold_7034_pnnx_fold_7034 v_1004 = (v_1002 + v_1003) v_1005 = v_1004.view(1, 36, 6, 64, 64) v_1006 = self.pnnx_fold_7044_pnnx_fold_7044 v_1007 = (v_1005 + v_1006) v_1008 = v_1007.view(-1, 6, 64, 64) v_1009 = self.layers_dfe_4_residual_group_blocks_5_attn_softmax(v_1008) v_1010 = torch.matmul(input=v_1009, other=v_999) v_1011 = torch.transpose(input=v_1010, dim0=1, dim1=2) v_1012 = v_1011.reshape(36, 64, 192) v_1013 = self.layers_dfe_4_residual_group_blocks_5_attn_proj(v_1012) v_1014 = v_1013.reshape(1, 6, 6, 8, 8, 192) v_1015 = torch.permute(input=v_1014, dims=(0,1,3,2,4,5)) v_1016 = v_1015.reshape(1, 48, 48, -1) v_1017 = torch.roll(input=v_1016, dims=(1,2), shifts=(4,4)) v_1018 = v_1017.view(1, 2304, 192) v_1019 = (v_987 + v_1018) v_1020 = self.layers_dfe_4_residual_group_blocks_5_norm2(v_1019) v_1021 = self.layers_dfe_4_residual_group_blocks_5_mlp_fc1(v_1020) v_1022 = self.layers_dfe_4_residual_group_blocks_5_mlp_act(v_1021) v_1023 = self.layers_dfe_4_residual_group_blocks_5_mlp_fc2(v_1022) v_1024 = (v_1019 + v_1023) v_1025 = torch.transpose(input=v_1024, dim0=1, dim1=2) v_1026 = v_1025.view(1, 192, 48, 48) v_1027 = self.layers_dfe_4_conv(v_1026) v_1028 = torch.flatten(input=v_1027, end_dim=-1, start_dim=2) v_1029 = torch.transpose(input=v_1028, dim0=1, dim1=2) v_1030 = (v_1029 + v_826) v_1031 = self.layers_dfe_5_residual_group_blocks_0_norm1(v_1030) v_1032 = v_1031.reshape(1, 6, 8, 6, 8, 192) v_1033 = torch.permute(input=v_1032, dims=(0,1,3,2,4,5)) v_1034 = v_1033.reshape(36, 64, 192) v_1035 = self.layers_dfe_5_residual_group_blocks_0_attn_qkv(v_1034) v_1036 = v_1035.reshape(36, 64, 3, 6, 32) v_1037 = torch.permute(input=v_1036, dims=(2,0,3,1,4)) v_1038, v_1039, v_1040 = torch.unbind(v_1037, dim=0) v_1041 = (v_1038 * 1.767767e-01) v_1042 = torch.transpose(input=v_1039, dim0=-2, dim1=-1) v_1043 = torch.matmul(input=v_1041, other=v_1042) v_1044 = self.pnnx_fold_7227_pnnx_fold_7227 v_1045 = (v_1043 + v_1044) v_1046 = self.layers_dfe_5_residual_group_blocks_0_attn_softmax(v_1045) v_1047 = torch.matmul(input=v_1046, other=v_1040) v_1048 = torch.transpose(input=v_1047, dim0=1, dim1=2) v_1049 = v_1048.reshape(36, 64, 192) v_1050 = self.layers_dfe_5_residual_group_blocks_0_attn_proj(v_1049) v_1051 = v_1050.reshape(1, 6, 6, 8, 8, 192) v_1052 = torch.permute(input=v_1051, dims=(0,1,3,2,4,5)) v_1053 = v_1052.reshape(1, 2304, 192) v_1054 = (v_1030 + v_1053) v_1055 = self.layers_dfe_5_residual_group_blocks_0_norm2(v_1054) v_1056 = self.layers_dfe_5_residual_group_blocks_0_mlp_fc1(v_1055) v_1057 = self.layers_dfe_5_residual_group_blocks_0_mlp_act(v_1056) v_1058 = self.layers_dfe_5_residual_group_blocks_0_mlp_fc2(v_1057) v_1059 = (v_1054 + v_1058) v_1060 = self.layers_dfe_5_residual_group_blocks_1_norm1(v_1059) v_1061 = v_1060.view(1, 48, 48, 192) v_1062 = torch.roll(input=v_1061, dims=(1,2), shifts=(-4,-4)) v_1063 = v_1062.view(1, 6, 8, 6, 8, 192) v_1064 = torch.permute(input=v_1063, dims=(0,1,3,2,4,5)) v_1065 = v_1064.reshape(36, 64, 192) v_1066 = self.layers_dfe_5_residual_group_blocks_1_attn_qkv(v_1065) v_1067 = v_1066.reshape(36, 64, 3, 6, 32) v_1068 = torch.permute(input=v_1067, dims=(2,0,3,1,4)) v_1069, v_1070, v_1071 = torch.unbind(v_1068, dim=0) v_1072 = (v_1069 * 1.767767e-01) v_1073 = torch.transpose(input=v_1070, dim0=-2, dim1=-1) v_1074 = torch.matmul(input=v_1072, other=v_1073) v_1075 = self.pnnx_fold_7380_pnnx_fold_7380 v_1076 = (v_1074 + v_1075) v_1077 = v_1076.view(1, 36, 6, 64, 64) v_1078 = self.pnnx_fold_7390_pnnx_fold_7390 v_1079 = (v_1077 + v_1078) v_1080 = v_1079.view(-1, 6, 64, 64) v_1081 = self.layers_dfe_5_residual_group_blocks_1_attn_softmax(v_1080) v_1082 = torch.matmul(input=v_1081, other=v_1071) v_1083 = torch.transpose(input=v_1082, dim0=1, dim1=2) v_1084 = v_1083.reshape(36, 64, 192) v_1085 = self.layers_dfe_5_residual_group_blocks_1_attn_proj(v_1084) v_1086 = v_1085.reshape(1, 6, 6, 8, 8, 192) v_1087 = torch.permute(input=v_1086, dims=(0,1,3,2,4,5)) v_1088 = v_1087.reshape(1, 48, 48, -1) v_1089 = torch.roll(input=v_1088, dims=(1,2), shifts=(4,4)) v_1090 = v_1089.view(1, 2304, 192) v_1091 = (v_1059 + v_1090) v_1092 = self.layers_dfe_5_residual_group_blocks_1_norm2(v_1091) v_1093 = self.layers_dfe_5_residual_group_blocks_1_mlp_fc1(v_1092) v_1094 = self.layers_dfe_5_residual_group_blocks_1_mlp_act(v_1093) v_1095 = self.layers_dfe_5_residual_group_blocks_1_mlp_fc2(v_1094) v_1096 = (v_1091 + v_1095) v_1097 = self.layers_dfe_5_residual_group_blocks_2_norm1(v_1096) v_1098 = v_1097.reshape(1, 6, 8, 6, 8, 192) v_1099 = torch.permute(input=v_1098, dims=(0,1,3,2,4,5)) v_1100 = v_1099.reshape(36, 64, 192) v_1101 = self.layers_dfe_5_residual_group_blocks_2_attn_qkv(v_1100) v_1102 = v_1101.reshape(36, 64, 3, 6, 32) v_1103 = torch.permute(input=v_1102, dims=(2,0,3,1,4)) v_1104, v_1105, v_1106 = torch.unbind(v_1103, dim=0) v_1107 = (v_1104 * 1.767767e-01) v_1108 = torch.transpose(input=v_1105, dim0=-2, dim1=-1) v_1109 = torch.matmul(input=v_1107, other=v_1108) v_1110 = self.pnnx_fold_7539_pnnx_fold_7539 v_1111 = (v_1109 + v_1110) v_1112 = self.layers_dfe_5_residual_group_blocks_2_attn_softmax(v_1111) v_1113 = torch.matmul(input=v_1112, other=v_1106) v_1114 = torch.transpose(input=v_1113, dim0=1, dim1=2) v_1115 = v_1114.reshape(36, 64, 192) v_1116 = self.layers_dfe_5_residual_group_blocks_2_attn_proj(v_1115) v_1117 = v_1116.reshape(1, 6, 6, 8, 8, 192) v_1118 = torch.permute(input=v_1117, dims=(0,1,3,2,4,5)) v_1119 = v_1118.reshape(1, 2304, 192) v_1120 = (v_1096 + v_1119) v_1121 = self.layers_dfe_5_residual_group_blocks_2_norm2(v_1120) v_1122 = self.layers_dfe_5_residual_group_blocks_2_mlp_fc1(v_1121) v_1123 = self.layers_dfe_5_residual_group_blocks_2_mlp_act(v_1122) v_1124 = self.layers_dfe_5_residual_group_blocks_2_mlp_fc2(v_1123) v_1125 = (v_1120 + v_1124) v_1126 = self.layers_dfe_5_residual_group_blocks_3_norm1(v_1125) v_1127 = v_1126.view(1, 48, 48, 192) v_1128 = torch.roll(input=v_1127, dims=(1,2), shifts=(-4,-4)) v_1129 = v_1128.view(1, 6, 8, 6, 8, 192) v_1130 = torch.permute(input=v_1129, dims=(0,1,3,2,4,5)) v_1131 = v_1130.reshape(36, 64, 192) v_1132 = self.layers_dfe_5_residual_group_blocks_3_attn_qkv(v_1131) v_1133 = v_1132.reshape(36, 64, 3, 6, 32) v_1134 = torch.permute(input=v_1133, dims=(2,0,3,1,4)) v_1135, v_1136, v_1137 = torch.unbind(v_1134, dim=0) v_1138 = (v_1135 * 1.767767e-01) v_1139 = torch.transpose(input=v_1136, dim0=-2, dim1=-1) v_1140 = torch.matmul(input=v_1138, other=v_1139) v_1141 = self.pnnx_fold_7692_pnnx_fold_7692 v_1142 = (v_1140 + v_1141) v_1143 = v_1142.view(1, 36, 6, 64, 64) v_1144 = self.pnnx_fold_7702_pnnx_fold_7702 v_1145 = (v_1143 + v_1144) v_1146 = v_1145.view(-1, 6, 64, 64) v_1147 = self.layers_dfe_5_residual_group_blocks_3_attn_softmax(v_1146) v_1148 = torch.matmul(input=v_1147, other=v_1137) v_1149 = torch.transpose(input=v_1148, dim0=1, dim1=2) v_1150 = v_1149.reshape(36, 64, 192) v_1151 = self.layers_dfe_5_residual_group_blocks_3_attn_proj(v_1150) v_1152 = v_1151.reshape(1, 6, 6, 8, 8, 192) v_1153 = torch.permute(input=v_1152, dims=(0,1,3,2,4,5)) v_1154 = v_1153.reshape(1, 48, 48, -1) v_1155 = torch.roll(input=v_1154, dims=(1,2), shifts=(4,4)) v_1156 = v_1155.view(1, 2304, 192) v_1157 = (v_1125 + v_1156) v_1158 = self.layers_dfe_5_residual_group_blocks_3_norm2(v_1157) v_1159 = self.layers_dfe_5_residual_group_blocks_3_mlp_fc1(v_1158) v_1160 = self.layers_dfe_5_residual_group_blocks_3_mlp_act(v_1159) v_1161 = self.layers_dfe_5_residual_group_blocks_3_mlp_fc2(v_1160) v_1162 = (v_1157 + v_1161) v_1163 = self.layers_dfe_5_residual_group_blocks_4_norm1(v_1162) v_1164 = v_1163.reshape(1, 6, 8, 6, 8, 192) v_1165 = torch.permute(input=v_1164, dims=(0,1,3,2,4,5)) v_1166 = v_1165.reshape(36, 64, 192) v_1167 = self.layers_dfe_5_residual_group_blocks_4_attn_qkv(v_1166) v_1168 = v_1167.reshape(36, 64, 3, 6, 32) v_1169 = torch.permute(input=v_1168, dims=(2,0,3,1,4)) v_1170, v_1171, v_1172 = torch.unbind(v_1169, dim=0) v_1173 = (v_1170 * 1.767767e-01) v_1174 = torch.transpose(input=v_1171, dim0=-2, dim1=-1) v_1175 = torch.matmul(input=v_1173, other=v_1174) v_1176 = self.pnnx_fold_7851_pnnx_fold_7851 v_1177 = (v_1175 + v_1176) v_1178 = self.layers_dfe_5_residual_group_blocks_4_attn_softmax(v_1177) v_1179 = torch.matmul(input=v_1178, other=v_1172) v_1180 = torch.transpose(input=v_1179, dim0=1, dim1=2) v_1181 = v_1180.reshape(36, 64, 192) v_1182 = self.layers_dfe_5_residual_group_blocks_4_attn_proj(v_1181) v_1183 = v_1182.reshape(1, 6, 6, 8, 8, 192) v_1184 = torch.permute(input=v_1183, dims=(0,1,3,2,4,5)) v_1185 = v_1184.reshape(1, 2304, 192) v_1186 = (v_1162 + v_1185) v_1187 = self.layers_dfe_5_residual_group_blocks_4_norm2(v_1186) v_1188 = self.layers_dfe_5_residual_group_blocks_4_mlp_fc1(v_1187) v_1189 = self.layers_dfe_5_residual_group_blocks_4_mlp_act(v_1188) v_1190 = self.layers_dfe_5_residual_group_blocks_4_mlp_fc2(v_1189) v_1191 = (v_1186 + v_1190) v_1192 = self.layers_dfe_5_residual_group_blocks_5_norm1(v_1191) v_1193 = v_1192.view(1, 48, 48, 192) v_1194 = torch.roll(input=v_1193, dims=(1,2), shifts=(-4,-4)) v_1195 = v_1194.view(1, 6, 8, 6, 8, 192) v_1196 = torch.permute(input=v_1195, dims=(0,1,3,2,4,5)) v_1197 = v_1196.reshape(36, 64, 192) v_1198 = self.layers_dfe_5_residual_group_blocks_5_attn_qkv(v_1197) v_1199 = v_1198.reshape(36, 64, 3, 6, 32) v_1200 = torch.permute(input=v_1199, dims=(2,0,3,1,4)) v_1201, v_1202, v_1203 = torch.unbind(v_1200, dim=0) v_1204 = (v_1201 * 1.767767e-01) v_1205 = torch.transpose(input=v_1202, dim0=-2, dim1=-1) v_1206 = torch.matmul(input=v_1204, other=v_1205) v_1207 = self.pnnx_fold_8004_pnnx_fold_8004 v_1208 = (v_1206 + v_1207) v_1209 = v_1208.view(1, 36, 6, 64, 64) v_1210 = self.pnnx_fold_8014_pnnx_fold_8014 v_1211 = (v_1209 + v_1210) v_1212 = v_1211.view(-1, 6, 64, 64) v_1213 = self.layers_dfe_5_residual_group_blocks_5_attn_softmax(v_1212) v_1214 = torch.matmul(input=v_1213, other=v_1203) v_1215 = torch.transpose(input=v_1214, dim0=1, dim1=2) v_1216 = v_1215.reshape(36, 64, 192) v_1217 = self.layers_dfe_5_residual_group_blocks_5_attn_proj(v_1216) v_1218 = v_1217.reshape(1, 6, 6, 8, 8, 192) v_1219 = torch.permute(input=v_1218, dims=(0,1,3,2,4,5)) v_1220 = v_1219.reshape(1, 48, 48, -1) v_1221 = torch.roll(input=v_1220, dims=(1,2), shifts=(4,4)) v_1222 = v_1221.view(1, 2304, 192) v_1223 = (v_1191 + v_1222) v_1224 = self.layers_dfe_5_residual_group_blocks_5_norm2(v_1223) v_1225 = self.layers_dfe_5_residual_group_blocks_5_mlp_fc1(v_1224) v_1226 = self.layers_dfe_5_residual_group_blocks_5_mlp_act(v_1225) v_1227 = self.layers_dfe_5_residual_group_blocks_5_mlp_fc2(v_1226) v_1228 = (v_1223 + v_1227) v_1229 = torch.transpose(input=v_1228, dim0=1, dim1=2) v_1230 = v_1229.view(1, 192, 48, 48) v_1231 = self.layers_dfe_5_conv(v_1230) v_1232 = torch.flatten(input=v_1231, end_dim=-1, start_dim=2) v_1233 = torch.transpose(input=v_1232, dim0=1, dim1=2) v_1234 = (v_1233 + v_1030) v_1235 = self.norm_dfe(v_1234) v_1236 = torch.transpose(input=v_1235, dim0=1, dim1=2) v_1237 = v_1236.view(1, 192, 48, 48) v_1238 = self.conv_after_body_dfe(v_1237) v_1239 = (v_1238 + v_6) v_1240 = torch.flatten(input=v_7, end_dim=-1, start_dim=2) v_1241 = torch.transpose(input=v_1240, dim0=1, dim1=2) v_1242 = self.pnnx_unique_73(v_1241) v_1243 = self.pnnx_unique_75(v_1242) v_1244 = v_1243.reshape(1, 6, 8, 6, 8, 192) v_1245 = torch.permute(input=v_1244, dims=(0,1,3,2,4,5)) v_1246 = v_1245.reshape(36, 64, 192) v_1247 = self.pnnx_unique_78(v_1246) v_1248 = v_1247.reshape(36, 64, 3, 6, 32) v_1249 = torch.permute(input=v_1248, dims=(2,0,3,1,4)) v_1250, v_1251, v_1252 = torch.unbind(v_1249, dim=0) v_1253 = (v_1250 * 1.767767e-01) v_1254 = torch.transpose(input=v_1251, dim0=-2, dim1=-1) v_1255 = torch.matmul(input=v_1253, other=v_1254) v_1256 = self.pnnx_fold_8214_pnnx_fold_8214 v_1257 = (v_1255 + v_1256) v_1258 = self.pnnx_unique_79(v_1257) v_1259 = torch.matmul(input=v_1258, other=v_1252) v_1260 = torch.transpose(input=v_1259, dim0=1, dim1=2) v_1261 = v_1260.reshape(36, 64, 192) v_1262 = self.pnnx_unique_81(v_1261) v_1263 = v_1262.reshape(1, 6, 6, 8, 8, 192) v_1264 = torch.permute(input=v_1263, dims=(0,1,3,2,4,5)) v_1265 = v_1264.reshape(1, 2304, 192) v_1266 = (v_1242 + v_1265) v_1267 = self.pnnx_unique_83(v_1266) v_1268 = self.pnnx_unique_84(v_1267) v_1269 = self.pnnx_unique_85(v_1268) v_1270 = self.pnnx_unique_87(v_1269) v_1271 = (v_1266 + v_1270) v_1272 = self.pnnx_unique_90(v_1271) v_1273 = v_1272.view(1, 48, 48, 192) v_1274 = torch.roll(input=v_1273, dims=(1,2), shifts=(-4,-4)) v_1275 = v_1274.view(1, 6, 8, 6, 8, 192) v_1276 = torch.permute(input=v_1275, dims=(0,1,3,2,4,5)) v_1277 = v_1276.reshape(36, 64, 192) v_1278 = self.pnnx_unique_93(v_1277) v_1279 = v_1278.reshape(36, 64, 3, 6, 32) v_1280 = torch.permute(input=v_1279, dims=(2,0,3,1,4)) v_1281, v_1282, v_1283 = torch.unbind(v_1280, dim=0) v_1284 = (v_1281 * 1.767767e-01) v_1285 = torch.transpose(input=v_1282, dim0=-2, dim1=-1) v_1286 = torch.matmul(input=v_1284, other=v_1285) v_1287 = self.pnnx_fold_8367_pnnx_fold_8367 v_1288 = (v_1286 + v_1287) v_1289 = v_1288.view(1, 36, 6, 64, 64) v_1290 = self.pnnx_fold_8377_pnnx_fold_8377 v_1291 = (v_1289 + v_1290) v_1292 = v_1291.view(-1, 6, 64, 64) v_1293 = self.pnnx_unique_94(v_1292) v_1294 = torch.matmul(input=v_1293, other=v_1283) v_1295 = torch.transpose(input=v_1294, dim0=1, dim1=2) v_1296 = v_1295.reshape(36, 64, 192) v_1297 = self.pnnx_unique_96(v_1296) v_1298 = v_1297.reshape(1, 6, 6, 8, 8, 192) v_1299 = torch.permute(input=v_1298, dims=(0,1,3,2,4,5)) v_1300 = v_1299.reshape(1, 48, 48, -1) v_1301 = torch.roll(input=v_1300, dims=(1,2), shifts=(4,4)) v_1302 = v_1301.view(1, 2304, 192) v_1303 = (v_1271 + v_1302) v_1304 = self.pnnx_unique_98(v_1303) v_1305 = self.pnnx_unique_99(v_1304) v_1306 = self.pnnx_unique_100(v_1305) v_1307 = self.pnnx_unique_102(v_1306) v_1308 = (v_1303 + v_1307) v_1309 = self.pnnx_unique_104(v_1308) v_1310 = v_1309.reshape(1, 6, 8, 6, 8, 192) v_1311 = torch.permute(input=v_1310, dims=(0,1,3,2,4,5)) v_1312 = v_1311.reshape(36, 64, 192) v_1313 = self.pnnx_unique_107(v_1312) v_1314 = v_1313.reshape(36, 64, 3, 6, 32) v_1315 = torch.permute(input=v_1314, dims=(2,0,3,1,4)) v_1316, v_1317, v_1318 = torch.unbind(v_1315, dim=0) v_1319 = (v_1316 * 1.767767e-01) v_1320 = torch.transpose(input=v_1317, dim0=-2, dim1=-1) v_1321 = torch.matmul(input=v_1319, other=v_1320) v_1322 = self.pnnx_fold_8526_pnnx_fold_8526 v_1323 = (v_1321 + v_1322) v_1324 = self.pnnx_unique_108(v_1323) v_1325 = torch.matmul(input=v_1324, other=v_1318) v_1326 = torch.transpose(input=v_1325, dim0=1, dim1=2) v_1327 = v_1326.reshape(36, 64, 192) v_1328 = self.pnnx_unique_110(v_1327) v_1329 = v_1328.reshape(1, 6, 6, 8, 8, 192) v_1330 = torch.permute(input=v_1329, dims=(0,1,3,2,4,5)) v_1331 = v_1330.reshape(1, 2304, 192) v_1332 = (v_1308 + v_1331) v_1333 = self.pnnx_unique_112(v_1332) v_1334 = self.pnnx_unique_113(v_1333) v_1335 = self.pnnx_unique_114(v_1334) v_1336 = self.pnnx_unique_116(v_1335) v_1337 = (v_1332 + v_1336) v_1338 = self.pnnx_unique_119(v_1337) v_1339 = v_1338.view(1, 48, 48, 192) v_1340 = torch.roll(input=v_1339, dims=(1,2), shifts=(-4,-4)) v_1341 = v_1340.view(1, 6, 8, 6, 8, 192) v_1342 = torch.permute(input=v_1341, dims=(0,1,3,2,4,5)) v_1343 = v_1342.reshape(36, 64, 192) v_1344 = self.pnnx_unique_122(v_1343) v_1345 = v_1344.reshape(36, 64, 3, 6, 32) v_1346 = torch.permute(input=v_1345, dims=(2,0,3,1,4)) v_1347, v_1348, v_1349 = torch.unbind(v_1346, dim=0) v_1350 = (v_1347 * 1.767767e-01) v_1351 = torch.transpose(input=v_1348, dim0=-2, dim1=-1) v_1352 = torch.matmul(input=v_1350, other=v_1351) v_1353 = self.pnnx_fold_8679_pnnx_fold_8679 v_1354 = (v_1352 + v_1353) v_1355 = v_1354.view(1, 36, 6, 64, 64) v_1356 = self.pnnx_fold_8689_pnnx_fold_8689 v_1357 = (v_1355 + v_1356) v_1358 = v_1357.view(-1, 6, 64, 64) v_1359 = self.pnnx_unique_123(v_1358) v_1360 = torch.matmul(input=v_1359, other=v_1349) v_1361 = torch.transpose(input=v_1360, dim0=1, dim1=2) v_1362 = v_1361.reshape(36, 64, 192) v_1363 = self.pnnx_unique_125(v_1362) v_1364 = v_1363.reshape(1, 6, 6, 8, 8, 192) v_1365 = torch.permute(input=v_1364, dims=(0,1,3,2,4,5)) v_1366 = v_1365.reshape(1, 48, 48, -1) v_1367 = torch.roll(input=v_1366, dims=(1,2), shifts=(4,4)) v_1368 = v_1367.view(1, 2304, 192) v_1369 = (v_1337 + v_1368) v_1370 = self.pnnx_unique_127(v_1369) v_1371 = self.pnnx_unique_128(v_1370) v_1372 = self.pnnx_unique_129(v_1371) v_1373 = self.pnnx_unique_131(v_1372) v_1374 = (v_1369 + v_1373) v_1375 = self.pnnx_unique_133(v_1374) v_1376 = v_1375.reshape(1, 6, 8, 6, 8, 192) v_1377 = torch.permute(input=v_1376, dims=(0,1,3,2,4,5)) v_1378 = v_1377.reshape(36, 64, 192) v_1379 = self.pnnx_unique_136(v_1378) v_1380 = v_1379.reshape(36, 64, 3, 6, 32) v_1381 = torch.permute(input=v_1380, dims=(2,0,3,1,4)) v_1382, v_1383, v_1384 = torch.unbind(v_1381, dim=0) v_1385 = (v_1382 * 1.767767e-01) v_1386 = torch.transpose(input=v_1383, dim0=-2, dim1=-1) v_1387 = torch.matmul(input=v_1385, other=v_1386) v_1388 = self.pnnx_fold_8838_pnnx_fold_8838 v_1389 = (v_1387 + v_1388) v_1390 = self.pnnx_unique_137(v_1389) v_1391 = torch.matmul(input=v_1390, other=v_1384) v_1392 = torch.transpose(input=v_1391, dim0=1, dim1=2) v_1393 = v_1392.reshape(36, 64, 192) v_1394 = self.pnnx_unique_139(v_1393) v_1395 = v_1394.reshape(1, 6, 6, 8, 8, 192) v_1396 = torch.permute(input=v_1395, dims=(0,1,3,2,4,5)) v_1397 = v_1396.reshape(1, 2304, 192) v_1398 = (v_1374 + v_1397) v_1399 = self.pnnx_unique_141(v_1398) v_1400 = self.pnnx_unique_142(v_1399) v_1401 = self.pnnx_unique_143(v_1400) v_1402 = self.pnnx_unique_145(v_1401) v_1403 = (v_1398 + v_1402) v_1404 = self.pnnx_unique_148(v_1403) v_1405 = v_1404.view(1, 48, 48, 192) v_1406 = torch.roll(input=v_1405, dims=(1,2), shifts=(-4,-4)) v_1407 = v_1406.view(1, 6, 8, 6, 8, 192) v_1408 = torch.permute(input=v_1407, dims=(0,1,3,2,4,5)) v_1409 = v_1408.reshape(36, 64, 192) v_1410 = self.pnnx_unique_151(v_1409) v_1411 = v_1410.reshape(36, 64, 3, 6, 32) v_1412 = torch.permute(input=v_1411, dims=(2,0,3,1,4)) v_1413, v_1414, v_1415 = torch.unbind(v_1412, dim=0) v_1416 = (v_1413 * 1.767767e-01) v_1417 = torch.transpose(input=v_1414, dim0=-2, dim1=-1) v_1418 = torch.matmul(input=v_1416, other=v_1417) v_1419 = self.pnnx_fold_8991_pnnx_fold_8991 v_1420 = (v_1418 + v_1419) v_1421 = v_1420.view(1, 36, 6, 64, 64) v_1422 = self.pnnx_fold_9001_pnnx_fold_9001 v_1423 = (v_1421 + v_1422) v_1424 = v_1423.view(-1, 6, 64, 64) v_1425 = self.pnnx_unique_152(v_1424) v_1426 = torch.matmul(input=v_1425, other=v_1415) v_1427 = torch.transpose(input=v_1426, dim0=1, dim1=2) v_1428 = v_1427.reshape(36, 64, 192) v_1429 = self.pnnx_unique_154(v_1428) v_1430 = v_1429.reshape(1, 6, 6, 8, 8, 192) v_1431 = torch.permute(input=v_1430, dims=(0,1,3,2,4,5)) v_1432 = v_1431.reshape(1, 48, 48, -1) v_1433 = torch.roll(input=v_1432, dims=(1,2), shifts=(4,4)) v_1434 = v_1433.view(1, 2304, 192) v_1435 = (v_1403 + v_1434) v_1436 = self.pnnx_unique_156(v_1435) v_1437 = self.pnnx_unique_157(v_1436) v_1438 = self.pnnx_unique_158(v_1437) v_1439 = self.pnnx_unique_160(v_1438) v_1440 = (v_1435 + v_1439) v_1441 = torch.transpose(input=v_1440, dim0=1, dim1=2) v_1442 = v_1441.view(1, 192, 48, 48) v_1443 = self.pnnx_unique_162(v_1442) v_1444 = torch.flatten(input=v_1443, end_dim=-1, start_dim=2) v_1445 = torch.transpose(input=v_1444, dim0=1, dim1=2) v_1446 = (v_1445 + v_1242) v_1447 = self.pnnx_unique_163(v_1446) v_1448 = v_1447.reshape(1, 6, 8, 6, 8, 192) v_1449 = torch.permute(input=v_1448, dims=(0,1,3,2,4,5)) v_1450 = v_1449.reshape(36, 64, 192) v_1451 = self.pnnx_unique_166(v_1450) v_1452 = v_1451.reshape(36, 64, 3, 6, 32) v_1453 = torch.permute(input=v_1452, dims=(2,0,3,1,4)) v_1454, v_1455, v_1456 = torch.unbind(v_1453, dim=0) v_1457 = (v_1454 * 1.767767e-01) v_1458 = torch.transpose(input=v_1455, dim0=-2, dim1=-1) v_1459 = torch.matmul(input=v_1457, other=v_1458) v_1460 = self.pnnx_fold_9184_pnnx_fold_9184 v_1461 = (v_1459 + v_1460) v_1462 = self.pnnx_unique_167(v_1461) v_1463 = torch.matmul(input=v_1462, other=v_1456) v_1464 = torch.transpose(input=v_1463, dim0=1, dim1=2) v_1465 = v_1464.reshape(36, 64, 192) v_1466 = self.pnnx_unique_169(v_1465) v_1467 = v_1466.reshape(1, 6, 6, 8, 8, 192) v_1468 = torch.permute(input=v_1467, dims=(0,1,3,2,4,5)) v_1469 = v_1468.reshape(1, 2304, 192) v_1470 = (v_1446 + v_1469) v_1471 = self.pnnx_unique_171(v_1470) v_1472 = self.pnnx_unique_172(v_1471) v_1473 = self.pnnx_unique_173(v_1472) v_1474 = self.pnnx_unique_175(v_1473) v_1475 = (v_1470 + v_1474) v_1476 = self.pnnx_unique_178(v_1475) v_1477 = v_1476.view(1, 48, 48, 192) v_1478 = torch.roll(input=v_1477, dims=(1,2), shifts=(-4,-4)) v_1479 = v_1478.view(1, 6, 8, 6, 8, 192) v_1480 = torch.permute(input=v_1479, dims=(0,1,3,2,4,5)) v_1481 = v_1480.reshape(36, 64, 192) v_1482 = self.pnnx_unique_181(v_1481) v_1483 = v_1482.reshape(36, 64, 3, 6, 32) v_1484 = torch.permute(input=v_1483, dims=(2,0,3,1,4)) v_1485, v_1486, v_1487 = torch.unbind(v_1484, dim=0) v_1488 = (v_1485 * 1.767767e-01) v_1489 = torch.transpose(input=v_1486, dim0=-2, dim1=-1) v_1490 = torch.matmul(input=v_1488, other=v_1489) v_1491 = self.pnnx_fold_9337_pnnx_fold_9337 v_1492 = (v_1490 + v_1491) v_1493 = v_1492.view(1, 36, 6, 64, 64) v_1494 = self.pnnx_fold_9347_pnnx_fold_9347 v_1495 = (v_1493 + v_1494) v_1496 = v_1495.view(-1, 6, 64, 64) v_1497 = self.pnnx_unique_182(v_1496) v_1498 = torch.matmul(input=v_1497, other=v_1487) v_1499 = torch.transpose(input=v_1498, dim0=1, dim1=2) v_1500 = v_1499.reshape(36, 64, 192) v_1501 = self.pnnx_unique_184(v_1500) v_1502 = v_1501.reshape(1, 6, 6, 8, 8, 192) v_1503 = torch.permute(input=v_1502, dims=(0,1,3,2,4,5)) v_1504 = v_1503.reshape(1, 48, 48, -1) v_1505 = torch.roll(input=v_1504, dims=(1,2), shifts=(4,4)) v_1506 = v_1505.view(1, 2304, 192) v_1507 = (v_1475 + v_1506) v_1508 = self.pnnx_unique_186(v_1507) v_1509 = self.pnnx_unique_187(v_1508) v_1510 = self.pnnx_unique_188(v_1509) v_1511 = self.pnnx_unique_190(v_1510) v_1512 = (v_1507 + v_1511) v_1513 = self.pnnx_unique_192(v_1512) v_1514 = v_1513.reshape(1, 6, 8, 6, 8, 192) v_1515 = torch.permute(input=v_1514, dims=(0,1,3,2,4,5)) v_1516 = v_1515.reshape(36, 64, 192) v_1517 = self.pnnx_unique_195(v_1516) v_1518 = v_1517.reshape(36, 64, 3, 6, 32) v_1519 = torch.permute(input=v_1518, dims=(2,0,3,1,4)) v_1520, v_1521, v_1522 = torch.unbind(v_1519, dim=0) v_1523 = (v_1520 * 1.767767e-01) v_1524 = torch.transpose(input=v_1521, dim0=-2, dim1=-1) v_1525 = torch.matmul(input=v_1523, other=v_1524) v_1526 = self.pnnx_fold_9496_pnnx_fold_9496 v_1527 = (v_1525 + v_1526) v_1528 = self.pnnx_unique_196(v_1527) v_1529 = torch.matmul(input=v_1528, other=v_1522) v_1530 = torch.transpose(input=v_1529, dim0=1, dim1=2) v_1531 = v_1530.reshape(36, 64, 192) v_1532 = self.pnnx_unique_198(v_1531) v_1533 = v_1532.reshape(1, 6, 6, 8, 8, 192) v_1534 = torch.permute(input=v_1533, dims=(0,1,3,2,4,5)) v_1535 = v_1534.reshape(1, 2304, 192) v_1536 = (v_1512 + v_1535) v_1537 = self.pnnx_unique_200(v_1536) v_1538 = self.pnnx_unique_201(v_1537) v_1539 = self.pnnx_unique_202(v_1538) v_1540 = self.pnnx_unique_204(v_1539) v_1541 = (v_1536 + v_1540) v_1542 = self.pnnx_unique_207(v_1541) v_1543 = v_1542.view(1, 48, 48, 192) v_1544 = torch.roll(input=v_1543, dims=(1,2), shifts=(-4,-4)) v_1545 = v_1544.view(1, 6, 8, 6, 8, 192) v_1546 = torch.permute(input=v_1545, dims=(0,1,3,2,4,5)) v_1547 = v_1546.reshape(36, 64, 192) v_1548 = self.pnnx_unique_210(v_1547) v_1549 = v_1548.reshape(36, 64, 3, 6, 32) v_1550 = torch.permute(input=v_1549, dims=(2,0,3,1,4)) v_1551, v_1552, v_1553 = torch.unbind(v_1550, dim=0) v_1554 = (v_1551 * 1.767767e-01) v_1555 = torch.transpose(input=v_1552, dim0=-2, dim1=-1) v_1556 = torch.matmul(input=v_1554, other=v_1555) v_1557 = self.pnnx_fold_9649_pnnx_fold_9649 v_1558 = (v_1556 + v_1557) v_1559 = v_1558.view(1, 36, 6, 64, 64) v_1560 = self.pnnx_fold_9659_pnnx_fold_9659 v_1561 = (v_1559 + v_1560) v_1562 = v_1561.view(-1, 6, 64, 64) v_1563 = self.pnnx_unique_211(v_1562) v_1564 = torch.matmul(input=v_1563, other=v_1553) v_1565 = torch.transpose(input=v_1564, dim0=1, dim1=2) v_1566 = v_1565.reshape(36, 64, 192) v_1567 = self.pnnx_unique_213(v_1566) v_1568 = v_1567.reshape(1, 6, 6, 8, 8, 192) v_1569 = torch.permute(input=v_1568, dims=(0,1,3,2,4,5)) v_1570 = v_1569.reshape(1, 48, 48, -1) v_1571 = torch.roll(input=v_1570, dims=(1,2), shifts=(4,4)) v_1572 = v_1571.view(1, 2304, 192) v_1573 = (v_1541 + v_1572) v_1574 = self.pnnx_unique_215(v_1573) v_1575 = self.pnnx_unique_216(v_1574) v_1576 = self.pnnx_unique_217(v_1575) v_1577 = self.pnnx_unique_219(v_1576) v_1578 = (v_1573 + v_1577) v_1579 = self.pnnx_unique_221(v_1578) v_1580 = v_1579.reshape(1, 6, 8, 6, 8, 192) v_1581 = torch.permute(input=v_1580, dims=(0,1,3,2,4,5)) v_1582 = v_1581.reshape(36, 64, 192) v_1583 = self.pnnx_unique_224(v_1582) v_1584 = v_1583.reshape(36, 64, 3, 6, 32) v_1585 = torch.permute(input=v_1584, dims=(2,0,3,1,4)) v_1586, v_1587, v_1588 = torch.unbind(v_1585, dim=0) v_1589 = (v_1586 * 1.767767e-01) v_1590 = torch.transpose(input=v_1587, dim0=-2, dim1=-1) v_1591 = torch.matmul(input=v_1589, other=v_1590) v_1592 = self.pnnx_fold_9808_pnnx_fold_9808 v_1593 = (v_1591 + v_1592) v_1594 = self.pnnx_unique_225(v_1593) v_1595 = torch.matmul(input=v_1594, other=v_1588) v_1596 = torch.transpose(input=v_1595, dim0=1, dim1=2) v_1597 = v_1596.reshape(36, 64, 192) v_1598 = self.pnnx_unique_227(v_1597) v_1599 = v_1598.reshape(1, 6, 6, 8, 8, 192) v_1600 = torch.permute(input=v_1599, dims=(0,1,3,2,4,5)) v_1601 = v_1600.reshape(1, 2304, 192) v_1602 = (v_1578 + v_1601) v_1603 = self.pnnx_unique_229(v_1602) v_1604 = self.pnnx_unique_230(v_1603) v_1605 = self.pnnx_unique_231(v_1604) v_1606 = self.pnnx_unique_233(v_1605) v_1607 = (v_1602 + v_1606) v_1608 = self.pnnx_unique_236(v_1607) v_1609 = v_1608.view(1, 48, 48, 192) v_1610 = torch.roll(input=v_1609, dims=(1,2), shifts=(-4,-4)) v_1611 = v_1610.view(1, 6, 8, 6, 8, 192) v_1612 = torch.permute(input=v_1611, dims=(0,1,3,2,4,5)) v_1613 = v_1612.reshape(36, 64, 192) v_1614 = self.pnnx_unique_239(v_1613) v_1615 = v_1614.reshape(36, 64, 3, 6, 32) v_1616 = torch.permute(input=v_1615, dims=(2,0,3,1,4)) v_1617, v_1618, v_1619 = torch.unbind(v_1616, dim=0) v_1620 = (v_1617 * 1.767767e-01) v_1621 = torch.transpose(input=v_1618, dim0=-2, dim1=-1) v_1622 = torch.matmul(input=v_1620, other=v_1621) v_1623 = self.pnnx_fold_9961_pnnx_fold_9961 v_1624 = (v_1622 + v_1623) v_1625 = v_1624.view(1, 36, 6, 64, 64) v_1626 = self.pnnx_fold_9971_pnnx_fold_9971 v_1627 = (v_1625 + v_1626) v_1628 = v_1627.view(-1, 6, 64, 64) v_1629 = self.pnnx_unique_240(v_1628) v_1630 = torch.matmul(input=v_1629, other=v_1619) v_1631 = torch.transpose(input=v_1630, dim0=1, dim1=2) v_1632 = v_1631.reshape(36, 64, 192) v_1633 = self.pnnx_unique_242(v_1632) v_1634 = v_1633.reshape(1, 6, 6, 8, 8, 192) v_1635 = torch.permute(input=v_1634, dims=(0,1,3,2,4,5)) v_1636 = v_1635.reshape(1, 48, 48, -1) v_1637 = torch.roll(input=v_1636, dims=(1,2), shifts=(4,4)) v_1638 = v_1637.view(1, 2304, 192) v_1639 = (v_1607 + v_1638) v_1640 = self.pnnx_unique_244(v_1639) v_1641 = self.pnnx_unique_245(v_1640) v_1642 = self.pnnx_unique_246(v_1641) v_1643 = self.pnnx_unique_248(v_1642) v_1644 = (v_1639 + v_1643) v_1645 = torch.transpose(input=v_1644, dim0=1, dim1=2) v_1646 = v_1645.view(1, 192, 48, 48) v_1647 = self.pnnx_unique_250(v_1646) v_1648 = torch.flatten(input=v_1647, end_dim=-1, start_dim=2) v_1649 = torch.transpose(input=v_1648, dim0=1, dim1=2) v_1650 = (v_1649 + v_1446) v_1651 = self.pnnx_unique_251(v_1650) v_1652 = v_1651.reshape(1, 6, 8, 6, 8, 192) v_1653 = torch.permute(input=v_1652, dims=(0,1,3,2,4,5)) v_1654 = v_1653.reshape(36, 64, 192) v_1655 = self.pnnx_unique_254(v_1654) v_1656 = v_1655.reshape(36, 64, 3, 6, 32) v_1657 = torch.permute(input=v_1656, dims=(2,0,3,1,4)) v_1658, v_1659, v_1660 = torch.unbind(v_1657, dim=0) v_1661 = (v_1658 * 1.767767e-01) v_1662 = torch.transpose(input=v_1659, dim0=-2, dim1=-1) v_1663 = torch.matmul(input=v_1661, other=v_1662) v_1664 = self.pnnx_fold_10154_pnnx_fold_10154 v_1665 = (v_1663 + v_1664) v_1666 = self.pnnx_unique_255(v_1665) v_1667 = torch.matmul(input=v_1666, other=v_1660) v_1668 = torch.transpose(input=v_1667, dim0=1, dim1=2) v_1669 = v_1668.reshape(36, 64, 192) v_1670 = self.pnnx_unique_257(v_1669) v_1671 = v_1670.reshape(1, 6, 6, 8, 8, 192) v_1672 = torch.permute(input=v_1671, dims=(0,1,3,2,4,5)) v_1673 = v_1672.reshape(1, 2304, 192) v_1674 = (v_1650 + v_1673) v_1675 = self.pnnx_unique_259(v_1674) v_1676 = self.pnnx_unique_260(v_1675) v_1677 = self.pnnx_unique_261(v_1676) v_1678 = self.pnnx_unique_263(v_1677) v_1679 = (v_1674 + v_1678) v_1680 = self.pnnx_unique_266(v_1679) v_1681 = v_1680.view(1, 48, 48, 192) v_1682 = torch.roll(input=v_1681, dims=(1,2), shifts=(-4,-4)) v_1683 = v_1682.view(1, 6, 8, 6, 8, 192) v_1684 = torch.permute(input=v_1683, dims=(0,1,3,2,4,5)) v_1685 = v_1684.reshape(36, 64, 192) v_1686 = self.pnnx_unique_269(v_1685) v_1687 = v_1686.reshape(36, 64, 3, 6, 32) v_1688 = torch.permute(input=v_1687, dims=(2,0,3,1,4)) v_1689, v_1690, v_1691 = torch.unbind(v_1688, dim=0) v_1692 = (v_1689 * 1.767767e-01) v_1693 = torch.transpose(input=v_1690, dim0=-2, dim1=-1) v_1694 = torch.matmul(input=v_1692, other=v_1693) v_1695 = self.pnnx_fold_10307_pnnx_fold_10307 v_1696 = (v_1694 + v_1695) v_1697 = v_1696.view(1, 36, 6, 64, 64) v_1698 = self.pnnx_fold_10317_pnnx_fold_10317 v_1699 = (v_1697 + v_1698) v_1700 = v_1699.view(-1, 6, 64, 64) v_1701 = self.pnnx_unique_270(v_1700) v_1702 = torch.matmul(input=v_1701, other=v_1691) v_1703 = torch.transpose(input=v_1702, dim0=1, dim1=2) v_1704 = v_1703.reshape(36, 64, 192) v_1705 = self.pnnx_unique_272(v_1704) v_1706 = v_1705.reshape(1, 6, 6, 8, 8, 192) v_1707 = torch.permute(input=v_1706, dims=(0,1,3,2,4,5)) v_1708 = v_1707.reshape(1, 48, 48, -1) v_1709 = torch.roll(input=v_1708, dims=(1,2), shifts=(4,4)) v_1710 = v_1709.view(1, 2304, 192) v_1711 = (v_1679 + v_1710) v_1712 = self.pnnx_unique_274(v_1711) v_1713 = self.pnnx_unique_275(v_1712) v_1714 = self.pnnx_unique_276(v_1713) v_1715 = self.pnnx_unique_278(v_1714) v_1716 = (v_1711 + v_1715) v_1717 = self.pnnx_unique_280(v_1716) v_1718 = v_1717.reshape(1, 6, 8, 6, 8, 192) v_1719 = torch.permute(input=v_1718, dims=(0,1,3,2,4,5)) v_1720 = v_1719.reshape(36, 64, 192) v_1721 = self.pnnx_unique_283(v_1720) v_1722 = v_1721.reshape(36, 64, 3, 6, 32) v_1723 = torch.permute(input=v_1722, dims=(2,0,3,1,4)) v_1724, v_1725, v_1726 = torch.unbind(v_1723, dim=0) v_1727 = (v_1724 * 1.767767e-01) v_1728 = torch.transpose(input=v_1725, dim0=-2, dim1=-1) v_1729 = torch.matmul(input=v_1727, other=v_1728) v_1730 = self.pnnx_fold_10466_pnnx_fold_10466 v_1731 = (v_1729 + v_1730) v_1732 = self.pnnx_unique_284(v_1731) v_1733 = torch.matmul(input=v_1732, other=v_1726) v_1734 = torch.transpose(input=v_1733, dim0=1, dim1=2) v_1735 = v_1734.reshape(36, 64, 192) v_1736 = self.pnnx_unique_286(v_1735) v_1737 = v_1736.reshape(1, 6, 6, 8, 8, 192) v_1738 = torch.permute(input=v_1737, dims=(0,1,3,2,4,5)) v_1739 = v_1738.reshape(1, 2304, 192) v_1740 = (v_1716 + v_1739) v_1741 = self.pnnx_unique_288(v_1740) v_1742 = self.pnnx_unique_289(v_1741) v_1743 = self.pnnx_unique_290(v_1742) v_1744 = self.pnnx_unique_292(v_1743) v_1745 = (v_1740 + v_1744) v_1746 = self.pnnx_unique_295(v_1745) v_1747 = v_1746.view(1, 48, 48, 192) v_1748 = torch.roll(input=v_1747, dims=(1,2), shifts=(-4,-4)) v_1749 = v_1748.view(1, 6, 8, 6, 8, 192) v_1750 = torch.permute(input=v_1749, dims=(0,1,3,2,4,5)) v_1751 = v_1750.reshape(36, 64, 192) v_1752 = self.pnnx_unique_298(v_1751) v_1753 = v_1752.reshape(36, 64, 3, 6, 32) v_1754 = torch.permute(input=v_1753, dims=(2,0,3,1,4)) v_1755, v_1756, v_1757 = torch.unbind(v_1754, dim=0) v_1758 = (v_1755 * 1.767767e-01) v_1759 = torch.transpose(input=v_1756, dim0=-2, dim1=-1) v_1760 = torch.matmul(input=v_1758, other=v_1759) v_1761 = self.pnnx_fold_10619_pnnx_fold_10619 v_1762 = (v_1760 + v_1761) v_1763 = v_1762.view(1, 36, 6, 64, 64) v_1764 = self.pnnx_fold_10629_pnnx_fold_10629 v_1765 = (v_1763 + v_1764) v_1766 = v_1765.view(-1, 6, 64, 64) v_1767 = self.pnnx_unique_299(v_1766) v_1768 = torch.matmul(input=v_1767, other=v_1757) v_1769 = torch.transpose(input=v_1768, dim0=1, dim1=2) v_1770 = v_1769.reshape(36, 64, 192) v_1771 = self.pnnx_unique_301(v_1770) v_1772 = v_1771.reshape(1, 6, 6, 8, 8, 192) v_1773 = torch.permute(input=v_1772, dims=(0,1,3,2,4,5)) v_1774 = v_1773.reshape(1, 48, 48, -1) v_1775 = torch.roll(input=v_1774, dims=(1,2), shifts=(4,4)) v_1776 = v_1775.view(1, 2304, 192) v_1777 = (v_1745 + v_1776) v_1778 = self.pnnx_unique_303(v_1777) v_1779 = self.pnnx_unique_304(v_1778) v_1780 = self.pnnx_unique_305(v_1779) v_1781 = self.pnnx_unique_307(v_1780) v_1782 = (v_1777 + v_1781) v_1783 = self.pnnx_unique_309(v_1782) v_1784 = v_1783.reshape(1, 6, 8, 6, 8, 192) v_1785 = torch.permute(input=v_1784, dims=(0,1,3,2,4,5)) v_1786 = v_1785.reshape(36, 64, 192) v_1787 = self.pnnx_unique_312(v_1786) v_1788 = v_1787.reshape(36, 64, 3, 6, 32) v_1789 = torch.permute(input=v_1788, dims=(2,0,3,1,4)) v_1790, v_1791, v_1792 = torch.unbind(v_1789, dim=0) v_1793 = (v_1790 * 1.767767e-01) v_1794 = torch.transpose(input=v_1791, dim0=-2, dim1=-1) v_1795 = torch.matmul(input=v_1793, other=v_1794) v_1796 = self.pnnx_fold_10778_pnnx_fold_10778 v_1797 = (v_1795 + v_1796) v_1798 = self.pnnx_unique_313(v_1797) v_1799 = torch.matmul(input=v_1798, other=v_1792) v_1800 = torch.transpose(input=v_1799, dim0=1, dim1=2) v_1801 = v_1800.reshape(36, 64, 192) v_1802 = self.pnnx_unique_315(v_1801) v_1803 = v_1802.reshape(1, 6, 6, 8, 8, 192) v_1804 = torch.permute(input=v_1803, dims=(0,1,3,2,4,5)) v_1805 = v_1804.reshape(1, 2304, 192) v_1806 = (v_1782 + v_1805) v_1807 = self.pnnx_unique_317(v_1806) v_1808 = self.pnnx_unique_318(v_1807) v_1809 = self.pnnx_unique_319(v_1808) v_1810 = self.pnnx_unique_321(v_1809) v_1811 = (v_1806 + v_1810) v_1812 = self.pnnx_unique_324(v_1811) v_1813 = v_1812.view(1, 48, 48, 192) v_1814 = torch.roll(input=v_1813, dims=(1,2), shifts=(-4,-4)) v_1815 = v_1814.view(1, 6, 8, 6, 8, 192) v_1816 = torch.permute(input=v_1815, dims=(0,1,3,2,4,5)) v_1817 = v_1816.reshape(36, 64, 192) v_1818 = self.pnnx_unique_327(v_1817) v_1819 = v_1818.reshape(36, 64, 3, 6, 32) v_1820 = torch.permute(input=v_1819, dims=(2,0,3,1,4)) v_1821, v_1822, v_1823 = torch.unbind(v_1820, dim=0) v_1824 = (v_1821 * 1.767767e-01) v_1825 = torch.transpose(input=v_1822, dim0=-2, dim1=-1) v_1826 = torch.matmul(input=v_1824, other=v_1825) v_1827 = self.pnnx_fold_10931_pnnx_fold_10931 v_1828 = (v_1826 + v_1827) v_1829 = v_1828.view(1, 36, 6, 64, 64) v_1830 = self.pnnx_fold_10941_pnnx_fold_10941 v_1831 = (v_1829 + v_1830) v_1832 = v_1831.view(-1, 6, 64, 64) v_1833 = self.pnnx_unique_328(v_1832) v_1834 = torch.matmul(input=v_1833, other=v_1823) v_1835 = torch.transpose(input=v_1834, dim0=1, dim1=2) v_1836 = v_1835.reshape(36, 64, 192) v_1837 = self.pnnx_unique_330(v_1836) v_1838 = v_1837.reshape(1, 6, 6, 8, 8, 192) v_1839 = torch.permute(input=v_1838, dims=(0,1,3,2,4,5)) v_1840 = v_1839.reshape(1, 48, 48, -1) v_1841 = torch.roll(input=v_1840, dims=(1,2), shifts=(4,4)) v_1842 = v_1841.view(1, 2304, 192) v_1843 = (v_1811 + v_1842) v_1844 = self.pnnx_unique_332(v_1843) v_1845 = self.pnnx_unique_333(v_1844) v_1846 = self.pnnx_unique_334(v_1845) v_1847 = self.pnnx_unique_336(v_1846) v_1848 = (v_1843 + v_1847) v_1849 = torch.transpose(input=v_1848, dim0=1, dim1=2) v_1850 = v_1849.view(1, 192, 48, 48) v_1851 = self.pnnx_unique_338(v_1850) v_1852 = torch.flatten(input=v_1851, end_dim=-1, start_dim=2) v_1853 = torch.transpose(input=v_1852, dim0=1, dim1=2) v_1854 = (v_1853 + v_1650) v_1855 = self.pnnx_unique_339(v_1854) v_1856 = v_1855.reshape(1, 6, 8, 6, 8, 192) v_1857 = torch.permute(input=v_1856, dims=(0,1,3,2,4,5)) v_1858 = v_1857.reshape(36, 64, 192) v_1859 = self.pnnx_unique_342(v_1858) v_1860 = v_1859.reshape(36, 64, 3, 6, 32) v_1861 = torch.permute(input=v_1860, dims=(2,0,3,1,4)) v_1862, v_1863, v_1864 = torch.unbind(v_1861, dim=0) v_1865 = (v_1862 * 1.767767e-01) v_1866 = torch.transpose(input=v_1863, dim0=-2, dim1=-1) v_1867 = torch.matmul(input=v_1865, other=v_1866) v_1868 = self.pnnx_fold_11124_pnnx_fold_11124 v_1869 = (v_1867 + v_1868) v_1870 = self.pnnx_unique_343(v_1869) v_1871 = torch.matmul(input=v_1870, other=v_1864) v_1872 = torch.transpose(input=v_1871, dim0=1, dim1=2) v_1873 = v_1872.reshape(36, 64, 192) v_1874 = self.pnnx_unique_345(v_1873) v_1875 = v_1874.reshape(1, 6, 6, 8, 8, 192) v_1876 = torch.permute(input=v_1875, dims=(0,1,3,2,4,5)) v_1877 = v_1876.reshape(1, 2304, 192) v_1878 = (v_1854 + v_1877) v_1879 = self.pnnx_unique_347(v_1878) v_1880 = self.pnnx_unique_348(v_1879) v_1881 = self.pnnx_unique_349(v_1880) v_1882 = self.pnnx_unique_351(v_1881) v_1883 = (v_1878 + v_1882) v_1884 = self.pnnx_unique_354(v_1883) v_1885 = v_1884.view(1, 48, 48, 192) v_1886 = torch.roll(input=v_1885, dims=(1,2), shifts=(-4,-4)) v_1887 = v_1886.view(1, 6, 8, 6, 8, 192) v_1888 = torch.permute(input=v_1887, dims=(0,1,3,2,4,5)) v_1889 = v_1888.reshape(36, 64, 192) v_1890 = self.pnnx_unique_357(v_1889) v_1891 = v_1890.reshape(36, 64, 3, 6, 32) v_1892 = torch.permute(input=v_1891, dims=(2,0,3,1,4)) v_1893, v_1894, v_1895 = torch.unbind(v_1892, dim=0) v_1896 = (v_1893 * 1.767767e-01) v_1897 = torch.transpose(input=v_1894, dim0=-2, dim1=-1) v_1898 = torch.matmul(input=v_1896, other=v_1897) v_1899 = self.pnnx_fold_11277_pnnx_fold_11277 v_1900 = (v_1898 + v_1899) v_1901 = v_1900.view(1, 36, 6, 64, 64) v_1902 = self.pnnx_fold_11287_pnnx_fold_11287 v_1903 = (v_1901 + v_1902) v_1904 = v_1903.view(-1, 6, 64, 64) v_1905 = self.pnnx_unique_358(v_1904) v_1906 = torch.matmul(input=v_1905, other=v_1895) v_1907 = torch.transpose(input=v_1906, dim0=1, dim1=2) v_1908 = v_1907.reshape(36, 64, 192) v_1909 = self.pnnx_unique_360(v_1908) v_1910 = v_1909.reshape(1, 6, 6, 8, 8, 192) v_1911 = torch.permute(input=v_1910, dims=(0,1,3,2,4,5)) v_1912 = v_1911.reshape(1, 48, 48, -1) v_1913 = torch.roll(input=v_1912, dims=(1,2), shifts=(4,4)) v_1914 = v_1913.view(1, 2304, 192) v_1915 = (v_1883 + v_1914) v_1916 = self.pnnx_unique_362(v_1915) v_1917 = self.pnnx_unique_363(v_1916) v_1918 = self.pnnx_unique_364(v_1917) v_1919 = self.pnnx_unique_366(v_1918) v_1920 = (v_1915 + v_1919) v_1921 = self.pnnx_unique_368(v_1920) v_1922 = v_1921.reshape(1, 6, 8, 6, 8, 192) v_1923 = torch.permute(input=v_1922, dims=(0,1,3,2,4,5)) v_1924 = v_1923.reshape(36, 64, 192) v_1925 = self.pnnx_unique_371(v_1924) v_1926 = v_1925.reshape(36, 64, 3, 6, 32) v_1927 = torch.permute(input=v_1926, dims=(2,0,3,1,4)) v_1928, v_1929, v_1930 = torch.unbind(v_1927, dim=0) v_1931 = (v_1928 * 1.767767e-01) v_1932 = torch.transpose(input=v_1929, dim0=-2, dim1=-1) v_1933 = torch.matmul(input=v_1931, other=v_1932) v_1934 = self.pnnx_fold_11436_pnnx_fold_11436 v_1935 = (v_1933 + v_1934) v_1936 = self.pnnx_unique_372(v_1935) v_1937 = torch.matmul(input=v_1936, other=v_1930) v_1938 = torch.transpose(input=v_1937, dim0=1, dim1=2) v_1939 = v_1938.reshape(36, 64, 192) v_1940 = self.pnnx_unique_374(v_1939) v_1941 = v_1940.reshape(1, 6, 6, 8, 8, 192) v_1942 = torch.permute(input=v_1941, dims=(0,1,3,2,4,5)) v_1943 = v_1942.reshape(1, 2304, 192) v_1944 = (v_1920 + v_1943) v_1945 = self.pnnx_unique_376(v_1944) v_1946 = self.pnnx_unique_377(v_1945) v_1947 = self.pnnx_unique_378(v_1946) v_1948 = self.pnnx_unique_380(v_1947) v_1949 = (v_1944 + v_1948) v_1950 = self.pnnx_unique_383(v_1949) v_1951 = v_1950.view(1, 48, 48, 192) v_1952 = torch.roll(input=v_1951, dims=(1,2), shifts=(-4,-4)) v_1953 = v_1952.view(1, 6, 8, 6, 8, 192) v_1954 = torch.permute(input=v_1953, dims=(0,1,3,2,4,5)) v_1955 = v_1954.reshape(36, 64, 192) v_1956 = self.pnnx_unique_386(v_1955) v_1957 = v_1956.reshape(36, 64, 3, 6, 32) v_1958 = torch.permute(input=v_1957, dims=(2,0,3,1,4)) v_1959, v_1960, v_1961 = torch.unbind(v_1958, dim=0) v_1962 = (v_1959 * 1.767767e-01) v_1963 = torch.transpose(input=v_1960, dim0=-2, dim1=-1) v_1964 = torch.matmul(input=v_1962, other=v_1963) v_1965 = self.pnnx_fold_11589_pnnx_fold_11589 v_1966 = (v_1964 + v_1965) v_1967 = v_1966.view(1, 36, 6, 64, 64) v_1968 = self.pnnx_fold_11599_pnnx_fold_11599 v_1969 = (v_1967 + v_1968) v_1970 = v_1969.view(-1, 6, 64, 64) v_1971 = self.pnnx_unique_387(v_1970) v_1972 = torch.matmul(input=v_1971, other=v_1961) v_1973 = torch.transpose(input=v_1972, dim0=1, dim1=2) v_1974 = v_1973.reshape(36, 64, 192) v_1975 = self.pnnx_unique_389(v_1974) v_1976 = v_1975.reshape(1, 6, 6, 8, 8, 192) v_1977 = torch.permute(input=v_1976, dims=(0,1,3,2,4,5)) v_1978 = v_1977.reshape(1, 48, 48, -1) v_1979 = torch.roll(input=v_1978, dims=(1,2), shifts=(4,4)) v_1980 = v_1979.view(1, 2304, 192) v_1981 = (v_1949 + v_1980) v_1982 = self.pnnx_unique_391(v_1981) v_1983 = self.pnnx_unique_392(v_1982) v_1984 = self.pnnx_unique_393(v_1983) v_1985 = self.pnnx_unique_395(v_1984) v_1986 = (v_1981 + v_1985) v_1987 = self.pnnx_unique_397(v_1986) v_1988 = v_1987.reshape(1, 6, 8, 6, 8, 192) v_1989 = torch.permute(input=v_1988, dims=(0,1,3,2,4,5)) v_1990 = v_1989.reshape(36, 64, 192) v_1991 = self.pnnx_unique_400(v_1990) v_1992 = v_1991.reshape(36, 64, 3, 6, 32) v_1993 = torch.permute(input=v_1992, dims=(2,0,3,1,4)) v_1994, v_1995, v_1996 = torch.unbind(v_1993, dim=0) v_1997 = (v_1994 * 1.767767e-01) v_1998 = torch.transpose(input=v_1995, dim0=-2, dim1=-1) v_1999 = torch.matmul(input=v_1997, other=v_1998) v_2000 = self.pnnx_fold_11748_pnnx_fold_11748 v_2001 = (v_1999 + v_2000) v_2002 = self.pnnx_unique_401(v_2001) v_2003 = torch.matmul(input=v_2002, other=v_1996) v_2004 = torch.transpose(input=v_2003, dim0=1, dim1=2) v_2005 = v_2004.reshape(36, 64, 192) v_2006 = self.pnnx_unique_403(v_2005) v_2007 = v_2006.reshape(1, 6, 6, 8, 8, 192) v_2008 = torch.permute(input=v_2007, dims=(0,1,3,2,4,5)) v_2009 = v_2008.reshape(1, 2304, 192) v_2010 = (v_1986 + v_2009) v_2011 = self.pnnx_unique_405(v_2010) v_2012 = self.pnnx_unique_406(v_2011) v_2013 = self.pnnx_unique_407(v_2012) v_2014 = self.pnnx_unique_409(v_2013) v_2015 = (v_2010 + v_2014) v_2016 = self.pnnx_unique_412(v_2015) v_2017 = v_2016.view(1, 48, 48, 192) v_2018 = torch.roll(input=v_2017, dims=(1,2), shifts=(-4,-4)) v_2019 = v_2018.view(1, 6, 8, 6, 8, 192) v_2020 = torch.permute(input=v_2019, dims=(0,1,3,2,4,5)) v_2021 = v_2020.reshape(36, 64, 192) v_2022 = self.pnnx_unique_415(v_2021) v_2023 = v_2022.reshape(36, 64, 3, 6, 32) v_2024 = torch.permute(input=v_2023, dims=(2,0,3,1,4)) v_2025, v_2026, v_2027 = torch.unbind(v_2024, dim=0) v_2028 = (v_2025 * 1.767767e-01) v_2029 = torch.transpose(input=v_2026, dim0=-2, dim1=-1) v_2030 = torch.matmul(input=v_2028, other=v_2029) v_2031 = self.pnnx_fold_11901_pnnx_fold_11901 v_2032 = (v_2030 + v_2031) v_2033 = v_2032.view(1, 36, 6, 64, 64) v_2034 = self.pnnx_fold_11911_pnnx_fold_11911 v_2035 = (v_2033 + v_2034) v_2036 = v_2035.view(-1, 6, 64, 64) v_2037 = self.pnnx_unique_416(v_2036) v_2038 = torch.matmul(input=v_2037, other=v_2027) v_2039 = torch.transpose(input=v_2038, dim0=1, dim1=2) v_2040 = v_2039.reshape(36, 64, 192) v_2041 = self.pnnx_unique_418(v_2040) v_2042 = v_2041.reshape(1, 6, 6, 8, 8, 192) v_2043 = torch.permute(input=v_2042, dims=(0,1,3,2,4,5)) v_2044 = v_2043.reshape(1, 48, 48, -1) v_2045 = torch.roll(input=v_2044, dims=(1,2), shifts=(4,4)) v_2046 = v_2045.view(1, 2304, 192) v_2047 = (v_2015 + v_2046) v_2048 = self.pnnx_unique_420(v_2047) v_2049 = self.pnnx_unique_421(v_2048) v_2050 = self.pnnx_unique_422(v_2049) v_2051 = self.pnnx_unique_424(v_2050) v_2052 = (v_2047 + v_2051) v_2053 = torch.transpose(input=v_2052, dim0=1, dim1=2) v_2054 = v_2053.view(1, 192, 48, 48) v_2055 = self.pnnx_unique_426(v_2054) v_2056 = torch.flatten(input=v_2055, end_dim=-1, start_dim=2) v_2057 = torch.transpose(input=v_2056, dim0=1, dim1=2) v_2058 = (v_2057 + v_1854) v_2059 = self.pnnx_unique_427(v_2058) v_2060 = v_2059.reshape(1, 6, 8, 6, 8, 192) v_2061 = torch.permute(input=v_2060, dims=(0,1,3,2,4,5)) v_2062 = v_2061.reshape(36, 64, 192) v_2063 = self.pnnx_unique_430(v_2062) v_2064 = v_2063.reshape(36, 64, 3, 6, 32) v_2065 = torch.permute(input=v_2064, dims=(2,0,3,1,4)) v_2066, v_2067, v_2068 = torch.unbind(v_2065, dim=0) v_2069 = (v_2066 * 1.767767e-01) v_2070 = torch.transpose(input=v_2067, dim0=-2, dim1=-1) v_2071 = torch.matmul(input=v_2069, other=v_2070) v_2072 = self.pnnx_fold_12094_pnnx_fold_12094 v_2073 = (v_2071 + v_2072) v_2074 = self.pnnx_unique_431(v_2073) v_2075 = torch.matmul(input=v_2074, other=v_2068) v_2076 = torch.transpose(input=v_2075, dim0=1, dim1=2) v_2077 = v_2076.reshape(36, 64, 192) v_2078 = self.pnnx_unique_433(v_2077) v_2079 = v_2078.reshape(1, 6, 6, 8, 8, 192) v_2080 = torch.permute(input=v_2079, dims=(0,1,3,2,4,5)) v_2081 = v_2080.reshape(1, 2304, 192) v_2082 = (v_2058 + v_2081) v_2083 = self.pnnx_unique_435(v_2082) v_2084 = self.pnnx_unique_436(v_2083) v_2085 = self.pnnx_unique_437(v_2084) v_2086 = self.pnnx_unique_439(v_2085) v_2087 = (v_2082 + v_2086) v_2088 = self.pnnx_unique_442(v_2087) v_2089 = v_2088.view(1, 48, 48, 192) v_2090 = torch.roll(input=v_2089, dims=(1,2), shifts=(-4,-4)) v_2091 = v_2090.view(1, 6, 8, 6, 8, 192) v_2092 = torch.permute(input=v_2091, dims=(0,1,3,2,4,5)) v_2093 = v_2092.reshape(36, 64, 192) v_2094 = self.pnnx_unique_445(v_2093) v_2095 = v_2094.reshape(36, 64, 3, 6, 32) v_2096 = torch.permute(input=v_2095, dims=(2,0,3,1,4)) v_2097, v_2098, v_2099 = torch.unbind(v_2096, dim=0) v_2100 = (v_2097 * 1.767767e-01) v_2101 = torch.transpose(input=v_2098, dim0=-2, dim1=-1) v_2102 = torch.matmul(input=v_2100, other=v_2101) v_2103 = self.pnnx_fold_12247_pnnx_fold_12247 v_2104 = (v_2102 + v_2103) v_2105 = v_2104.view(1, 36, 6, 64, 64) v_2106 = self.pnnx_fold_12257_pnnx_fold_12257 v_2107 = (v_2105 + v_2106) v_2108 = v_2107.view(-1, 6, 64, 64) v_2109 = self.pnnx_unique_446(v_2108) v_2110 = torch.matmul(input=v_2109, other=v_2099) v_2111 = torch.transpose(input=v_2110, dim0=1, dim1=2) v_2112 = v_2111.reshape(36, 64, 192) v_2113 = self.pnnx_unique_448(v_2112) v_2114 = v_2113.reshape(1, 6, 6, 8, 8, 192) v_2115 = torch.permute(input=v_2114, dims=(0,1,3,2,4,5)) v_2116 = v_2115.reshape(1, 48, 48, -1) v_2117 = torch.roll(input=v_2116, dims=(1,2), shifts=(4,4)) v_2118 = v_2117.view(1, 2304, 192) v_2119 = (v_2087 + v_2118) v_2120 = self.pnnx_unique_450(v_2119) v_2121 = self.pnnx_unique_451(v_2120) v_2122 = self.pnnx_unique_452(v_2121) v_2123 = self.pnnx_unique_454(v_2122) v_2124 = (v_2119 + v_2123) v_2125 = self.pnnx_unique_456(v_2124) v_2126 = v_2125.reshape(1, 6, 8, 6, 8, 192) v_2127 = torch.permute(input=v_2126, dims=(0,1,3,2,4,5)) v_2128 = v_2127.reshape(36, 64, 192) v_2129 = self.pnnx_unique_459(v_2128) v_2130 = v_2129.reshape(36, 64, 3, 6, 32) v_2131 = torch.permute(input=v_2130, dims=(2,0,3,1,4)) v_2132, v_2133, v_2134 = torch.unbind(v_2131, dim=0) v_2135 = (v_2132 * 1.767767e-01) v_2136 = torch.transpose(input=v_2133, dim0=-2, dim1=-1) v_2137 = torch.matmul(input=v_2135, other=v_2136) v_2138 = self.pnnx_fold_12406_pnnx_fold_12406 v_2139 = (v_2137 + v_2138) v_2140 = self.pnnx_unique_460(v_2139) v_2141 = torch.matmul(input=v_2140, other=v_2134) v_2142 = torch.transpose(input=v_2141, dim0=1, dim1=2) v_2143 = v_2142.reshape(36, 64, 192) v_2144 = self.pnnx_unique_462(v_2143) v_2145 = v_2144.reshape(1, 6, 6, 8, 8, 192) v_2146 = torch.permute(input=v_2145, dims=(0,1,3,2,4,5)) v_2147 = v_2146.reshape(1, 2304, 192) v_2148 = (v_2124 + v_2147) v_2149 = self.pnnx_unique_464(v_2148) v_2150 = self.pnnx_unique_465(v_2149) v_2151 = self.pnnx_unique_466(v_2150) v_2152 = self.pnnx_unique_468(v_2151) v_2153 = (v_2148 + v_2152) v_2154 = self.pnnx_unique_471(v_2153) v_2155 = v_2154.view(1, 48, 48, 192) v_2156 = torch.roll(input=v_2155, dims=(1,2), shifts=(-4,-4)) v_2157 = v_2156.view(1, 6, 8, 6, 8, 192) v_2158 = torch.permute(input=v_2157, dims=(0,1,3,2,4,5)) v_2159 = v_2158.reshape(36, 64, 192) v_2160 = self.pnnx_unique_474(v_2159) v_2161 = v_2160.reshape(36, 64, 3, 6, 32) v_2162 = torch.permute(input=v_2161, dims=(2,0,3,1,4)) v_2163, v_2164, v_2165 = torch.unbind(v_2162, dim=0) v_2166 = (v_2163 * 1.767767e-01) v_2167 = torch.transpose(input=v_2164, dim0=-2, dim1=-1) v_2168 = torch.matmul(input=v_2166, other=v_2167) v_2169 = self.pnnx_fold_12559_pnnx_fold_12559 v_2170 = (v_2168 + v_2169) v_2171 = v_2170.view(1, 36, 6, 64, 64) v_2172 = self.pnnx_fold_12569_pnnx_fold_12569 v_2173 = (v_2171 + v_2172) v_2174 = v_2173.view(-1, 6, 64, 64) v_2175 = self.pnnx_unique_475(v_2174) v_2176 = torch.matmul(input=v_2175, other=v_2165) v_2177 = torch.transpose(input=v_2176, dim0=1, dim1=2) v_2178 = v_2177.reshape(36, 64, 192) v_2179 = self.pnnx_unique_477(v_2178) v_2180 = v_2179.reshape(1, 6, 6, 8, 8, 192) v_2181 = torch.permute(input=v_2180, dims=(0,1,3,2,4,5)) v_2182 = v_2181.reshape(1, 48, 48, -1) v_2183 = torch.roll(input=v_2182, dims=(1,2), shifts=(4,4)) v_2184 = v_2183.view(1, 2304, 192) v_2185 = (v_2153 + v_2184) v_2186 = self.pnnx_unique_479(v_2185) v_2187 = self.pnnx_unique_480(v_2186) v_2188 = self.pnnx_unique_481(v_2187) v_2189 = self.pnnx_unique_483(v_2188) v_2190 = (v_2185 + v_2189) v_2191 = self.pnnx_unique_485(v_2190) v_2192 = v_2191.reshape(1, 6, 8, 6, 8, 192) v_2193 = torch.permute(input=v_2192, dims=(0,1,3,2,4,5)) v_2194 = v_2193.reshape(36, 64, 192) v_2195 = self.pnnx_unique_488(v_2194) v_2196 = v_2195.reshape(36, 64, 3, 6, 32) v_2197 = torch.permute(input=v_2196, dims=(2,0,3,1,4)) v_2198, v_2199, v_2200 = torch.unbind(v_2197, dim=0) v_2201 = (v_2198 * 1.767767e-01) v_2202 = torch.transpose(input=v_2199, dim0=-2, dim1=-1) v_2203 = torch.matmul(input=v_2201, other=v_2202) v_2204 = self.pnnx_fold_12718_pnnx_fold_12718 v_2205 = (v_2203 + v_2204) v_2206 = self.pnnx_unique_489(v_2205) v_2207 = torch.matmul(input=v_2206, other=v_2200) v_2208 = torch.transpose(input=v_2207, dim0=1, dim1=2) v_2209 = v_2208.reshape(36, 64, 192) v_2210 = self.pnnx_unique_491(v_2209) v_2211 = v_2210.reshape(1, 6, 6, 8, 8, 192) v_2212 = torch.permute(input=v_2211, dims=(0,1,3,2,4,5)) v_2213 = v_2212.reshape(1, 2304, 192) v_2214 = (v_2190 + v_2213) v_2215 = self.pnnx_unique_493(v_2214) v_2216 = self.pnnx_unique_494(v_2215) v_2217 = self.pnnx_unique_495(v_2216) v_2218 = self.pnnx_unique_497(v_2217) v_2219 = (v_2214 + v_2218) v_2220 = self.pnnx_unique_500(v_2219) v_2221 = v_2220.view(1, 48, 48, 192) v_2222 = torch.roll(input=v_2221, dims=(1,2), shifts=(-4,-4)) v_2223 = v_2222.view(1, 6, 8, 6, 8, 192) v_2224 = torch.permute(input=v_2223, dims=(0,1,3,2,4,5)) v_2225 = v_2224.reshape(36, 64, 192) v_2226 = self.pnnx_unique_503(v_2225) v_2227 = v_2226.reshape(36, 64, 3, 6, 32) v_2228 = torch.permute(input=v_2227, dims=(2,0,3,1,4)) v_2229, v_2230, v_2231 = torch.unbind(v_2228, dim=0) v_2232 = (v_2229 * 1.767767e-01) v_2233 = torch.transpose(input=v_2230, dim0=-2, dim1=-1) v_2234 = torch.matmul(input=v_2232, other=v_2233) v_2235 = self.pnnx_fold_12871_pnnx_fold_12871 v_2236 = (v_2234 + v_2235) v_2237 = v_2236.view(1, 36, 6, 64, 64) v_2238 = self.pnnx_fold_12881_pnnx_fold_12881 v_2239 = (v_2237 + v_2238) v_2240 = v_2239.view(-1, 6, 64, 64) v_2241 = self.pnnx_unique_504(v_2240) v_2242 = torch.matmul(input=v_2241, other=v_2231) v_2243 = torch.transpose(input=v_2242, dim0=1, dim1=2) v_2244 = v_2243.reshape(36, 64, 192) v_2245 = self.pnnx_unique_506(v_2244) v_2246 = v_2245.reshape(1, 6, 6, 8, 8, 192) v_2247 = torch.permute(input=v_2246, dims=(0,1,3,2,4,5)) v_2248 = v_2247.reshape(1, 48, 48, -1) v_2249 = torch.roll(input=v_2248, dims=(1,2), shifts=(4,4)) v_2250 = v_2249.view(1, 2304, 192) v_2251 = (v_2219 + v_2250) v_2252 = self.pnnx_unique_508(v_2251) v_2253 = self.pnnx_unique_509(v_2252) v_2254 = self.pnnx_unique_510(v_2253) v_2255 = self.pnnx_unique_512(v_2254) v_2256 = (v_2251 + v_2255) v_2257 = torch.transpose(input=v_2256, dim0=1, dim1=2) v_2258 = v_2257.view(1, 192, 48, 48) v_2259 = self.pnnx_unique_514(v_2258) v_2260 = torch.flatten(input=v_2259, end_dim=-1, start_dim=2) v_2261 = torch.transpose(input=v_2260, dim0=1, dim1=2) v_2262 = (v_2261 + v_2058) v_2263 = self.pnnx_unique_515(v_2262) v_2264 = v_2263.reshape(1, 6, 8, 6, 8, 192) v_2265 = torch.permute(input=v_2264, dims=(0,1,3,2,4,5)) v_2266 = v_2265.reshape(36, 64, 192) v_2267 = self.pnnx_unique_518(v_2266) v_2268 = v_2267.reshape(36, 64, 3, 6, 32) v_2269 = torch.permute(input=v_2268, dims=(2,0,3,1,4)) v_2270, v_2271, v_2272 = torch.unbind(v_2269, dim=0) v_2273 = (v_2270 * 1.767767e-01) v_2274 = torch.transpose(input=v_2271, dim0=-2, dim1=-1) v_2275 = torch.matmul(input=v_2273, other=v_2274) v_2276 = self.pnnx_fold_13064_pnnx_fold_13064 v_2277 = (v_2275 + v_2276) v_2278 = self.pnnx_unique_519(v_2277) v_2279 = torch.matmul(input=v_2278, other=v_2272) v_2280 = torch.transpose(input=v_2279, dim0=1, dim1=2) v_2281 = v_2280.reshape(36, 64, 192) v_2282 = self.pnnx_unique_521(v_2281) v_2283 = v_2282.reshape(1, 6, 6, 8, 8, 192) v_2284 = torch.permute(input=v_2283, dims=(0,1,3,2,4,5)) v_2285 = v_2284.reshape(1, 2304, 192) v_2286 = (v_2262 + v_2285) v_2287 = self.pnnx_unique_523(v_2286) v_2288 = self.pnnx_unique_524(v_2287) v_2289 = self.pnnx_unique_525(v_2288) v_2290 = self.pnnx_unique_527(v_2289) v_2291 = (v_2286 + v_2290) v_2292 = self.pnnx_unique_530(v_2291) v_2293 = v_2292.view(1, 48, 48, 192) v_2294 = torch.roll(input=v_2293, dims=(1,2), shifts=(-4,-4)) v_2295 = v_2294.view(1, 6, 8, 6, 8, 192) v_2296 = torch.permute(input=v_2295, dims=(0,1,3,2,4,5)) v_2297 = v_2296.reshape(36, 64, 192) v_2298 = self.pnnx_unique_533(v_2297) v_2299 = v_2298.reshape(36, 64, 3, 6, 32) v_2300 = torch.permute(input=v_2299, dims=(2,0,3,1,4)) v_2301, v_2302, v_2303 = torch.unbind(v_2300, dim=0) v_2304 = (v_2301 * 1.767767e-01) v_2305 = torch.transpose(input=v_2302, dim0=-2, dim1=-1) v_2306 = torch.matmul(input=v_2304, other=v_2305) v_2307 = self.pnnx_fold_13217_pnnx_fold_13217 v_2308 = (v_2306 + v_2307) v_2309 = v_2308.view(1, 36, 6, 64, 64) v_2310 = self.pnnx_fold_13227_pnnx_fold_13227 v_2311 = (v_2309 + v_2310) v_2312 = v_2311.view(-1, 6, 64, 64) v_2313 = self.pnnx_unique_534(v_2312) v_2314 = torch.matmul(input=v_2313, other=v_2303) v_2315 = torch.transpose(input=v_2314, dim0=1, dim1=2) v_2316 = v_2315.reshape(36, 64, 192) v_2317 = self.pnnx_unique_536(v_2316) v_2318 = v_2317.reshape(1, 6, 6, 8, 8, 192) v_2319 = torch.permute(input=v_2318, dims=(0,1,3,2,4,5)) v_2320 = v_2319.reshape(1, 48, 48, -1) v_2321 = torch.roll(input=v_2320, dims=(1,2), shifts=(4,4)) v_2322 = v_2321.view(1, 2304, 192) v_2323 = (v_2291 + v_2322) v_2324 = self.pnnx_unique_538(v_2323) v_2325 = self.pnnx_unique_539(v_2324) v_2326 = self.pnnx_unique_540(v_2325) v_2327 = self.pnnx_unique_542(v_2326) v_2328 = (v_2323 + v_2327) v_2329 = self.pnnx_unique_544(v_2328) v_2330 = v_2329.reshape(1, 6, 8, 6, 8, 192) v_2331 = torch.permute(input=v_2330, dims=(0,1,3,2,4,5)) v_2332 = v_2331.reshape(36, 64, 192) v_2333 = self.pnnx_unique_547(v_2332) v_2334 = v_2333.reshape(36, 64, 3, 6, 32) v_2335 = torch.permute(input=v_2334, dims=(2,0,3,1,4)) v_2336, v_2337, v_2338 = torch.unbind(v_2335, dim=0) v_2339 = (v_2336 * 1.767767e-01) v_2340 = torch.transpose(input=v_2337, dim0=-2, dim1=-1) v_2341 = torch.matmul(input=v_2339, other=v_2340) v_2342 = self.pnnx_fold_13376_pnnx_fold_13376 v_2343 = (v_2341 + v_2342) v_2344 = self.pnnx_unique_548(v_2343) v_2345 = torch.matmul(input=v_2344, other=v_2338) v_2346 = torch.transpose(input=v_2345, dim0=1, dim1=2) v_2347 = v_2346.reshape(36, 64, 192) v_2348 = self.pnnx_unique_550(v_2347) v_2349 = v_2348.reshape(1, 6, 6, 8, 8, 192) v_2350 = torch.permute(input=v_2349, dims=(0,1,3,2,4,5)) v_2351 = v_2350.reshape(1, 2304, 192) v_2352 = (v_2328 + v_2351) v_2353 = self.pnnx_unique_552(v_2352) v_2354 = self.pnnx_unique_553(v_2353) v_2355 = self.pnnx_unique_554(v_2354) v_2356 = self.pnnx_unique_556(v_2355) v_2357 = (v_2352 + v_2356) v_2358 = self.pnnx_unique_559(v_2357) v_2359 = v_2358.view(1, 48, 48, 192) v_2360 = torch.roll(input=v_2359, dims=(1,2), shifts=(-4,-4)) v_2361 = v_2360.view(1, 6, 8, 6, 8, 192) v_2362 = torch.permute(input=v_2361, dims=(0,1,3,2,4,5)) v_2363 = v_2362.reshape(36, 64, 192) v_2364 = self.pnnx_unique_562(v_2363) v_2365 = v_2364.reshape(36, 64, 3, 6, 32) v_2366 = torch.permute(input=v_2365, dims=(2,0,3,1,4)) v_2367, v_2368, v_2369 = torch.unbind(v_2366, dim=0) v_2370 = (v_2367 * 1.767767e-01) v_2371 = torch.transpose(input=v_2368, dim0=-2, dim1=-1) v_2372 = torch.matmul(input=v_2370, other=v_2371) v_2373 = self.pnnx_fold_13529_pnnx_fold_13529 v_2374 = (v_2372 + v_2373) v_2375 = v_2374.view(1, 36, 6, 64, 64) v_2376 = self.pnnx_fold_13539_pnnx_fold_13539 v_2377 = (v_2375 + v_2376) v_2378 = v_2377.view(-1, 6, 64, 64) v_2379 = self.pnnx_unique_563(v_2378) v_2380 = torch.matmul(input=v_2379, other=v_2369) v_2381 = torch.transpose(input=v_2380, dim0=1, dim1=2) v_2382 = v_2381.reshape(36, 64, 192) v_2383 = self.pnnx_unique_565(v_2382) v_2384 = v_2383.reshape(1, 6, 6, 8, 8, 192) v_2385 = torch.permute(input=v_2384, dims=(0,1,3,2,4,5)) v_2386 = v_2385.reshape(1, 48, 48, -1) v_2387 = torch.roll(input=v_2386, dims=(1,2), shifts=(4,4)) v_2388 = v_2387.view(1, 2304, 192) v_2389 = (v_2357 + v_2388) v_2390 = self.pnnx_unique_567(v_2389) v_2391 = self.pnnx_unique_568(v_2390) v_2392 = self.pnnx_unique_569(v_2391) v_2393 = self.pnnx_unique_571(v_2392) v_2394 = (v_2389 + v_2393) v_2395 = self.pnnx_unique_573(v_2394) v_2396 = v_2395.reshape(1, 6, 8, 6, 8, 192) v_2397 = torch.permute(input=v_2396, dims=(0,1,3,2,4,5)) v_2398 = v_2397.reshape(36, 64, 192) v_2399 = self.pnnx_unique_576(v_2398) v_2400 = v_2399.reshape(36, 64, 3, 6, 32) v_2401 = torch.permute(input=v_2400, dims=(2,0,3,1,4)) v_2402, v_2403, v_2404 = torch.unbind(v_2401, dim=0) v_2405 = (v_2402 * 1.767767e-01) v_2406 = torch.transpose(input=v_2403, dim0=-2, dim1=-1) v_2407 = torch.matmul(input=v_2405, other=v_2406) v_2408 = self.pnnx_fold_13688_pnnx_fold_13688 v_2409 = (v_2407 + v_2408) v_2410 = self.pnnx_unique_577(v_2409) v_2411 = torch.matmul(input=v_2410, other=v_2404) v_2412 = torch.transpose(input=v_2411, dim0=1, dim1=2) v_2413 = v_2412.reshape(36, 64, 192) v_2414 = self.pnnx_unique_579(v_2413) v_2415 = v_2414.reshape(1, 6, 6, 8, 8, 192) v_2416 = torch.permute(input=v_2415, dims=(0,1,3,2,4,5)) v_2417 = v_2416.reshape(1, 2304, 192) v_2418 = (v_2394 + v_2417) v_2419 = self.pnnx_unique_581(v_2418) v_2420 = self.pnnx_unique_582(v_2419) v_2421 = self.pnnx_unique_583(v_2420) v_2422 = self.pnnx_unique_585(v_2421) v_2423 = (v_2418 + v_2422) v_2424 = self.pnnx_unique_588(v_2423) v_2425 = v_2424.view(1, 48, 48, 192) v_2426 = torch.roll(input=v_2425, dims=(1,2), shifts=(-4,-4)) v_2427 = v_2426.view(1, 6, 8, 6, 8, 192) v_2428 = torch.permute(input=v_2427, dims=(0,1,3,2,4,5)) v_2429 = v_2428.reshape(36, 64, 192) v_2430 = self.pnnx_unique_591(v_2429) v_2431 = v_2430.reshape(36, 64, 3, 6, 32) v_2432 = torch.permute(input=v_2431, dims=(2,0,3,1,4)) v_2433, v_2434, v_2435 = torch.unbind(v_2432, dim=0) v_2436 = (v_2433 * 1.767767e-01) v_2437 = torch.transpose(input=v_2434, dim0=-2, dim1=-1) v_2438 = torch.matmul(input=v_2436, other=v_2437) v_2439 = self.pnnx_fold_13841_pnnx_fold_13841 v_2440 = (v_2438 + v_2439) v_2441 = v_2440.view(1, 36, 6, 64, 64) v_2442 = self.pnnx_fold_13851_pnnx_fold_13851 v_2443 = (v_2441 + v_2442) v_2444 = v_2443.view(-1, 6, 64, 64) v_2445 = self.pnnx_unique_592(v_2444) v_2446 = torch.matmul(input=v_2445, other=v_2435) v_2447 = torch.transpose(input=v_2446, dim0=1, dim1=2) v_2448 = v_2447.reshape(36, 64, 192) v_2449 = self.pnnx_unique_594(v_2448) v_2450 = v_2449.reshape(1, 6, 6, 8, 8, 192) v_2451 = torch.permute(input=v_2450, dims=(0,1,3,2,4,5)) v_2452 = v_2451.reshape(1, 48, 48, -1) v_2453 = torch.roll(input=v_2452, dims=(1,2), shifts=(4,4)) v_2454 = v_2453.view(1, 2304, 192) v_2455 = (v_2423 + v_2454) v_2456 = self.pnnx_unique_596(v_2455) v_2457 = self.pnnx_unique_597(v_2456) v_2458 = self.pnnx_unique_598(v_2457) v_2459 = self.pnnx_unique_600(v_2458) v_2460 = (v_2455 + v_2459) v_2461 = torch.transpose(input=v_2460, dim0=1, dim1=2) v_2462 = v_2461.view(1, 192, 48, 48) v_2463 = self.pnnx_unique_602(v_2462) v_2464 = torch.flatten(input=v_2463, end_dim=-1, start_dim=2) v_2465 = torch.transpose(input=v_2464, dim0=1, dim1=2) v_2466 = (v_2465 + v_2262) v_2467 = self.pnnx_unique_603(v_2466) v_2468 = torch.transpose(input=v_2467, dim0=1, dim1=2) v_2469 = v_2468.view(1, 192, 48, 48) v_2470 = self.pnnx_unique_604(v_2469) v_2471 = (v_2470 + v_7) v_2472 = (v_2471 - v_1239) v_2473 = self.conv2d_0(v_2472) v_2474 = self.manipulator_convblks_0_relu(v_2473) v_2475 = (v_2474 * 1.900000e+01) v_2476 = self.conv2d_1(v_2475) v_2477 = self.conv2d_2(v_2476) v_2478 = self.manipulator_resblks_0_relu(v_2477) v_2479 = self.conv2d_3(v_2478) v_2480 = (v_2471 + (v_2479 + v_2476)) v_2481 = torch.flatten(input=v_2480, end_dim=-1, start_dim=2) v_2482 = torch.transpose(input=v_2481, dim0=1, dim1=2) v_2483 = self.patch_embed_mmsa_norm(v_2482) v_2484 = self.layers_mmsa_0_residual_group_blocks_0_norm1(v_2483) v_2485 = v_2484.reshape(1, 6, 8, 6, 8, 192) v_2486 = torch.permute(input=v_2485, dims=(0,1,3,2,4,5)) v_2487 = v_2486.reshape(36, 64, 192) v_2488 = self.layers_mmsa_0_residual_group_blocks_0_attn_qkv(v_2487) v_2489 = v_2488.reshape(36, 64, 3, 6, 32) v_2490 = torch.permute(input=v_2489, dims=(2,0,3,1,4)) v_2491, v_2492, v_2493 = torch.unbind(v_2490, dim=0) v_2494 = (v_2491 * 1.767767e-01) v_2495 = torch.transpose(input=v_2492, dim0=-2, dim1=-1) v_2496 = torch.matmul(input=v_2494, other=v_2495) v_2497 = self.pnnx_fold_14084_pnnx_fold_14084 v_2498 = (v_2496 + v_2497) v_2499 = self.layers_mmsa_0_residual_group_blocks_0_attn_softmax(v_2498) v_2500 = torch.matmul(input=v_2499, other=v_2493) v_2501 = torch.transpose(input=v_2500, dim0=1, dim1=2) v_2502 = v_2501.reshape(36, 64, 192) v_2503 = self.layers_mmsa_0_residual_group_blocks_0_attn_proj(v_2502) v_2504 = v_2503.reshape(1, 6, 6, 8, 8, 192) v_2505 = torch.permute(input=v_2504, dims=(0,1,3,2,4,5)) v_2506 = v_2505.reshape(1, 2304, 192) v_2507 = (v_2483 + v_2506) v_2508 = self.layers_mmsa_0_residual_group_blocks_0_norm2(v_2507) v_2509 = self.layers_mmsa_0_residual_group_blocks_0_mlp_fc1(v_2508) v_2510 = self.layers_mmsa_0_residual_group_blocks_0_mlp_act(v_2509) v_2511 = self.layers_mmsa_0_residual_group_blocks_0_mlp_fc2(v_2510) v_2512 = (v_2507 + v_2511) v_2513 = self.layers_mmsa_0_residual_group_blocks_1_norm1(v_2512) v_2514 = v_2513.view(1, 48, 48, 192) v_2515 = torch.roll(input=v_2514, dims=(1,2), shifts=(-4,-4)) v_2516 = v_2515.view(1, 6, 8, 6, 8, 192) v_2517 = torch.permute(input=v_2516, dims=(0,1,3,2,4,5)) v_2518 = v_2517.reshape(36, 64, 192) v_2519 = self.layers_mmsa_0_residual_group_blocks_1_attn_qkv(v_2518) v_2520 = v_2519.reshape(36, 64, 3, 6, 32) v_2521 = torch.permute(input=v_2520, dims=(2,0,3,1,4)) v_2522, v_2523, v_2524 = torch.unbind(v_2521, dim=0) v_2525 = (v_2522 * 1.767767e-01) v_2526 = torch.transpose(input=v_2523, dim0=-2, dim1=-1) v_2527 = torch.matmul(input=v_2525, other=v_2526) v_2528 = self.pnnx_fold_14237_pnnx_fold_14237 v_2529 = (v_2527 + v_2528) v_2530 = v_2529.view(1, 36, 6, 64, 64) v_2531 = self.pnnx_fold_14247_pnnx_fold_14247 v_2532 = (v_2530 + v_2531) v_2533 = v_2532.view(-1, 6, 64, 64) v_2534 = self.layers_mmsa_0_residual_group_blocks_1_attn_softmax(v_2533) v_2535 = torch.matmul(input=v_2534, other=v_2524) v_2536 = torch.transpose(input=v_2535, dim0=1, dim1=2) v_2537 = v_2536.reshape(36, 64, 192) v_2538 = self.layers_mmsa_0_residual_group_blocks_1_attn_proj(v_2537) v_2539 = v_2538.reshape(1, 6, 6, 8, 8, 192) v_2540 = torch.permute(input=v_2539, dims=(0,1,3,2,4,5)) v_2541 = v_2540.reshape(1, 48, 48, -1) v_2542 = torch.roll(input=v_2541, dims=(1,2), shifts=(4,4)) v_2543 = v_2542.view(1, 2304, 192) v_2544 = (v_2512 + v_2543) v_2545 = self.layers_mmsa_0_residual_group_blocks_1_norm2(v_2544) v_2546 = self.layers_mmsa_0_residual_group_blocks_1_mlp_fc1(v_2545) v_2547 = self.layers_mmsa_0_residual_group_blocks_1_mlp_act(v_2546) v_2548 = self.layers_mmsa_0_residual_group_blocks_1_mlp_fc2(v_2547) v_2549 = (v_2544 + v_2548) v_2550 = self.layers_mmsa_0_residual_group_blocks_2_norm1(v_2549) v_2551 = v_2550.reshape(1, 6, 8, 6, 8, 192) v_2552 = torch.permute(input=v_2551, dims=(0,1,3,2,4,5)) v_2553 = v_2552.reshape(36, 64, 192) v_2554 = self.layers_mmsa_0_residual_group_blocks_2_attn_qkv(v_2553) v_2555 = v_2554.reshape(36, 64, 3, 6, 32) v_2556 = torch.permute(input=v_2555, dims=(2,0,3,1,4)) v_2557, v_2558, v_2559 = torch.unbind(v_2556, dim=0) v_2560 = (v_2557 * 1.767767e-01) v_2561 = torch.transpose(input=v_2558, dim0=-2, dim1=-1) v_2562 = torch.matmul(input=v_2560, other=v_2561) v_2563 = self.pnnx_fold_14396_pnnx_fold_14396 v_2564 = (v_2562 + v_2563) v_2565 = self.layers_mmsa_0_residual_group_blocks_2_attn_softmax(v_2564) v_2566 = torch.matmul(input=v_2565, other=v_2559) v_2567 = torch.transpose(input=v_2566, dim0=1, dim1=2) v_2568 = v_2567.reshape(36, 64, 192) v_2569 = self.layers_mmsa_0_residual_group_blocks_2_attn_proj(v_2568) v_2570 = v_2569.reshape(1, 6, 6, 8, 8, 192) v_2571 = torch.permute(input=v_2570, dims=(0,1,3,2,4,5)) v_2572 = v_2571.reshape(1, 2304, 192) v_2573 = (v_2549 + v_2572) v_2574 = self.layers_mmsa_0_residual_group_blocks_2_norm2(v_2573) v_2575 = self.layers_mmsa_0_residual_group_blocks_2_mlp_fc1(v_2574) v_2576 = self.layers_mmsa_0_residual_group_blocks_2_mlp_act(v_2575) v_2577 = self.layers_mmsa_0_residual_group_blocks_2_mlp_fc2(v_2576) v_2578 = (v_2573 + v_2577) v_2579 = self.layers_mmsa_0_residual_group_blocks_3_norm1(v_2578) v_2580 = v_2579.view(1, 48, 48, 192) v_2581 = torch.roll(input=v_2580, dims=(1,2), shifts=(-4,-4)) v_2582 = v_2581.view(1, 6, 8, 6, 8, 192) v_2583 = torch.permute(input=v_2582, dims=(0,1,3,2,4,5)) v_2584 = v_2583.reshape(36, 64, 192) v_2585 = self.layers_mmsa_0_residual_group_blocks_3_attn_qkv(v_2584) v_2586 = v_2585.reshape(36, 64, 3, 6, 32) v_2587 = torch.permute(input=v_2586, dims=(2,0,3,1,4)) v_2588, v_2589, v_2590 = torch.unbind(v_2587, dim=0) v_2591 = (v_2588 * 1.767767e-01) v_2592 = torch.transpose(input=v_2589, dim0=-2, dim1=-1) v_2593 = torch.matmul(input=v_2591, other=v_2592) v_2594 = self.pnnx_fold_14549_pnnx_fold_14549 v_2595 = (v_2593 + v_2594) v_2596 = v_2595.view(1, 36, 6, 64, 64) v_2597 = self.pnnx_fold_14559_pnnx_fold_14559 v_2598 = (v_2596 + v_2597) v_2599 = v_2598.view(-1, 6, 64, 64) v_2600 = self.layers_mmsa_0_residual_group_blocks_3_attn_softmax(v_2599) v_2601 = torch.matmul(input=v_2600, other=v_2590) v_2602 = torch.transpose(input=v_2601, dim0=1, dim1=2) v_2603 = v_2602.reshape(36, 64, 192) v_2604 = self.layers_mmsa_0_residual_group_blocks_3_attn_proj(v_2603) v_2605 = v_2604.reshape(1, 6, 6, 8, 8, 192) v_2606 = torch.permute(input=v_2605, dims=(0,1,3,2,4,5)) v_2607 = v_2606.reshape(1, 48, 48, -1) v_2608 = torch.roll(input=v_2607, dims=(1,2), shifts=(4,4)) v_2609 = v_2608.view(1, 2304, 192) v_2610 = (v_2578 + v_2609) v_2611 = self.layers_mmsa_0_residual_group_blocks_3_norm2(v_2610) v_2612 = self.layers_mmsa_0_residual_group_blocks_3_mlp_fc1(v_2611) v_2613 = self.layers_mmsa_0_residual_group_blocks_3_mlp_act(v_2612) v_2614 = self.layers_mmsa_0_residual_group_blocks_3_mlp_fc2(v_2613) v_2615 = (v_2610 + v_2614) v_2616 = self.layers_mmsa_0_residual_group_blocks_4_norm1(v_2615) v_2617 = v_2616.reshape(1, 6, 8, 6, 8, 192) v_2618 = torch.permute(input=v_2617, dims=(0,1,3,2,4,5)) v_2619 = v_2618.reshape(36, 64, 192) v_2620 = self.layers_mmsa_0_residual_group_blocks_4_attn_qkv(v_2619) v_2621 = v_2620.reshape(36, 64, 3, 6, 32) v_2622 = torch.permute(input=v_2621, dims=(2,0,3,1,4)) v_2623, v_2624, v_2625 = torch.unbind(v_2622, dim=0) v_2626 = (v_2623 * 1.767767e-01) v_2627 = torch.transpose(input=v_2624, dim0=-2, dim1=-1) v_2628 = torch.matmul(input=v_2626, other=v_2627) v_2629 = self.pnnx_fold_14708_pnnx_fold_14708 v_2630 = (v_2628 + v_2629) v_2631 = self.layers_mmsa_0_residual_group_blocks_4_attn_softmax(v_2630) v_2632 = torch.matmul(input=v_2631, other=v_2625) v_2633 = torch.transpose(input=v_2632, dim0=1, dim1=2) v_2634 = v_2633.reshape(36, 64, 192) v_2635 = self.layers_mmsa_0_residual_group_blocks_4_attn_proj(v_2634) v_2636 = v_2635.reshape(1, 6, 6, 8, 8, 192) v_2637 = torch.permute(input=v_2636, dims=(0,1,3,2,4,5)) v_2638 = v_2637.reshape(1, 2304, 192) v_2639 = (v_2615 + v_2638) v_2640 = self.layers_mmsa_0_residual_group_blocks_4_norm2(v_2639) v_2641 = self.layers_mmsa_0_residual_group_blocks_4_mlp_fc1(v_2640) v_2642 = self.layers_mmsa_0_residual_group_blocks_4_mlp_act(v_2641) v_2643 = self.layers_mmsa_0_residual_group_blocks_4_mlp_fc2(v_2642) v_2644 = (v_2639 + v_2643) v_2645 = self.layers_mmsa_0_residual_group_blocks_5_norm1(v_2644) v_2646 = v_2645.view(1, 48, 48, 192) v_2647 = torch.roll(input=v_2646, dims=(1,2), shifts=(-4,-4)) v_2648 = v_2647.view(1, 6, 8, 6, 8, 192) v_2649 = torch.permute(input=v_2648, dims=(0,1,3,2,4,5)) v_2650 = v_2649.reshape(36, 64, 192) v_2651 = self.layers_mmsa_0_residual_group_blocks_5_attn_qkv(v_2650) v_2652 = v_2651.reshape(36, 64, 3, 6, 32) v_2653 = torch.permute(input=v_2652, dims=(2,0,3,1,4)) v_2654, v_2655, v_2656 = torch.unbind(v_2653, dim=0) v_2657 = (v_2654 * 1.767767e-01) v_2658 = torch.transpose(input=v_2655, dim0=-2, dim1=-1) v_2659 = torch.matmul(input=v_2657, other=v_2658) v_2660 = self.pnnx_fold_14861_pnnx_fold_14861 v_2661 = (v_2659 + v_2660) v_2662 = v_2661.view(1, 36, 6, 64, 64) v_2663 = self.pnnx_fold_14871_pnnx_fold_14871 v_2664 = (v_2662 + v_2663) v_2665 = v_2664.view(-1, 6, 64, 64) v_2666 = self.layers_mmsa_0_residual_group_blocks_5_attn_softmax(v_2665) v_2667 = torch.matmul(input=v_2666, other=v_2656) v_2668 = torch.transpose(input=v_2667, dim0=1, dim1=2) v_2669 = v_2668.reshape(36, 64, 192) v_2670 = self.layers_mmsa_0_residual_group_blocks_5_attn_proj(v_2669) v_2671 = v_2670.reshape(1, 6, 6, 8, 8, 192) v_2672 = torch.permute(input=v_2671, dims=(0,1,3,2,4,5)) v_2673 = v_2672.reshape(1, 48, 48, -1) v_2674 = torch.roll(input=v_2673, dims=(1,2), shifts=(4,4)) v_2675 = v_2674.view(1, 2304, 192) v_2676 = (v_2644 + v_2675) v_2677 = self.layers_mmsa_0_residual_group_blocks_5_norm2(v_2676) v_2678 = self.layers_mmsa_0_residual_group_blocks_5_mlp_fc1(v_2677) v_2679 = self.layers_mmsa_0_residual_group_blocks_5_mlp_act(v_2678) v_2680 = self.layers_mmsa_0_residual_group_blocks_5_mlp_fc2(v_2679) v_2681 = (v_2676 + v_2680) v_2682 = torch.transpose(input=v_2681, dim0=1, dim1=2) v_2683 = v_2682.view(1, 192, 48, 48) v_2684 = self.layers_mmsa_0_conv(v_2683) v_2685 = torch.flatten(input=v_2684, end_dim=-1, start_dim=2) v_2686 = torch.transpose(input=v_2685, dim0=1, dim1=2) v_2687 = (v_2686 + v_2483) v_2688 = self.layers_mmsa_1_residual_group_blocks_0_norm1(v_2687) v_2689 = v_2688.reshape(1, 6, 8, 6, 8, 192) v_2690 = torch.permute(input=v_2689, dims=(0,1,3,2,4,5)) v_2691 = v_2690.reshape(36, 64, 192) v_2692 = self.layers_mmsa_1_residual_group_blocks_0_attn_qkv(v_2691) v_2693 = v_2692.reshape(36, 64, 3, 6, 32) v_2694 = torch.permute(input=v_2693, dims=(2,0,3,1,4)) v_2695, v_2696, v_2697 = torch.unbind(v_2694, dim=0) v_2698 = (v_2695 * 1.767767e-01) v_2699 = torch.transpose(input=v_2696, dim0=-2, dim1=-1) v_2700 = torch.matmul(input=v_2698, other=v_2699) v_2701 = self.pnnx_fold_15054_pnnx_fold_15054 v_2702 = (v_2700 + v_2701) v_2703 = self.layers_mmsa_1_residual_group_blocks_0_attn_softmax(v_2702) v_2704 = torch.matmul(input=v_2703, other=v_2697) v_2705 = torch.transpose(input=v_2704, dim0=1, dim1=2) v_2706 = v_2705.reshape(36, 64, 192) v_2707 = self.layers_mmsa_1_residual_group_blocks_0_attn_proj(v_2706) v_2708 = v_2707.reshape(1, 6, 6, 8, 8, 192) v_2709 = torch.permute(input=v_2708, dims=(0,1,3,2,4,5)) v_2710 = v_2709.reshape(1, 2304, 192) v_2711 = (v_2687 + v_2710) v_2712 = self.layers_mmsa_1_residual_group_blocks_0_norm2(v_2711) v_2713 = self.layers_mmsa_1_residual_group_blocks_0_mlp_fc1(v_2712) v_2714 = self.layers_mmsa_1_residual_group_blocks_0_mlp_act(v_2713) v_2715 = self.layers_mmsa_1_residual_group_blocks_0_mlp_fc2(v_2714) v_2716 = (v_2711 + v_2715) v_2717 = self.layers_mmsa_1_residual_group_blocks_1_norm1(v_2716) v_2718 = v_2717.view(1, 48, 48, 192) v_2719 = torch.roll(input=v_2718, dims=(1,2), shifts=(-4,-4)) v_2720 = v_2719.view(1, 6, 8, 6, 8, 192) v_2721 = torch.permute(input=v_2720, dims=(0,1,3,2,4,5)) v_2722 = v_2721.reshape(36, 64, 192) v_2723 = self.layers_mmsa_1_residual_group_blocks_1_attn_qkv(v_2722) v_2724 = v_2723.reshape(36, 64, 3, 6, 32) v_2725 = torch.permute(input=v_2724, dims=(2,0,3,1,4)) v_2726, v_2727, v_2728 = torch.unbind(v_2725, dim=0) v_2729 = (v_2726 * 1.767767e-01) v_2730 = torch.transpose(input=v_2727, dim0=-2, dim1=-1) v_2731 = torch.matmul(input=v_2729, other=v_2730) v_2732 = self.pnnx_fold_15207_pnnx_fold_15207 v_2733 = (v_2731 + v_2732) v_2734 = v_2733.view(1, 36, 6, 64, 64) v_2735 = self.pnnx_fold_15217_pnnx_fold_15217 v_2736 = (v_2734 + v_2735) v_2737 = v_2736.view(-1, 6, 64, 64) v_2738 = self.layers_mmsa_1_residual_group_blocks_1_attn_softmax(v_2737) v_2739 = torch.matmul(input=v_2738, other=v_2728) v_2740 = torch.transpose(input=v_2739, dim0=1, dim1=2) v_2741 = v_2740.reshape(36, 64, 192) v_2742 = self.layers_mmsa_1_residual_group_blocks_1_attn_proj(v_2741) v_2743 = v_2742.reshape(1, 6, 6, 8, 8, 192) v_2744 = torch.permute(input=v_2743, dims=(0,1,3,2,4,5)) v_2745 = v_2744.reshape(1, 48, 48, -1) v_2746 = torch.roll(input=v_2745, dims=(1,2), shifts=(4,4)) v_2747 = v_2746.view(1, 2304, 192) v_2748 = (v_2716 + v_2747) v_2749 = self.layers_mmsa_1_residual_group_blocks_1_norm2(v_2748) v_2750 = self.layers_mmsa_1_residual_group_blocks_1_mlp_fc1(v_2749) v_2751 = self.layers_mmsa_1_residual_group_blocks_1_mlp_act(v_2750) v_2752 = self.layers_mmsa_1_residual_group_blocks_1_mlp_fc2(v_2751) v_2753 = (v_2748 + v_2752) v_2754 = self.layers_mmsa_1_residual_group_blocks_2_norm1(v_2753) v_2755 = v_2754.reshape(1, 6, 8, 6, 8, 192) v_2756 = torch.permute(input=v_2755, dims=(0,1,3,2,4,5)) v_2757 = v_2756.reshape(36, 64, 192) v_2758 = self.layers_mmsa_1_residual_group_blocks_2_attn_qkv(v_2757) v_2759 = v_2758.reshape(36, 64, 3, 6, 32) v_2760 = torch.permute(input=v_2759, dims=(2,0,3,1,4)) v_2761, v_2762, v_2763 = torch.unbind(v_2760, dim=0) v_2764 = (v_2761 * 1.767767e-01) v_2765 = torch.transpose(input=v_2762, dim0=-2, dim1=-1) v_2766 = torch.matmul(input=v_2764, other=v_2765) v_2767 = self.pnnx_fold_15366_pnnx_fold_15366 v_2768 = (v_2766 + v_2767) v_2769 = self.layers_mmsa_1_residual_group_blocks_2_attn_softmax(v_2768) v_2770 = torch.matmul(input=v_2769, other=v_2763) v_2771 = torch.transpose(input=v_2770, dim0=1, dim1=2) v_2772 = v_2771.reshape(36, 64, 192) v_2773 = self.layers_mmsa_1_residual_group_blocks_2_attn_proj(v_2772) v_2774 = v_2773.reshape(1, 6, 6, 8, 8, 192) v_2775 = torch.permute(input=v_2774, dims=(0,1,3,2,4,5)) v_2776 = v_2775.reshape(1, 2304, 192) v_2777 = (v_2753 + v_2776) v_2778 = self.layers_mmsa_1_residual_group_blocks_2_norm2(v_2777) v_2779 = self.layers_mmsa_1_residual_group_blocks_2_mlp_fc1(v_2778) v_2780 = self.layers_mmsa_1_residual_group_blocks_2_mlp_act(v_2779) v_2781 = self.layers_mmsa_1_residual_group_blocks_2_mlp_fc2(v_2780) v_2782 = (v_2777 + v_2781) v_2783 = self.layers_mmsa_1_residual_group_blocks_3_norm1(v_2782) v_2784 = v_2783.view(1, 48, 48, 192) v_2785 = torch.roll(input=v_2784, dims=(1,2), shifts=(-4,-4)) v_2786 = v_2785.view(1, 6, 8, 6, 8, 192) v_2787 = torch.permute(input=v_2786, dims=(0,1,3,2,4,5)) v_2788 = v_2787.reshape(36, 64, 192) v_2789 = self.layers_mmsa_1_residual_group_blocks_3_attn_qkv(v_2788) v_2790 = v_2789.reshape(36, 64, 3, 6, 32) v_2791 = torch.permute(input=v_2790, dims=(2,0,3,1,4)) v_2792, v_2793, v_2794 = torch.unbind(v_2791, dim=0) v_2795 = (v_2792 * 1.767767e-01) v_2796 = torch.transpose(input=v_2793, dim0=-2, dim1=-1) v_2797 = torch.matmul(input=v_2795, other=v_2796) v_2798 = self.pnnx_fold_15519_pnnx_fold_15519 v_2799 = (v_2797 + v_2798) v_2800 = v_2799.view(1, 36, 6, 64, 64) v_2801 = self.pnnx_fold_15529_pnnx_fold_15529 v_2802 = (v_2800 + v_2801) v_2803 = v_2802.view(-1, 6, 64, 64) v_2804 = self.layers_mmsa_1_residual_group_blocks_3_attn_softmax(v_2803) v_2805 = torch.matmul(input=v_2804, other=v_2794) v_2806 = torch.transpose(input=v_2805, dim0=1, dim1=2) v_2807 = v_2806.reshape(36, 64, 192) v_2808 = self.layers_mmsa_1_residual_group_blocks_3_attn_proj(v_2807) v_2809 = v_2808.reshape(1, 6, 6, 8, 8, 192) v_2810 = torch.permute(input=v_2809, dims=(0,1,3,2,4,5)) v_2811 = v_2810.reshape(1, 48, 48, -1) v_2812 = torch.roll(input=v_2811, dims=(1,2), shifts=(4,4)) v_2813 = v_2812.view(1, 2304, 192) v_2814 = (v_2782 + v_2813) v_2815 = self.layers_mmsa_1_residual_group_blocks_3_norm2(v_2814) v_2816 = self.layers_mmsa_1_residual_group_blocks_3_mlp_fc1(v_2815) v_2817 = self.layers_mmsa_1_residual_group_blocks_3_mlp_act(v_2816) v_2818 = self.layers_mmsa_1_residual_group_blocks_3_mlp_fc2(v_2817) v_2819 = (v_2814 + v_2818) v_2820 = self.layers_mmsa_1_residual_group_blocks_4_norm1(v_2819) v_2821 = v_2820.reshape(1, 6, 8, 6, 8, 192) v_2822 = torch.permute(input=v_2821, dims=(0,1,3,2,4,5)) v_2823 = v_2822.reshape(36, 64, 192) v_2824 = self.layers_mmsa_1_residual_group_blocks_4_attn_qkv(v_2823) v_2825 = v_2824.reshape(36, 64, 3, 6, 32) v_2826 = torch.permute(input=v_2825, dims=(2,0,3,1,4)) v_2827, v_2828, v_2829 = torch.unbind(v_2826, dim=0) v_2830 = (v_2827 * 1.767767e-01) v_2831 = torch.transpose(input=v_2828, dim0=-2, dim1=-1) v_2832 = torch.matmul(input=v_2830, other=v_2831) v_2833 = self.pnnx_fold_15678_pnnx_fold_15678 v_2834 = (v_2832 + v_2833) v_2835 = self.layers_mmsa_1_residual_group_blocks_4_attn_softmax(v_2834) v_2836 = torch.matmul(input=v_2835, other=v_2829) v_2837 = torch.transpose(input=v_2836, dim0=1, dim1=2) v_2838 = v_2837.reshape(36, 64, 192) v_2839 = self.layers_mmsa_1_residual_group_blocks_4_attn_proj(v_2838) v_2840 = v_2839.reshape(1, 6, 6, 8, 8, 192) v_2841 = torch.permute(input=v_2840, dims=(0,1,3,2,4,5)) v_2842 = v_2841.reshape(1, 2304, 192) v_2843 = (v_2819 + v_2842) v_2844 = self.layers_mmsa_1_residual_group_blocks_4_norm2(v_2843) v_2845 = self.layers_mmsa_1_residual_group_blocks_4_mlp_fc1(v_2844) v_2846 = self.layers_mmsa_1_residual_group_blocks_4_mlp_act(v_2845) v_2847 = self.layers_mmsa_1_residual_group_blocks_4_mlp_fc2(v_2846) v_2848 = (v_2843 + v_2847) v_2849 = self.layers_mmsa_1_residual_group_blocks_5_norm1(v_2848) v_2850 = v_2849.view(1, 48, 48, 192) v_2851 = torch.roll(input=v_2850, dims=(1,2), shifts=(-4,-4)) v_2852 = v_2851.view(1, 6, 8, 6, 8, 192) v_2853 = torch.permute(input=v_2852, dims=(0,1,3,2,4,5)) v_2854 = v_2853.reshape(36, 64, 192) v_2855 = self.layers_mmsa_1_residual_group_blocks_5_attn_qkv(v_2854) v_2856 = v_2855.reshape(36, 64, 3, 6, 32) v_2857 = torch.permute(input=v_2856, dims=(2,0,3,1,4)) v_2858, v_2859, v_2860 = torch.unbind(v_2857, dim=0) v_2861 = (v_2858 * 1.767767e-01) v_2862 = torch.transpose(input=v_2859, dim0=-2, dim1=-1) v_2863 = torch.matmul(input=v_2861, other=v_2862) v_2864 = self.pnnx_fold_15831_pnnx_fold_15831 v_2865 = (v_2863 + v_2864) v_2866 = v_2865.view(1, 36, 6, 64, 64) v_2867 = self.pnnx_fold_15841_pnnx_fold_15841 v_2868 = (v_2866 + v_2867) v_2869 = v_2868.view(-1, 6, 64, 64) v_2870 = self.layers_mmsa_1_residual_group_blocks_5_attn_softmax(v_2869) v_2871 = torch.matmul(input=v_2870, other=v_2860) v_2872 = torch.transpose(input=v_2871, dim0=1, dim1=2) v_2873 = v_2872.reshape(36, 64, 192) v_2874 = self.layers_mmsa_1_residual_group_blocks_5_attn_proj(v_2873) v_2875 = v_2874.reshape(1, 6, 6, 8, 8, 192) v_2876 = torch.permute(input=v_2875, dims=(0,1,3,2,4,5)) v_2877 = v_2876.reshape(1, 48, 48, -1) v_2878 = torch.roll(input=v_2877, dims=(1,2), shifts=(4,4)) v_2879 = v_2878.view(1, 2304, 192) v_2880 = (v_2848 + v_2879) v_2881 = self.layers_mmsa_1_residual_group_blocks_5_norm2(v_2880) v_2882 = self.layers_mmsa_1_residual_group_blocks_5_mlp_fc1(v_2881) v_2883 = self.layers_mmsa_1_residual_group_blocks_5_mlp_act(v_2882) v_2884 = self.layers_mmsa_1_residual_group_blocks_5_mlp_fc2(v_2883) v_2885 = (v_2880 + v_2884) v_2886 = torch.transpose(input=v_2885, dim0=1, dim1=2) v_2887 = v_2886.view(1, 192, 48, 48) v_2888 = self.layers_mmsa_1_conv(v_2887) v_2889 = torch.flatten(input=v_2888, end_dim=-1, start_dim=2) v_2890 = torch.transpose(input=v_2889, dim0=1, dim1=2) v_2891 = (v_2890 + v_2687) v_2892 = self.layers_mmsa_2_residual_group_blocks_0_norm1(v_2891) v_2893 = v_2892.reshape(1, 6, 8, 6, 8, 192) v_2894 = torch.permute(input=v_2893, dims=(0,1,3,2,4,5)) v_2895 = v_2894.reshape(36, 64, 192) v_2896 = self.layers_mmsa_2_residual_group_blocks_0_attn_qkv(v_2895) v_2897 = v_2896.reshape(36, 64, 3, 6, 32) v_2898 = torch.permute(input=v_2897, dims=(2,0,3,1,4)) v_2899, v_2900, v_2901 = torch.unbind(v_2898, dim=0) v_2902 = (v_2899 * 1.767767e-01) v_2903 = torch.transpose(input=v_2900, dim0=-2, dim1=-1) v_2904 = torch.matmul(input=v_2902, other=v_2903) v_2905 = self.pnnx_fold_16024_pnnx_fold_16024 v_2906 = (v_2904 + v_2905) v_2907 = self.layers_mmsa_2_residual_group_blocks_0_attn_softmax(v_2906) v_2908 = torch.matmul(input=v_2907, other=v_2901) v_2909 = torch.transpose(input=v_2908, dim0=1, dim1=2) v_2910 = v_2909.reshape(36, 64, 192) v_2911 = self.layers_mmsa_2_residual_group_blocks_0_attn_proj(v_2910) v_2912 = v_2911.reshape(1, 6, 6, 8, 8, 192) v_2913 = torch.permute(input=v_2912, dims=(0,1,3,2,4,5)) v_2914 = v_2913.reshape(1, 2304, 192) v_2915 = (v_2891 + v_2914) v_2916 = self.layers_mmsa_2_residual_group_blocks_0_norm2(v_2915) v_2917 = self.layers_mmsa_2_residual_group_blocks_0_mlp_fc1(v_2916) v_2918 = self.layers_mmsa_2_residual_group_blocks_0_mlp_act(v_2917) v_2919 = self.layers_mmsa_2_residual_group_blocks_0_mlp_fc2(v_2918) v_2920 = (v_2915 + v_2919) v_2921 = self.layers_mmsa_2_residual_group_blocks_1_norm1(v_2920) v_2922 = v_2921.view(1, 48, 48, 192) v_2923 = torch.roll(input=v_2922, dims=(1,2), shifts=(-4,-4)) v_2924 = v_2923.view(1, 6, 8, 6, 8, 192) v_2925 = torch.permute(input=v_2924, dims=(0,1,3,2,4,5)) v_2926 = v_2925.reshape(36, 64, 192) v_2927 = self.layers_mmsa_2_residual_group_blocks_1_attn_qkv(v_2926) v_2928 = v_2927.reshape(36, 64, 3, 6, 32) v_2929 = torch.permute(input=v_2928, dims=(2,0,3,1,4)) v_2930, v_2931, v_2932 = torch.unbind(v_2929, dim=0) v_2933 = (v_2930 * 1.767767e-01) v_2934 = torch.transpose(input=v_2931, dim0=-2, dim1=-1) v_2935 = torch.matmul(input=v_2933, other=v_2934) v_2936 = self.pnnx_fold_16177_pnnx_fold_16177 v_2937 = (v_2935 + v_2936) v_2938 = v_2937.view(1, 36, 6, 64, 64) v_2939 = self.pnnx_fold_16187_pnnx_fold_16187 v_2940 = (v_2938 + v_2939) v_2941 = v_2940.view(-1, 6, 64, 64) v_2942 = self.layers_mmsa_2_residual_group_blocks_1_attn_softmax(v_2941) v_2943 = torch.matmul(input=v_2942, other=v_2932) v_2944 = torch.transpose(input=v_2943, dim0=1, dim1=2) v_2945 = v_2944.reshape(36, 64, 192) v_2946 = self.layers_mmsa_2_residual_group_blocks_1_attn_proj(v_2945) v_2947 = v_2946.reshape(1, 6, 6, 8, 8, 192) v_2948 = torch.permute(input=v_2947, dims=(0,1,3,2,4,5)) v_2949 = v_2948.reshape(1, 48, 48, -1) v_2950 = torch.roll(input=v_2949, dims=(1,2), shifts=(4,4)) v_2951 = v_2950.view(1, 2304, 192) v_2952 = (v_2920 + v_2951) v_2953 = self.layers_mmsa_2_residual_group_blocks_1_norm2(v_2952) v_2954 = self.layers_mmsa_2_residual_group_blocks_1_mlp_fc1(v_2953) v_2955 = self.layers_mmsa_2_residual_group_blocks_1_mlp_act(v_2954) v_2956 = self.layers_mmsa_2_residual_group_blocks_1_mlp_fc2(v_2955) v_2957 = (v_2952 + v_2956) v_2958 = self.layers_mmsa_2_residual_group_blocks_2_norm1(v_2957) v_2959 = v_2958.reshape(1, 6, 8, 6, 8, 192) v_2960 = torch.permute(input=v_2959, dims=(0,1,3,2,4,5)) v_2961 = v_2960.reshape(36, 64, 192) v_2962 = self.layers_mmsa_2_residual_group_blocks_2_attn_qkv(v_2961) v_2963 = v_2962.reshape(36, 64, 3, 6, 32) v_2964 = torch.permute(input=v_2963, dims=(2,0,3,1,4)) v_2965, v_2966, v_2967 = torch.unbind(v_2964, dim=0) v_2968 = (v_2965 * 1.767767e-01) v_2969 = torch.transpose(input=v_2966, dim0=-2, dim1=-1) v_2970 = torch.matmul(input=v_2968, other=v_2969) v_2971 = self.pnnx_fold_16336_pnnx_fold_16336 v_2972 = (v_2970 + v_2971) v_2973 = self.layers_mmsa_2_residual_group_blocks_2_attn_softmax(v_2972) v_2974 = torch.matmul(input=v_2973, other=v_2967) v_2975 = torch.transpose(input=v_2974, dim0=1, dim1=2) v_2976 = v_2975.reshape(36, 64, 192) v_2977 = self.layers_mmsa_2_residual_group_blocks_2_attn_proj(v_2976) v_2978 = v_2977.reshape(1, 6, 6, 8, 8, 192) v_2979 = torch.permute(input=v_2978, dims=(0,1,3,2,4,5)) v_2980 = v_2979.reshape(1, 2304, 192) v_2981 = (v_2957 + v_2980) v_2982 = self.layers_mmsa_2_residual_group_blocks_2_norm2(v_2981) v_2983 = self.layers_mmsa_2_residual_group_blocks_2_mlp_fc1(v_2982) v_2984 = self.layers_mmsa_2_residual_group_blocks_2_mlp_act(v_2983) v_2985 = self.layers_mmsa_2_residual_group_blocks_2_mlp_fc2(v_2984) v_2986 = (v_2981 + v_2985) v_2987 = self.layers_mmsa_2_residual_group_blocks_3_norm1(v_2986) v_2988 = v_2987.view(1, 48, 48, 192) v_2989 = torch.roll(input=v_2988, dims=(1,2), shifts=(-4,-4)) v_2990 = v_2989.view(1, 6, 8, 6, 8, 192) v_2991 = torch.permute(input=v_2990, dims=(0,1,3,2,4,5)) v_2992 = v_2991.reshape(36, 64, 192) v_2993 = self.layers_mmsa_2_residual_group_blocks_3_attn_qkv(v_2992) v_2994 = v_2993.reshape(36, 64, 3, 6, 32) v_2995 = torch.permute(input=v_2994, dims=(2,0,3,1,4)) v_2996, v_2997, v_2998 = torch.unbind(v_2995, dim=0) v_2999 = (v_2996 * 1.767767e-01) v_3000 = torch.transpose(input=v_2997, dim0=-2, dim1=-1) v_3001 = torch.matmul(input=v_2999, other=v_3000) v_3002 = self.pnnx_fold_16489_pnnx_fold_16489 v_3003 = (v_3001 + v_3002) v_3004 = v_3003.view(1, 36, 6, 64, 64) v_3005 = self.pnnx_fold_16499_pnnx_fold_16499 v_3006 = (v_3004 + v_3005) v_3007 = v_3006.view(-1, 6, 64, 64) v_3008 = self.layers_mmsa_2_residual_group_blocks_3_attn_softmax(v_3007) v_3009 = torch.matmul(input=v_3008, other=v_2998) v_3010 = torch.transpose(input=v_3009, dim0=1, dim1=2) v_3011 = v_3010.reshape(36, 64, 192) v_3012 = self.layers_mmsa_2_residual_group_blocks_3_attn_proj(v_3011) v_3013 = v_3012.reshape(1, 6, 6, 8, 8, 192) v_3014 = torch.permute(input=v_3013, dims=(0,1,3,2,4,5)) v_3015 = v_3014.reshape(1, 48, 48, -1) v_3016 = torch.roll(input=v_3015, dims=(1,2), shifts=(4,4)) v_3017 = v_3016.view(1, 2304, 192) v_3018 = (v_2986 + v_3017) v_3019 = self.layers_mmsa_2_residual_group_blocks_3_norm2(v_3018) v_3020 = self.layers_mmsa_2_residual_group_blocks_3_mlp_fc1(v_3019) v_3021 = self.layers_mmsa_2_residual_group_blocks_3_mlp_act(v_3020) v_3022 = self.layers_mmsa_2_residual_group_blocks_3_mlp_fc2(v_3021) v_3023 = (v_3018 + v_3022) v_3024 = self.layers_mmsa_2_residual_group_blocks_4_norm1(v_3023) v_3025 = v_3024.reshape(1, 6, 8, 6, 8, 192) v_3026 = torch.permute(input=v_3025, dims=(0,1,3,2,4,5)) v_3027 = v_3026.reshape(36, 64, 192) v_3028 = self.layers_mmsa_2_residual_group_blocks_4_attn_qkv(v_3027) v_3029 = v_3028.reshape(36, 64, 3, 6, 32) v_3030 = torch.permute(input=v_3029, dims=(2,0,3,1,4)) v_3031, v_3032, v_3033 = torch.unbind(v_3030, dim=0) v_3034 = (v_3031 * 1.767767e-01) v_3035 = torch.transpose(input=v_3032, dim0=-2, dim1=-1) v_3036 = torch.matmul(input=v_3034, other=v_3035) v_3037 = self.pnnx_fold_16648_pnnx_fold_16648 v_3038 = (v_3036 + v_3037) v_3039 = self.layers_mmsa_2_residual_group_blocks_4_attn_softmax(v_3038) v_3040 = torch.matmul(input=v_3039, other=v_3033) v_3041 = torch.transpose(input=v_3040, dim0=1, dim1=2) v_3042 = v_3041.reshape(36, 64, 192) v_3043 = self.layers_mmsa_2_residual_group_blocks_4_attn_proj(v_3042) v_3044 = v_3043.reshape(1, 6, 6, 8, 8, 192) v_3045 = torch.permute(input=v_3044, dims=(0,1,3,2,4,5)) v_3046 = v_3045.reshape(1, 2304, 192) v_3047 = (v_3023 + v_3046) v_3048 = self.layers_mmsa_2_residual_group_blocks_4_norm2(v_3047) v_3049 = self.layers_mmsa_2_residual_group_blocks_4_mlp_fc1(v_3048) v_3050 = self.layers_mmsa_2_residual_group_blocks_4_mlp_act(v_3049) v_3051 = self.layers_mmsa_2_residual_group_blocks_4_mlp_fc2(v_3050) v_3052 = (v_3047 + v_3051) v_3053 = self.layers_mmsa_2_residual_group_blocks_5_norm1(v_3052) v_3054 = v_3053.view(1, 48, 48, 192) v_3055 = torch.roll(input=v_3054, dims=(1,2), shifts=(-4,-4)) v_3056 = v_3055.view(1, 6, 8, 6, 8, 192) v_3057 = torch.permute(input=v_3056, dims=(0,1,3,2,4,5)) v_3058 = v_3057.reshape(36, 64, 192) v_3059 = self.layers_mmsa_2_residual_group_blocks_5_attn_qkv(v_3058) v_3060 = v_3059.reshape(36, 64, 3, 6, 32) v_3061 = torch.permute(input=v_3060, dims=(2,0,3,1,4)) v_3062, v_3063, v_3064 = torch.unbind(v_3061, dim=0) v_3065 = (v_3062 * 1.767767e-01) v_3066 = torch.transpose(input=v_3063, dim0=-2, dim1=-1) v_3067 = torch.matmul(input=v_3065, other=v_3066) v_3068 = self.pnnx_fold_16801_pnnx_fold_16801 v_3069 = (v_3067 + v_3068) v_3070 = v_3069.view(1, 36, 6, 64, 64) v_3071 = self.pnnx_fold_16811_pnnx_fold_16811 v_3072 = (v_3070 + v_3071) v_3073 = v_3072.view(-1, 6, 64, 64) v_3074 = self.layers_mmsa_2_residual_group_blocks_5_attn_softmax(v_3073) v_3075 = torch.matmul(input=v_3074, other=v_3064) v_3076 = torch.transpose(input=v_3075, dim0=1, dim1=2) v_3077 = v_3076.reshape(36, 64, 192) v_3078 = self.layers_mmsa_2_residual_group_blocks_5_attn_proj(v_3077) v_3079 = v_3078.reshape(1, 6, 6, 8, 8, 192) v_3080 = torch.permute(input=v_3079, dims=(0,1,3,2,4,5)) v_3081 = v_3080.reshape(1, 48, 48, -1) v_3082 = torch.roll(input=v_3081, dims=(1,2), shifts=(4,4)) v_3083 = v_3082.view(1, 2304, 192) v_3084 = (v_3052 + v_3083) v_3085 = self.layers_mmsa_2_residual_group_blocks_5_norm2(v_3084) v_3086 = self.layers_mmsa_2_residual_group_blocks_5_mlp_fc1(v_3085) v_3087 = self.layers_mmsa_2_residual_group_blocks_5_mlp_act(v_3086) v_3088 = self.layers_mmsa_2_residual_group_blocks_5_mlp_fc2(v_3087) v_3089 = (v_3084 + v_3088) v_3090 = torch.transpose(input=v_3089, dim0=1, dim1=2) v_3091 = v_3090.view(1, 192, 48, 48) v_3092 = self.layers_mmsa_2_conv(v_3091) v_3093 = torch.flatten(input=v_3092, end_dim=-1, start_dim=2) v_3094 = torch.transpose(input=v_3093, dim0=1, dim1=2) v_3095 = (v_3094 + v_2891) v_3096 = self.layers_mmsa_3_residual_group_blocks_0_norm1(v_3095) v_3097 = v_3096.reshape(1, 6, 8, 6, 8, 192) v_3098 = torch.permute(input=v_3097, dims=(0,1,3,2,4,5)) v_3099 = v_3098.reshape(36, 64, 192) v_3100 = self.layers_mmsa_3_residual_group_blocks_0_attn_qkv(v_3099) v_3101 = v_3100.reshape(36, 64, 3, 6, 32) v_3102 = torch.permute(input=v_3101, dims=(2,0,3,1,4)) v_3103, v_3104, v_3105 = torch.unbind(v_3102, dim=0) v_3106 = (v_3103 * 1.767767e-01) v_3107 = torch.transpose(input=v_3104, dim0=-2, dim1=-1) v_3108 = torch.matmul(input=v_3106, other=v_3107) v_3109 = self.pnnx_fold_16994_pnnx_fold_16994 v_3110 = (v_3108 + v_3109) v_3111 = self.layers_mmsa_3_residual_group_blocks_0_attn_softmax(v_3110) v_3112 = torch.matmul(input=v_3111, other=v_3105) v_3113 = torch.transpose(input=v_3112, dim0=1, dim1=2) v_3114 = v_3113.reshape(36, 64, 192) v_3115 = self.layers_mmsa_3_residual_group_blocks_0_attn_proj(v_3114) v_3116 = v_3115.reshape(1, 6, 6, 8, 8, 192) v_3117 = torch.permute(input=v_3116, dims=(0,1,3,2,4,5)) v_3118 = v_3117.reshape(1, 2304, 192) v_3119 = (v_3095 + v_3118) v_3120 = self.layers_mmsa_3_residual_group_blocks_0_norm2(v_3119) v_3121 = self.layers_mmsa_3_residual_group_blocks_0_mlp_fc1(v_3120) v_3122 = self.layers_mmsa_3_residual_group_blocks_0_mlp_act(v_3121) v_3123 = self.layers_mmsa_3_residual_group_blocks_0_mlp_fc2(v_3122) v_3124 = (v_3119 + v_3123) v_3125 = self.layers_mmsa_3_residual_group_blocks_1_norm1(v_3124) v_3126 = v_3125.view(1, 48, 48, 192) v_3127 = torch.roll(input=v_3126, dims=(1,2), shifts=(-4,-4)) v_3128 = v_3127.view(1, 6, 8, 6, 8, 192) v_3129 = torch.permute(input=v_3128, dims=(0,1,3,2,4,5)) v_3130 = v_3129.reshape(36, 64, 192) v_3131 = self.layers_mmsa_3_residual_group_blocks_1_attn_qkv(v_3130) v_3132 = v_3131.reshape(36, 64, 3, 6, 32) v_3133 = torch.permute(input=v_3132, dims=(2,0,3,1,4)) v_3134, v_3135, v_3136 = torch.unbind(v_3133, dim=0) v_3137 = (v_3134 * 1.767767e-01) v_3138 = torch.transpose(input=v_3135, dim0=-2, dim1=-1) v_3139 = torch.matmul(input=v_3137, other=v_3138) v_3140 = self.pnnx_fold_17147_pnnx_fold_17147 v_3141 = (v_3139 + v_3140) v_3142 = v_3141.view(1, 36, 6, 64, 64) v_3143 = self.pnnx_fold_17157_pnnx_fold_17157 v_3144 = (v_3142 + v_3143) v_3145 = v_3144.view(-1, 6, 64, 64) v_3146 = self.layers_mmsa_3_residual_group_blocks_1_attn_softmax(v_3145) v_3147 = torch.matmul(input=v_3146, other=v_3136) v_3148 = torch.transpose(input=v_3147, dim0=1, dim1=2) v_3149 = v_3148.reshape(36, 64, 192) v_3150 = self.layers_mmsa_3_residual_group_blocks_1_attn_proj(v_3149) v_3151 = v_3150.reshape(1, 6, 6, 8, 8, 192) v_3152 = torch.permute(input=v_3151, dims=(0,1,3,2,4,5)) v_3153 = v_3152.reshape(1, 48, 48, -1) v_3154 = torch.roll(input=v_3153, dims=(1,2), shifts=(4,4)) v_3155 = v_3154.view(1, 2304, 192) v_3156 = (v_3124 + v_3155) v_3157 = self.layers_mmsa_3_residual_group_blocks_1_norm2(v_3156) v_3158 = self.layers_mmsa_3_residual_group_blocks_1_mlp_fc1(v_3157) v_3159 = self.layers_mmsa_3_residual_group_blocks_1_mlp_act(v_3158) v_3160 = self.layers_mmsa_3_residual_group_blocks_1_mlp_fc2(v_3159) v_3161 = (v_3156 + v_3160) v_3162 = self.layers_mmsa_3_residual_group_blocks_2_norm1(v_3161) v_3163 = v_3162.reshape(1, 6, 8, 6, 8, 192) v_3164 = torch.permute(input=v_3163, dims=(0,1,3,2,4,5)) v_3165 = v_3164.reshape(36, 64, 192) v_3166 = self.layers_mmsa_3_residual_group_blocks_2_attn_qkv(v_3165) v_3167 = v_3166.reshape(36, 64, 3, 6, 32) v_3168 = torch.permute(input=v_3167, dims=(2,0,3,1,4)) v_3169, v_3170, v_3171 = torch.unbind(v_3168, dim=0) v_3172 = (v_3169 * 1.767767e-01) v_3173 = torch.transpose(input=v_3170, dim0=-2, dim1=-1) v_3174 = torch.matmul(input=v_3172, other=v_3173) v_3175 = self.pnnx_fold_17306_pnnx_fold_17306 v_3176 = (v_3174 + v_3175) v_3177 = self.layers_mmsa_3_residual_group_blocks_2_attn_softmax(v_3176) v_3178 = torch.matmul(input=v_3177, other=v_3171) v_3179 = torch.transpose(input=v_3178, dim0=1, dim1=2) v_3180 = v_3179.reshape(36, 64, 192) v_3181 = self.layers_mmsa_3_residual_group_blocks_2_attn_proj(v_3180) v_3182 = v_3181.reshape(1, 6, 6, 8, 8, 192) v_3183 = torch.permute(input=v_3182, dims=(0,1,3,2,4,5)) v_3184 = v_3183.reshape(1, 2304, 192) v_3185 = (v_3161 + v_3184) v_3186 = self.layers_mmsa_3_residual_group_blocks_2_norm2(v_3185) v_3187 = self.layers_mmsa_3_residual_group_blocks_2_mlp_fc1(v_3186) v_3188 = self.layers_mmsa_3_residual_group_blocks_2_mlp_act(v_3187) v_3189 = self.layers_mmsa_3_residual_group_blocks_2_mlp_fc2(v_3188) v_3190 = (v_3185 + v_3189) v_3191 = self.layers_mmsa_3_residual_group_blocks_3_norm1(v_3190) v_3192 = v_3191.view(1, 48, 48, 192) v_3193 = torch.roll(input=v_3192, dims=(1,2), shifts=(-4,-4)) v_3194 = v_3193.view(1, 6, 8, 6, 8, 192) v_3195 = torch.permute(input=v_3194, dims=(0,1,3,2,4,5)) v_3196 = v_3195.reshape(36, 64, 192) v_3197 = self.layers_mmsa_3_residual_group_blocks_3_attn_qkv(v_3196) v_3198 = v_3197.reshape(36, 64, 3, 6, 32) v_3199 = torch.permute(input=v_3198, dims=(2,0,3,1,4)) v_3200, v_3201, v_3202 = torch.unbind(v_3199, dim=0) v_3203 = (v_3200 * 1.767767e-01) v_3204 = torch.transpose(input=v_3201, dim0=-2, dim1=-1) v_3205 = torch.matmul(input=v_3203, other=v_3204) v_3206 = self.pnnx_fold_17459_pnnx_fold_17459 v_3207 = (v_3205 + v_3206) v_3208 = v_3207.view(1, 36, 6, 64, 64) v_3209 = self.pnnx_fold_17469_pnnx_fold_17469 v_3210 = (v_3208 + v_3209) v_3211 = v_3210.view(-1, 6, 64, 64) v_3212 = self.layers_mmsa_3_residual_group_blocks_3_attn_softmax(v_3211) v_3213 = torch.matmul(input=v_3212, other=v_3202) v_3214 = torch.transpose(input=v_3213, dim0=1, dim1=2) v_3215 = v_3214.reshape(36, 64, 192) v_3216 = self.layers_mmsa_3_residual_group_blocks_3_attn_proj(v_3215) v_3217 = v_3216.reshape(1, 6, 6, 8, 8, 192) v_3218 = torch.permute(input=v_3217, dims=(0,1,3,2,4,5)) v_3219 = v_3218.reshape(1, 48, 48, -1) v_3220 = torch.roll(input=v_3219, dims=(1,2), shifts=(4,4)) v_3221 = v_3220.view(1, 2304, 192) v_3222 = (v_3190 + v_3221) v_3223 = self.layers_mmsa_3_residual_group_blocks_3_norm2(v_3222) v_3224 = self.layers_mmsa_3_residual_group_blocks_3_mlp_fc1(v_3223) v_3225 = self.layers_mmsa_3_residual_group_blocks_3_mlp_act(v_3224) v_3226 = self.layers_mmsa_3_residual_group_blocks_3_mlp_fc2(v_3225) v_3227 = (v_3222 + v_3226) v_3228 = self.layers_mmsa_3_residual_group_blocks_4_norm1(v_3227) v_3229 = v_3228.reshape(1, 6, 8, 6, 8, 192) v_3230 = torch.permute(input=v_3229, dims=(0,1,3,2,4,5)) v_3231 = v_3230.reshape(36, 64, 192) v_3232 = self.layers_mmsa_3_residual_group_blocks_4_attn_qkv(v_3231) v_3233 = v_3232.reshape(36, 64, 3, 6, 32) v_3234 = torch.permute(input=v_3233, dims=(2,0,3,1,4)) v_3235, v_3236, v_3237 = torch.unbind(v_3234, dim=0) v_3238 = (v_3235 * 1.767767e-01) v_3239 = torch.transpose(input=v_3236, dim0=-2, dim1=-1) v_3240 = torch.matmul(input=v_3238, other=v_3239) v_3241 = self.pnnx_fold_17618_pnnx_fold_17618 v_3242 = (v_3240 + v_3241) v_3243 = self.layers_mmsa_3_residual_group_blocks_4_attn_softmax(v_3242) v_3244 = torch.matmul(input=v_3243, other=v_3237) v_3245 = torch.transpose(input=v_3244, dim0=1, dim1=2) v_3246 = v_3245.reshape(36, 64, 192) v_3247 = self.layers_mmsa_3_residual_group_blocks_4_attn_proj(v_3246) v_3248 = v_3247.reshape(1, 6, 6, 8, 8, 192) v_3249 = torch.permute(input=v_3248, dims=(0,1,3,2,4,5)) v_3250 = v_3249.reshape(1, 2304, 192) v_3251 = (v_3227 + v_3250) v_3252 = self.layers_mmsa_3_residual_group_blocks_4_norm2(v_3251) v_3253 = self.layers_mmsa_3_residual_group_blocks_4_mlp_fc1(v_3252) v_3254 = self.layers_mmsa_3_residual_group_blocks_4_mlp_act(v_3253) v_3255 = self.layers_mmsa_3_residual_group_blocks_4_mlp_fc2(v_3254) v_3256 = (v_3251 + v_3255) v_3257 = self.layers_mmsa_3_residual_group_blocks_5_norm1(v_3256) v_3258 = v_3257.view(1, 48, 48, 192) v_3259 = torch.roll(input=v_3258, dims=(1,2), shifts=(-4,-4)) v_3260 = v_3259.view(1, 6, 8, 6, 8, 192) v_3261 = torch.permute(input=v_3260, dims=(0,1,3,2,4,5)) v_3262 = v_3261.reshape(36, 64, 192) v_3263 = self.layers_mmsa_3_residual_group_blocks_5_attn_qkv(v_3262) v_3264 = v_3263.reshape(36, 64, 3, 6, 32) v_3265 = torch.permute(input=v_3264, dims=(2,0,3,1,4)) v_3266, v_3267, v_3268 = torch.unbind(v_3265, dim=0) v_3269 = (v_3266 * 1.767767e-01) v_3270 = torch.transpose(input=v_3267, dim0=-2, dim1=-1) v_3271 = torch.matmul(input=v_3269, other=v_3270) v_3272 = self.pnnx_fold_17771_pnnx_fold_17771 v_3273 = (v_3271 + v_3272) v_3274 = v_3273.view(1, 36, 6, 64, 64) v_3275 = self.pnnx_fold_17781_pnnx_fold_17781 v_3276 = (v_3274 + v_3275) v_3277 = v_3276.view(-1, 6, 64, 64) v_3278 = self.layers_mmsa_3_residual_group_blocks_5_attn_softmax(v_3277) v_3279 = torch.matmul(input=v_3278, other=v_3268) v_3280 = torch.transpose(input=v_3279, dim0=1, dim1=2) v_3281 = v_3280.reshape(36, 64, 192) v_3282 = self.layers_mmsa_3_residual_group_blocks_5_attn_proj(v_3281) v_3283 = v_3282.reshape(1, 6, 6, 8, 8, 192) v_3284 = torch.permute(input=v_3283, dims=(0,1,3,2,4,5)) v_3285 = v_3284.reshape(1, 48, 48, -1) v_3286 = torch.roll(input=v_3285, dims=(1,2), shifts=(4,4)) v_3287 = v_3286.view(1, 2304, 192) v_3288 = (v_3256 + v_3287) v_3289 = self.layers_mmsa_3_residual_group_blocks_5_norm2(v_3288) v_3290 = self.layers_mmsa_3_residual_group_blocks_5_mlp_fc1(v_3289) v_3291 = self.layers_mmsa_3_residual_group_blocks_5_mlp_act(v_3290) v_3292 = self.layers_mmsa_3_residual_group_blocks_5_mlp_fc2(v_3291) v_3293 = (v_3288 + v_3292) v_3294 = torch.transpose(input=v_3293, dim0=1, dim1=2) v_3295 = v_3294.view(1, 192, 48, 48) v_3296 = self.layers_mmsa_3_conv(v_3295) v_3297 = torch.flatten(input=v_3296, end_dim=-1, start_dim=2) v_3298 = torch.transpose(input=v_3297, dim0=1, dim1=2) v_3299 = (v_3298 + v_3095) v_3300 = self.layers_mmsa_4_residual_group_blocks_0_norm1(v_3299) v_3301 = v_3300.reshape(1, 6, 8, 6, 8, 192) v_3302 = torch.permute(input=v_3301, dims=(0,1,3,2,4,5)) v_3303 = v_3302.reshape(36, 64, 192) v_3304 = self.layers_mmsa_4_residual_group_blocks_0_attn_qkv(v_3303) v_3305 = v_3304.reshape(36, 64, 3, 6, 32) v_3306 = torch.permute(input=v_3305, dims=(2,0,3,1,4)) v_3307, v_3308, v_3309 = torch.unbind(v_3306, dim=0) v_3310 = (v_3307 * 1.767767e-01) v_3311 = torch.transpose(input=v_3308, dim0=-2, dim1=-1) v_3312 = torch.matmul(input=v_3310, other=v_3311) v_3313 = self.pnnx_fold_17964_pnnx_fold_17964 v_3314 = (v_3312 + v_3313) v_3315 = self.layers_mmsa_4_residual_group_blocks_0_attn_softmax(v_3314) v_3316 = torch.matmul(input=v_3315, other=v_3309) v_3317 = torch.transpose(input=v_3316, dim0=1, dim1=2) v_3318 = v_3317.reshape(36, 64, 192) v_3319 = self.layers_mmsa_4_residual_group_blocks_0_attn_proj(v_3318) v_3320 = v_3319.reshape(1, 6, 6, 8, 8, 192) v_3321 = torch.permute(input=v_3320, dims=(0,1,3,2,4,5)) v_3322 = v_3321.reshape(1, 2304, 192) v_3323 = (v_3299 + v_3322) v_3324 = self.layers_mmsa_4_residual_group_blocks_0_norm2(v_3323) v_3325 = self.layers_mmsa_4_residual_group_blocks_0_mlp_fc1(v_3324) v_3326 = self.layers_mmsa_4_residual_group_blocks_0_mlp_act(v_3325) v_3327 = self.layers_mmsa_4_residual_group_blocks_0_mlp_fc2(v_3326) v_3328 = (v_3323 + v_3327) v_3329 = self.layers_mmsa_4_residual_group_blocks_1_norm1(v_3328) v_3330 = v_3329.view(1, 48, 48, 192) v_3331 = torch.roll(input=v_3330, dims=(1,2), shifts=(-4,-4)) v_3332 = v_3331.view(1, 6, 8, 6, 8, 192) v_3333 = torch.permute(input=v_3332, dims=(0,1,3,2,4,5)) v_3334 = v_3333.reshape(36, 64, 192) v_3335 = self.layers_mmsa_4_residual_group_blocks_1_attn_qkv(v_3334) v_3336 = v_3335.reshape(36, 64, 3, 6, 32) v_3337 = torch.permute(input=v_3336, dims=(2,0,3,1,4)) v_3338, v_3339, v_3340 = torch.unbind(v_3337, dim=0) v_3341 = (v_3338 * 1.767767e-01) v_3342 = torch.transpose(input=v_3339, dim0=-2, dim1=-1) v_3343 = torch.matmul(input=v_3341, other=v_3342) v_3344 = self.pnnx_fold_18117_pnnx_fold_18117 v_3345 = (v_3343 + v_3344) v_3346 = v_3345.view(1, 36, 6, 64, 64) v_3347 = self.pnnx_fold_18127_pnnx_fold_18127 v_3348 = (v_3346 + v_3347) v_3349 = v_3348.view(-1, 6, 64, 64) v_3350 = self.layers_mmsa_4_residual_group_blocks_1_attn_softmax(v_3349) v_3351 = torch.matmul(input=v_3350, other=v_3340) v_3352 = torch.transpose(input=v_3351, dim0=1, dim1=2) v_3353 = v_3352.reshape(36, 64, 192) v_3354 = self.layers_mmsa_4_residual_group_blocks_1_attn_proj(v_3353) v_3355 = v_3354.reshape(1, 6, 6, 8, 8, 192) v_3356 = torch.permute(input=v_3355, dims=(0,1,3,2,4,5)) v_3357 = v_3356.reshape(1, 48, 48, -1) v_3358 = torch.roll(input=v_3357, dims=(1,2), shifts=(4,4)) v_3359 = v_3358.view(1, 2304, 192) v_3360 = (v_3328 + v_3359) v_3361 = self.layers_mmsa_4_residual_group_blocks_1_norm2(v_3360) v_3362 = self.layers_mmsa_4_residual_group_blocks_1_mlp_fc1(v_3361) v_3363 = self.layers_mmsa_4_residual_group_blocks_1_mlp_act(v_3362) v_3364 = self.layers_mmsa_4_residual_group_blocks_1_mlp_fc2(v_3363) v_3365 = (v_3360 + v_3364) v_3366 = self.layers_mmsa_4_residual_group_blocks_2_norm1(v_3365) v_3367 = v_3366.reshape(1, 6, 8, 6, 8, 192) v_3368 = torch.permute(input=v_3367, dims=(0,1,3,2,4,5)) v_3369 = v_3368.reshape(36, 64, 192) v_3370 = self.layers_mmsa_4_residual_group_blocks_2_attn_qkv(v_3369) v_3371 = v_3370.reshape(36, 64, 3, 6, 32) v_3372 = torch.permute(input=v_3371, dims=(2,0,3,1,4)) v_3373, v_3374, v_3375 = torch.unbind(v_3372, dim=0) v_3376 = (v_3373 * 1.767767e-01) v_3377 = torch.transpose(input=v_3374, dim0=-2, dim1=-1) v_3378 = torch.matmul(input=v_3376, other=v_3377) v_3379 = self.pnnx_fold_18276_pnnx_fold_18276 v_3380 = (v_3378 + v_3379) v_3381 = self.layers_mmsa_4_residual_group_blocks_2_attn_softmax(v_3380) v_3382 = torch.matmul(input=v_3381, other=v_3375) v_3383 = torch.transpose(input=v_3382, dim0=1, dim1=2) v_3384 = v_3383.reshape(36, 64, 192) v_3385 = self.layers_mmsa_4_residual_group_blocks_2_attn_proj(v_3384) v_3386 = v_3385.reshape(1, 6, 6, 8, 8, 192) v_3387 = torch.permute(input=v_3386, dims=(0,1,3,2,4,5)) v_3388 = v_3387.reshape(1, 2304, 192) v_3389 = (v_3365 + v_3388) v_3390 = self.layers_mmsa_4_residual_group_blocks_2_norm2(v_3389) v_3391 = self.layers_mmsa_4_residual_group_blocks_2_mlp_fc1(v_3390) v_3392 = self.layers_mmsa_4_residual_group_blocks_2_mlp_act(v_3391) v_3393 = self.layers_mmsa_4_residual_group_blocks_2_mlp_fc2(v_3392) v_3394 = (v_3389 + v_3393) v_3395 = self.layers_mmsa_4_residual_group_blocks_3_norm1(v_3394) v_3396 = v_3395.view(1, 48, 48, 192) v_3397 = torch.roll(input=v_3396, dims=(1,2), shifts=(-4,-4)) v_3398 = v_3397.view(1, 6, 8, 6, 8, 192) v_3399 = torch.permute(input=v_3398, dims=(0,1,3,2,4,5)) v_3400 = v_3399.reshape(36, 64, 192) v_3401 = self.layers_mmsa_4_residual_group_blocks_3_attn_qkv(v_3400) v_3402 = v_3401.reshape(36, 64, 3, 6, 32) v_3403 = torch.permute(input=v_3402, dims=(2,0,3,1,4)) v_3404, v_3405, v_3406 = torch.unbind(v_3403, dim=0) v_3407 = (v_3404 * 1.767767e-01) v_3408 = torch.transpose(input=v_3405, dim0=-2, dim1=-1) v_3409 = torch.matmul(input=v_3407, other=v_3408) v_3410 = self.pnnx_fold_18429_pnnx_fold_18429 v_3411 = (v_3409 + v_3410) v_3412 = v_3411.view(1, 36, 6, 64, 64) v_3413 = self.pnnx_fold_18439_pnnx_fold_18439 v_3414 = (v_3412 + v_3413) v_3415 = v_3414.view(-1, 6, 64, 64) v_3416 = self.layers_mmsa_4_residual_group_blocks_3_attn_softmax(v_3415) v_3417 = torch.matmul(input=v_3416, other=v_3406) v_3418 = torch.transpose(input=v_3417, dim0=1, dim1=2) v_3419 = v_3418.reshape(36, 64, 192) v_3420 = self.layers_mmsa_4_residual_group_blocks_3_attn_proj(v_3419) v_3421 = v_3420.reshape(1, 6, 6, 8, 8, 192) v_3422 = torch.permute(input=v_3421, dims=(0,1,3,2,4,5)) v_3423 = v_3422.reshape(1, 48, 48, -1) v_3424 = torch.roll(input=v_3423, dims=(1,2), shifts=(4,4)) v_3425 = v_3424.view(1, 2304, 192) v_3426 = (v_3394 + v_3425) v_3427 = self.layers_mmsa_4_residual_group_blocks_3_norm2(v_3426) v_3428 = self.layers_mmsa_4_residual_group_blocks_3_mlp_fc1(v_3427) v_3429 = self.layers_mmsa_4_residual_group_blocks_3_mlp_act(v_3428) v_3430 = self.layers_mmsa_4_residual_group_blocks_3_mlp_fc2(v_3429) v_3431 = (v_3426 + v_3430) v_3432 = self.layers_mmsa_4_residual_group_blocks_4_norm1(v_3431) v_3433 = v_3432.reshape(1, 6, 8, 6, 8, 192) v_3434 = torch.permute(input=v_3433, dims=(0,1,3,2,4,5)) v_3435 = v_3434.reshape(36, 64, 192) v_3436 = self.layers_mmsa_4_residual_group_blocks_4_attn_qkv(v_3435) v_3437 = v_3436.reshape(36, 64, 3, 6, 32) v_3438 = torch.permute(input=v_3437, dims=(2,0,3,1,4)) v_3439, v_3440, v_3441 = torch.unbind(v_3438, dim=0) v_3442 = (v_3439 * 1.767767e-01) v_3443 = torch.transpose(input=v_3440, dim0=-2, dim1=-1) v_3444 = torch.matmul(input=v_3442, other=v_3443) v_3445 = self.pnnx_fold_18588_pnnx_fold_18588 v_3446 = (v_3444 + v_3445) v_3447 = self.layers_mmsa_4_residual_group_blocks_4_attn_softmax(v_3446) v_3448 = torch.matmul(input=v_3447, other=v_3441) v_3449 = torch.transpose(input=v_3448, dim0=1, dim1=2) v_3450 = v_3449.reshape(36, 64, 192) v_3451 = self.layers_mmsa_4_residual_group_blocks_4_attn_proj(v_3450) v_3452 = v_3451.reshape(1, 6, 6, 8, 8, 192) v_3453 = torch.permute(input=v_3452, dims=(0,1,3,2,4,5)) v_3454 = v_3453.reshape(1, 2304, 192) v_3455 = (v_3431 + v_3454) v_3456 = self.layers_mmsa_4_residual_group_blocks_4_norm2(v_3455) v_3457 = self.layers_mmsa_4_residual_group_blocks_4_mlp_fc1(v_3456) v_3458 = self.layers_mmsa_4_residual_group_blocks_4_mlp_act(v_3457) v_3459 = self.layers_mmsa_4_residual_group_blocks_4_mlp_fc2(v_3458) v_3460 = (v_3455 + v_3459) v_3461 = self.layers_mmsa_4_residual_group_blocks_5_norm1(v_3460) v_3462 = v_3461.view(1, 48, 48, 192) v_3463 = torch.roll(input=v_3462, dims=(1,2), shifts=(-4,-4)) v_3464 = v_3463.view(1, 6, 8, 6, 8, 192) v_3465 = torch.permute(input=v_3464, dims=(0,1,3,2,4,5)) v_3466 = v_3465.reshape(36, 64, 192) v_3467 = self.layers_mmsa_4_residual_group_blocks_5_attn_qkv(v_3466) v_3468 = v_3467.reshape(36, 64, 3, 6, 32) v_3469 = torch.permute(input=v_3468, dims=(2,0,3,1,4)) v_3470, v_3471, v_3472 = torch.unbind(v_3469, dim=0) v_3473 = (v_3470 * 1.767767e-01) v_3474 = torch.transpose(input=v_3471, dim0=-2, dim1=-1) v_3475 = torch.matmul(input=v_3473, other=v_3474) v_3476 = self.pnnx_fold_18741_pnnx_fold_18741 v_3477 = (v_3475 + v_3476) v_3478 = v_3477.view(1, 36, 6, 64, 64) v_3479 = self.pnnx_fold_18751_pnnx_fold_18751 v_3480 = (v_3478 + v_3479) v_3481 = v_3480.view(-1, 6, 64, 64) v_3482 = self.layers_mmsa_4_residual_group_blocks_5_attn_softmax(v_3481) v_3483 = torch.matmul(input=v_3482, other=v_3472) v_3484 = torch.transpose(input=v_3483, dim0=1, dim1=2) v_3485 = v_3484.reshape(36, 64, 192) v_3486 = self.layers_mmsa_4_residual_group_blocks_5_attn_proj(v_3485) v_3487 = v_3486.reshape(1, 6, 6, 8, 8, 192) v_3488 = torch.permute(input=v_3487, dims=(0,1,3,2,4,5)) v_3489 = v_3488.reshape(1, 48, 48, -1) v_3490 = torch.roll(input=v_3489, dims=(1,2), shifts=(4,4)) v_3491 = v_3490.view(1, 2304, 192) v_3492 = (v_3460 + v_3491) v_3493 = self.layers_mmsa_4_residual_group_blocks_5_norm2(v_3492) v_3494 = self.layers_mmsa_4_residual_group_blocks_5_mlp_fc1(v_3493) v_3495 = self.layers_mmsa_4_residual_group_blocks_5_mlp_act(v_3494) v_3496 = self.layers_mmsa_4_residual_group_blocks_5_mlp_fc2(v_3495) v_3497 = (v_3492 + v_3496) v_3498 = torch.transpose(input=v_3497, dim0=1, dim1=2) v_3499 = v_3498.view(1, 192, 48, 48) v_3500 = self.layers_mmsa_4_conv(v_3499) v_3501 = torch.flatten(input=v_3500, end_dim=-1, start_dim=2) v_3502 = torch.transpose(input=v_3501, dim0=1, dim1=2) v_3503 = (v_3502 + v_3299) v_3504 = self.layers_mmsa_5_residual_group_blocks_0_norm1(v_3503) v_3505 = v_3504.reshape(1, 6, 8, 6, 8, 192) v_3506 = torch.permute(input=v_3505, dims=(0,1,3,2,4,5)) v_3507 = v_3506.reshape(36, 64, 192) v_3508 = self.layers_mmsa_5_residual_group_blocks_0_attn_qkv(v_3507) v_3509 = v_3508.reshape(36, 64, 3, 6, 32) v_3510 = torch.permute(input=v_3509, dims=(2,0,3,1,4)) v_3511, v_3512, v_3513 = torch.unbind(v_3510, dim=0) v_3514 = (v_3511 * 1.767767e-01) v_3515 = torch.transpose(input=v_3512, dim0=-2, dim1=-1) v_3516 = torch.matmul(input=v_3514, other=v_3515) v_3517 = self.pnnx_fold_18934_pnnx_fold_18934 v_3518 = (v_3516 + v_3517) v_3519 = self.layers_mmsa_5_residual_group_blocks_0_attn_softmax(v_3518) v_3520 = torch.matmul(input=v_3519, other=v_3513) v_3521 = torch.transpose(input=v_3520, dim0=1, dim1=2) v_3522 = v_3521.reshape(36, 64, 192) v_3523 = self.layers_mmsa_5_residual_group_blocks_0_attn_proj(v_3522) v_3524 = v_3523.reshape(1, 6, 6, 8, 8, 192) v_3525 = torch.permute(input=v_3524, dims=(0,1,3,2,4,5)) v_3526 = v_3525.reshape(1, 2304, 192) v_3527 = (v_3503 + v_3526) v_3528 = self.layers_mmsa_5_residual_group_blocks_0_norm2(v_3527) v_3529 = self.layers_mmsa_5_residual_group_blocks_0_mlp_fc1(v_3528) v_3530 = self.layers_mmsa_5_residual_group_blocks_0_mlp_act(v_3529) v_3531 = self.layers_mmsa_5_residual_group_blocks_0_mlp_fc2(v_3530) v_3532 = (v_3527 + v_3531) v_3533 = self.layers_mmsa_5_residual_group_blocks_1_norm1(v_3532) v_3534 = v_3533.view(1, 48, 48, 192) v_3535 = torch.roll(input=v_3534, dims=(1,2), shifts=(-4,-4)) v_3536 = v_3535.view(1, 6, 8, 6, 8, 192) v_3537 = torch.permute(input=v_3536, dims=(0,1,3,2,4,5)) v_3538 = v_3537.reshape(36, 64, 192) v_3539 = self.layers_mmsa_5_residual_group_blocks_1_attn_qkv(v_3538) v_3540 = v_3539.reshape(36, 64, 3, 6, 32) v_3541 = torch.permute(input=v_3540, dims=(2,0,3,1,4)) v_3542, v_3543, v_3544 = torch.unbind(v_3541, dim=0) v_3545 = (v_3542 * 1.767767e-01) v_3546 = torch.transpose(input=v_3543, dim0=-2, dim1=-1) v_3547 = torch.matmul(input=v_3545, other=v_3546) v_3548 = self.pnnx_fold_19087_pnnx_fold_19087 v_3549 = (v_3547 + v_3548) v_3550 = v_3549.view(1, 36, 6, 64, 64) v_3551 = self.pnnx_fold_19097_pnnx_fold_19097 v_3552 = (v_3550 + v_3551) v_3553 = v_3552.view(-1, 6, 64, 64) v_3554 = self.layers_mmsa_5_residual_group_blocks_1_attn_softmax(v_3553) v_3555 = torch.matmul(input=v_3554, other=v_3544) v_3556 = torch.transpose(input=v_3555, dim0=1, dim1=2) v_3557 = v_3556.reshape(36, 64, 192) v_3558 = self.layers_mmsa_5_residual_group_blocks_1_attn_proj(v_3557) v_3559 = v_3558.reshape(1, 6, 6, 8, 8, 192) v_3560 = torch.permute(input=v_3559, dims=(0,1,3,2,4,5)) v_3561 = v_3560.reshape(1, 48, 48, -1) v_3562 = torch.roll(input=v_3561, dims=(1,2), shifts=(4,4)) v_3563 = v_3562.view(1, 2304, 192) v_3564 = (v_3532 + v_3563) v_3565 = self.layers_mmsa_5_residual_group_blocks_1_norm2(v_3564) v_3566 = self.layers_mmsa_5_residual_group_blocks_1_mlp_fc1(v_3565) v_3567 = self.layers_mmsa_5_residual_group_blocks_1_mlp_act(v_3566) v_3568 = self.layers_mmsa_5_residual_group_blocks_1_mlp_fc2(v_3567) v_3569 = (v_3564 + v_3568) v_3570 = self.layers_mmsa_5_residual_group_blocks_2_norm1(v_3569) v_3571 = v_3570.reshape(1, 6, 8, 6, 8, 192) v_3572 = torch.permute(input=v_3571, dims=(0,1,3,2,4,5)) v_3573 = v_3572.reshape(36, 64, 192) v_3574 = self.layers_mmsa_5_residual_group_blocks_2_attn_qkv(v_3573) v_3575 = v_3574.reshape(36, 64, 3, 6, 32) v_3576 = torch.permute(input=v_3575, dims=(2,0,3,1,4)) v_3577, v_3578, v_3579 = torch.unbind(v_3576, dim=0) v_3580 = (v_3577 * 1.767767e-01) v_3581 = torch.transpose(input=v_3578, dim0=-2, dim1=-1) v_3582 = torch.matmul(input=v_3580, other=v_3581) v_3583 = self.pnnx_fold_19246_pnnx_fold_19246 v_3584 = (v_3582 + v_3583) v_3585 = self.layers_mmsa_5_residual_group_blocks_2_attn_softmax(v_3584) v_3586 = torch.matmul(input=v_3585, other=v_3579) v_3587 = torch.transpose(input=v_3586, dim0=1, dim1=2) v_3588 = v_3587.reshape(36, 64, 192) v_3589 = self.layers_mmsa_5_residual_group_blocks_2_attn_proj(v_3588) v_3590 = v_3589.reshape(1, 6, 6, 8, 8, 192) v_3591 = torch.permute(input=v_3590, dims=(0,1,3,2,4,5)) v_3592 = v_3591.reshape(1, 2304, 192) v_3593 = (v_3569 + v_3592) v_3594 = self.layers_mmsa_5_residual_group_blocks_2_norm2(v_3593) v_3595 = self.layers_mmsa_5_residual_group_blocks_2_mlp_fc1(v_3594) v_3596 = self.layers_mmsa_5_residual_group_blocks_2_mlp_act(v_3595) v_3597 = self.layers_mmsa_5_residual_group_blocks_2_mlp_fc2(v_3596) v_3598 = (v_3593 + v_3597) v_3599 = self.layers_mmsa_5_residual_group_blocks_3_norm1(v_3598) v_3600 = v_3599.view(1, 48, 48, 192) v_3601 = torch.roll(input=v_3600, dims=(1,2), shifts=(-4,-4)) v_3602 = v_3601.view(1, 6, 8, 6, 8, 192) v_3603 = torch.permute(input=v_3602, dims=(0,1,3,2,4,5)) v_3604 = v_3603.reshape(36, 64, 192) v_3605 = self.layers_mmsa_5_residual_group_blocks_3_attn_qkv(v_3604) v_3606 = v_3605.reshape(36, 64, 3, 6, 32) v_3607 = torch.permute(input=v_3606, dims=(2,0,3,1,4)) v_3608, v_3609, v_3610 = torch.unbind(v_3607, dim=0) v_3611 = (v_3608 * 1.767767e-01) v_3612 = torch.transpose(input=v_3609, dim0=-2, dim1=-1) v_3613 = torch.matmul(input=v_3611, other=v_3612) v_3614 = self.pnnx_fold_19399_pnnx_fold_19399 v_3615 = (v_3613 + v_3614) v_3616 = v_3615.view(1, 36, 6, 64, 64) v_3617 = self.pnnx_fold_19409_pnnx_fold_19409 v_3618 = (v_3616 + v_3617) v_3619 = v_3618.view(-1, 6, 64, 64) v_3620 = self.layers_mmsa_5_residual_group_blocks_3_attn_softmax(v_3619) v_3621 = torch.matmul(input=v_3620, other=v_3610) v_3622 = torch.transpose(input=v_3621, dim0=1, dim1=2) v_3623 = v_3622.reshape(36, 64, 192) v_3624 = self.layers_mmsa_5_residual_group_blocks_3_attn_proj(v_3623) v_3625 = v_3624.reshape(1, 6, 6, 8, 8, 192) v_3626 = torch.permute(input=v_3625, dims=(0,1,3,2,4,5)) v_3627 = v_3626.reshape(1, 48, 48, -1) v_3628 = torch.roll(input=v_3627, dims=(1,2), shifts=(4,4)) v_3629 = v_3628.view(1, 2304, 192) v_3630 = (v_3598 + v_3629) v_3631 = self.layers_mmsa_5_residual_group_blocks_3_norm2(v_3630) v_3632 = self.layers_mmsa_5_residual_group_blocks_3_mlp_fc1(v_3631) v_3633 = self.layers_mmsa_5_residual_group_blocks_3_mlp_act(v_3632) v_3634 = self.layers_mmsa_5_residual_group_blocks_3_mlp_fc2(v_3633) v_3635 = (v_3630 + v_3634) v_3636 = self.layers_mmsa_5_residual_group_blocks_4_norm1(v_3635) v_3637 = v_3636.reshape(1, 6, 8, 6, 8, 192) v_3638 = torch.permute(input=v_3637, dims=(0,1,3,2,4,5)) v_3639 = v_3638.reshape(36, 64, 192) v_3640 = self.layers_mmsa_5_residual_group_blocks_4_attn_qkv(v_3639) v_3641 = v_3640.reshape(36, 64, 3, 6, 32) v_3642 = torch.permute(input=v_3641, dims=(2,0,3,1,4)) v_3643, v_3644, v_3645 = torch.unbind(v_3642, dim=0) v_3646 = (v_3643 * 1.767767e-01) v_3647 = torch.transpose(input=v_3644, dim0=-2, dim1=-1) v_3648 = torch.matmul(input=v_3646, other=v_3647) v_3649 = self.pnnx_fold_19558_pnnx_fold_19558 v_3650 = (v_3648 + v_3649) v_3651 = self.layers_mmsa_5_residual_group_blocks_4_attn_softmax(v_3650) v_3652 = torch.matmul(input=v_3651, other=v_3645) v_3653 = torch.transpose(input=v_3652, dim0=1, dim1=2) v_3654 = v_3653.reshape(36, 64, 192) v_3655 = self.layers_mmsa_5_residual_group_blocks_4_attn_proj(v_3654) v_3656 = v_3655.reshape(1, 6, 6, 8, 8, 192) v_3657 = torch.permute(input=v_3656, dims=(0,1,3,2,4,5)) v_3658 = v_3657.reshape(1, 2304, 192) v_3659 = (v_3635 + v_3658) v_3660 = self.layers_mmsa_5_residual_group_blocks_4_norm2(v_3659) v_3661 = self.layers_mmsa_5_residual_group_blocks_4_mlp_fc1(v_3660) v_3662 = self.layers_mmsa_5_residual_group_blocks_4_mlp_act(v_3661) v_3663 = self.layers_mmsa_5_residual_group_blocks_4_mlp_fc2(v_3662) v_3664 = (v_3659 + v_3663) v_3665 = self.layers_mmsa_5_residual_group_blocks_5_norm1(v_3664) v_3666 = v_3665.view(1, 48, 48, 192) v_3667 = torch.roll(input=v_3666, dims=(1,2), shifts=(-4,-4)) v_3668 = v_3667.view(1, 6, 8, 6, 8, 192) v_3669 = torch.permute(input=v_3668, dims=(0,1,3,2,4,5)) v_3670 = v_3669.reshape(36, 64, 192) v_3671 = self.layers_mmsa_5_residual_group_blocks_5_attn_qkv(v_3670) v_3672 = v_3671.reshape(36, 64, 3, 6, 32) v_3673 = torch.permute(input=v_3672, dims=(2,0,3,1,4)) v_3674, v_3675, v_3676 = torch.unbind(v_3673, dim=0) v_3677 = (v_3674 * 1.767767e-01) v_3678 = torch.transpose(input=v_3675, dim0=-2, dim1=-1) v_3679 = torch.matmul(input=v_3677, other=v_3678) v_3680 = self.pnnx_fold_19711_pnnx_fold_19711 v_3681 = (v_3679 + v_3680) v_3682 = v_3681.view(1, 36, 6, 64, 64) v_3683 = self.pnnx_fold_19721_pnnx_fold_19721 v_3684 = (v_3682 + v_3683) v_3685 = v_3684.view(-1, 6, 64, 64) v_3686 = self.layers_mmsa_5_residual_group_blocks_5_attn_softmax(v_3685) v_3687 = torch.matmul(input=v_3686, other=v_3676) v_3688 = torch.transpose(input=v_3687, dim0=1, dim1=2) v_3689 = v_3688.reshape(36, 64, 192) v_3690 = self.layers_mmsa_5_residual_group_blocks_5_attn_proj(v_3689) v_3691 = v_3690.reshape(1, 6, 6, 8, 8, 192) v_3692 = torch.permute(input=v_3691, dims=(0,1,3,2,4,5)) v_3693 = v_3692.reshape(1, 48, 48, -1) v_3694 = torch.roll(input=v_3693, dims=(1,2), shifts=(4,4)) v_3695 = v_3694.view(1, 2304, 192) v_3696 = (v_3664 + v_3695) v_3697 = self.layers_mmsa_5_residual_group_blocks_5_norm2(v_3696) v_3698 = self.layers_mmsa_5_residual_group_blocks_5_mlp_fc1(v_3697) v_3699 = self.layers_mmsa_5_residual_group_blocks_5_mlp_act(v_3698) v_3700 = self.layers_mmsa_5_residual_group_blocks_5_mlp_fc2(v_3699) v_3701 = (v_3696 + v_3700) v_3702 = torch.transpose(input=v_3701, dim0=1, dim1=2) v_3703 = v_3702.view(1, 192, 48, 48) v_3704 = self.layers_mmsa_5_conv(v_3703) v_3705 = torch.flatten(input=v_3704, end_dim=-1, start_dim=2) v_3706 = torch.transpose(input=v_3705, dim0=1, dim1=2) v_3707 = (v_3706 + v_3503) v_3708 = self.norm_mmsa(v_3707) v_3709 = torch.transpose(input=v_3708, dim0=1, dim1=2) v_3710 = v_3709.view(1, 192, 48, 48) v_3711 = self.conv_after_body_mmsa(v_3710) v_3712 = (v_3711 + v_2480) v_3713 = self.upsample_conv(v_3712) v_3714 = self.conv_last(v_3713) v_3715 = self.pnnx_fold_2234_pnnx_fold_2234 v_3716 = (v_3714, v_1239, v_2471, v_3715, ) return v_3716 def export_torchscript(): net = Model() net.eval() torch.manual_seed(0) v_0 = torch.rand(1, 3, 384, 384, dtype=torch.float) v_1 = torch.rand(1, 3, 384, 384, dtype=torch.float) mod = torch.jit.trace(net, (v_0, v_1)) mod.save("/Users/raoulritter/STB-VMM/20x/modelpnnx20x_pnnx.py.pt") def export_onnx(): net = Model() net.eval() torch.manual_seed(0) v_0 = torch.rand(1, 3, 384, 384, dtype=torch.float) v_1 = torch.rand(1, 3, 384, 384, dtype=torch.float) torch.onnx._export(net, (v_0, v_1), "/Users/raoulritter/STB-VMM/20x/modelpnnx20x_pnnx.py.onnx", export_params=True, operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK, opset_version=13, input_names=['in0', 'in1'], output_names=['out0']) def test_inference(): net = Model() net.eval() torch.manual_seed(0) v_0 = torch.rand(1, 3, 384, 384, dtype=torch.float) v_1 = torch.rand(1, 3, 384, 384, dtype=torch.float) return net(v_0, v_1)