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add MobileOne reparam
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Your Name committed Jun 28, 2022
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1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -10,3 +10,4 @@ vendor/
*.pyc
*.egg-info/

weights/
15 changes: 8 additions & 7 deletions nb/torch/backbones/mobileone.py
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Expand Up @@ -350,13 +350,14 @@ def __init__(
module.append(block)
setattr(self, f"stage{idx}", nn.Sequential(*module))

self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Sequential(
nn.Linear(
out_channels,
num_classes,
),
)
if num_classes is not None:
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Sequential(
nn.Linear(
out_channels,
num_classes,
),
)

def forward(self, x):
# for s_idx in range(self.stage_num):
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20 changes: 20 additions & 0 deletions nb/torch/utils/checkpoint.py
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import torch

def load_ckp_unwrap_module(ckp_p):
sd = torch.load(ckp_p, map_location='cpu')

state_dict = None
if 'state_dict' in sd.keys():
state_dict = sd['state_dict']
elif 'model' in sd.keys():
state_dict = sd['model']
else:
state_dict = sd

new_sd = {}
for k, v in state_dict.items():
if 'module.' in k:
new_sd[k.replace('module.', '')] = v
else:
new_sd[k] = v
return new_sd
40 changes: 40 additions & 0 deletions test.py
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from nb.torch.backbones.mobileone import MobileOneNet, make_mobileone_s0
from nb.torch.utils.checkpoint import load_ckp_unwrap_module
import sys
import torch
import os

a = sys.argv[1]

sd = load_ckp_unwrap_module(a)

x = torch.randn(1, 3, 224, 224)

model = make_mobileone_s0(deploy=False)
model.load_state_dict(sd)
print("original model loaded.")

for module in model.modules():
if hasattr(module, "switch_to_deploy"):
module.switch_to_deploy()

o1 = model(x)

deploy_model = make_mobileone_s0(deploy=True)
deploy_model.eval()
deploy_model.load_state_dict(model.state_dict())
o = deploy_model(x)

print((o1 - o).sum())

n_f = os.path.join(
os.path.dirname(a), os.path.basename(a).split(".")[0] + "_reparam.pth"
)
torch.save(model.state_dict(), n_f)

mod = torch.jit.trace(deploy_model, x)

n_f2 = os.path.join(
os.path.dirname(a), os.path.basename(a).split(".")[0] + "_reparam.pt"
)
mod.save(n_f2)
93 changes: 93 additions & 0 deletions test.txt
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7767517
91 90
pnnx.Input pnnx_input_0 0 1 0 #0=(1,3,224,224)f32
nn.Conv2d convbn2d_0 1 1 0 1 bias=True dilation=(1,1) groups=1 in_channels=3 kernel_size=(3,3) out_channels=48 padding=(1,1) padding_mode=zeros stride=(2,2) @bias=(48)f32 @weight=(48,3,3,3)f32 $input=0 #0=(1,3,224,224)f32 #1=(1,48,112,112)f32
nn.ReLU stage0.2 1 1 1 2 #1=(1,48,112,112)f32 #2=(1,48,112,112)f32
nn.Conv2d stage1.0.dw_reparam 1 1 2 3 bias=True dilation=(1,1) groups=48 in_channels=48 kernel_size=(3,3) out_channels=48 padding=(1,1) padding_mode=zeros stride=(2,2) @bias=(48)f32 @weight=(48,1,3,3)f32 #2=(1,48,112,112)f32 #3=(1,48,56,56)f32
nn.ReLU stage1.0.nonlinearity 1 1 3 4 #3=(1,48,56,56)f32 #4=(1,48,56,56)f32
nn.Conv2d stage1.0.pw_reparam 1 1 4 5 bias=True dilation=(1,1) groups=1 in_channels=48 kernel_size=(1,1) out_channels=48 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(48)f32 @weight=(48,48,1,1)f32 #4=(1,48,56,56)f32 #5=(1,48,56,56)f32
nn.ReLU pnnx_unique_0 1 1 5 6 #5=(1,48,56,56)f32 #6=(1,48,56,56)f32
nn.Conv2d stage1.1.dw_reparam 1 1 6 7 bias=True dilation=(1,1) groups=48 in_channels=48 kernel_size=(3,3) out_channels=48 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(48)f32 @weight=(48,1,3,3)f32 #6=(1,48,56,56)f32 #7=(1,48,56,56)f32
nn.ReLU stage1.1.nonlinearity 1 1 7 8 #7=(1,48,56,56)f32 #8=(1,48,56,56)f32
nn.Conv2d stage1.1.pw_reparam 1 1 8 9 bias=True dilation=(1,1) groups=1 in_channels=48 kernel_size=(1,1) out_channels=48 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(48)f32 @weight=(48,48,1,1)f32 #8=(1,48,56,56)f32 #9=(1,48,56,56)f32
nn.ReLU pnnx_unique_1 1 1 9 10 #9=(1,48,56,56)f32 #10=(1,48,56,56)f32
nn.Conv2d stage2.0.dw_reparam 1 1 10 11 bias=True dilation=(1,1) groups=48 in_channels=48 kernel_size=(3,3) out_channels=48 padding=(1,1) padding_mode=zeros stride=(2,2) @bias=(48)f32 @weight=(48,1,3,3)f32 #10=(1,48,56,56)f32 #11=(1,48,28,28)f32
nn.ReLU stage2.0.nonlinearity 1 1 11 12 #11=(1,48,28,28)f32 #12=(1,48,28,28)f32
nn.Conv2d stage2.0.pw_reparam 1 1 12 13 bias=True dilation=(1,1) groups=1 in_channels=48 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,48,1,1)f32 #12=(1,48,28,28)f32 #13=(1,128,28,28)f32
nn.ReLU pnnx_unique_2 1 1 13 14 #13=(1,128,28,28)f32 #14=(1,128,28,28)f32
nn.Conv2d stage2.1.dw_reparam 1 1 14 15 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #14=(1,128,28,28)f32 #15=(1,128,28,28)f32
nn.ReLU stage2.1.nonlinearity 1 1 15 16 #15=(1,128,28,28)f32 #16=(1,128,28,28)f32
nn.Conv2d stage2.1.pw_reparam 1 1 16 17 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #16=(1,128,28,28)f32 #17=(1,128,28,28)f32
nn.ReLU pnnx_unique_3 1 1 17 18 #17=(1,128,28,28)f32 #18=(1,128,28,28)f32
nn.Conv2d stage2.2.dw_reparam 1 1 18 19 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #18=(1,128,28,28)f32 #19=(1,128,28,28)f32
nn.ReLU stage2.2.nonlinearity 1 1 19 20 #19=(1,128,28,28)f32 #20=(1,128,28,28)f32
nn.Conv2d stage2.2.pw_reparam 1 1 20 21 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #20=(1,128,28,28)f32 #21=(1,128,28,28)f32
nn.ReLU pnnx_unique_4 1 1 21 22 #21=(1,128,28,28)f32 #22=(1,128,28,28)f32
nn.Conv2d stage2.3.dw_reparam 1 1 22 23 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #22=(1,128,28,28)f32 #23=(1,128,28,28)f32
nn.ReLU stage2.3.nonlinearity 1 1 23 24 #23=(1,128,28,28)f32 #24=(1,128,28,28)f32
nn.Conv2d stage2.3.pw_reparam 1 1 24 25 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #24=(1,128,28,28)f32 #25=(1,128,28,28)f32
nn.ReLU pnnx_unique_5 1 1 25 26 #25=(1,128,28,28)f32 #26=(1,128,28,28)f32
nn.Conv2d stage2.4.dw_reparam 1 1 26 27 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #26=(1,128,28,28)f32 #27=(1,128,28,28)f32
nn.ReLU stage2.4.nonlinearity 1 1 27 28 #27=(1,128,28,28)f32 #28=(1,128,28,28)f32
nn.Conv2d stage2.4.pw_reparam 1 1 28 29 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #28=(1,128,28,28)f32 #29=(1,128,28,28)f32
nn.ReLU pnnx_unique_6 1 1 29 30 #29=(1,128,28,28)f32 #30=(1,128,28,28)f32
nn.Conv2d stage2.5.dw_reparam 1 1 30 31 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #30=(1,128,28,28)f32 #31=(1,128,28,28)f32
nn.ReLU stage2.5.nonlinearity 1 1 31 32 #31=(1,128,28,28)f32 #32=(1,128,28,28)f32
nn.Conv2d stage2.5.pw_reparam 1 1 32 33 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #32=(1,128,28,28)f32 #33=(1,128,28,28)f32
nn.ReLU pnnx_unique_7 1 1 33 34 #33=(1,128,28,28)f32 #34=(1,128,28,28)f32
nn.Conv2d stage2.6.dw_reparam 1 1 34 35 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #34=(1,128,28,28)f32 #35=(1,128,28,28)f32
nn.ReLU stage2.6.nonlinearity 1 1 35 36 #35=(1,128,28,28)f32 #36=(1,128,28,28)f32
nn.Conv2d stage2.6.pw_reparam 1 1 36 37 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #36=(1,128,28,28)f32 #37=(1,128,28,28)f32
nn.ReLU pnnx_unique_8 1 1 37 38 #37=(1,128,28,28)f32 #38=(1,128,28,28)f32
nn.Conv2d stage2.7.dw_reparam 1 1 38 39 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,1,3,3)f32 #38=(1,128,28,28)f32 #39=(1,128,28,28)f32
nn.ReLU stage2.7.nonlinearity 1 1 39 40 #39=(1,128,28,28)f32 #40=(1,128,28,28)f32
nn.Conv2d stage2.7.pw_reparam 1 1 40 41 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=128 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(128)f32 @weight=(128,128,1,1)f32 #40=(1,128,28,28)f32 #41=(1,128,28,28)f32
nn.ReLU pnnx_unique_9 1 1 41 42 #41=(1,128,28,28)f32 #42=(1,128,28,28)f32
nn.Conv2d stage3.0.dw_reparam 1 1 42 43 bias=True dilation=(1,1) groups=128 in_channels=128 kernel_size=(3,3) out_channels=128 padding=(1,1) padding_mode=zeros stride=(2,2) @bias=(128)f32 @weight=(128,1,3,3)f32 #42=(1,128,28,28)f32 #43=(1,128,14,14)f32
nn.ReLU stage3.0.nonlinearity 1 1 43 44 #43=(1,128,14,14)f32 #44=(1,128,14,14)f32
nn.Conv2d stage3.0.pw_reparam 1 1 44 45 bias=True dilation=(1,1) groups=1 in_channels=128 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,128,1,1)f32 #44=(1,128,14,14)f32 #45=(1,256,14,14)f32
nn.ReLU pnnx_unique_10 1 1 45 46 #45=(1,256,14,14)f32 #46=(1,256,14,14)f32
nn.Conv2d stage3.1.dw_reparam 1 1 46 47 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #46=(1,256,14,14)f32 #47=(1,256,14,14)f32
nn.ReLU stage3.1.nonlinearity 1 1 47 48 #47=(1,256,14,14)f32 #48=(1,256,14,14)f32
nn.Conv2d stage3.1.pw_reparam 1 1 48 49 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #48=(1,256,14,14)f32 #49=(1,256,14,14)f32
nn.ReLU pnnx_unique_11 1 1 49 50 #49=(1,256,14,14)f32 #50=(1,256,14,14)f32
nn.Conv2d stage3.2.dw_reparam 1 1 50 51 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #50=(1,256,14,14)f32 #51=(1,256,14,14)f32
nn.ReLU stage3.2.nonlinearity 1 1 51 52 #51=(1,256,14,14)f32 #52=(1,256,14,14)f32
nn.Conv2d stage3.2.pw_reparam 1 1 52 53 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #52=(1,256,14,14)f32 #53=(1,256,14,14)f32
nn.ReLU pnnx_unique_12 1 1 53 54 #53=(1,256,14,14)f32 #54=(1,256,14,14)f32
nn.Conv2d stage3.3.dw_reparam 1 1 54 55 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #54=(1,256,14,14)f32 #55=(1,256,14,14)f32
nn.ReLU stage3.3.nonlinearity 1 1 55 56 #55=(1,256,14,14)f32 #56=(1,256,14,14)f32
nn.Conv2d stage3.3.pw_reparam 1 1 56 57 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #56=(1,256,14,14)f32 #57=(1,256,14,14)f32
nn.ReLU pnnx_unique_13 1 1 57 58 #57=(1,256,14,14)f32 #58=(1,256,14,14)f32
nn.Conv2d stage3.4.dw_reparam 1 1 58 59 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #58=(1,256,14,14)f32 #59=(1,256,14,14)f32
nn.ReLU stage3.4.nonlinearity 1 1 59 60 #59=(1,256,14,14)f32 #60=(1,256,14,14)f32
nn.Conv2d stage3.4.pw_reparam 1 1 60 61 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #60=(1,256,14,14)f32 #61=(1,256,14,14)f32
nn.ReLU pnnx_unique_14 1 1 61 62 #61=(1,256,14,14)f32 #62=(1,256,14,14)f32
nn.Conv2d stage4.0.dw_reparam 1 1 62 63 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #62=(1,256,14,14)f32 #63=(1,256,14,14)f32
nn.ReLU stage4.0.nonlinearity 1 1 63 64 #63=(1,256,14,14)f32 #64=(1,256,14,14)f32
nn.Conv2d stage4.0.pw_reparam 1 1 64 65 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #64=(1,256,14,14)f32 #65=(1,256,14,14)f32
nn.ReLU pnnx_unique_15 1 1 65 66 #65=(1,256,14,14)f32 #66=(1,256,14,14)f32
nn.Conv2d stage4.1.dw_reparam 1 1 66 67 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #66=(1,256,14,14)f32 #67=(1,256,14,14)f32
nn.ReLU stage4.1.nonlinearity 1 1 67 68 #67=(1,256,14,14)f32 #68=(1,256,14,14)f32
nn.Conv2d stage4.1.pw_reparam 1 1 68 69 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #68=(1,256,14,14)f32 #69=(1,256,14,14)f32
nn.ReLU pnnx_unique_16 1 1 69 70 #69=(1,256,14,14)f32 #70=(1,256,14,14)f32
nn.Conv2d stage4.2.dw_reparam 1 1 70 71 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #70=(1,256,14,14)f32 #71=(1,256,14,14)f32
nn.ReLU stage4.2.nonlinearity 1 1 71 72 #71=(1,256,14,14)f32 #72=(1,256,14,14)f32
nn.Conv2d stage4.2.pw_reparam 1 1 72 73 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #72=(1,256,14,14)f32 #73=(1,256,14,14)f32
nn.ReLU pnnx_unique_17 1 1 73 74 #73=(1,256,14,14)f32 #74=(1,256,14,14)f32
nn.Conv2d stage4.3.dw_reparam 1 1 74 75 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #74=(1,256,14,14)f32 #75=(1,256,14,14)f32
nn.ReLU stage4.3.nonlinearity 1 1 75 76 #75=(1,256,14,14)f32 #76=(1,256,14,14)f32
nn.Conv2d stage4.3.pw_reparam 1 1 76 77 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #76=(1,256,14,14)f32 #77=(1,256,14,14)f32
nn.ReLU pnnx_unique_18 1 1 77 78 #77=(1,256,14,14)f32 #78=(1,256,14,14)f32
nn.Conv2d stage4.4.dw_reparam 1 1 78 79 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,1,3,3)f32 #78=(1,256,14,14)f32 #79=(1,256,14,14)f32
nn.ReLU stage4.4.nonlinearity 1 1 79 80 #79=(1,256,14,14)f32 #80=(1,256,14,14)f32
nn.Conv2d stage4.4.pw_reparam 1 1 80 81 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=256 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(256)f32 @weight=(256,256,1,1)f32 #80=(1,256,14,14)f32 #81=(1,256,14,14)f32
nn.ReLU pnnx_unique_19 1 1 81 82 #81=(1,256,14,14)f32 #82=(1,256,14,14)f32
nn.Conv2d stage5.0.dw_reparam 1 1 82 83 bias=True dilation=(1,1) groups=256 in_channels=256 kernel_size=(3,3) out_channels=256 padding=(1,1) padding_mode=zeros stride=(2,2) @bias=(256)f32 @weight=(256,1,3,3)f32 #82=(1,256,14,14)f32 #83=(1,256,7,7)f32
nn.ReLU stage5.0.nonlinearity 1 1 83 84 #83=(1,256,7,7)f32 #84=(1,256,7,7)f32
nn.Conv2d stage5.0.pw_reparam 1 1 84 85 bias=True dilation=(1,1) groups=1 in_channels=256 kernel_size=(1,1) out_channels=1024 padding=(0,0) padding_mode=zeros stride=(1,1) @bias=(1024)f32 @weight=(1024,256,1,1)f32 #84=(1,256,7,7)f32 #85=(1,1024,7,7)f32
nn.ReLU pnnx_unique_20 1 1 85 86 #85=(1,1024,7,7)f32 #86=(1,1024,7,7)f32
nn.AdaptiveAvgPool2d avg_pool 1 1 86 87 output_size=(1,1) #86=(1,1024,7,7)f32 #87=(1,1024,1,1)f32
torch.flatten torch.flatten_0 1 1 87 88 end_dim=-1 start_dim=1 $input=87 #87=(1,1024,1,1)f32 #88=(1,1024)f32
nn.Linear fc1.0 1 1 88 89 bias=True in_features=1024 out_features=1000 @bias=(1000)f32 @weight=(1000,1024)f32 #88=(1,1024)f32 #89=(1,1000)f32
pnnx.Output pnnx_output_0 1 0 89 #89=(1,1000)f32

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