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f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae f9f499d f3ee5ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import os
import sys
import torch
import glob
# Ensure project root and CodeFormer directory are in sys.path
tools_dir = os.path.dirname(os.path.abspath(__file__))
project_dir = os.path.dirname(tools_dir)
codeformer_dir = os.path.join(project_dir, "models", "CodeFormer")
if codeformer_dir not in sys.path:
sys.path.insert(0, codeformer_dir)
from basicsr.utils.registry import ARCH_REGISTRY
import basicsr.archs.codeformer_arch
from basicsr.archs.rrdbnet_arch import RRDBNet
class CodeFormerONNXWrapper(torch.nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def forward(self, x, w):
# We pass adain=True for face restoration
out, _, _ = self.net(x, w=w, adain=True)
return out
def get_latest_checkpoint(search_pattern):
checkpoints = glob.glob(search_pattern)
if not checkpoints:
return None
# Auto-sort checkpoints by iteration number in their filenames (e.g., net_g_1200.pth)
def extract_iter(path):
basename = os.path.basename(path)
# e.g., net_g_1200.pth -> 1200
numbers = [int(s) for s in basename.replace(".pth", "").split("_") if s.isdigit()]
return numbers[0] if numbers else 0
return max(checkpoints, key=extract_iter)
def export_codeformer():
print("\n--- Exporting CodeFormer to ONNX ---")
device = torch.device("cpu")
# 1. Instantiate CodeFormer model
net = ARCH_REGISTRY.get('CodeFormer')(
dim_embd=512,
codebook_size=1024,
n_head=8,
n_layers=9,
connect_list=['32', '64', '128', '256']
)
# 2. Check for latest custom checkpoint, fallback to pretrained
custom_pattern = os.path.join(codeformer_dir, "experiments", "*_CodeFormer_stage3_custom", "models", "net_g_*.pth")
checkpoint_path = get_latest_checkpoint(custom_pattern)
if checkpoint_path:
print(f"Found custom CodeFormer checkpoint: {checkpoint_path}")
else:
checkpoint_path = os.path.join(project_dir, "weights", "CodeFormer", "codeformer.pth")
print(f"No custom checkpoint found. Using pretrained weights: {checkpoint_path}")
if not os.path.exists(checkpoint_path):
print(f"[ERROR] CodeFormer weights not found at: {checkpoint_path}")
return False
# 3. Load state dict
checkpoint = torch.load(checkpoint_path, map_location=device)
if 'params_ema' in checkpoint:
net.load_state_dict(checkpoint['params_ema'])
else:
net.load_state_dict(checkpoint['params'])
net.eval()
wrapper = CodeFormerONNXWrapper(net).to(device)
# Dummy inputs: x (1, 3, 512, 512) and w (scalar / 1D tensor)
dummy_x = torch.randn(1, 3, 512, 512, dtype=torch.float32)
dummy_w = torch.tensor([0.5], dtype=torch.float32) # Default w value
output_dir = os.path.join(project_dir, "weights", "CodeFormer")
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, "codeformer.onnx")
print(f"Exporting to {output_path}...")
torch.onnx.export(
wrapper,
(dummy_x, dummy_w),
output_path,
input_names=['input', 'w'],
output_names=['output'],
opset_version=16,
do_constant_folding=True
)
# Verify model
try:
import onnx
onnx_model = onnx.load(output_path)
onnx.checker.check_model(onnx_model)
print("[SUCCESS] CodeFormer ONNX model is valid.")
return True
except Exception as e:
print(f"[WARNING] ONNX validation failed: {e}")
return True
def export_realesrgan(num_block=None):
print("\n--- Exporting Real-ESRGAN to ONNX ---")
device = torch.device("cpu")
# 1. Check for latest custom checkpoint, fallback to pretrained
custom_pattern = os.path.join(project_dir, "models", "Real-ESRGAN", "experiments", "train_RealESRGAN_custom", "models", "net_g_*.pth")
checkpoint_path = get_latest_checkpoint(custom_pattern)
if checkpoint_path:
print(f"Found custom Real-ESRGAN checkpoint: {checkpoint_path}")
scale = 4
default_blocks = 6
else:
checkpoint_path = os.path.join(project_dir, "weights", "realesrgan", "RealESRGAN_x2plus.pth")
print(f"No custom checkpoint found. Using pretrained weights: {checkpoint_path}")
scale = 2
default_blocks = 23
if not os.path.exists(checkpoint_path):
print(f"[ERROR] Real-ESRGAN weights not found at: {checkpoint_path}")
return False
blocks = num_block if num_block is not None else default_blocks
print(f"Instantiating RRDBNet with scale={scale}, num_block={blocks}")
# 2. Instantiate RRDBNet architecture with correct scale
net = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=blocks,
num_grow_ch=32,
scale=scale
)
checkpoint = torch.load(checkpoint_path, map_location=device)
if 'params_ema' in checkpoint:
net.load_state_dict(checkpoint['params_ema'])
else:
net.load_state_dict(checkpoint['params'])
net.eval()
# Dummy input: x (1, 3, 256, 256) with dynamic H & W
dummy_x = torch.randn(1, 3, 256, 256, dtype=torch.float32)
output_dir = os.path.join(project_dir, "weights", "realesrgan")
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, "realesrgan.onnx")
print(f"Exporting to {output_path}...")
torch.onnx.export(
net,
dummy_x,
output_path,
input_names=['input'],
output_names=['output'],
dynamic_axes={
'input': {0: 'batch', 2: 'height', 3: 'width'},
'output': {0: 'batch', 2: 'height', 3: 'width'}
},
opset_version=16,
do_constant_folding=True
)
# Verify model
try:
import onnx
onnx_model = onnx.load(output_path)
onnx.checker.check_model(onnx_model)
print("[SUCCESS] Real-ESRGAN ONNX model is valid.")
return True
except Exception as e:
print(f"[WARNING] ONNX validation failed: {e}")
return True
def main():
import argparse
parser = argparse.ArgumentParser(description="Export CodeFormer and Real-ESRGAN models to ONNX")
parser.add_argument('--num-block', type=int, default=None, help="Number of RRDB blocks for Real-ESRGAN (defaults to 6 for custom checkpoint, 23 for pretrained)")
args = parser.parse_args()
success_cf = export_codeformer()
success_re = export_realesrgan(num_block=args.num_block)
if success_cf and success_re:
print("\n=== ALL MODELS EXPORTED SUCCESSFULLY ===")
else:
print("\n=== SOME MODEL EXPORTS FAILED ===")
if __name__ == "__main__":
main()
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