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import types
import argparse
import torch
import torch.nn.functional as F
import numpy as np
import onnx
import onnxsim
from basicsr.archs.ddcolor_arch import DDColor
from onnx import load_model, save_model, shape_inference
from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference
def parse_args():
parser = argparse.ArgumentParser(description="Export DDColor model to ONNX.")
parser.add_argument(
"--input_size",
type=int,
default=512,
help="Input image dimension.",
)
parser.add_argument(
"--batch_size",
type=int,
default=1,
help="Input batch size.",
)
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to export ONNX model.",
)
parser.add_argument(
"--model_size",
type=str,
default="tiny",
help="Path to export ONNX model.",
)
parser.add_argument(
"--decoder_type",
type=str,
default="MultiScaleColorDecoder",
help="Path to export ONNX model.",
)
parser.add_argument(
"--export_path",
type=str,
default="./model.onnx",
help="Path to export ONNX model.",
)
parser.add_argument(
"--opset",
type=int,
default=12,
help="ONNX opset version.",
)
return parser.parse_args()
def create_onnx_export(args):
input_size = args.input_size
device = torch.device('cpu')
if args.model_size == 'tiny':
encoder_name = 'convnext-t'
else:
encoder_name = 'convnext-l'
# hardcoded in inference/colorization_pipeline.py
# decoder_type = "MultiScaleColorDecoder"
if args.decoder_type == 'MultiScaleColorDecoder':
model = DDColor(
encoder_name=encoder_name,
decoder_name='MultiScaleColorDecoder',
input_size=[input_size, input_size],
num_output_channels=2,
last_norm='Spectral',
do_normalize=False,
num_queries=100,
num_scales=3,
dec_layers=9,
).to(device)
elif args.decoder_type == 'SingleColorDecoder':
model = DDColor(
encoder_name=encoder_name,
decoder_name='SingleColorDecoder',
input_size=[input_size, input_size],
num_output_channels=2,
last_norm='Spectral',
do_normalize=False,
num_queries=256,
).to(device)
else:
raise("decoder_type not implemented.")
model.load_state_dict(
torch.load(args.model_path, map_location=device)['params'],
strict=False)
model.eval()
channels = 3 # RGB image has 3 channels
random_input = torch.rand((args.batch_size, channels, input_size, input_size), dtype=torch.float32)
dynamic_axes = {}
if args.batch_size == 0:
dynamic_axes[0] = "batch"
if input_size == 0:
dynamic_axes[2] = "height"
dynamic_axes[3] = "width"
torch.onnx.export(
model,
random_input,
args.export_path,
opset_version=args.opset,
input_names=["input"],
output_names=["output"],
dynamic_axes={
"input": dynamic_axes,
"output": dynamic_axes
},
)
def check_onnx_export(export_path):
save_model(
shape_inference.infer_shapes(
load_model(export_path),
check_type=True,
strict_mode=True,
data_prop=True
),
export_path
)
save_model(
SymbolicShapeInference.infer_shapes(load_model(export_path),
auto_merge=True,
guess_output_rank=True
),
export_path,
)
model_onnx = onnx.load(export_path) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
model_onnx, check = onnxsim.simplify(model_onnx)
assert check, "assert check failed"
onnx.save(model_onnx, export_path)
if __name__ == '__main__':
args = parse_args()
create_onnx_export(args)
print(f'ONNX file successfully created at {args.export_path}')
check_onnx_export(args.export_path)
print(f'ONNX file at {args.export_path} verifed shapes and simplified')
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