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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import numpy as np
import torch
import torch._C
import torch.serialization
from mmengine import Config
from mmengine.runner import load_checkpoint
from torch import nn
from mmseg.models import build_segmentor
torch.manual_seed(3)
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_version = x.split('rc')
digit_version.append(int(patch_version[0]) - 1)
digit_version.append(int(patch_version[1]))
return digit_version
def check_torch_version():
torch_minimum_version = '1.8.0'
torch_version = digit_version(torch.__version__)
assert (torch_version >= digit_version(torch_minimum_version)), \
f'Torch=={torch.__version__} is not support for converting to ' \
f'torchscript. Please install pytorch>={torch_minimum_version}.'
def _convert_batchnorm(module):
module_output = module
if isinstance(module, torch.nn.SyncBatchNorm):
module_output = torch.nn.BatchNorm2d(module.num_features, module.eps,
module.momentum, module.affine,
module.track_running_stats)
if module.affine:
module_output.weight.data = module.weight.data.clone().detach()
module_output.bias.data = module.bias.data.clone().detach()
# keep requires_grad unchanged
module_output.weight.requires_grad = module.weight.requires_grad
module_output.bias.requires_grad = module.bias.requires_grad
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
for name, child in module.named_children():
module_output.add_module(name, _convert_batchnorm(child))
del module
return module_output
def _demo_mm_inputs(input_shape, num_classes):
"""Create a superset of inputs needed to run test or train batches.
Args:
input_shape (tuple):
input batch dimensions
num_classes (int):
number of semantic classes
"""
(N, C, H, W) = input_shape
rng = np.random.RandomState(0)
imgs = rng.rand(*input_shape)
segs = rng.randint(
low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8)
img_metas = [{
'img_shape': (H, W, C),
'ori_shape': (H, W, C),
'pad_shape': (H, W, C),
'filename': '<demo>.png',
'scale_factor': 1.0,
'flip': False,
} for _ in range(N)]
mm_inputs = {
'imgs': torch.FloatTensor(imgs).requires_grad_(True),
'img_metas': img_metas,
'gt_semantic_seg': torch.LongTensor(segs)
}
return mm_inputs
def pytorch2libtorch(model,
input_shape,
show=False,
output_file='tmp.pt',
verify=False):
"""Export Pytorch model to TorchScript model and verify the outputs are
same between Pytorch and TorchScript.
Args:
model (nn.Module): Pytorch model we want to export.
input_shape (tuple): Use this input shape to construct
the corresponding dummy input and execute the model.
show (bool): Whether print the computation graph. Default: False.
output_file (string): The path to where we store the
output TorchScript model. Default: `tmp.pt`.
verify (bool): Whether compare the outputs between
Pytorch and TorchScript. Default: False.
"""
if isinstance(model.decode_head, nn.ModuleList):
num_classes = model.decode_head[-1].num_classes
else:
num_classes = model.decode_head.num_classes
mm_inputs = _demo_mm_inputs(input_shape, num_classes)
imgs = mm_inputs.pop('imgs')
# replace the original forword with forward_dummy
model.forward = model.forward_dummy
model.eval()
traced_model = torch.jit.trace(
model,
example_inputs=imgs,
check_trace=verify,
)
if show:
print(traced_model.graph)
traced_model.save(output_file)
print(f'Successfully exported TorchScript model: {output_file}')
def parse_args():
parser = argparse.ArgumentParser(
description='Convert MMSeg to TorchScript')
parser.add_argument('config', help='test config file path')
parser.add_argument('--checkpoint', help='checkpoint file', default=None)
parser.add_argument(
'--show', action='store_true', help='show TorchScript graph')
parser.add_argument(
'--verify', action='store_true', help='verify the TorchScript model')
parser.add_argument('--output-file', type=str, default='tmp.pt')
parser.add_argument(
'--shape',
type=int,
nargs='+',
default=[512, 512],
help='input image size (height, width)')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
check_torch_version()
if len(args.shape) == 1:
input_shape = (1, 3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (
1,
3,
) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
cfg.model.pretrained = None
# build the model and load checkpoint
cfg.model.train_cfg = None
segmentor = build_segmentor(
cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
# convert SyncBN to BN
segmentor = _convert_batchnorm(segmentor)
if args.checkpoint:
load_checkpoint(segmentor, args.checkpoint, map_location='cpu')
# convert the PyTorch model to LibTorch model
pytorch2libtorch(
segmentor,
input_shape,
show=args.show,
output_file=args.output_file,
verify=args.verify)
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