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openseg-group / openseg.pytorch

The official Pytorch implementation of OCNet, OCRNet, and SegFix.
MIT License
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Models can't match for torch1.7 branch #52

Closed TiankaiHang closed 3 years ago

TiankaiHang commented 3 years ago

Thanks for your nice work! When i'm trying to use the branch pytorch1.7, i meet some problems.

For example bash scripts/cityscapes/hrnet/run_h_48_d_4_ocr.sh val 1

I got `Logging to ./log/cityscapes/hrnet_w48_ocr_1.log 2021-02-18 13:51:54,123 INFO [offset_helper.py, 54] engery/max-distance: 5 engery/min-distance: 0 2021-02-18 13:51:54,123 INFO [offset_helper.py, 61] direction/num_classes: 8 scale: 1 2021-02-18 13:51:54,123 INFO [offset_helper.py, 66] c4 align axis: False 2021-02-18 13:51:54,175 INFO [module_runner.py, 44] BN Type is torchsyncbn. 2021-02-18 13:52:06,206 INFO [module_runner.py, 84] Loading checkpoint from ./checkpoints/cityscapes/hrnet_w48_ocr_1_latest.pth... Traceback (most recent call last): File "main.py", line 217, in model = Tester(configer)
File "/home/v-tihang/code_base/MyCodeBase/torch-learning/openseg.pytorch/segmentor/tester.py", line 69, in init self._init_model() File "/home/v-tihang/code_base/MyCodeBase/torch-learning/openseg.pytorch/segmentor/tester.py", line 73, in _init_model self.seg_net = self.module_runner.load_net(self.seg_net) File "/home/v-tihang/code_base/MyCodeBase/torch-learning/openseg.pytorch/segmentor/tools/module_runner.py", line 104, in load_net self.load_state_dict(net.module, checkpoint_dict, self.configer.get('network', 'resume_strict')) File "/home/v-tihang/code_base/MyCodeBase/torch-learning/openseg.pytorch/segmentor/tools/module_runner.py", line 156, in load_state_dict raise RuntimeError(err_msg) RuntimeError: unexpected key in source state_dict: conv3x3.1.weight, conv3x3.1.bias, conv3x3.1.running_mean, conv3x3.1.running_var, ocr_distri_head.object_context_block.f_pixel.1.weight, ocr_distri_head.object_context_block.f_pixel.1.bias, ocr_distri_head.object_context_block.f_pixel.1.running_mean, ocr_distri_head.object_context_block.f_pixel.1.running_var, ocr_distri_head.object_context_block.f_pixel.3.weight, ocr_distri_head.object_context_block.f_pixel.3.bias, ocr_distri_head.object_context_block.f_pixel.3.running_mean, ocr_distri_head.object_context_block.f_pixel.3.running_var, ocr_distri_head.object_context_block.f_object.1.weight, ocr_distri_head.object_context_block.f_object.1.bias, ocr_distri_head.object_context_block.f_object.1.running_mean, ocr_distri_head.object_context_block.f_object.1.running_var, ocr_distri_head.object_context_block.f_object.3.weight, ocr_distri_head.object_context_block.f_object.3.bias, ocr_distri_head.object_context_block.f_object.3.running_mean, ocr_distri_head.object_context_block.f_object.3.running_var, ocr_distri_head.object_context_block.f_down.1.weight, ocr_distri_head.object_context_block.f_down.1.bias, ocr_distri_head.object_context_block.f_down.1.running_mean, ocr_distri_head.object_context_block.f_down.1.running_var, ocr_distri_head.object_context_block.f_up.1.weight, ocr_distri_head.object_context_block.f_up.1.bias, ocr_distri_head.object_context_block.f_up.1.running_mean, ocr_distri_head.object_context_block.f_up.1.running_var, ocr_distri_head.conv_bn_dropout.1.weight, ocr_distri_head.conv_bn_dropout.1.bias, ocr_distri_head.conv_bn_dropout.1.running_mean, ocr_distri_head.conv_bn_dropout.1.running_var, aux_head.1.weight, aux_head.1.bias, aux_head.1.running_mean, aux_head.1.running_var

missing keys in source state_dict: backbone.stage3.1.branches.0.0.bn1.num_batches_tracked, backbone.stage4.0.fuse_layers.3.2.0.1.num_batches_tracked, backbone.stage4.0.branches.3.2.bn1.num_batches_tracked, backbone.stage3.0.branches.0.1.bn1.num_batches_tracked, backbone.stage4.1.branches.0.1.bn1.num_batches_tracked, backbone.stage3.0.branches.1.3.bn1.num_batches_tracked, backbone.stage4.1.branches.3.2.bn1.num_batches_tracked, backbone.stage3.2.fuse_layers.2.0.0.1.num_batches_tracked, backbone.stage3.1.branches.2.2.bn1.num_batches_tracked, backbone.stage4.0.branches.3.1.bn2.num_batches_tracked, backbone.stage3.3.branches.1.2.bn2.num_batches_tracked, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.stage4.1.branches.2.1.bn1.num_batches_tracked, backbone.stage4.2.fuse_layers.0.1.1.num_batches_tracked, backbone.stage4.0.branches.1.0.bn2.num_batches_tracked, backbone.layer1.0.bn2.num_batches_tracked, backbone.stage4.1.branches.1.2.bn1.num_batches_tracked, 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evaluate the result... ERROR: Cannot find any ground truth images to use for evaluation. Searched for: /cityscapes/val/label/*.png`

PkuRainBow commented 3 years ago

@TiankaiHang Thanks for your interest in our work. All the released checkpoints are previously trained with pytorch-0.4.1. We will the main checkpoints based on pytorch-1.7 very soon.

TiankaiHang commented 3 years ago

@TiankaiHang Thanks for your interest in our work. All the released checkpoints are previously trained with pytorch-0.4.1. We will the main checkpoints based on pytorch-1.7 very soon.

thanks :-)

PkuRainBow commented 3 years ago

If you have access to 8x GPUs, it is very easy to reproduce the 83+% performance on Cityscapes val set based on the https://github.com/openseg-group/openseg.pytorch/blob/pytorch-1.7/scripts/cityscapes/hrnet/run_h_48_d_4_ocr_rmi_paddle.sh within several hours.