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60 changes: 30 additions & 30 deletions
60
configs/dest/dest_simpatt-b0_1024x1024_160k_cityscapes.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,30 +1,30 @@ | ||
| _base_ = [ | ||
| '../_base_/models/dest_simpatt-b0.py', | ||
| '../_base_/datasets/cityscapes_1024x1024.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | ||
| ] | ||
|
|
||
| optimizer = dict( | ||
| _delete_=True, | ||
| type='AdamW', | ||
| lr=0.00006, | ||
| betas=(0.9, 0.999), | ||
| weight_decay=0.01, | ||
| paramwise_cfg=dict( | ||
| custom_keys={ | ||
| 'pos_block': dict(decay_mult=0.), | ||
| 'norm': dict(decay_mult=0.), | ||
| 'head': dict(lr_mult=10.) | ||
| })) | ||
|
|
||
| lr_config = dict( | ||
| _delete_=True, | ||
| policy='poly', | ||
| warmup='linear', | ||
| warmup_iters=1500, | ||
| warmup_ratio=1e-6, | ||
| power=1.0, | ||
| min_lr=0.0, | ||
| by_epoch=False) | ||
|
|
||
| data = dict(samples_per_gpu=1, workers_per_gpu=1) | ||
| _base_ = [ | ||
| '../_base_/models/dest_simpatt-b0.py', | ||
| '../_base_/datasets/cityscapes_1024x1024.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | ||
| ] | ||
|
|
||
| optimizer = dict( | ||
| _delete_=True, | ||
| type='AdamW', | ||
| lr=0.00006, | ||
| betas=(0.9, 0.999), | ||
| weight_decay=0.01, | ||
| paramwise_cfg=dict( | ||
| custom_keys={ | ||
| 'pos_block': dict(decay_mult=0.), | ||
| 'norm': dict(decay_mult=0.), | ||
| 'head': dict(lr_mult=10.) | ||
| })) | ||
|
|
||
| lr_config = dict( | ||
| _delete_=True, | ||
| policy='poly', | ||
| warmup='linear', | ||
| warmup_iters=1500, | ||
| warmup_ratio=1e-6, | ||
| power=1.0, | ||
| min_lr=0.0, | ||
| by_epoch=False) | ||
|
|
||
| data = dict(samples_per_gpu=1, workers_per_gpu=1) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,9 +1,9 @@ | ||
| _base_ = ['./dest_simpatt-b0_1024x1024_160k_cityscapes.py'] | ||
|
|
||
| embed_dims = [64, 128, 250, 320] | ||
|
|
||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=None, | ||
| backbone=dict(embed_dims=embed_dims), | ||
| decode_head=dict(in_channels=embed_dims, channels=64)) | ||
| _base_ = ['./dest_simpatt-b0_1024x1024_160k_cityscapes.py'] | ||
|
|
||
| embed_dims = [64, 128, 250, 320] | ||
|
|
||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=None, | ||
| backbone=dict(embed_dims=embed_dims), | ||
| decode_head=dict(in_channels=embed_dims, channels=64)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,9 +1,9 @@ | ||
| _base_ = ['./dest_simpatt-b0_1024x1024_160k_cityscapes.py'] | ||
|
|
||
| embed_dims = [64, 128, 250, 320] | ||
|
|
||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=None, | ||
| backbone=dict(embed_dims=embed_dims, num_layers=[3, 3, 6, 3]), | ||
| decode_head=dict(in_channels=embed_dims, channels=64)) | ||
| _base_ = ['./dest_simpatt-b0_1024x1024_160k_cityscapes.py'] | ||
|
|
||
| embed_dims = [64, 128, 250, 320] | ||
|
|
||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained=None, | ||
| backbone=dict(embed_dims=embed_dims, num_layers=[3, 3, 6, 3]), | ||
| decode_head=dict(in_channels=embed_dims, channels=64)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,52 +1,54 @@ | ||
| import torch | ||
| import torch.nn as nn | ||
|
|
||
| from mmcv.cnn import ConvModule | ||
| from mmseg.models.builder import HEADS | ||
| from mmseg.models.decode_heads.decode_head import BaseDecodeHead | ||
|
|
||
|
|
||
| @HEADS.register_module() | ||
| class DESTHead(BaseDecodeHead): | ||
|
|
||
| def __init__(self, interpolate_mode='bilinear', **kwargs): | ||
| super().__init__(input_transform='multiple_select', **kwargs) | ||
| self.interpolate_mode = interpolate_mode | ||
| num_inputs = len(self.in_channels) | ||
| assert num_inputs == len(self.in_index) | ||
| self.fuse_in_channels = self.in_channels.copy() | ||
| for i in range(num_inputs - 1): | ||
| self.fuse_in_channels[i] += self.fuse_in_channels[i + 1] | ||
| self.convs = nn.ModuleList() | ||
| for i in range(num_inputs): | ||
| self.convs.append( | ||
| ConvModule( | ||
| in_channels=self.in_channels[i], | ||
| out_channels=self.in_channels[i], | ||
| kernel_size=1, | ||
| stride=1, | ||
| act_cfg=self.act_cfg)) | ||
|
|
||
| self.fuse_convs = nn.ModuleList() | ||
| for i in range(num_inputs): | ||
| self.fuse_convs.append( | ||
| ConvModule( | ||
| in_channels=self.fuse_in_channels[i], | ||
| out_channels=self.in_channels[i], | ||
| kernel_size=3, | ||
| stride=1, | ||
| padding=1, | ||
| act_cfg=self.act_cfg)) | ||
|
|
||
| self.upsample = nn.ModuleList([ | ||
| nn.Sequential(nn.Upsample(scale_factor=2, mode=interpolate_mode)) | ||
| ] * len(self.in_channels)) | ||
|
|
||
| def forward(self, inputs): | ||
| for idx in reversed(range(len(inputs))): | ||
| x = self.convs[idx](inputs[idx]) | ||
| if idx != len(inputs) - 1: | ||
| x = torch.concat([feat, x], dim=1) | ||
| x = self.upsample[idx](x) | ||
| feat = self.fuse_convs[idx](x) | ||
| return self.cls_seg(feat) | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import torch | ||
| import torch.nn as nn | ||
| from mmcv.cnn import ConvModule | ||
|
|
||
| from mmseg.models.builder import HEADS | ||
| from mmseg.models.decode_heads.decode_head import BaseDecodeHead | ||
|
|
||
|
|
||
| @HEADS.register_module() | ||
| class DESTHead(BaseDecodeHead): | ||
|
|
||
| def __init__(self, interpolate_mode='bilinear', **kwargs): | ||
| super().__init__(input_transform='multiple_select', **kwargs) | ||
| self.interpolate_mode = interpolate_mode | ||
| num_inputs = len(self.in_channels) | ||
| assert num_inputs == len(self.in_index) | ||
| self.fuse_in_channels = self.in_channels.copy() | ||
| for i in range(num_inputs - 1): | ||
| self.fuse_in_channels[i] += self.fuse_in_channels[i + 1] | ||
| self.convs = nn.ModuleList() | ||
| for i in range(num_inputs): | ||
| self.convs.append( | ||
| ConvModule( | ||
| in_channels=self.in_channels[i], | ||
| out_channels=self.in_channels[i], | ||
| kernel_size=1, | ||
| stride=1, | ||
| act_cfg=self.act_cfg)) | ||
|
|
||
| self.fuse_convs = nn.ModuleList() | ||
| for i in range(num_inputs): | ||
| self.fuse_convs.append( | ||
| ConvModule( | ||
| in_channels=self.fuse_in_channels[i], | ||
| out_channels=self.in_channels[i], | ||
| kernel_size=3, | ||
| stride=1, | ||
| padding=1, | ||
| act_cfg=self.act_cfg)) | ||
|
|
||
| self.upsample = nn.ModuleList([ | ||
| nn.Sequential(nn.Upsample(scale_factor=2, mode=interpolate_mode)) | ||
| ] * len(self.in_channels)) | ||
|
|
||
| def forward(self, inputs): | ||
| feat = None | ||
| for idx in reversed(range(len(inputs))): | ||
| x = self.convs[idx](inputs[idx]) | ||
| if idx != len(inputs) - 1: | ||
| x = torch.concat([feat, x], dim=1) | ||
| x = self.upsample[idx](x) | ||
| feat = self.fuse_convs[idx](x) | ||
| return self.cls_seg(feat) |
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