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* [Feature]Segformer re-implementation * Using act_cfg and norm_cfg to control activation and normalization * Split this PR into several little PRs * Fix lint error * Remove SegFormerHead * [Feature] Add segformer decode head and related train config * Add ade20K trainval support for segformer 1. Add related train and val configs; 2. Add AlignedResize; * Set arg: find_unused_parameters = True * parameters init refactor * 1. Refactor segformer backbone parameters init; 2. Remove rebundant functions and unit tests; * Remove rebundant codes * Replace Linear Layer to 1X1 Conv * Use nn.ModuleList to refactor segformer head. * Remove local to_xtuple * 1. Remove rebundant codes; 2. Modify module name; * Refactor the backbone of segformer using mmcv.cnn.bricks.transformer.py * Fix some code logic bugs. * Add mit_convert.py to match pretrain keys of segformer. * Resolve some comments. * 1. Add some assert to ensure right params; 2. Support flexible peconv position; * Add pe_index assert and fix unit test. * 1. Add doc string for MixVisionTransformer; 2. Add some unit tests for MixVisionTransformer; * Use hw_shape to pass shape of feature map. * 1. Fix doc string of MixVisionTransformer; 2. Simplify MixFFN; 3. Modify H, W to hw_shape; * Add more unit tests. * Add doc string for shape convertion functions. * Add some unit tests to improve code coverage. * Fix Segformer backbone pretrain weights match bug. * Modify configs of segformer. * resolve the shape convertion functions doc string. * Add pad_to_patch_size arg. * Support progressive test with fewer memory cost. * Modify default value of pad_to_patch_size arg. * Temp code * Using processor to refactor evaluation workflow. * refactor eval hook. * Fix process bar. * Fix middle save argument. * Modify some variable name of dataset evaluate api. * Modify some viriable name of eval hook. * Fix some priority bugs of eval hook. * Fix some bugs about model loading and eval hook. * Add ade20k 640x640 dataset. * Fix related segformer configs. * Depreciated efficient_test. * Fix training progress blocked by eval hook. * Depreciated old test api. * Modify error patch size. * Fix pretrain of mit_b0 * Fix the test api error. * Modify dataset base config. * Fix test api error. * Modify outer api. * Build a sampler test api. * TODO: Refactor format_results. * Modify variable names. * Fix num_classes bug. * Fix sampler index bug. * Fix grammaly bug. * Add part of benchmark results. * Support batch sampler. * More readable test api. * Remove some command arg and fix eval hook bug. * Support format-only arg. * Modify format_results of datasets. * Modify tool which use test apis. * Update readme. * Update readme of segformer. * Updata readme of segformer. * Update segformer readme and fix segformer mit_b4. * Update readme of segformer. * Clean AlignedResize related config. * Clean code from pr #709 * Clean code from pr #709 * Add 512x512 segformer_mit-b5. * Fix lint. * Fix some segformer head bugs. * Add segformer unit tests. * Replace AlignedResize to ResizeToMultiple. * Modify readme of segformer. * Fix bug of ResizeToMultiple. * Add ResizeToMultiple unit tests. * Resolve conflict. * Simplify the implementation of ResizeToMultiple. * Update test results. * Fix multi-scale test error when resize_ratio=1.75 and input size=640x640. * Update segformer results. * Update Segformer results. * Fix some url bugs and pipelines bug. * Move ckpt convertion to tools. * Add segformer official pretrain weights usage. * Clean redundant codes. * Remove redundant codes. * Unfied format. * Add description for segformer converter. * Update workers.
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule
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from mmseg.models.builder import HEADS
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from mmseg.models.decode_heads.decode_head import BaseDecodeHead
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from mmseg.ops import resize
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@HEADS.register_module()
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class SegformerHead(BaseDecodeHead):
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"""The all mlp Head of segformer.
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This head is the implementation of
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`Segformer <https://arxiv.org/abs/2105.15203>` _.
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Args:
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interpolate_mode: The interpolate mode of MLP head upsample operation.
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Default: 'bilinear'.
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"""
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def __init__(self, interpolate_mode='bilinear', **kwargs):
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super().__init__(input_transform='multiple_select', **kwargs)
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self.interpolate_mode = interpolate_mode
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num_inputs = len(self.in_channels)
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assert num_inputs == len(self.in_index)
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self.convs = nn.ModuleList()
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for i in range(num_inputs):
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self.convs.append(
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ConvModule(
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in_channels=self.in_channels[i],
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out_channels=self.channels,
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kernel_size=1,
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stride=1,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg))
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self.fusion_conv = ConvModule(
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in_channels=self.channels * num_inputs,
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out_channels=self.channels,
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kernel_size=1,
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norm_cfg=self.norm_cfg)
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def forward(self, inputs):
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# Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
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inputs = self._transform_inputs(inputs)
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outs = []
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for idx in range(len(inputs)):
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x = inputs[idx]
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conv = self.convs[idx]
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outs.append(
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resize(
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input=conv(x),
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size=inputs[0].shape[2:],
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mode=self.interpolate_mode,
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align_corners=self.align_corners))
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out = self.fusion_conv(torch.cat(outs, dim=1))
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out = self.cls_seg(out)
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return out
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