From c9d54bc1c31ff02cc9f095a7a138e66c89bb8c2d Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Tue, 21 Jul 2020 17:39:29 -0700 Subject: [PATCH] Add HRNet feature extraction, fix senet type, lower feature testing res to 96x96 --- tests/test_models.py | 5 +- timm/models/hrnet.py | 200 ++++++++++++++++++++++++++----------------- timm/models/senet.py | 4 +- 3 files changed, 127 insertions(+), 82 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index b7831ef0..966c5bd8 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -109,12 +109,13 @@ def test_model_forward_torchscript(model_name, batch_size): EXCLUDE_FEAT_FILTERS = [ - 'hrnet*', '*pruned*', # hopefully fix at some point + '*pruned*', # hopefully fix at some point ] if 'GITHUB_ACTIONS' in os.environ and 'Linux' in platform.system(): # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d'] + @pytest.mark.timeout(120) @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS)) @pytest.mark.parametrize('batch_size', [1]) @@ -124,7 +125,7 @@ def test_model_forward_features(model_name, batch_size): model.eval() expected_channels = model.feature_info.channels() assert len(expected_channels) >= 4 # all models here should have at least 4 feature levels by default, some 5 or 6 - input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already... + input_size = (3, 96, 96) # jit compile is already a bit slow and we've tested normal res already... outputs = model(torch.randn((batch_size, *input_size))) assert len(expected_channels) == len(outputs) for e, o in zip(expected_channels, outputs): diff --git a/timm/models/hrnet.py b/timm/models/hrnet.py index f4d47fc2..7796b8a4 100644 --- a/timm/models/hrnet.py +++ b/timm/models/hrnet.py @@ -8,17 +8,15 @@ Original header: Written by Bin Xiao (Bin.Xiao@microsoft.com) Modified by Ke Sun (sunk@mail.ustc.edu.cn) """ - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import logging +from typing import List +import torch import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from .features import FeatureInfo from .helpers import build_model_with_cfg from .layers import SelectAdaptivePool2d from .registry import register_model @@ -403,32 +401,23 @@ class HighResolutionModule(nn.Module): self.branches = self._make_branches( num_branches, blocks, num_blocks, num_channels) self.fuse_layers = self._make_fuse_layers() - self.relu = nn.ReLU(False) + self.fuse_act = nn.ReLU(False) def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels): + error_msg = '' if num_branches != len(num_blocks): - error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( - num_branches, len(num_blocks)) - logger.error(error_msg) - raise ValueError(error_msg) - - if num_branches != len(num_channels): - error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( - num_branches, len(num_channels)) + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(num_branches, len(num_blocks)) + elif num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(num_branches, len(num_channels)) + elif num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(num_branches, len(num_inchannels)) + if error_msg: logger.error(error_msg) raise ValueError(error_msg) - if num_branches != len(num_inchannels): - error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( - num_branches, len(num_inchannels)) - logger.error(error_msg) - raise ValueError(error_msg) - - def _make_one_branch(self, branch_index, block, num_blocks, num_channels, - stride=1): + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): downsample = None - if stride != 1 or \ - self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + if stride != 1 or self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: downsample = nn.Sequential( nn.Conv2d( self.num_inchannels[branch_index], num_channels[branch_index] * block.expansion, @@ -489,22 +478,22 @@ class HighResolutionModule(nn.Module): def get_num_inchannels(self): return self.num_inchannels - def forward(self, x): + def forward(self, x: List[torch.Tensor]): if self.num_branches == 1: return [self.branches[0](x[0])] - for i in range(self.num_branches): - x[i] = self.branches[i](x[i]) + for i, branch in enumerate(self.branches): + x[i] = branch(x[i]) x_fuse = [] - for i in range(len(self.fuse_layers)): - y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for i, fuse_outer in enumerate(self.fuse_layers): + y = x[0] if i == 0 else fuse_outer[0](x[0]) for j in range(1, self.num_branches): if i == j: y = y + x[j] else: - y = y + self.fuse_layers[i][j](x[j]) - x_fuse.append(self.relu(y)) + y = y + fuse_outer[j](x[j]) + x_fuse.append(self.fuse_act(y)) return x_fuse @@ -517,7 +506,7 @@ blocks_dict = { class HighResolutionNet(nn.Module): - def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0): + def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0, head='classification'): super(HighResolutionNet, self).__init__() self.num_classes = num_classes self.drop_rate = drop_rate @@ -525,9 +514,10 @@ class HighResolutionNet(nn.Module): stem_width = cfg['STEM_WIDTH'] self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(stem_width, momentum=_BN_MOMENTUM) + self.act1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(stem_width, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(64, momentum=_BN_MOMENTUM) - self.relu = nn.ReLU(inplace=True) + self.act2 = nn.ReLU(inplace=True) self.stage1_cfg = cfg['STAGE1'] num_channels = self.stage1_cfg['NUM_CHANNELS'][0] @@ -557,31 +547,49 @@ class HighResolutionNet(nn.Module): self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) self.stage4, pre_stage_channels = self._make_stage(self.stage4_cfg, num_channels, multi_scale_output=True) - # Classification Head - self.num_features = 2048 - self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels) - self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) - self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) + self.head = head + self.head_channels = None # set if _make_head called + if head == 'classification': + # Classification Head + self.num_features = 2048 + self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(pre_stage_channels) + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) + elif head == 'incre': + self.num_features = 2048 + self.incre_modules, _, _ = self._make_head(pre_stage_channels, True) + else: + self.incre_modules = None + self.num_features = 256 + + curr_stride = 2 + # module names aren't actually valid here, hook or FeatureNet based extraction would not work + self.feature_info = [dict(num_chs=64, reduction=curr_stride, module='stem')] + for i, c in enumerate(self.head_channels if self.head_channels else num_channels): + curr_stride *= 2 + c = c * 4 if self.head_channels else c # head block expansion factor of 4 + self.feature_info += [dict(num_chs=c, reduction=curr_stride, module=f'stage{i + 1}')] self.init_weights() - def _make_head(self, pre_stage_channels): + def _make_head(self, pre_stage_channels, incre_only=False): head_block = Bottleneck - head_channels = [32, 64, 128, 256] + self.head_channels = [32, 64, 128, 256] # Increasing the #channels on each resolution # from C, 2C, 4C, 8C to 128, 256, 512, 1024 incre_modules = [] for i, channels in enumerate(pre_stage_channels): - incre_modules.append( - self._make_layer(head_block, channels, head_channels[i], 1, stride=1)) + incre_modules.append(self._make_layer(head_block, channels, self.head_channels[i], 1, stride=1)) incre_modules = nn.ModuleList(incre_modules) + if incre_only: + return incre_modules, None, None # downsampling modules downsamp_modules = [] for i in range(len(pre_stage_channels) - 1): - in_channels = head_channels[i] * head_block.expansion - out_channels = head_channels[i + 1] * head_block.expansion + in_channels = self.head_channels[i] * head_block.expansion + out_channels = self.head_channels[i + 1] * head_block.expansion downsamp_module = nn.Sequential( nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=2, padding=1), @@ -593,7 +601,7 @@ class HighResolutionNet(nn.Module): final_layer = nn.Sequential( nn.Conv2d( - in_channels=head_channels[3] * head_block.expansion, + in_channels=self.head_channels[3] * head_block.expansion, out_channels=self.num_features, kernel_size=1, stride=1, padding=0 ), nn.BatchNorm2d(self.num_features, momentum=_BN_MOMENTUM), @@ -655,11 +663,7 @@ class HighResolutionNet(nn.Module): modules = [] for i in range(num_modules): # multi_scale_output is only used last module - if not multi_scale_output and i == num_modules - 1: - reset_multi_scale_output = False - else: - reset_multi_scale_output = True - + reset_multi_scale_output = multi_scale_output or i < num_modules - 1 modules.append(HighResolutionModule( num_branches, block, num_blocks, num_inchannels, num_channels, fuse_method, reset_multi_scale_output) ) @@ -688,40 +692,35 @@ class HighResolutionNet(nn.Module): else: self.classifier = nn.Identity() + def stages(self, x) -> List[torch.Tensor]: + x = self.layer1(x) + + xl = [t(x) for i, t in enumerate(self.transition1)] + yl = self.stage2(xl) + + xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition2)] + yl = self.stage3(xl) + + xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition3)] + yl = self.stage4(xl) + return yl + def forward_features(self, x): + # Stem x = self.conv1(x) x = self.bn1(x) - x = self.relu(x) + x = self.act1(x) x = self.conv2(x) x = self.bn2(x) - x = self.relu(x) - x = self.layer1(x) - - x_list = [] - for i in range(len(self.transition1)): - x_list.append(self.transition1[i](x)) - y_list = self.stage2(x_list) - - x_list = [] - for i in range(len(self.transition2)): - if not isinstance(self.transition2[i], nn.Identity): - x_list.append(self.transition2[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage3(x_list) + x = self.act2(x) - x_list = [] - for i in range(len(self.transition3)): - if not isinstance(self.transition3[i], nn.Identity): - x_list.append(self.transition3[i](y_list[-1])) - else: - x_list.append(y_list[i]) - y_list = self.stage4(x_list) + # Stages + yl = self.stages(x) # Classification Head - y = self.incre_modules[0](y_list[0]) - for i in range(len(self.downsamp_modules)): - y = self.incre_modules[i + 1](y_list[i + 1]) + self.downsamp_modules[i](y) + y = self.incre_modules[0](yl[0]) + for i, down in enumerate(self.downsamp_modules): + y = self.incre_modules[i + 1](yl[i + 1]) + down(y) y = self.final_layer(y) return y @@ -734,10 +733,55 @@ class HighResolutionNet(nn.Module): return x +class HighResolutionNetFeatures(HighResolutionNet): + """HighResolutionNet feature extraction + + The design of HRNet makes it easy to grab feature maps, this class provides a simple wrapper to do so. + It would be more complicated to use the FeatureNet helpers. + + The `feature_location=incre` allows grabbing increased channel count features using part of the + classification head. If `feature_location=''` the default HRNet features are returned. First stem + conv is used for stride 2 features. + """ + + def __init__(self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0.0, + feature_location='incre', out_indices=(0, 1, 2, 3, 4)): + assert feature_location in ('incre', '') + super(HighResolutionNetFeatures, self).__init__( + cfg, in_chans=in_chans, num_classes=num_classes, global_pool=global_pool, + drop_rate=drop_rate, head=feature_location) + self.feature_info = FeatureInfo(self.feature_info, out_indices) + self._out_idx = {i for i in out_indices} + + def forward_features(self, x): + assert False, 'Not supported' + + def forward(self, x) -> List[torch.tensor]: + out = [] + x = self.conv1(x) + x = self.bn1(x) + x = self.act1(x) + if 0 in self._out_idx: + out.append(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.act2(x) + x = self.stages(x) + if self.incre_modules is not None: + x = [incre(f) for f, incre in zip(x, self.incre_modules)] + for i, f in enumerate(x): + if i + 1 in self._out_idx: + out.append(f) + return out + + def _create_hrnet(variant, pretrained, **model_kwargs): - assert not model_kwargs.pop('features_only', False) # feature extraction not figured out yet + model_cls = HighResolutionNet + if model_kwargs.pop('features_only', False): + model_cls = HighResolutionNetFeatures + return build_model_with_cfg( - HighResolutionNet, variant, pretrained, default_cfg=default_cfgs[variant], + model_cls, variant, pretrained, default_cfg=default_cfgs[variant], model_cfg=cfg_cls[variant], **model_kwargs) diff --git a/timm/models/senet.py b/timm/models/senet.py index 2156e4cd..b0cf8de2 100644 --- a/timm/models/senet.py +++ b/timm/models/senet.py @@ -423,14 +423,14 @@ def legacy_seresnet34(pretrained=False, **kwargs): @register_model -def legacy_seresnet50(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def legacy_seresnet50(pretrained=False, **kwargs): model_args = dict( block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, **kwargs) return _create_senet('seresnet50', pretrained, **model_args) @register_model -def legacy_seresnet101(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def legacy_seresnet101(pretrained=False, **kwargs): model_args = dict( block=SEResNetBottleneck, layers=[3, 4, 23, 3], groups=1, reduction=16, **kwargs) return _create_senet('seresnet101', pretrained, **model_args)