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Merge pull request #413 from rwightman/eca-weights

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Add new weights for ecaresnet26t/50t/269d models.
pull/418/head
Ross Wightman 3 years ago committed by GitHub
parent 3a7aa95f7e a39c3ee216
commit a7f95818e4
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 107 additions and 132 deletions
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8
README.md
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@ -2,6 +2,14 @@
## What's New
### Feb 8, 2021
* Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352.
* `ecaresnet26t` - 79.88 top-1 @ 320x320, 79.08 @ 256x256
* `ecaresnet50t` - 82.35 top-1 @ 320x320, 81.52 @ 256x256
* `ecaresnet269d` - 84.93 top-1 @ 352x352, 84.87 @ 320x320
* Remove separate tiered (`t`) vs tiered_narrow (`tn`) ResNet model defs, all `tn` changed to `t` and `t` models removed (`seresnext26t_32x4d` only model w/ weights that was removed).
* Support model default_cfgs with separate train vs test resolution `test_input_size`
### Jan 30, 2021
* Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on [paper](https://arxiv.org/abs/2101.08692)

2
sotabench.py
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@ -122,8 +122,6 @@ model_list = [
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
_entry('seresnext26t_32x4d', 'SE-ResNeXt-26-T 32x4d', '1812.01187',
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
_entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),

9
timm/data/config.py
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@ -5,7 +5,7 @@ from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=True):
new_config = {}
default_cfg = default_cfg
if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
@ -25,8 +25,11 @@ def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
elif 'img_size' in args and args['img_size'] is not None:
assert isinstance(args['img_size'], int)
input_size = (in_chans, args['img_size'], args['img_size'])
elif 'input_size' in default_cfg:
input_size = default_cfg['input_size']
else:
if use_test_size and 'test_input_size' in default_cfg:
input_size = default_cfg['test_input_size']
elif 'input_size' in default_cfg:
input_size = default_cfg['input_size']
new_config['input_size'] = input_size
# resolve interpolation method

6
timm/models/efficientnet_blocks.py
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@ -350,7 +350,8 @@ class EdgeResidual(nn.Module):
self.drop_path_rate = drop_path_rate
# Expansion convolution
self.conv_exp = create_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type)
self.conv_exp = create_conv2d(
in_chs, mid_chs, exp_kernel_size, stride=stride, dilation=dilation, padding=pad_type)
self.bn1 = norm_layer(mid_chs, **norm_kwargs)
self.act1 = act_layer(inplace=True)
@ -362,8 +363,7 @@ class EdgeResidual(nn.Module):
self.se = None
# Point-wise linear projection
self.conv_pwl = create_conv2d(
mid_chs, out_chs, pw_kernel_size, stride=stride, dilation=dilation, padding=pad_type)
self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)
self.bn2 = norm_layer(out_chs, **norm_kwargs)
def feature_info(self, location):

11
timm/models/layers/test_time_pool.py
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@ -36,14 +36,17 @@ class TestTimePoolHead(nn.Module):
return x.view(x.size(0), -1)
def apply_test_time_pool(model, config):
def apply_test_time_pool(model, config, use_test_size=True):
test_time_pool = False
if not hasattr(model, 'default_cfg') or not model.default_cfg:
return model, False
if (config['input_size'][-1] > model.default_cfg['input_size'][-1] and
config['input_size'][-2] > model.default_cfg['input_size'][-2]):
if use_test_size and 'test_input_size' in model.default_cfg:
df_input_size = model.default_cfg['test_input_size']
else:
df_input_size = model.default_cfg['input_size']
if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]:
_logger.info('Target input size %s > pretrained default %s, using test time pooling' %
(str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
(str(config['input_size'][-2:]), str(df_input_size[-2:])))
model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
test_time_pool = True
return model, test_time_pool

195
timm/models/resnet.py
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@ -7,7 +7,6 @@ ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered ste
Copyright 2020 Ross Wightman
"""
import math
import copy
import torch
import torch.nn as nn
@ -58,24 +57,18 @@ default_cfgs = {
'resnet101': _cfg(url='', interpolation='bicubic'),
'resnet101d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'resnet101d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet152': _cfg(url='', interpolation='bicubic'),
'resnet152d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'resnet152d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'resnet200': _cfg(url='', interpolation='bicubic'),
'resnet200d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'resnet200d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)),
'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
'tv_resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
@ -146,7 +139,7 @@ default_cfgs = {
'seresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth',
interpolation='bicubic'),
'seresnet50tn': _cfg(
'seresnet50t': _cfg(
url='',
interpolation='bicubic',
first_conv='conv1.0'),
@ -158,10 +151,9 @@ default_cfgs = {
interpolation='bicubic'),
'seresnet152d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'seresnet152d_320': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=1.0, test_input_size=(3, 320, 320)
),
'seresnet200d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
@ -171,18 +163,11 @@ default_cfgs = {
# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
'seresnext26_32x4d': _cfg(
url='',
interpolation='bicubic'),
'seresnext26d_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'seresnext26t_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'seresnext26tn_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
interpolation='bicubic',
first_conv='conv1.0'),
@ -201,8 +186,10 @@ default_cfgs = {
first_conv='conv1.0'),
# Efficient Channel Attention ResNets
'ecaresnet18': _cfg(),
'ecaresnet50': _cfg(),
'ecaresnet26t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnetlight': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth',
interpolation='bicubic'),
@ -214,10 +201,13 @@ default_cfgs = {
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth',
interpolation='bicubic',
first_conv='conv1.0'),
'ecaresnet50t': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnet101d': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth',
interpolation='bicubic',
first_conv='conv1.0'),
interpolation='bicubic', first_conv='conv1.0'),
'ecaresnet101d_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
interpolation='bicubic',
@ -226,17 +216,17 @@ default_cfgs = {
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
'ecaresnet269d': _cfg(
url='',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(10, 10),
crop_pct=1.0, test_input_size=(3, 352, 352)),
# Efficient Channel Attention ResNeXts
'ecaresnext26tn_32x4d': _cfg(
'ecaresnext26t_32x4d': _cfg(
url='',
interpolation='bicubic',
first_conv='conv1.0'),
'ecaresnext50_32x4d': _cfg(
interpolation='bicubic', first_conv='conv1.0'),
'ecaresnext50t_32x4d': _cfg(
url='',
interpolation='bicubic'),
interpolation='bicubic', first_conv='conv1.0'),
# ResNets with anti-aliasing blur pool
'resnetblur18': _cfg(
@ -529,8 +519,7 @@ class ResNet(nn.Module):
The type of stem:
* '', default - a single 7x7 conv with a width of stem_width
* 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width//4 * 6, stem_width * 2
* 'deep_tiered_narrow' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
block_reduce_first: int, default 1
Reduction factor for first convolution output width of residual blocks,
1 for all archs except senets, where 2
@ -564,18 +553,17 @@ class ResNet(nn.Module):
deep_stem = 'deep' in stem_type
inplanes = stem_width * 2 if deep_stem else 64
if deep_stem:
stem_chs_1 = stem_chs_2 = stem_width
stem_chs = (stem_width, stem_width)
if 'tiered' in stem_type:
stem_chs_1 = 3 * (stem_width // 4)
stem_chs_2 = stem_width if 'narrow' in stem_type else 6 * (stem_width // 4)
stem_chs = (3 * (stem_width // 4), stem_width)
self.conv1 = nn.Sequential(*[
nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False),
norm_layer(stem_chs_1),
nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
norm_layer(stem_chs[0]),
act_layer(inplace=True),
nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False),
norm_layer(stem_chs_2),
nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
norm_layer(stem_chs[1]),
act_layer(inplace=True),
nn.Conv2d(stem_chs_2, inplanes, 3, stride=1, padding=1, bias=False)])
nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
else:
self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(inplanes)
@ -732,14 +720,6 @@ def resnet101d(pretrained=False, **kwargs):
return _create_resnet('resnet101d', pretrained, **model_args)
@register_model
def resnet101d_320(pretrained=False, **kwargs):
"""Constructs a ResNet-101-D model.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet101d_320', pretrained, **model_args)
@register_model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
@ -757,15 +737,6 @@ def resnet152d(pretrained=False, **kwargs):
return _create_resnet('resnet152d', pretrained, **model_args)
@register_model
def resnet152d_320(pretrained=False, **kwargs):
"""Constructs a ResNet-152-D model.
"""
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet152d_320', pretrained, **model_args)
@register_model
def resnet200(pretrained=False, **kwargs):
"""Constructs a ResNet-200 model.
@ -783,15 +754,6 @@ def resnet200d(pretrained=False, **kwargs):
return _create_resnet('resnet200d', pretrained, **model_args)
@register_model
def resnet200d_320(pretrained=False, **kwargs):
"""Constructs a ResNet-200-D model. NOTE: Duplicate of 200D above w/ diff default cfg for 320x320.
"""
model_args = dict(
block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet200d_320', pretrained, **model_args)
@register_model
def tv_resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model with original Torchvision weights.
@ -1068,19 +1030,15 @@ def swsl_resnext101_32x16d(pretrained=True, **kwargs):
@register_model
def ecaresnet18(pretrained=False, **kwargs):
""" Constructs an ECA-ResNet-18 model.
"""
model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnet18', pretrained, **model_args)
@register_model
def ecaresnet50(pretrained=False, **kwargs):
"""Constructs an ECA-ResNet-50 model.
def ecaresnet26t(pretrained=False, **kwargs):
"""Constructs an ECA-ResNeXt-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem and ECA attn.
"""
model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnet50', pretrained, **model_args)
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnet26t', pretrained, **model_args)
@register_model
@ -1104,6 +1062,17 @@ def ecaresnet50d_pruned(pretrained=False, **kwargs):
return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **model_args)
@register_model
def ecaresnet50t(pretrained=False, **kwargs):
"""Constructs an ECA-ResNet-50-T model.
Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn.
"""
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnet50t', pretrained, **model_args)
@register_model
def ecaresnetlight(pretrained=False, **kwargs):
"""Constructs a ResNet-50-D light model with eca.
@ -1156,16 +1125,27 @@ def ecaresnet269d(pretrained=False, **kwargs):
@register_model
def ecaresnext26tn_32x4d(pretrained=False, **kwargs):
"""Constructs an ECA-ResNeXt-26-TN model.
def ecaresnext26t_32x4d(pretrained=False, **kwargs):
"""Constructs an ECA-ResNeXt-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.
this model replaces SE module with the ECA module
in the deep stem. This model replaces SE module with the ECA module
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered_narrow', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnext26tn_32x4d', pretrained, **model_args)
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnext26t_32x4d', pretrained, **model_args)
@register_model
def ecaresnext50t_32x4d(pretrained=False, **kwargs):
"""Constructs an ECA-ResNeXt-50-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. This model replaces SE module with the ECA module
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
return _create_resnet('ecaresnext50t_32x4d', pretrained, **model_args)
@register_model
@ -1203,11 +1183,11 @@ def seresnet50(pretrained=False, **kwargs):
@register_model
def seresnet50tn(pretrained=False, **kwargs):
def seresnet50t(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered_narrow', avg_down=True,
block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True,
block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnet50tn', pretrained, **model_args)
return _create_resnet('seresnet50t', pretrained, **model_args)
@register_model
@ -1250,22 +1230,6 @@ def seresnet269d(pretrained=False, **kwargs):
return _create_resnet('seresnet269d', pretrained, **model_args)
@register_model
def seresnet152d_320(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnet152d_320', pretrained, **model_args)
@register_model
def seresnext26_32x4d(pretrained=False, **kwargs):
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4,
block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnext26_32x4d', pretrained, **model_args)
@register_model
def seresnext26d_32x4d(pretrained=False, **kwargs):
"""Constructs a SE-ResNeXt-26-D model.`
@ -1281,7 +1245,7 @@ def seresnext26d_32x4d(pretrained=False, **kwargs):
@register_model
def seresnext26t_32x4d(pretrained=False, **kwargs):
"""Constructs a SE-ResNet-26-T model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 48, 64 channels
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem.
"""
model_args = dict(
@ -1292,14 +1256,11 @@ def seresnext26t_32x4d(pretrained=False, **kwargs):
@register_model
def seresnext26tn_32x4d(pretrained=False, **kwargs):
"""Constructs a SE-ResNeXt-26-TN model.
This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
in the deep stem. The channel number of the middle stem conv is narrower than the 'T' variant.
"""Constructs a SE-ResNeXt-26-T model.
NOTE I deprecated previous 't' model defs and replaced 't' with 'tn', this was the only tn model of note
so keeping this def for backwards compat with any uses out there. Old 't' model is lost.
"""
model_args = dict(
block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
stem_type='deep_tiered_narrow', avg_down=True, block_args=dict(attn_layer='se'), **kwargs)
return _create_resnet('seresnext26tn_32x4d', pretrained, **model_args)
return seresnext26t_32x4d(pretrained=pretrained, **kwargs)
@register_model

2
timm/version.py
Unescape View File

@ -1 +1 @@
__version__ = '0.4.1'
__version__ = '0.4.2'

6
validate.py
Unescape View File

@ -147,8 +147,10 @@ def validate(args):
param_count = sum([m.numel() for m in model.parameters()])
_logger.info('Model %s created, param count: %d' % (args.model, param_count))
data_config = resolve_data_config(vars(args), model=model)
model, test_time_pool = (model, False) if args.no_test_pool else apply_test_time_pool(model, data_config)
data_config = resolve_data_config(vars(args), model=model, use_test_size=True)
test_time_pool = False
if not args.no_test_pool:
model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)
if args.torchscript:
torch.jit.optimized_execution(True)

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