mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Add new weights for ecaresnet26t/50t/269d models. Remove distinction between 't' and 'tn' (tiered models), tn is now t. Add test time img size spec to default cfg.
This commit is contained in:
Ross Wightman
parent
4203efa36d
commit
68a4144882
@ -122,8 +122,6 @@ model_list = [
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
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_entry('seresnext26t_32x4d', 'SE-ResNeXt-26-T 32x4d', '1812.01187',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
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_entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
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_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
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_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),
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@ -5,7 +5,7 @@ from .constants import *
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_logger = logging.getLogger(__name__)
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def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
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def resolve_data_config(args, default_cfg={}, model=None, use_test_size=False, verbose=True):
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new_config = {}
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default_cfg = default_cfg
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if not default_cfg and model is not None and hasattr(model, 'default_cfg'):
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@ -25,8 +25,11 @@ def resolve_data_config(args, default_cfg={}, model=None, verbose=True):
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elif 'img_size' in args and args['img_size'] is not None:
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assert isinstance(args['img_size'], int)
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input_size = (in_chans, args['img_size'], args['img_size'])
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elif 'input_size' in default_cfg:
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input_size = default_cfg['input_size']
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else:
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if use_test_size and 'test_input_size' in default_cfg:
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input_size = default_cfg['test_input_size']
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elif 'input_size' in default_cfg:
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input_size = default_cfg['input_size']
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new_config['input_size'] = input_size
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# resolve interpolation method
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@ -36,14 +36,17 @@ class TestTimePoolHead(nn.Module):
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return x.view(x.size(0), -1)
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def apply_test_time_pool(model, config):
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def apply_test_time_pool(model, config, use_test_size=True):
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test_time_pool = False
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if not hasattr(model, 'default_cfg') or not model.default_cfg:
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return model, False
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if (config['input_size'][-1] > model.default_cfg['input_size'][-1] and
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config['input_size'][-2] > model.default_cfg['input_size'][-2]):
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if use_test_size and 'test_input_size' in model.default_cfg:
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df_input_size = model.default_cfg['test_input_size']
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else:
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df_input_size = model.default_cfg['input_size']
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if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]:
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_logger.info('Target input size %s > pretrained default %s, using test time pooling' %
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(str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
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(str(config['input_size'][-2:]), str(df_input_size[-2:])))
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model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
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test_time_pool = True
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return model, test_time_pool
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@ -7,7 +7,6 @@ ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered ste
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Copyright 2020 Ross Wightman
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"""
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import math
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import copy
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import torch
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import torch.nn as nn
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@ -58,24 +57,18 @@ default_cfgs = {
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'resnet101': _cfg(url='', interpolation='bicubic'),
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'resnet101d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'resnet101d_320': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'resnet152': _cfg(url='', interpolation='bicubic'),
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'resnet152d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'resnet152d_320': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'resnet200': _cfg(url='', interpolation='bicubic'),
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'resnet200d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'resnet200d_320': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)),
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'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
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'tv_resnet50': _cfg(url='https://download.pytorch.org/models/resnet50-19c8e357.pth'),
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'tv_resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
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@ -146,7 +139,7 @@ default_cfgs = {
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'seresnet50': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth',
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interpolation='bicubic'),
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'seresnet50tn': _cfg(
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'seresnet50t': _cfg(
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url='',
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interpolation='bicubic',
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first_conv='conv1.0'),
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@ -158,10 +151,9 @@ default_cfgs = {
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interpolation='bicubic'),
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'seresnet152d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'seresnet152d_320': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 320, 320)
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),
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'seresnet200d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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@ -171,18 +163,11 @@ default_cfgs = {
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# Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
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'seresnext26_32x4d': _cfg(
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url='',
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interpolation='bicubic'),
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'seresnext26d_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'seresnext26t_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'seresnext26tn_32x4d': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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@ -201,8 +186,10 @@ default_cfgs = {
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first_conv='conv1.0'),
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# Efficient Channel Attention ResNets
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'ecaresnet18': _cfg(),
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'ecaresnet50': _cfg(),
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'ecaresnet26t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=0.95, test_input_size=(3, 320, 320)),
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'ecaresnetlight': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth',
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interpolation='bicubic'),
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@ -214,10 +201,13 @@ default_cfgs = {
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'ecaresnet50t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
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crop_pct=0.95, test_input_size=(3, 320, 320)),
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'ecaresnet101d': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth',
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interpolation='bicubic',
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first_conv='conv1.0'),
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interpolation='bicubic', first_conv='conv1.0'),
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'ecaresnet101d_pruned': _cfg(
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url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
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interpolation='bicubic',
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@ -226,17 +216,17 @@ default_cfgs = {
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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'ecaresnet269d': _cfg(
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url='',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
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interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(8, 8),
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crop_pct=1.0, test_input_size=(3, 352, 352)),
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# Efficient Channel Attention ResNeXts
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'ecaresnext26tn_32x4d': _cfg(
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'ecaresnext26t_32x4d': _cfg(
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url='',
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interpolation='bicubic',
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first_conv='conv1.0'),
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'ecaresnext50_32x4d': _cfg(
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interpolation='bicubic', first_conv='conv1.0'),
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'ecaresnext50t_32x4d': _cfg(
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url='',
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interpolation='bicubic'),
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interpolation='bicubic', first_conv='conv1.0'),
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# ResNets with anti-aliasing blur pool
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'resnetblur18': _cfg(
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@ -529,8 +519,7 @@ class ResNet(nn.Module):
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The type of stem:
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* '', default - a single 7x7 conv with a width of stem_width
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* 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
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* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width//4 * 6, stem_width * 2
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* 'deep_tiered_narrow' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
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* 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
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block_reduce_first: int, default 1
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Reduction factor for first convolution output width of residual blocks,
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1 for all archs except senets, where 2
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@ -564,18 +553,17 @@ class ResNet(nn.Module):
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deep_stem = 'deep' in stem_type
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inplanes = stem_width * 2 if deep_stem else 64
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if deep_stem:
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stem_chs_1 = stem_chs_2 = stem_width
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stem_chs = (stem_width, stem_width)
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if 'tiered' in stem_type:
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stem_chs_1 = 3 * (stem_width // 4)
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stem_chs_2 = stem_width if 'narrow' in stem_type else 6 * (stem_width // 4)
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stem_chs = (3 * (stem_width // 4), stem_width)
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self.conv1 = nn.Sequential(*[
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nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False),
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norm_layer(stem_chs_1),
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nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
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norm_layer(stem_chs[0]),
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act_layer(inplace=True),
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nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False),
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norm_layer(stem_chs_2),
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nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
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norm_layer(stem_chs[1]),
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act_layer(inplace=True),
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nn.Conv2d(stem_chs_2, inplanes, 3, stride=1, padding=1, bias=False)])
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nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
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else:
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self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = norm_layer(inplanes)
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@ -732,14 +720,6 @@ def resnet101d(pretrained=False, **kwargs):
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return _create_resnet('resnet101d', pretrained, **model_args)
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@register_model
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def resnet101d_320(pretrained=False, **kwargs):
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"""Constructs a ResNet-101-D model.
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"""
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model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet101d_320', pretrained, **model_args)
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@register_model
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def resnet152(pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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@ -757,15 +737,6 @@ def resnet152d(pretrained=False, **kwargs):
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return _create_resnet('resnet152d', pretrained, **model_args)
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@register_model
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def resnet152d_320(pretrained=False, **kwargs):
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"""Constructs a ResNet-152-D model.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet152d_320', pretrained, **model_args)
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@register_model
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def resnet200(pretrained=False, **kwargs):
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"""Constructs a ResNet-200 model.
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@ -783,15 +754,6 @@ def resnet200d(pretrained=False, **kwargs):
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return _create_resnet('resnet200d', pretrained, **model_args)
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@register_model
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def resnet200d_320(pretrained=False, **kwargs):
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"""Constructs a ResNet-200-D model. NOTE: Duplicate of 200D above w/ diff default cfg for 320x320.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
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return _create_resnet('resnet200d_320', pretrained, **model_args)
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@register_model
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def tv_resnet34(pretrained=False, **kwargs):
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"""Constructs a ResNet-34 model with original Torchvision weights.
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@ -1075,6 +1037,18 @@ def ecaresnet18(pretrained=False, **kwargs):
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return _create_resnet('ecaresnet18', pretrained, **model_args)
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@register_model
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def ecaresnet26t(pretrained=False, **kwargs):
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"""Constructs an ECA-ResNeXt-26-T model.
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This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
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in the deep stem and ECA attn.
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"""
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model_args = dict(
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block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32,
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stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
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return _create_resnet('ecaresnet26t', pretrained, **model_args)
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@register_model
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def ecaresnet50(pretrained=False, **kwargs):
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"""Constructs an ECA-ResNet-50 model.
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@ -1104,6 +1078,17 @@ def ecaresnet50d_pruned(pretrained=False, **kwargs):
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return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **model_args)
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@register_model
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def ecaresnet50t(pretrained=False, **kwargs):
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"""Constructs an ECA-ResNet-50-T model.
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Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn.
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"""
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model_args = dict(
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block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32,
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stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs)
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return _create_resnet('ecaresnet50t', pretrained, **model_args)
|
||||
|
||||
|
||||
@register_model
|
||||
def ecaresnetlight(pretrained=False, **kwargs):
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||||
"""Constructs a ResNet-50-D light model with eca.
|
||||
@ -1156,16 +1141,27 @@ 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 +1199,11 @@ 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 +1246,6 @@ 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 +1261,7 @@ 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 +1272,11 @@ 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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user