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Fix memory_efficient mode for DenseNets. Add AntiAliasing (Blur) support for DenseNets and create one test model. Add lr cycle/mul params to train args.

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This commit is contained in:
Ross Wightman 2020-05-22 16:16:45 -07:00
parent 7df83258c9
commit 6441e9cc1b
3 changed files with 27 additions and 9 deletions
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22
timm/models/densenet.py
View File

@ -14,7 +14,7 @@ from torch.jit.annotations import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import load_pretrained
from .layers import SelectAdaptivePool2d, BatchNormAct2d, create_norm_act
from .layers import SelectAdaptivePool2d, BatchNormAct2d, create_norm_act, BlurPool2d
from .registry import register_model
__all__ = ['DenseNet']
@ -71,9 +71,9 @@ class DenseLayer(nn.Module):
def call_checkpoint_bottleneck(self, x):
# type: (List[torch.Tensor]) -> torch.Tensor
def closure(*xs):
return self.bottleneck_fn(*xs)
return self.bottleneck_fn(xs)
return cp.checkpoint(closure, x)
return cp.checkpoint(closure, *x)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
@ -132,11 +132,14 @@ class DenseBlock(nn.ModuleDict):
class DenseTransition(nn.Sequential):
def __init__(self, num_input_features, num_output_features, norm_act_layer=nn.BatchNorm2d):
def __init__(self, num_input_features, num_output_features, norm_act_layer=nn.BatchNorm2d, aa_layer=None):
super(DenseTransition, self).__init__()
self.add_module('norm', norm_act_layer(num_input_features))
self.add_module('conv', nn.Conv2d(
num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
if aa_layer is not None:
self.add_module('pool', aa_layer(num_output_features, stride=2))
else:
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
@ -301,6 +304,17 @@ def densenet121(pretrained=False, **kwargs):
return model
@register_model
def densenetblur121d(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = _densenet(
'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep',
aa_layer=BlurPool2d, **kwargs)
return model
@register_model
def densenet121d(pretrained=False, **kwargs):
r"""Densenet-121 model from

8
timm/scheduler/scheduler_factory.py
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@ -23,12 +23,12 @@ def create_scheduler(args, optimizer):
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=num_epochs,
t_mul=1.0,
t_mul=args.lr_cycle_mul,
lr_min=args.min_lr,
decay_rate=args.decay_rate,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
cycle_limit=1,
cycle_limit=args.lr_cycle_limit,
t_in_epochs=True,
noise_range_t=noise_range,
noise_pct=args.lr_noise_pct,
@ -40,11 +40,11 @@ def create_scheduler(args, optimizer):
lr_scheduler = TanhLRScheduler(
optimizer,
t_initial=num_epochs,
t_mul=1.0,
t_mul=args.lr_cycle_mul,
lr_min=args.min_lr,
warmup_lr_init=args.warmup_lr,
warmup_t=args.warmup_epochs,
cycle_limit=1,
cycle_limit=args.lr_cycle_limit,
t_in_epochs=True,
noise_range_t=noise_range,
noise_pct=args.lr_noise_pct,

4
train.py
View File

@ -111,6 +111,10 @@ parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT
help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
help='learning rate cycle len multiplier (default: 1.0)')
parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
help='learning rate cycle limit')
parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
help='warmup learning rate (default: 0.0001)')
parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',