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Update some comments re preliminary EfficientNet-V2 assumptions

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This commit is contained in:
Ross Wightman 2021-04-03 12:00:25 -07:00
parent 2b49ab7a36
commit fb896c0b26
2 changed files with 26 additions and 5 deletions
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11
timm/models/efficientnet.py
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@ -829,11 +829,16 @@ def _gen_efficientnet_v2s(variant, channel_multiplier=1.0, depth_multiplier=1.0,
and weights
Ref impl:
Paper: https://arxiv.org/abs/2104.00298
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['er_r2_k3_s1_e1_c24_noskip'],
# FIXME it's not clear if the FusedMBConv layers have SE enabled for the Small variant,
# Table 4 suggests no. 23.94M params w/o, 23.98 with which is closer to 24M.
# ['er_r2_k3_s1_e1_c24_se0.25'],
# ['er_r4_k3_s2_e4_c48_se0.25'],
# ['er_r4_k3_s2_e4_c64_se0.25'],
['er_r2_k3_s1_e1_c24'],
['er_r4_k3_s2_e4_c48'],
['er_r4_k3_s2_e4_c64'],
['ir_r6_k3_s2_e4_c128_se0.25'],
@ -846,7 +851,7 @@ def _gen_efficientnet_v2s(variant, channel_multiplier=1.0, depth_multiplier=1.0,
stem_size=24,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=resolve_act_layer(kwargs, 'silu'),
act_layer=resolve_act_layer(kwargs, 'silu'), # FIXME this is an assumption, paper does not mention
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)

20
timm/models/efficientnet_blocks.py
View File

@ -205,7 +205,14 @@ class DepthwiseSeparableConv(nn.Module):
class InvertedResidual(nn.Module):
""" Inverted residual block w/ optional SE and CondConv routing"""
""" Inverted residual block w/ optional SE
Originally used in MobileNet-V2 - https://arxiv.org/abs/1801.04381v4, this layer is often
referred to as 'MBConv' for (Mobile inverted bottleneck conv) and is also used in
* MNasNet - https://arxiv.org/abs/1807.11626
* EfficientNet - https://arxiv.org/abs/1905.11946
* MobileNet-V3 - https://arxiv.org/abs/1905.02244
"""
def __init__(self, in_chs, out_chs, dw_kernel_size=3,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
@ -333,7 +340,16 @@ class CondConvResidual(InvertedResidual):
class EdgeResidual(nn.Module):
""" Residual block with expansion convolution followed by pointwise-linear w/ stride"""
""" Residual block with expansion convolution followed by pointwise-linear w/ stride
Originally introduced in `EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML`
- https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
This layer is also called FusedMBConv in the MobileDet, EfficientNet-X, and EfficientNet-V2 papers
* MobileDet - https://arxiv.org/abs/2004.14525
* EfficientNet-X - https://arxiv.org/abs/2102.05610
* EfficientNet-V2 - https://arxiv.org/abs/2104.00298
"""
def __init__(self, in_chs, out_chs, exp_kernel_size=3, exp_ratio=1.0, fake_in_chs=0,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False, pw_kernel_size=1,