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Add mobilenet edgetpu defs for exp, add ol mobilenet v1 back for completeness / comparison

more_mobile
Ross Wightman 2024-06-13 17:33:04 -07:00
parent 22de845add
commit 9613c76844
3 changed files with 191 additions and 4 deletions
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2
timm/models/_efficientnet_blocks.py
View File

@ -662,7 +662,7 @@ class EdgeResidual(nn.Module):
mid_chs = make_divisible(force_in_chs * exp_ratio)
else:
mid_chs = make_divisible(in_chs * exp_ratio)
groups = num_groups(group_size, in_chs)
groups = num_groups(group_size, mid_chs) # NOTE: Using out_chs of conv_exp for groups calc
self.has_skip = (in_chs == out_chs and stride == 1) and not noskip
use_aa = aa_layer is not None and stride > 1 # FIXME handle dilation

3
timm/models/_efficientnet_builder.py
View File

@ -221,8 +221,9 @@ def _decode_block_str(block_str):
))
else:
assert False, 'Unknown block type (%s)' % block_type
if 'gs' in options:
block_args['group_size'] = options['gs']
block_args['group_size'] = int(options['gs'])
return block_args, num_repeat

190
timm/models/efficientnet.py
View File

@ -80,6 +80,7 @@ class EfficientNet(nn.Module):
in_chans: int = 3,
stem_size: int = 32,
fix_stem: bool = False,
stem_kernel_size: int = 3,
output_stride: int = 32,
pad_type: str = '',
act_layer: Optional[LayerType] = None,
@ -104,7 +105,7 @@ class EfficientNet(nn.Module):
# Stem
if not fix_stem:
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.conv_stem = create_conv2d(in_chans, stem_size, stem_kernel_size, stride=2, padding=pad_type)
self.bn1 = norm_act_layer(stem_size, inplace=True)
# Middle stages (IR/ER/DS Blocks)
@ -478,6 +479,34 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
return model
def _gen_mobilenet_v1(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
"""
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381
"""
arch_def = [
['dsa_r1_k3_s1_c64'],
['dsa_r2_k3_s2_c128'],
['dsa_r2_k3_s2_c256'],
['dsa_r6_k3_s2_c512'],
['dsa_r2_k3_s2_c1024'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
num_features=1024 if fix_stem_head else max(1024, round_chs_fn(1024)),
stem_size=32,
fix_stem=fix_stem_head,
round_chs_fn=round_chs_fn,
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu6'),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
""" Generate MobileNet-V2 network
@ -1056,6 +1085,95 @@ def _gen_tinynet(
return model
def _gen_mobilenet_edgetpu(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
"""
Based on definitions in: https://github.com/tensorflow/models/tree/d2427a562f401c9af118e47af2f030a0a5599f55/official/projects/edgetpu/vision
"""
if 'edgetpu_v2' in variant:
stem_size = 64
stem_kernel_size = 5
group_size = 64
num_features = 1280
act_layer = resolve_act_layer(kwargs, 'relu')
def _arch_def(chs: List[int], group_size: int):
return [
# stage 0, 112x112 in
[f'cn_r1_k1_s1_c{chs[0]}'], # NOTE with expansion==1, official impl block ends just 1x1 pwl
# stage 1, 112x112 in
[f'er_r1_k3_s2_e8_c{chs[1]}', f'er_r1_k3_s1_e4_gs{group_size}_c{chs[1]}'],
# stage 2, 56x56 in
[
f'er_r1_k3_s2_e8_c{chs[2]}',
f'er_r1_k3_s1_e4_gs{group_size}_c{chs[2]}',
f'er_r1_k3_s1_e4_c{chs[2]}',
f'er_r1_k3_s1_e4_gs{group_size}_c{chs[2]}',
],
# stage 3, 28x28 in
[f'er_r1_k3_s2_e8_c{chs[3]}', f'ir_r3_k3_s1_e4_c{chs[3]}'],
# stage 4, 14x14in
[f'ir_r1_k3_s1_e8_c{chs[4]}', f'ir_r3_k3_s1_e4_c{chs[4]}'],
# stage 5, 14x14in
[f'ir_r1_k3_s2_e8_c{chs[5]}', f'ir_r3_k3_s1_e4_c{chs[5]}'],
# stage 6, 7x7 in
[f'ir_r1_k3_s1_e8_c{chs[6]}'],
]
if 'edgetpu_v2_xs' in variant:
stem_size = 32
stem_kernel_size = 3
channels = [16, 32, 48, 96, 144, 160, 192]
elif 'edgetpu_v2_s' in variant:
channels = [24, 48, 64, 128, 160, 192, 256]
elif 'edgetpu_v2_m' in variant:
channels = [32, 64, 80, 160, 192, 240, 320]
num_features = 1344
elif 'edgetpu_v2_l' in variant:
stem_kernel_size = 7
group_size = 128
channels = [32, 64, 96, 192, 240, 256, 384]
num_features = 1408
else:
assert False
arch_def = _arch_def(channels, group_size)
else:
# v1
stem_size = 32
stem_kernel_size = 3
num_features = 1280
act_layer = resolve_act_layer(kwargs, 'relu')
arch_def = [
# stage 0, 112x112 in
['cn_r1_k1_s1_c16'],
# stage 1, 112x112 in
['er_r1_k3_s2_e8_c32', 'er_r3_k3_s1_e4_c32'],
# stage 2, 56x56 in
['er_r1_k3_s2_e8_c48', 'er_r3_k3_s1_e4_c48'],
# stage 3, 28x28 in
['ir_r1_k3_s2_e8_c96', 'ir_r3_k3_s1_e4_c96'],
# stage 4, 14x14in
['ir_r1_k3_s1_e8_c96_noskip', 'ir_r3_k3_s1_e4_c96'],
# stage 5, 14x14in
['ir_r1_k5_s2_e8_c160', 'ir_r3_k5_s1_e4_c160'],
# stage 6, 7x7 in
['ir_r1_k3_s1_e8_c192'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=num_features,
stem_size=stem_size,
stem_kernel_size=stem_kernel_size,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
@ -1086,6 +1204,9 @@ default_cfgs = generate_default_cfgs({
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth',
hf_hub_id='timm/'),
'mobilenet_100.untrained': _cfg(),
'mobilenet_125.untrained': _cfg(),
'mobilenetv2_035.untrained': _cfg(),
'mobilenetv2_050.lamb_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth',
@ -1395,7 +1516,6 @@ default_cfgs = generate_default_cfgs({
hf_hub_id='timm/',
input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'tf_efficientnet_es.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
hf_hub_id='timm/',
@ -1584,6 +1704,23 @@ default_cfgs = generate_default_cfgs({
input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475)
url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth',
hf_hub_id='timm/'),
'mobilenet_edgetpu_100.untrained': _cfg(
# hf_hub_id='timm/',
input_size=(3, 224, 224), crop_pct=0.9),
'mobilenet_edgetpu_v2_xs.untrained': _cfg(
# hf_hub_id='timm/',
input_size=(3, 224, 224), crop_pct=0.9),
'mobilenet_edgetpu_v2_s.untrained': _cfg(
#hf_hub_id='timm/',
input_size=(3, 224, 224), crop_pct=0.9),
'mobilenet_edgetpu_v2_m.untrained': _cfg(
#hf_hub_id='timm/',
input_size=(3, 224, 224), crop_pct=0.9),
'mobilenet_edgetpu_v2_l.untrained': _cfg(
#hf_hub_id='timm/',
input_size=(3, 224, 224), crop_pct=0.9),
})
@ -1650,6 +1787,20 @@ def mnasnet_small(pretrained=False, **kwargs) -> EfficientNet:
return model
@register_model
def mobilenet_100(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V1 """
model = _gen_mobilenet_v1('mobilenet_100', 1.0, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenet_125(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V1 """
model = _gen_mobilenet_v1('mobilenet_125', 1.25, pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenetv2_035(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet V2 w/ 0.35 channel multiplier """
@ -2510,6 +2661,41 @@ def tinynet_e(pretrained=False, **kwargs) -> EfficientNet:
return model
@register_model
def mobilenet_edgetpu_100(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet-EdgeTPU-v1 100. """
model = _gen_mobilenet_edgetpu('mobilenet_edgetpu_100', pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenet_edgetpu_v2_xs(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet-EdgeTPU-v2 Extra Small. """
model = _gen_mobilenet_edgetpu('mobilenet_edgetpu_v2_xs', pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenet_edgetpu_v2_s(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet-EdgeTPU-v2 Small. """
model = _gen_mobilenet_edgetpu('mobilenet_edgetpu_v2_s', pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenet_edgetpu_v2_m(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet-EdgeTPU-v2 Medium. """
model = _gen_mobilenet_edgetpu('mobilenet_edgetpu_v2_m', pretrained=pretrained, **kwargs)
return model
@register_model
def mobilenet_edgetpu_v2_l(pretrained=False, **kwargs) -> EfficientNet:
""" MobileNet-EdgeTPU-v2 Large. """
model = _gen_mobilenet_edgetpu('mobilenet_edgetpu_v2_l', pretrained=pretrained, **kwargs)
return model
register_model_deprecations(__name__, {
'tf_efficientnet_b0_ap': 'tf_efficientnet_b0.ap_in1k',
'tf_efficientnet_b1_ap': 'tf_efficientnet_b1.ap_in1k',