EfficientNet-Lite model added w/ converted checkpoints, validation in progress...

5 years ago · bd05258f7b
parent 7deacf5477
commit bd05258f7b
2 changed files with 204 additions and 6 deletions
--- a/timm/models/efficientnet.py
+++ b/timm/models/efficientnet.py
@ -52,12 +52,14 @@ default_cfgs = {
    'mnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'),
    'mnasnet_140': _cfg(url=''),
+
    'semnasnet_050': _cfg(url=''),
    'semnasnet_075': _cfg(url=''),
    'semnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'),
    'semnasnet_140': _cfg(url=''),
    'mnasnet_small': _cfg(url=''),
+
    'mobilenetv2_100': _cfg(url=''),
    'fbnetc_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
@ -65,6 +67,7 @@ default_cfgs = {
    'spnasnet_100': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
        interpolation='bilinear'),
+
    'efficientnet_b0': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'),
    'efficientnet_b1': _cfg(
@ -94,15 +97,32 @@ default_cfgs = {
        url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
    'efficientnet_l2': _cfg(
        url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961),
+
    'efficientnet_es': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth'),
    'efficientnet_em': _cfg(
        url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
    'efficientnet_el': _cfg(
        url='', input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
+
    'efficientnet_cc_b0_4e': _cfg(url=''),
    'efficientnet_cc_b0_8e': _cfg(url=''),
    'efficientnet_cc_b1_8e': _cfg(url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
+
+    'efficientnet_lite0': _cfg(
+        url=''),
+    'efficientnet_lite1': _cfg(
+        url='',
+        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
+    'efficientnet_lite2': _cfg(
+        url='',
+        input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890),
+    'efficientnet_lite3': _cfg(
+        url='',
+        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
+    'efficientnet_lite4': _cfg(
+        url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
+
    'tf_efficientnet_b0': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
        input_size=(3, 224, 224)),
@ -130,6 +150,7 @@ default_cfgs = {
    'tf_efficientnet_b8': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth',
        input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
+
    'tf_efficientnet_b0_ap': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth',
        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)),
@ -165,6 +186,7 @@ default_cfgs = {
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth',
        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
        input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954),
+
    'tf_efficientnet_b0_ns': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth',
        input_size=(3, 224, 224)),
@ -195,6 +217,7 @@ default_cfgs = {
    'tf_efficientnet_l2_ns': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth',
        input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96),
+
    'tf_efficientnet_es': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth',
        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
@ -207,6 +230,7 @@ default_cfgs = {
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth',
        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
+
    'tf_efficientnet_cc_b0_4e': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth',
        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
@ -217,6 +241,33 @@ default_cfgs = {
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth',
        mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
+
+    'tf_efficientnet_lite0': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        interpolation='bicubic',  # should be bilinear but bicubic better match for TF bilinear at low res
+    ),
+    'tf_efficientnet_lite1': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882,
+        interpolation='bicubic',  # should be bilinear but bicubic better match for TF bilinear at low res
+    ),
+    'tf_efficientnet_lite2': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890,
+        interpolation='bicubic',  # should be bilinear but bicubic better match for TF bilinear at low res
+    ),
+    'tf_efficientnet_lite3': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, interpolation='bilinear'),
+    'tf_efficientnet_lite4': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922, interpolation='bilinear'),
+
    'mixnet_s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth'),
    'mixnet_m': _cfg(
@ -226,6 +277,7 @@ default_cfgs = {
    'mixnet_xl': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth'),
    'mixnet_xxl': _cfg(),
+
    'tf_mixnet_s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
    'tf_mixnet_m': _cfg(
@ -253,7 +305,7 @@ class EfficientNet(nn.Module):

    def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32,
                 channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
+                 output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
        super(EfficientNet, self).__init__()
        norm_kwargs = norm_kwargs or {}
@ -264,6 +316,7 @@ class EfficientNet(nn.Module):
        self._in_chs = in_chans

        # Stem
+        if not fix_stem:
            stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
        self.bn1 = norm_layer(stem_size, **norm_kwargs)
@ -333,7 +386,7 @@ class EfficientNetFeatures(nn.Module):

    def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='pre_pwl',
                 in_chans=3, stem_size=32, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
-                 output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
+                 output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
                 se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
        super(EfficientNetFeatures, self).__init__()
        norm_kwargs = norm_kwargs or {}
@ -346,6 +399,7 @@ class EfficientNetFeatures(nn.Module):
        self._in_chs = in_chans

        # Stem
+        if not fix_stem:
            stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
        self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type)
        self.bn1 = norm_layer(stem_size, **norm_kwargs)
@ -707,6 +761,47 @@ def _gen_efficientnet_condconv(
    return model


+def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+    """Creates an EfficientNet-Lite model.
+
+    Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
+    Paper: https://arxiv.org/abs/1905.11946
+
+    EfficientNet params
+    name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
+      'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
+      'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
+      'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
+      'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
+      'efficientnet-lite4': (1.4, 1.8, 300, 0.3),
+
+    Args:
+      channel_multiplier: multiplier to number of channels per layer
+      depth_multiplier: multiplier to number of repeats per stage
+    """
+    arch_def = [
+        ['ds_r1_k3_s1_e1_c16'],
+        ['ir_r2_k3_s2_e6_c24'],
+        ['ir_r2_k5_s2_e6_c40'],
+        ['ir_r3_k3_s2_e6_c80'],
+        ['ir_r3_k5_s1_e6_c112'],
+        ['ir_r4_k5_s2_e6_c192'],
+        ['ir_r1_k3_s1_e6_c320'],
+    ]
+    model_kwargs = dict(
+        block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True),
+        num_features=1280,
+        stem_size=32,
+        fix_stem=True,
+        channel_multiplier=channel_multiplier,
+        act_layer=nn.ReLU6,
+        norm_kwargs=resolve_bn_args(kwargs),
+        **kwargs,
+    )
+    model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
+    return model
+
+
 def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
    """Creates a MixNet Small model.

@ -1032,6 +1127,51 @@ def efficientnet_cc_b1_8e(pretrained=False, **kwargs):
    return model


+@register_model
+def efficientnet_lite0(pretrained=False, **kwargs):
+    """ EfficientNet-Lite0 """
+    # NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
+    model = _gen_efficientnet_lite(
+        'efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def efficientnet_lite1(pretrained=False, **kwargs):
+    """ EfficientNet-Lite1 """
+    # NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
+    model = _gen_efficientnet_lite(
+        'efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def efficientnet_lite2(pretrained=False, **kwargs):
+    """ EfficientNet-Lite2 """
+    # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
+    model = _gen_efficientnet_lite(
+        'efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def efficientnet_lite3(pretrained=False, **kwargs):
+    """ EfficientNet-Lite3 """
+    # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
+    model = _gen_efficientnet_lite(
+        'efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def efficientnet_lite4(pretrained=False, **kwargs):
+    """ EfficientNet-Lite4 """
+    # NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
+    model = _gen_efficientnet_lite(
+        'efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
+    return model
+
+
@register_model
 def tf_efficientnet_b0(pretrained=False, **kwargs):
    """ EfficientNet-B0. Tensorflow compatible variant  """
@ -1386,6 +1526,61 @@ def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs):
    return model


+@register_model
+def tf_efficientnet_lite0(pretrained=False, **kwargs):
+    """ EfficientNet-Lite0 """
+    # NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
+    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_efficientnet_lite(
+        'tf_efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tf_efficientnet_lite1(pretrained=False, **kwargs):
+    """ EfficientNet-Lite1 """
+    # NOTE for train, drop_rate should be 0.2, drop_path_rate should be 0.2
+    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_efficientnet_lite(
+        'tf_efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tf_efficientnet_lite2(pretrained=False, **kwargs):
+    """ EfficientNet-Lite2 """
+    # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
+    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_efficientnet_lite(
+        'tf_efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tf_efficientnet_lite3(pretrained=False, **kwargs):
+    """ EfficientNet-Lite3 """
+    # NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
+    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_efficientnet_lite(
+        'tf_efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tf_efficientnet_lite4(pretrained=False, **kwargs):
+    """ EfficientNet-Lite4 """
+    # NOTE for train, drop_rate should be 0.4, drop_path_rate should be 0.2
+    kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
+    kwargs['pad_type'] = 'same'
+    model = _gen_efficientnet_lite(
+        'tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs)
+    return model
+
+
@register_model
 def mixnet_s(pretrained=False, **kwargs):
    """Creates a MixNet Small model.
--- a/timm/models/efficientnet_builder.py
+++ b/timm/models/efficientnet_builder.py
@ -174,7 +174,7 @@ def _scale_stage_depth(stack_args, repeats, depth_multiplier=1.0, depth_trunc='c
    return sa_scaled


-def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1):
+def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_multiplier=1, fix_first_last=False):
    arch_args = []
    for stack_idx, block_strings in enumerate(arch_def):
        assert isinstance(block_strings, list)
@ -187,6 +187,9 @@ def decode_arch_def(arch_def, depth_multiplier=1.0, depth_trunc='ceil', experts_
                ba['num_experts'] *= experts_multiplier
            stack_args.append(ba)
            repeats.append(rep)
+        if fix_first_last and (stack_idx == 0 or stack_idx == len(arch_def) - 1):
+            arch_args.append(_scale_stage_depth(stack_args, repeats, 1.0, depth_trunc))
+        else:
            arch_args.append(_scale_stage_depth(stack_args, repeats, depth_multiplier, depth_trunc))
    return arch_args