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

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.

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