EfficientNet_V2¶
ConvNet
- class lucid.models.EfficientNet_V2(config: EfficientNetV2Config)¶
EfficientNet_V2 builds on the EfficientNet architecture, which employs a compound scaling method to balance depth, width, and resolution for optimal performance. The V2 variant introduces further improvements such as training with larger batch sizes, using higher-resolution images, and advanced regularization techniques like stochastic depth and progressive learning. Model structure is defined through EfficientNetV2Config.
%%{init: {"flowchart":{"curve":"monotoneX","nodeSpacing":50,"rankSpacing":50}} }%%
flowchart LR
linkStyle default stroke-width:2.0px
subgraph sg_m0["<span style='font-size:20px;font-weight:700'>efficientnet_v2_m</span>"]
style sg_m0 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
subgraph sg_m1["stem"]
style sg_m1 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
m2["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,3,224,224) → (1,24,112,112)</span>"];
m3["BatchNorm2d"];
m4["Swish"];
end
subgraph sg_m5["features"]
style sg_m5 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
subgraph sg_m6["_FusedMBConv x 13"]
style sg_m6 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
m6_in(["Input"]);
m6_out(["Output"]);
style m6_in fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
style m6_out fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
m7["Sequential<br/><span style='font-size:11px;font-weight:400'>(1,24,112,112) → (1,24,56,56)</span>"];
m8["Identity"];
end
subgraph sg_m9["_MBConv x 44"]
style sg_m9 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
m9_in(["Input"]);
m9_out(["Output"]);
style m9_in fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
style m9_out fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
m10(["Sequential x 2<br/><span style='font-size:11px;font-weight:400'>(1,80,14,14) → (1,320,7,7)</span>"]);
m11["_SEBlock<br/><span style='font-size:11px;font-weight:400'>(1,320,7,7) → (1,320,1,1)</span>"];
m12["Sequential<br/><span style='font-size:11px;font-weight:400'>(1,320,7,7) → (1,160,7,7)</span>"];
end
end
subgraph sg_m13["head"]
style sg_m13 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
m14["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,512,4,4) → (1,2048,4,4)</span>"];
m15["BatchNorm2d"];
m16["Swish"];
end
m17["AdaptiveAvgPool2d<br/><span style='font-size:11px;color:#b7791f;font-weight:400'>(1,2048,4,4) → (1,2048,1,1)</span>"];
m18["Dropout"];
m19["Linear<br/><span style='font-size:11px;color:#2b6cb0;font-weight:400'>(1,2048) → (1,1000)</span>"];
end
input["Input<br/><span style='font-size:11px;color:#a67c00;font-weight:400'>(1,3,224,224)</span>"];
output["Output<br/><span style='font-size:11px;color:#a67c00;font-weight:400'>(1,1000)</span>"];
style input fill:#fff3cd,stroke:#a67c00,stroke-width:1px;
style output fill:#fff3cd,stroke:#a67c00,stroke-width:1px;
style m2 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
style m3 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
style m4 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
style m8 fill:#ebf8ff,stroke:#2b6cb0,stroke-width:1px;
style m14 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
style m15 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
style m16 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
style m17 fill:#fefcbf,stroke:#b7791f,stroke-width:1px;
style m18 fill:#edf2f7,stroke:#4a5568,stroke-width:1px;
style m19 fill:#ebf8ff,stroke:#2b6cb0,stroke-width:1px;
input --> m2;
m10 --> m11;
m11 --> m12;
m12 -.-> m9_in;
m12 --> m9_out;
m14 --> m15;
m15 --> m16;
m16 --> m17;
m17 --> m18;
m18 --> m19;
m19 --> output;
m2 --> m3;
m3 --> m4;
m4 -.-> m7;
m6_in -.-> m7;
m6_out -.-> m10;
m6_out -.-> m6_in;
m7 --> m8;
m8 -.-> m6_in;
m8 --> m6_out;
m9_in -.-> m10;
m9_out --> m14;
m9_out -.-> m9_in;
Class Signature¶
class EfficientNet_V2(nn.Module):
def __init__(self, config: EfficientNetV2Config) -> None
Parameters¶
config (EfficientNetV2Config): Configuration object describing the V2 block sequence, classifier size, dropout, and progressive drop-path rate.
Warning
Ensure the block_cfg inside EfficientNetV2Config is well-defined to avoid shape mismatches or runtime errors during the forward pass.
Examples¶
import lucid
from lucid.models import EfficientNet_V2, EfficientNetV2Config
config = EfficientNetV2Config(
block_cfg=[
(True, 24, 3, 1, 1, 1, 0),
(False, 48, 3, 2, 4, 2, 4),
],
num_classes=100,
dropout=0.2,
drop_path_rate=0.1,
)
model = EfficientNet_V2(config)
input_tensor = lucid.random.randn(1, 3, 224, 224)
output = model(input_tensor)
print(output.shape)