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README.md

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+---
+license: apache-2.0
+tags:
+- adafactor
+- batchnorm
+- concat-mlp
+- efficientformer
+- linear-warmup
+- multitask
+- orthogonal
+- standard
+- swish
+- xlarge
+---
+
+# model_395024253_efficientformer_xlarge.py
+
+## Model Overview
+
+A **xlarge**-scale implementation of the **efficientformer** architecture, built for **multitask** tasks.
+
+## Architecture
+
+- **Architecture**: efficientformer
+- **Scale**: xlarge
+- **Attention**: standard
+- **Fusion strategy**: concat mlp
+- **Task head**: multitask
+- **Activation**: swish
+- **Normalization**: batchnorm
+- **Initialization**: orthogonal
+
+## Training
+
+- **Optimizer**: adafactor
+- **LR scheduler**: linear warmup
+
+## Files
+
+- `model_395024253_efficientformer_xlarge.py` — main artifact of this repository
+
+## License
+
+See the license field above.

model_395024253_efficientformer_xlarge.py

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+import torch, torch.nn as nn, math
+
+class M(nn.Module):
+    def __init__(self, d=512, L=10, H=8, nc=10):
+        super().__init__()
+        self.pe = nn.Conv2d(3, d, 16, 16)
+        self.cls = nn.Parameter(torch.randn(1,1,d)*.02)
+        self.pos = nn.Parameter(torch.randn(1,197,d)*.02)
+        self.blks = nn.ModuleList([nn.TransformerEncoderLayer(d, H, d*4, .1, activation='gelu', batch_first=True, norm_first=True) for _ in range(L)])
+        self.ln = nn.LayerNorm(d)
+        self.te = nn.Embedding(30522, d, padding_idx=0)
+        self.tpos = nn.Parameter(torch.randn(1,128,d)*.02)
+        self.tblks = nn.ModuleList([nn.TransformerEncoderLayer(d, H, d*4, .1, activation='gelu', batch_first=True, norm_first=True) for _ in range(L)])
+        self.tln = nn.LayerNorm(d)
+        self.fuse = nn.ModuleList([nn.TransformerEncoderLayer(d, H, d*4, .1, activation='gelu', batch_first=True, norm_first=True) for _ in range(2)])
+        self.fln = nn.LayerNorm(d)
+        self.head = nn.Sequential(nn.Linear(d,d), nn.SiLU(), nn.Dropout(.1), nn.Linear(d,nc))
+        self._init()
+
+    def _init(self):
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.orthogonal_(m.weight)
+                if m.bias is not None: nn.init.zeros_(m.bias)
+
+    def enc_img(self, x):
+        x = self.pe(x).flatten(2).transpose(1,2)
+        x = torch.cat([self.cls.expand(x.size(0),-1,-1), x], 1)
+        x = x + self.pos
+        for b in self.blks: x = b(x)
+        return self.ln(x)
+
+    def enc_txt(self, ids, mask=None):
+        x = self.te(ids) + self.tpos[:, :ids.size(1)]
+        m = (mask == 0) if mask is not None else None
+        for b in self.tblks: x = b(x, src_key_padding_mask=m)
+        return self.tln(x)
+
+    def forward(self, img, ids, mask=None, lbl=None):
+        fi = self.enc_img(img)
+        ft = self.enc_txt(ids, mask)
+        x = ft
+        for f in self.fuse: x = f(x)
+        x = self.fln(x[:, 0])
+        logits = self.head(x)
+        loss = nn.functional.cross_entropy(logits, lbl) if lbl is not None else None
+        return {'logits': logits, 'loss': loss}
+
+if __name__ == '__main__':
+    m = M()
+    print(f'Params: {sum(p.numel() for p in m.parameters()):,}')
+    o = m(torch.randn(2,3,224,224), torch.randint(0,30522,(2,128)), torch.ones(2,128), torch.tensor([0,1]))
+    print(o['logits'].shape, o['loss'].item())