Upload 12 files

.gitignore

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+__pycache__/
+*.pyc
+.venv/
+.cache/

LICENSE

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README.md

@@ -1,14 +1,47 @@
 ---
-title: OpenHTR Rec
-emoji: 😻
-colorFrom: red
-colorTo: green
+title: OpenHTR-HS Chinese Handwriting Recognition
+emoji: ✍️
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
 sdk_version: 6.31.0
-python_version: '3.12'
+python_version: "3.12.12"
 app_file: app.py
-pinned: false
-license: apache-2.0
+short_description: Recognize a single line of handwritten Chinese
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# OpenHTR-HS
+
+[English](#english) · [简体中文](#简体中文)
+
+## English
+
+OpenHTR-HS converts images of Chinese handwriting into text. 
+
+### How to use
+
+1. Upload or paste a clear image cropped to a single handwritten line.
+2. Click **Recognize** to transcribe the line.
+3. Use the copy button in **Transcription** to copy the result.
+
+The demo can output up to 50 characters per image.
+
+## 简体中文
+
+OpenHTR-HS 将中文手写图片转为文本。
+
+### 使用方法
+
+1. 上传或粘贴一张裁成单行的清晰手写图片。
+2. 点击 **Recognize** 识别该行文字。
+3. 点击 **Transcription** 结果框中的复制按钮。
+
+每张图片最多可输出 50 个字符。
+
+## Code attribution / 代码来源
+
+The recognizer incorporates code adapted from OpenOCR under its upstream Apache 2.0 license.
+
+识别器使用了源自 OpenOCR 的代码，遵循上游 Apache 2.0 许可证。
+
+[OpenOCR](https://github.com/Topdu/OpenOCR) · [Apache 2.0 license](LICENSE)

app.py

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+import os
+
+import spaces  # Initialize ZeroGPU before importing torch or the model.
+import gradio as gr
+import torch
+from PIL import Image
+
+from openhtr.inference import Recognizer
+
+
+# ZeroGPU exposes a CUDA device at startup to register the model for GPU workers.
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if device == "cpu":
+    torch.set_num_threads(min(2, os.cpu_count() or 1))
+recognizer = Recognizer(device)
+
+
+@spaces.GPU(duration=15)
+def recognize(image: Image.Image | None) -> str:
+    if image is None:
+        raise gr.Error("Upload or paste a handwritten line image first.")
+    if not isinstance(image, Image.Image):
+        raise gr.Error("Upload an image in a supported format such as PNG, JPEG, or WebP.")
+    text = recognizer.recognize(image)
+    if not text:
+        gr.Warning("No text was recognized. Try a clearer crop of a single line.")
+    return text
+
+
+with gr.Blocks(title="OpenHTR-HS Chinese Handwriting Recognition", analytics_enabled=False) as demo:
+    gr.Markdown("# OpenHTR-HS\nUpload or paste a cropped image of a single line of Chinese handwriting.")
+    with gr.Row():
+        image = gr.Image(
+            type="pil", image_mode=None, sources=["upload", "clipboard"], format="png",
+            label="Handwritten Line Image", height=280,
+        )
+        output = gr.Textbox(label="Transcription", interactive=False, lines=9, buttons=["copy"])
+    with gr.Row():
+        submit = gr.Button("Recognize", variant="primary")
+        gr.ClearButton([image, output], value="Clear")
+
+    submit.click(recognize, inputs=image, outputs=output, api_name="recognize")
+
+demo.queue(default_concurrency_limit=1, max_size=16)
+
+if __name__ == "__main__":
+    demo.launch()

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assets/openhtr-hs.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:020799dfc57a47c64d21f77957aed1dd6041415bbd5e039ffc6777ee99cab2aa
+size 143649704

openhtr/__init__.py

@@ -0,0 +1 @@
+"""OpenHTR model components."""

openhtr/decoder.py

@@ -0,0 +1,345 @@
+"""OpenHTR autoregressive decoder with visual cross-attention and KV caching."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+__all__ = ['OpenHTR_Decoder']
+
+
+class RMSNorm(nn.Module):
+
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        dtype = x.dtype
+        x = x.float()
+        x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+        return self.weight * x.to(dtype)
+
+
+class SwiGLU(nn.Module):
+
+    def __init__(self, dim, hidden_dim, drop=0.0):
+        super().__init__()
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)  # gate
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)  # up
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)  # down
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        return self.drop(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+def build_rope_1d(head_dim, positions, device, dtype=torch.float32,
+                  base=10000.0):
+    """RoPE cos/sin for the given absolute positions (interleaved pairs).
+
+    positions: LongTensor [T] or an int for a single position.
+    Returns cos/sin of shape [T, head_dim // 2].
+    """
+    half = head_dim // 2
+    if not torch.is_tensor(positions):
+        positions = torch.tensor([positions], device=device)
+    inv_freq = 1.0 / (base**(torch.arange(0, head_dim, 2, device=device,
+                                          dtype=dtype) / head_dim))
+    angles = positions.to(dtype)[:, None] * inv_freq[None, :]  # T, half
+    return angles.cos(), angles.sin()
+
+
+def rotate_interleaved(x, cos, sin):
+    """RoPE rotation, interleaved-pair convention.
+
+    x: [B, H, T, D]; cos/sin: [T, D//2] (broadcastable).
+    """
+    x1 = x[..., 0::2]
+    x2 = x[..., 1::2]
+    out1 = x1 * cos - x2 * sin
+    out2 = x1 * sin + x2 * cos
+    return torch.stack([out1, out2], dim=-1).flatten(-2)
+
+
+class NeoSelfAttention(nn.Module):
+    """Fused-QKV self-attention with RoPE and KV-cache support."""
+
+    def __init__(self, dim, num_heads, dropout=0.0):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        assert self.head_dim * num_heads == dim
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        self.out_proj = nn.Linear(dim, dim, bias=False)
+        self.dropout = dropout
+
+    def forward(self, x, cos=None, sin=None, kv_cache=None, is_causal=False):
+        B, T, D = x.shape
+        qkv = self.qkv(x).reshape(B, T, 3, self.num_heads,
+                                  self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        if cos is not None:
+            cos = cos.to(q.dtype)[None, None]  # 1 1 T hd/2
+            sin = sin.to(q.dtype)[None, None]
+            q = rotate_interleaved(q, cos, sin)
+            k = rotate_interleaved(k, cos, sin)
+        if kv_cache is not None and kv_cache[0] is not None:
+            k = torch.cat([kv_cache[0], k], dim=2)
+            v = torch.cat([kv_cache[1], v], dim=2)
+        y = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            dropout_p=self.dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+        y = y.transpose(1, 2).reshape(B, T, D)
+        return self.out_proj(y), k, v
+
+
+class NeoCrossAttention(nn.Module):
+    """Cross-attention against a fixed memory (K/V precomputed once)."""
+
+    def __init__(self, dim, num_heads, dropout=0.0):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.embed_dim = dim
+        self.q = nn.Linear(dim, dim, bias=False)
+        self.kv = nn.Linear(dim, dim * 2, bias=False)
+        self.out_proj = nn.Linear(dim, dim, bias=False)
+        self.dropout = dropout
+
+    def project_kv(self, memory):
+        """memory: [B, N, D] -> (k, v) each [B, H, N, head_dim]."""
+        B, N, _ = memory.shape
+        kv = self.kv(memory).reshape(B, N, 2, self.num_heads,
+                                     self.head_dim).permute(2, 0, 3, 1, 4)
+        return kv.unbind(0)
+
+    def forward(self, x, mem_kv):
+        B, T, D = x.shape
+        q = self.q(x).reshape(B, T, self.num_heads,
+                              self.head_dim).transpose(1, 2)
+        k, v = mem_kv
+        y = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            dropout_p=self.dropout if self.training else 0.0,
+        )
+        y = y.transpose(1, 2).reshape(B, T, D)
+        return self.out_proj(y)
+
+
+class NeoDecoderBlock(nn.Module):
+    """Pre-norm block: RoPE self-attn -> cross-attn -> SwiGLU FFN."""
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 mlp_ratio=2.67,
+                 attention_dropout_rate=0.0,
+                 residual_dropout_rate=0.1,
+                 rope=True):
+        super().__init__()
+        hidden = max(64, int(d_model * mlp_ratio) // 64 * 64)
+        self.self_attn = NeoSelfAttention(d_model,
+                                          nhead,
+                                          dropout=attention_dropout_rate)
+        self.norm1 = RMSNorm(d_model)
+        self.cross_attn = NeoCrossAttention(d_model,
+                                            nhead,
+                                            dropout=attention_dropout_rate)
+        self.norm2 = RMSNorm(d_model)
+        self.ffn = SwiGLU(d_model, hidden, drop=residual_dropout_rate)
+        self.norm3 = RMSNorm(d_model)
+        self.dropout = nn.Dropout(residual_dropout_rate)
+        self.rope = rope
+
+    def _rope(self, positions, device, dtype):
+        if not self.rope:
+            return None, None
+        return build_rope_1d(self.self_attn.head_dim, positions, device,
+                             dtype)
+
+    def forward_train(self, x, mem_kv):
+        """Full-sequence causal pass. x: [B, T, D]."""
+        T = x.shape[1]
+        pos = torch.arange(T, device=x.device)
+        cos, sin = self._rope(pos, x.device, x.dtype)
+        a, _, _ = self.self_attn(self.norm1(x), cos, sin, is_causal=T > 1)
+        x = x + self.dropout(a)
+        x = x + self.dropout(self.cross_attn(self.norm2(x), mem_kv))
+        x = x + self.dropout(self.ffn(self.norm3(x)))
+        return x
+
+    def step(self, x, mem_kv, kv_cache, pos):
+        """Single-token incremental pass.
+
+        x: [B, 1, D]; kv_cache: [k, v] list updated in place;
+        pos: absolute RoPE position of the incoming token.
+        """
+        cos, sin = self._rope(pos, x.device, x.dtype)
+        a, k, v = self.self_attn(self.norm1(x), cos, sin, kv_cache=kv_cache)
+        kv_cache[0], kv_cache[1] = k, v
+        x = x + self.dropout(a)
+        x = x + self.dropout(self.cross_attn(self.norm2(x), mem_kv))
+        x = x + self.dropout(self.ffn(self.norm3(x)))
+        return x
+
+
+class NeoEncoderBlock(nn.Module):
+    """Pre-norm RoPE self-attention + SwiGLU block (optional memory side)."""
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 mlp_ratio=2.67,
+                 attention_dropout_rate=0.0,
+                 residual_dropout_rate=0.1):
+        super().__init__()
+        hidden = max(64, int(d_model * mlp_ratio) // 64 * 64)
+        self.self_attn = NeoSelfAttention(d_model,
+                                          nhead,
+                                          dropout=attention_dropout_rate)
+        self.norm1 = RMSNorm(d_model)
+        self.ffn = SwiGLU(d_model, hidden, drop=residual_dropout_rate)
+        self.norm2 = RMSNorm(d_model)
+        self.dropout = nn.Dropout(residual_dropout_rate)
+
+    def forward(self, x):
+        T = x.shape[1]
+        pos = torch.arange(T, device=x.device)
+        cos, sin = build_rope_1d(self.self_attn.head_dim, pos, x.device,
+                                 x.dtype)
+        a, _, _ = self.self_attn(self.norm1(x), cos, sin, is_causal=False)
+        x = x + self.dropout(a)
+        x = x + self.dropout(self.ffn(self.norm2(x)))
+        return x
+
+
+class OpenHTR_Decoder(nn.Module):
+    """Decode text from visual tokens with causal self-attention and cross-attention."""
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        nhead=None,
+        num_encoder_layers=0,
+        beam_size=0,  # unused, greedy decoding only; kept for config compat
+        num_decoder_layers=2,
+        max_len=25,
+        attention_dropout_rate=0.0,
+        residual_dropout_rate=0.1,
+        mlp_ratio=2.67,
+        **kwargs,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.ignore_index = out_channels - 1
+        self.bos = out_channels - 2
+        self.eos = 0
+        self.max_len = max_len
+        d_model = in_channels
+        nhead = nhead if nhead is not None else d_model // 64
+        assert d_model % nhead == 0, \
+            f'd_model ({d_model}) must be divisible by nhead ({nhead})'
+
+        self.embedding = nn.Embedding(out_channels, d_model, padding_idx=0)
+
+        if num_encoder_layers is not None and num_encoder_layers > 0:
+            self.encoder = nn.ModuleList([
+                NeoEncoderBlock(
+                    d_model,
+                    nhead,
+                    mlp_ratio=mlp_ratio,
+                    attention_dropout_rate=attention_dropout_rate,
+                    residual_dropout_rate=residual_dropout_rate,
+                ) for _ in range(num_encoder_layers)
+            ])
+        else:
+            self.encoder = None
+
+        self.decoder = nn.ModuleList([
+            NeoDecoderBlock(
+                d_model,
+                nhead,
+                mlp_ratio=mlp_ratio,
+                attention_dropout_rate=attention_dropout_rate,
+                residual_dropout_rate=residual_dropout_rate,
+            ) for _ in range(num_decoder_layers)
+        ])
+
+        self.norm_out = RMSNorm(d_model)
+        self.tgt_word_prj = nn.Linear(d_model, self.out_channels - 2,
+                                      bias=False)
+        self.d_model = d_model
+        self.apply(self._init_weights)
+        nn.init.zeros_(self.embedding.weight.data[0])  # padding idx
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+        elif isinstance(m, nn.Embedding):
+            nn.init.normal_(m.weight, mean=0.0, std=0.02)
+
+    def _encode(self, src):
+        if self.encoder is not None:
+            for layer in self.encoder:
+                src = layer(src)
+        return src
+
+    def forward_train(self, src, tgt):
+        tgt = tgt[:, :-1]
+        memory = self._encode(src)
+        mem_kv = [layer.cross_attn.project_kv(memory)
+                  for layer in self.decoder]
+        x = self.embedding(tgt)
+        for i, layer in enumerate(self.decoder):
+            x = layer.forward_train(x, mem_kv[i])
+        x = self.norm_out(x)
+        return self.tgt_word_prj(x)
+
+    def forward_test(self, src):
+        bs = src.shape[0]
+        device = src.device
+        memory = self._encode(src)
+        mem_kv = [layer.cross_attn.project_kv(memory)
+                  for layer in self.decoder]
+
+        dec_seq = torch.full((bs, self.max_len + 1),
+                             self.ignore_index,
+                             dtype=torch.int64,
+                             device=device)
+        dec_seq[:, 0] = self.bos
+
+        logits = []
+        kv_caches = [[None, None] for _ in self.decoder]
+
+        with torch.no_grad():
+            for step in range(0, self.max_len):
+                tok = dec_seq[:, step:step + 1]  # B 1
+                x = self.embedding(tok)
+                for i, layer in enumerate(self.decoder):
+                    x = layer.step(x, mem_kv[i], kv_caches[i], pos=step)
+                x = self.norm_out(x)
+                word_prob = F.softmax(self.tgt_word_prj(x[:, -1, :]),
+                                      dim=-1).unsqueeze(1)  # B 1 V
+                logits.append(word_prob)
+                dec_seq[:, step + 1] = word_prob.squeeze(1).argmax(-1)
+                # stop once every sequence has emitted at least one EOS
+                if (dec_seq == self.eos).any(dim=-1).all():
+                    break
+
+        logits = torch.cat(logits, dim=1)
+        return logits
+
+    def forward(self, src, data=None):
+        if self.training:
+            max_len = data[1].max()
+            tgt = data[0][:, :2 + max_len]
+            return self.forward_train(src, tgt)
+        return self.forward_test(src)

openhtr/encoder.py

@@ -0,0 +1,398 @@
+"""OpenHTR visual encoder with local and global feature mixing."""
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.init import kaiming_normal_, ones_, trunc_normal_, zeros_
+
+from .layers import DropPath, Identity
+
+__all__ = ['OpenHTR_Encoder']
+
+
+class ConvBNLayer(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=3,
+        stride=1,
+        padding=0,
+        bias=False,
+        groups=1,
+        act=nn.GELU,
+    ):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias=bias,
+        )
+        self.norm = nn.BatchNorm2d(out_channels)
+        self.act = act()
+
+    def forward(self, inputs):
+        out = self.conv(inputs)
+        out = self.norm(out)
+        out = self.act(out)
+        return out
+
+
+class LastStage(nn.Module):
+
+    def __init__(self, in_channels, out_channels, last_drop, out_char_num=0):
+        super().__init__()
+        self.last_conv = nn.Linear(in_channels, out_channels, bias=False)
+        self.hardswish = nn.Hardswish()
+        self.dropout = nn.Dropout(p=last_drop)
+
+    def forward(self, x, sz):
+        x = x.reshape(-1, sz[0], sz[1], x.shape[-1])
+        x = x.mean(1)
+        x = self.last_conv(x)
+        x = self.hardswish(x)
+        x = self.dropout(x)
+        return x, [1, sz[1]]
+
+
+class Feat2D(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, sz):
+        C = x.shape[-1]
+        x = x.transpose(1, 2).reshape(-1, C, sz[0], sz[1])
+        return x, sz
+
+
+def build_rope_2d(head_dim, H, W, device, dtype=torch.float32):
+    """2D RoPE angles for a h-major flattened grid (H*W, w fastest).
+
+    Returns cos/sin of shape [N, head_dim // 2] for the interleaved
+    rotation convention (token dims are rotated in (2i, 2i+1) pairs).
+    The head dim is split evenly: first half of the rotation frequencies
+    encode the H axis, second half the W axis.
+    """
+    half = head_dim // 2
+    assert half % 2 == 0, 'head_dim must be divisible by 4 for 2D RoPE'
+    inv_freq = 1.0 / (10000.0**(torch.arange(0, half, 2, device=device,
+                                             dtype=dtype) / half))
+    h_pos = torch.arange(H, device=device, dtype=dtype)
+    w_pos = torch.arange(W, device=device, dtype=dtype)
+    ang_h = h_pos[:, None] * inv_freq[None, :]  # [H, half//2]
+    ang_w = w_pos[:, None] * inv_freq[None, :]  # [W, half//2]
+    ang_h = ang_h.repeat_interleave(W, dim=0)  # [H*W, half//2] (h-major)
+    ang_w = ang_w.repeat(H, 1)  # [H*W, half//2]
+    angles = torch.cat([ang_h, ang_w], dim=1)  # [N, half]
+    return angles.cos(), angles.sin()
+
+
+def rotate_interleaved(x, cos, sin):
+    """RoPE rotation, interleaved-pair convention.
+
+    x: [B, heads, N, D]; cos/sin: [N, D//2] (broadcastable).
+    """
+    x1 = x[..., 0::2]
+    x2 = x[..., 1::2]
+    out1 = x1 * cos - x2 * sin
+    out2 = x1 * sin + x2 * cos
+    return torch.stack([out1, out2], dim=-1).flatten(-2)
+
+
+class RMSNorm(nn.Module):
+
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        dtype = x.dtype
+        x = x.float()
+        x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+        return self.weight * x.to(dtype)
+
+
+class SwiGLU(nn.Module):
+
+    def __init__(self, dim, hidden_dim, drop=0.0):
+        super().__init__()
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)  # gate
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)  # up
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)  # down
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        return self.drop(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class ConvNeXtBlock(nn.Module):
+    """ConvNeXt block operating on [B, C, H, W] feature maps."""
+
+    def __init__(self, dim, kernel_size=7, mlp_ratio=4, drop_path=0.0):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim,
+                                dim,
+                                kernel_size,
+                                1,
+                                kernel_size // 2,
+                                groups=dim)
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        hidden = int(dim * mlp_ratio)
+        self.pw1 = nn.Linear(dim, hidden)
+        self.act = nn.GELU()
+        self.pw2 = nn.Linear(hidden, dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        y = self.dwconv(x).permute(0, 2, 3, 1)  # B H W C
+        y = self.norm(y)
+        y = self.pw2(self.act(self.pw1(y)))
+        y = y.permute(0, 3, 1, 2)
+        return x + self.drop_path(y)
+
+
+class GlobalAttentionBlock(nn.Module):
+    """Pre-norm attention block with 2D RoPE, RMSNorm and SwiGLU.
+
+    Input/output: [B, C, H, W]; attention runs on the flattened (h-major)
+    sequence so global mixing covers the whole line.
+    """
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 mlp_ratio=2.67,
+                 drop=0.0,
+                 attn_drop=0.0,
+                 drop_path=0.0,
+                 qkv_bias=True):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        assert self.head_dim * num_heads == dim
+        assert self.head_dim % 4 == 0, 'head_dim must be divisible by 4'
+        self.norm1 = RMSNorm(dim)
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(drop)
+        self.attn_drop = attn_drop
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
+        self.norm2 = RMSNorm(dim)
+        hidden = max(64, int(dim * mlp_ratio) // 64 * 64)
+        self.mlp = SwiGLU(dim, hidden, drop=drop)
+
+    def forward(self, x):
+        # x: [B, C, H, W]
+        B, C, H, W = x.shape
+        y = x.flatten(2).transpose(1, 2)  # B N C
+        y2 = self.norm1(y)
+        qkv = self.qkv(y2).reshape(B, -1, 3, self.num_heads,
+                                   self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        cos, sin = build_rope_2d(self.head_dim, H, W, x.device,
+                                 q.dtype)
+        cos = cos.to(q.dtype)[None, None]  # 1 1 N hd/2
+        sin = sin.to(q.dtype)[None, None]
+        q = rotate_interleaved(q, cos, sin)
+        k = rotate_interleaved(k, cos, sin)
+        y = F.scaled_dot_product_attention(
+            q, k, v, dropout_p=self.attn_drop if self.training else 0.0)
+        y = y.transpose(1, 2).reshape(B, -1, C)
+        y = self.proj_drop(self.proj(y))
+        x = x.flatten(2).transpose(1, 2) + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x.transpose(1, 2).reshape(B, C, H, W)
+
+
+class EncoderStage(nn.Module):
+
+    def __init__(self,
+                 dim=64,
+                 out_dim=256,
+                 depth=3,
+                 mixer=['Conv'] * 3,
+                 kernel_sizes=[7] * 3,
+                 sub_k=[2, 1],
+                 num_heads=2,
+                 mlp_ratio=4,
+                 attn_mlp_ratio=2.67,
+                 qkv_bias=True,
+                 drop_rate=0.0,
+                 attn_drop_rate=0.0,
+                 drop_path=[0.1] * 3,
+                 downsample=None,
+                 **kwargs):
+        super().__init__()
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            if mixer[i] == 'Conv':
+                self.blocks.append(
+                    ConvNeXtBlock(dim=dim,
+                                  kernel_size=kernel_sizes[i],
+                                  mlp_ratio=mlp_ratio,
+                                  drop_path=drop_path[i]))
+            else:
+                self.blocks.append(
+                    GlobalAttentionBlock(
+                        dim=dim,
+                        num_heads=num_heads,
+                        mlp_ratio=attn_mlp_ratio,
+                        drop=drop_rate,
+                        attn_drop=attn_drop_rate,
+                        drop_path=drop_path[i],
+                        qkv_bias=qkv_bias,
+                    ))
+
+        if downsample:
+            self.downsample = EncoderDownsample(dim, out_dim, stride=sub_k)
+        else:
+            self.downsample = None
+
+    def forward(self, x, sz):
+        for blk in self.blocks:
+            x = blk(x)
+        if self.downsample is not None:
+            x, sz = self.downsample(x, sz)
+        return x, sz
+
+
+class EncoderDownsample(nn.Module):
+    """Strided 3x3 conv + RMSNorm, operating on [B, C, H, W] (stays 4D)."""
+
+    def __init__(self, in_channels, out_channels, stride=[2, 1]):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels,
+                              out_channels,
+                              kernel_size=3,
+                              stride=stride,
+                              padding=1)
+        self.norm = RMSNorm(out_channels)
+
+    def forward(self, x, sz):
+        x = self.conv(x)
+        C, H, W = x.shape[1:]
+        y = self.norm(x.permute(0, 2, 3, 1))  # B H W C
+        return y.permute(0, 3, 1, 2).contiguous(), [H, W]
+
+
+class OpenHTR_Encoder(nn.Module):
+    """Encode local strokes and global context as a sequence of visual tokens."""
+
+    def __init__(self,
+                 max_sz=[32, 128],
+                 in_channels=3,
+                 out_channels=192,
+                 depths=[3, 6, 3],
+                 dims=[64, 128, 256],
+                 mixer=[['Conv'] * 3, ['Conv'] * 3 + ['Global'] * 3,
+                        ['Global'] * 3],
+                 sub_k=[[1, 1], [2, 1], [1, 1]],
+                 num_heads=[2, 4, 8],
+                 mlp_ratio=4,
+                 attn_mlp_ratio=2.67,
+                 qkv_bias=True,
+                 drop_rate=0.0,
+                 last_drop=0.1,
+                 attn_drop_rate=0.0,
+                 drop_path_rate=0.1,
+                 last_stage=False,
+                 feat2d=False,
+                 num_convs=None,  # unused, kept for config compat
+                 kernel_sizes=[[7] * 3, [7] * 3 + [7] * 3, [7] * 3],
+                 **kwargs):
+        super().__init__()
+        num_stages = len(depths)
+        self.num_features = dims[-1]
+
+        # Two stride-2 convolutions reduce the input resolution by a factor of four.
+        self.pope = nn.Sequential(
+            ConvBNLayer(in_channels=in_channels,
+                        out_channels=dims[0] // 2,
+                        kernel_size=3,
+                        stride=2,
+                        padding=1,
+                        act=nn.GELU),
+            ConvBNLayer(in_channels=dims[0] // 2,
+                        out_channels=dims[0],
+                        kernel_size=3,
+                        stride=2,
+                        padding=1,
+                        act=nn.GELU),
+        )
+
+        dpr = np.linspace(0, drop_path_rate, sum(depths))
+
+        self.stages = nn.ModuleList()
+        for i_stage in range(num_stages):
+            stage = EncoderStage(
+                dim=dims[i_stage],
+                out_dim=dims[i_stage + 1] if i_stage < num_stages - 1 else 0,
+                depth=depths[i_stage],
+                mixer=mixer[i_stage],
+                kernel_sizes=kernel_sizes[i_stage] if len(
+                    kernel_sizes[i_stage]) == len(mixer[i_stage]) else [7] *
+                len(mixer[i_stage]),
+                sub_k=sub_k[i_stage],
+                num_heads=num_heads[i_stage],
+                mlp_ratio=mlp_ratio,
+                attn_mlp_ratio=attn_mlp_ratio,
+                qkv_bias=qkv_bias,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_stage]):sum(depths[:i_stage + 1])],
+                downsample=False if i_stage == num_stages - 1 else True,
+            )
+            self.stages.append(stage)
+
+        self.out_channels = self.num_features
+        self.last_stage = last_stage
+        self.last_stage_layer = None
+        self.feat2d_layer = None
+        if last_stage:
+            self.out_channels = out_channels
+            self.last_stage_layer = LastStage(self.num_features, out_channels,
+                                              last_drop)
+        if feat2d:
+            self.feat2d_layer = Feat2D()
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, mean=0, std=0.02)
+            if m.bias is not None:
+                zeros_(m.bias)
+        if isinstance(m, nn.LayerNorm):
+            zeros_(m.bias)
+            ones_(m.weight)
+        if isinstance(m, nn.Conv2d):
+            kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'patch_embed', 'downsample', 'pos_embed'}
+
+    def forward(self, x):
+        if len(x.shape) == 5:
+            x = x.flatten(0, 1)
+        sz = x.shape[2:]
+        x = self.pope(x)
+        sz = [sz[0] // 4, sz[1] // 4]
+        for stage in self.stages:
+            x, sz = stage(x, sz)
+        # All EncoderStage blocks keep [B, C, H, W]; convert once at the end.
+        if self.last_stage_layer is not None:
+            x, sz = self.last_stage_layer(x.flatten(2).transpose(1, 2), sz)
+        elif self.feat2d_layer is not None:
+            pass  # already [B, C, H, W]
+        else:
+            x = x.flatten(2).transpose(1, 2)  # B N C
+        return x

openhtr/inference.py

@@ -0,0 +1,94 @@
+"""Single-line inference for the OpenHTR-HS checkpoint."""
+
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from safetensors.torch import load_file
+from torch import nn
+
+from .decoder import OpenHTR_Decoder
+from .encoder import OpenHTR_Encoder
+
+
+ASSETS = Path(__file__).resolve().parent.parent / "assets"
+BASE_SHAPES = ((64, 64), (96, 48), (112, 40), (128, 32))
+MAX_RATIO = 12
+BASE_HEIGHT = 32
+MAX_LENGTH = 50
+
+
+def preprocess_image(image: Image.Image) -> torch.Tensor:
+    """Use the RGB ratio buckets, bicubic resize and normalization of evaluation."""
+    image = image.convert("RGB")
+    width, height = image.size
+    if height > 1.5 * width:
+        image = image.transpose(Image.Transpose.ROTATE_90)
+        width, height = image.size
+
+    bucket = min(MAX_RATIO, max(1, round(width / height)))
+    if bucket <= len(BASE_SHAPES):
+        size = BASE_SHAPES[bucket - 1]
+    else:
+        size = (BASE_HEIGHT * bucket, BASE_HEIGHT)
+
+    resized = image.resize(size, Image.Resampling.BICUBIC)
+    pixels = torch.from_numpy(np.array(resized, dtype=np.uint8))
+    return pixels.permute(2, 0, 1).contiguous().float().div_(255).sub_(0.5).div_(0.5)
+
+
+class OpenHTR(nn.Module):
+    def __init__(self, vocabulary_size: int):
+        super().__init__()
+        self.encoder = OpenHTR_Encoder(
+            in_channels=3,
+            out_channels=256,
+            dims=[128, 256, 384],
+            depths=[6, 6, 6],
+            num_heads=[4, 8, 12],
+            mixer=[['Conv'] * 6, ['Conv'] * 2 + ['Global'] * 4, ['Global'] * 6],
+            sub_k=[[1, 1], [2, 1], [-1, -1]],
+            last_stage=False,
+            feat2d=False,
+        )
+        self.decoder = OpenHTR_Decoder(
+            in_channels=self.encoder.out_channels,
+            out_channels=vocabulary_size,
+            nhead=12,
+            num_encoder_layers=-1,
+            num_decoder_layers=2,
+            max_len=MAX_LENGTH,
+        )
+
+    def forward(self, pixels: torch.Tensor) -> torch.Tensor:
+        return self.decoder(self.encoder(pixels))
+
+
+class Recognizer:
+    def __init__(self, device: str | torch.device):
+        self.device = torch.device(device)
+        with (ASSETS / "charset.txt").open("rb") as file:
+            self.characters = tuple(line.decode("utf-8").rstrip("\r\n") for line in file)
+
+        # The checkpoint orders tokens as EOS, dictionary entries, BOS and PAD.
+        self.model = OpenHTR(len(self.characters) + 3)
+        self.model.load_state_dict(load_file(ASSETS / "openhtr-hs.safetensors"), strict=True)
+        self.model.to(self.device).eval()
+
+    @torch.inference_mode()
+    def recognize(self, image: Image.Image) -> str:
+        # Composite transparent PNG strokes on paper white before OCR.
+        if "A" in image.getbands() or "transparency" in image.info:
+            image = image.convert("RGBA")
+            paper = Image.new("RGBA", image.size, "white")
+            image = Image.alpha_composite(paper, image)
+
+        pixels = preprocess_image(image).unsqueeze(0).to(self.device)
+        token_ids = self.model(pixels)[0].argmax(dim=-1).tolist()
+        text = []
+        for token in token_ids:
+            if token == 0:
+                break
+            text.append(self.characters[token - 1])
+        return "".join(text)

openhtr/layers.py

@@ -0,0 +1,52 @@
+"""Layers used by the OpenHTR encoder."""
+
+from torch import nn
+
+
+def drop_path(x,
+              drop_prob: float = 0.0,
+              training: bool = False,
+              scale_by_keep: bool = True):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of
+    residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0], ) + (1, ) * (
+        x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
+    residual blocks)."""
+
+    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob,3):0.3f}'
+
+
+class Identity(nn.Module):
+
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, input):
+        return input

requirements.txt

@@ -0,0 +1,4 @@
+torch==2.11.0
+safetensors==0.8.0
+pillow==12.3.0
+numpy==2.2.6