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ppocr/modeling/backbones/rec_vitstr.py

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+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/roatienza/deep-text-recognition-benchmark/blob/master/modules/vitstr.py
+"""
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+from ppocr.modeling.backbones.rec_svtrnet import (
+    Block,
+    PatchEmbed,
+    zeros_,
+    trunc_normal_,
+    ones_,
+)
+
+scale_dim_heads = {"tiny": [192, 3], "small": [384, 6], "base": [768, 12]}
+
+
+class ViTSTR(nn.Layer):
+    def __init__(
+        self,
+        img_size=[224, 224],
+        in_channels=1,
+        scale="tiny",
+        seqlen=27,
+        patch_size=[16, 16],
+        embed_dim=None,
+        depth=12,
+        num_heads=None,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_path_rate=0.0,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        norm_layer="nn.LayerNorm",
+        act_layer="nn.GELU",
+        epsilon=1e-6,
+        out_channels=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.seqlen = seqlen
+        embed_dim = embed_dim if embed_dim is not None else scale_dim_heads[scale][0]
+        num_heads = num_heads if num_heads is not None else scale_dim_heads[scale][1]
+        out_channels = out_channels if out_channels is not None else embed_dim
+        self.patch_embed = PatchEmbed(
+            img_size=img_size,
+            in_channels=in_channels,
+            embed_dim=embed_dim,
+            patch_size=patch_size,
+            mode="linear",
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.pos_embed = self.create_parameter(
+            shape=[1, num_patches + 1, embed_dim], default_initializer=zeros_
+        )
+        self.add_parameter("pos_embed", self.pos_embed)
+        self.cls_token = self.create_parameter(
+            shape=[1, 1, embed_dim], default_initializer=zeros_
+        )
+        self.add_parameter("cls_token", self.cls_token)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = np.linspace(0, drop_path_rate, depth)
+        self.blocks = nn.LayerList(
+            [
+                Block(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=eval(act_layer),
+                    epsilon=epsilon,
+                    prenorm=False,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
+
+        self.out_channels = out_channels
+
+        trunc_normal_(self.pos_embed)
+        trunc_normal_(self.cls_token)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                zeros_(m.bias)
+        elif isinstance(m, nn.LayerNorm):
+            zeros_(m.bias)
+            ones_(m.weight)
+
+    def forward_features(self, x):
+        B = x.shape[0]
+        x = self.patch_embed(x)
+        cls_tokens = paddle.tile(self.cls_token, repeat_times=[B, 1, 1])
+        x = paddle.concat((cls_tokens, x), axis=1)
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = x[:, : self.seqlen]
+        return x.transpose([0, 2, 1]).unsqueeze(2)