fix arange to static for inference (#7279)

ppdet/modeling/necks/custom_pan.py

@@ -184,7 +184,10 @@ class TransformerEncoder(nn.Layer):
 @register
 @serializable
 class CustomCSPPAN(nn.Layer):
-    __shared__ = ['norm_type', 'data_format', 'width_mult', 'depth_mult', 'trt']
+    __shared__ = [
+        'norm_type', 'data_format', 'width_mult', 'depth_mult', 'trt',
+        'eval_size'
+    ]
 
     def __init__(self,
                  in_channels=[256, 512, 1024],
@@ -212,7 +215,8 @@ class CustomCSPPAN(nn.Layer):
                  attn_dropout=None,
                  act_dropout=None,
                  normalize_before=False,
-                 use_trans=False):
+                 use_trans=False,
+                 eval_size=None):
 
         super(CustomCSPPAN, self).__init__()
         out_channels = [max(round(c * width_mult), 1) for c in out_channels]
@@ -223,19 +227,29 @@ class CustomCSPPAN(nn.Layer):
         self.num_blocks = len(in_channels)
         self.data_format = data_format
         self._out_channels = out_channels
+
         self.hidden_dim = in_channels[-1]
         in_channels = in_channels[::-1]
-        self.nhead = nhead
-        self.num_layers = num_layers
+
         self.use_trans = use_trans
+        self.eval_size = eval_size
         if use_trans:
+            if eval_size is not None:
+                self.pos_embed = self.build_2d_sincos_position_embedding(
+                    eval_size[1] // 32,
+                    eval_size[0] // 32,
+                    embed_dim=self.hidden_dim)
+            else:
+                self.pos_embed = None
+
             encoder_layer = TransformerEncoderLayer(
                 self.hidden_dim, nhead, dim_feedforward, dropout, activation,
                 attn_dropout, act_dropout, normalize_before)
             encoder_norm = nn.LayerNorm(
                 self.hidden_dim) if normalize_before else None
-            self.encoder = TransformerEncoder(encoder_layer, self.num_layers,
+            self.encoder = TransformerEncoder(encoder_layer, num_layers,
                                               encoder_norm)
+
         fpn_stages = []
         fpn_routes = []
         for i, (ch_in, ch_out) in enumerate(zip(in_channels, out_channels)):
@@ -340,8 +354,12 @@ class CustomCSPPAN(nn.Layer):
 
             # flatten [B, C, H, W] to [B, HxW, C]
             src_flatten = last_feat.flatten(2).transpose([0, 2, 1])
-            pos_embed = self.build_2d_sincos_position_embedding(
-                w=w, h=h, embed_dim=self.hidden_dim)
+            if self.eval_size is not None:
+                pos_embed = self.pos_embed
+            else:
+                pos_embed = self.build_2d_sincos_position_embedding(
+                    w=w, h=h, embed_dim=self.hidden_dim)
+
             memory = self.encoder(src_flatten, pos_embed=pos_embed)
             last_feat_encode = memory.transpose([0, 2, 1]).reshape([n, c, h, w])
             blocks[-1] = last_feat_encode