add_solider

ppcls/arch/backbone/legendary_models/swin_transformer.py

@@ -27,21 +27,22 @@
 
 MODEL_URLS = {
     "SwinTransformer_tiny_patch4_window7_224":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
     "SwinTransformer_small_patch4_window7_224":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
     "SwinTransformer_base_patch4_window7_224":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
     "SwinTransformer_base_patch4_window12_384":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
     "SwinTransformer_large_patch4_window7_224":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_pretrained.pdparams",
     "SwinTransformer_large_patch4_window12_384":
-    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_pretrained.pdparams",
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_pretrained.pdparams",
 }
 
 __all__ = list(MODEL_URLS.keys())
 
+
 # The following re-implementation of roll is inspired by
 # https://gitee.com/ascend/pytorch/blob/master/torch_npu/contrib/function/roll.py
 
@@ -195,7 +196,7 @@ def __init__(self,
         self.window_size = window_size  # Wh, Ww
         self.num_heads = num_heads
         head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
+        self.scale = qk_scale or head_dim ** -0.5
 
         # define a parameter table of relative position bias
         # 2*Wh-1 * 2*Ww-1, nH
@@ -220,7 +221,7 @@ def __init__(self,
         relative_coords = relative_coords.transpose(
             [1, 2, 0])  # Wh*Ww, Wh*Ww, 2
         relative_coords[:, :, 0] += self.window_size[
-            0] - 1  # shift to start from 0
+                                        0] - 1  # shift to start from 0
         relative_coords[:, :, 1] += self.window_size[1] - 1
         relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
         relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
@@ -262,7 +263,7 @@ def forward(self, x, mask=None):
         B_, N, C = x.shape
         qkv = self.qkv(x).reshape(
             [B_, N, 3, self.num_heads, C // self.num_heads]).transpose(
-                [2, 0, 3, 1, 4])
+            [2, 0, 3, 1, 4])
         q, k, v = qkv[0], qkv[1], qkv[2]
 
         q = q * self.scale
@@ -407,12 +408,14 @@ def __init__(self,
             attn_mask = None
 
         self.register_buffer("attn_mask", attn_mask)
+
     def check_condition(self):
         if min(self.input_resolution) <= self.window_size:
             # if window size is larger than input resolution, we don't partition windows
             self.shift_size = 0
             self.window_size = min(self.input_resolution)
         assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
     def forward(self, x):
         H, W = self.input_resolution
         B, L, C = x.shape
@@ -676,7 +679,7 @@ def forward(self, x):
     def flops(self):
         Ho, Wo = self.patches_resolution
         flops = Ho * Wo * self.embed_dim * self.in_chans * (
-            self.patch_size[0] * self.patch_size[1])
+                self.patch_size[0] * self.patch_size[1])
         if self.norm is not None:
             flops += Ho * Wo * self.embed_dim
         return flops
@@ -735,7 +738,7 @@ def __init__(self,
         self.embed_dim = embed_dim
         self.ape = ape
         self.patch_norm = patch_norm
-        self.num_features = int(embed_dim * 2**(self.num_layers - 1))
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
         self.mlp_ratio = mlp_ratio
 
         # split image into non-overlapping patches
@@ -766,9 +769,9 @@ def __init__(self,
         self.layers = nn.LayerList()
         for i_layer in range(self.num_layers):
             layer = BasicLayer(
-                dim=int(embed_dim * 2**i_layer),
-                input_resolution=(patches_resolution[0] // (2**i_layer),
-                                  patches_resolution[1] // (2**i_layer)),
+                dim=int(embed_dim * 2 ** i_layer),
+                input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                  patches_resolution[1] // (2 ** i_layer)),
                 depth=depths[i_layer],
                 num_heads=num_heads[i_layer],
                 window_size=window_size,
@@ -826,7 +829,7 @@ def flops(self):
         for _, layer in enumerate(self.layers):
             flops += layer.flops()
         flops += self.num_features * self.patches_resolution[
-            0] * self.patches_resolution[1] // (2**self.num_layers)
+            0] * self.patches_resolution[1] // (2 ** self.num_layers)
         flops += self.num_features * self.num_classes
         return flops
 
@@ -836,7 +839,8 @@ def _load_pretrained(pretrained,
                      model_url,
                      use_ssld=False,
                      use_imagenet22k_pretrained=False,
-                     use_imagenet22kto1k_pretrained=False):
+                     use_imagenet22kto1k_pretrained=False,
+                     **kwargs):
     if pretrained is False:
         pass
     elif pretrained is True:

ppcls/arch/backbone/variant_models/swin_transformer_variant.py

@@ -2,11 +2,18 @@
 import paddle
 import paddle.nn as nn
 from ..legendary_models.swin_transformer import SwinTransformer, _load_pretrained, \
-    MODEL_URLS, PatchEmbed, BasicLayer,SwinTransformerBlock
+    PatchEmbed, BasicLayer, SwinTransformerBlock
 
-__all__ = ["SwinTransformer_tiny_patch4_window7_224_SOLIDER",
-           "SwinTransformer_small_patch4_window7_224_SOLIDER",
-           "SwinTransformer_base_patch4_window7_224_SOLIDER"]
+MODEL_URLS_SOLIDER = {
+    "SwinTransformer_tiny_patch4_window7_224_SOLIDER":
+        'https://paddleclas.bj.bcebos.com/models/SOILDER/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams',
+    "SwinTransformer_small_patch4_window7_224_SOLIDER":
+        'https://paddleclas.bj.bcebos.com/models/SOILDER/SwinTransformer_small_patch4_window7_224_pretrained.pdparams',
+    "SwinTransformer_base_patch4_window7_224_SOLIDER":
+        'https://paddleclas.bj.bcebos.com/models/SOILDER/SwinTransformer_base_patch4_window7_224_pretrained.pdparams'
+}
+
+__all__ = list(MODEL_URLS_SOLIDER.keys())
 
 
 class PatchEmbed_SOLIDER(PatchEmbed):
@@ -18,6 +25,7 @@ def forward(self, x):
             x = self.norm(x)
         return x, out_size
 
+
 class SwinTransformerBlock_SOLIDER(SwinTransformerBlock):
     r""" Swin Transformer Block.
 
@@ -51,7 +59,7 @@ def __init__(self,
                  drop_path=0.,
                  act_layer=nn.GELU,
                  norm_layer=nn.LayerNorm):
-        super(SwinTransformerBlock_SOLIDER,self).__init__(
+        super(SwinTransformerBlock_SOLIDER, self).__init__(
             dim=dim,
             input_resolution=input_resolution,
             num_heads=num_heads,
@@ -73,12 +81,15 @@ def __init__(self,
         self.shift_size = shift_size
         self.mlp_ratio = mlp_ratio
         self.check_condition()
+
     def check_condition(self):
         if min(self.input_resolution) < self.window_size:
             # if window size is larger than input resolution, we don't partition windows
             self.shift_size = 0
             self.window_size = min(self.input_resolution)
         assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+
 class BasicLayer_SOLIDER(BasicLayer):
     def __init__(self,
                  dim,
@@ -96,7 +107,7 @@ def __init__(self,
                  downsample=None,
                  use_checkpoint=False):
 
-        super(BasicLayer_SOLIDER,self).__init__(
+        super(BasicLayer_SOLIDER, self).__init__(
             dim=dim,
             input_resolution=input_resolution,
             depth=depth,
@@ -291,9 +302,6 @@ def forward_features(self, x, semantic_weight=None):
 
 def SwinTransformer_tiny_patch4_window7_224_SOLIDER(
         pretrained=False,
-        use_ssld=False,
-        use_imagenet22k_pretrained=False,
-        use_imagenet22kto1k_pretrained=False,
         **kwargs):
     model = SwinTransformer_SOLIDER(
         embed_dim=96,
@@ -304,19 +312,14 @@ def SwinTransformer_tiny_patch4_window7_224_SOLIDER(
         **kwargs)
     _load_pretrained(
         pretrained,
-        model,
-        MODEL_URLS["SwinTransformer_tiny_patch4_window7_224"],
-        use_ssld=use_ssld,
-        use_imagenet22k_pretrained=use_imagenet22k_pretrained,
-        use_imagenet22kto1k_pretrained=use_imagenet22kto1k_pretrained)
+        model=model,
+        model_url=MODEL_URLS_SOLIDER["SwinTransformer_tiny_patch4_window7_224_SOLIDER"],
+        **kwargs)
     return model
 
 
 def SwinTransformer_small_patch4_window7_224_SOLIDER(
         pretrained=False,
-        use_ssld=False,
-        use_imagenet22k_pretrained=False,
-        use_imagenet22kto1k_pretrained=False,
         **kwargs):
     model = SwinTransformer_SOLIDER(
         embed_dim=96,
@@ -327,19 +330,14 @@ def SwinTransformer_small_patch4_window7_224_SOLIDER(
         **kwargs)
     _load_pretrained(
         pretrained,
-        model,
-        MODEL_URLS["SwinTransformer_small_patch4_window7_224"],
-        use_ssld=use_ssld,
-        use_imagenet22k_pretrained=use_imagenet22k_pretrained,
-        use_imagenet22kto1k_pretrained=use_imagenet22kto1k_pretrained)
+        model=model,
+        model_url=MODEL_URLS_SOLIDER["SwinTransformer_small_patch4_window7_224_SOLIDER"],
+        **kwargs)
     return model
 
 
 def SwinTransformer_base_patch4_window7_224_SOLIDER(
         pretrained=False,
-        use_ssld=False,
-        use_imagenet22k_pretrained=False,
-        use_imagenet22kto1k_pretrained=False,
         **kwargs):
     model = SwinTransformer_SOLIDER(
         embed_dim=128,
@@ -350,9 +348,7 @@ def SwinTransformer_base_patch4_window7_224_SOLIDER(
         **kwargs)
     _load_pretrained(
         pretrained,
-        model,
-        MODEL_URLS["SwinTransformer_base_patch4_window7_224"],
-        use_ssld=use_ssld,
-        use_imagenet22k_pretrained=use_imagenet22k_pretrained,
-        use_imagenet22kto1k_pretrained=use_imagenet22kto1k_pretrained)
+        model=model,
+        model_url=MODEL_URLS_SOLIDER["SwinTransformer_base_patch4_window7_224_SOLIDER"],
+        **kwargs)
     return model

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml

@@ -32,6 +32,7 @@ AMP:
 Arch:
   name: SwinTransformer_base_patch4_window12_384
   class_num: 1000
+  pretrained: True
 
 # loss function config for traing/eval process
 Loss:

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml

@@ -32,7 +32,6 @@ AMP:
 Arch:
   name: SwinTransformer_base_patch4_window7_224
   class_num: 1000
-  pretrained: True
 
 # loss function config for traing/eval process
 Loss:

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml

@@ -32,7 +32,7 @@ AMP:
 Arch:
   name: SwinTransformer_large_patch4_window12_384
   class_num: 1000
- 
+
 # loss function config for traing/eval process
 Loss:
   Train:

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml

@@ -32,7 +32,7 @@ AMP:
 Arch:
   name: SwinTransformer_large_patch4_window7_224
   class_num: 1000
- 
+
 # loss function config for traing/eval process
 Loss:
   Train:

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml

@@ -32,7 +32,7 @@ AMP:
 Arch:
   name: SwinTransformer_small_patch4_window7_224
   class_num: 1000
- 
+
 # loss function config for traing/eval process
 Loss:
   Train:

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml

@@ -32,7 +32,7 @@ AMP:
 Arch:
   name: SwinTransformer_tiny_patch4_window7_224
   class_num: 1000
- 
+
 # loss function config for traing/eval process
 Loss:
   Train: