fix export model eval (#710)

* adapt to net.eval for the framework just contains training flag setting
* fix bug when export swin transformer

ppcls/modeling/architectures/swin_transformer.py

@@ -63,7 +63,7 @@ def window_partition(x, window_size):
     return windows
 
 
-def window_reverse(windows, window_size, H, W):
+def window_reverse(windows, window_size, H, W, C):
     """
     Args:
         windows: (num_windows*B, window_size, window_size, C)
@@ -74,10 +74,9 @@ def window_reverse(windows, window_size, H, W):
     Returns:
         x: (B, H, W, C)
     """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
     x = windows.reshape(
-        [B, H // window_size, W // window_size, window_size, window_size, -1])
-    x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([B, H, W, -1])
+        [-1, H // window_size, W // window_size, window_size, window_size, C])
+    x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([-1, H, W, C])
     return x
 
 
@@ -334,8 +333,8 @@ class SwinTransformerBlock(nn.Layer):
         # merge windows
         attn_windows = attn_windows.reshape(
             [-1, self.window_size, self.window_size, C])
-        shifted_x = window_reverse(attn_windows, self.window_size, H,
-                                   W)  # B H' W' C
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W,
+                                   C)  # B H' W' C
 
         # reverse cyclic shift
         if self.shift_size > 0:
@@ -406,7 +405,7 @@ class PatchMerging(nn.Layer):
         x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
         x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
         x = paddle.concat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.reshape([B, -1, 4 * C])  # B H/2*W/2 4*C
+        x = x.reshape([B, H * W // 4, 4 * C])  # B H/2*W/2 4*C
 
         x = self.norm(x)
         x = self.reduction(x)
@@ -551,10 +550,8 @@ class PatchEmbed(nn.Layer):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            "Input image size ({H}*{W}) doesn't match model ({}*{}).".format(
-                H, W, self.img_size[0], self.img_size[1])
+        # TODO (littletomatodonkey), uncomment the line will cause failure of jit.save
+        # assert [H, W] == self.img_size[:2], "Input image size ({H}*{W}) doesn't match model ({}*{}).".format(H, W, self.img_size[0], self.img_size[1])
         x = self.proj(x)
 
         x = x.flatten(2).transpose([0, 2, 1])  # B Ph*Pw C

tools/export_model.py

@@ -47,6 +47,12 @@ class Net(paddle.nn.Layer):
         self.pre_net = net(class_dim=class_dim)
         self.model = model
 
+    def eval(self):
+        self.training = False
+        for layer in self.sublayers():
+            layer.training = False
+            layer.eval()
+
     def forward(self, inputs):
         x = self.pre_net(inputs)
         if self.model == "GoogLeNet":