x.shape->paddle.shape(x)

paddlers/custom_models/cd/bit.py

@@ -138,12 +138,12 @@ class BIT(nn.Layer):
             Conv3x3(EBD_DIM, num_classes))
 
     def _get_semantic_tokens(self, x):
-        b, c = x.shape[:2]
+        b, c = paddle.shape(x)[:2]
         att_map = self.conv_att(x)
         att_map = att_map.reshape(
-            (b, self.token_len, 1, calc_product(*att_map.shape[2:])))
+            (b, self.token_len, 1, calc_product(*paddle.shape(att_map)[2:])))
         att_map = F.softmax(att_map, axis=-1)
-        x = x.reshape((b, 1, c, att_map.shape[-1]))
+        x = x.reshape((b, 1, c, paddle.shape(att_map)[-1]))
         tokens = (x * att_map).sum(-1)
         return tokens
 
@@ -164,7 +164,7 @@ class BIT(nn.Layer):
         return x
 
     def decode(self, x, m):
-        b, c, h, w = x.shape
+        b, c, h, w = paddle.shape(x)
         x = x.transpose((0, 2, 3, 1)).flatten(1, 2)
         x = self.decoder(x, m)
         x = x.transpose((0, 2, 1)).reshape((b, c, h, w))
@@ -276,7 +276,7 @@ class CrossAttention(nn.Layer):
             nn.Linear(inner_dim, dim), nn.Dropout(dropout_rate))
 
     def forward(self, x, ref):
-        b, n = x.shape[:2]
+        b, n = paddle.shape(x)[:2]
         h = self.n_heads
 
         q = self.fc_q(x)
@@ -284,7 +284,7 @@ class CrossAttention(nn.Layer):
         v = self.fc_v(ref)
 
         q = q.reshape((b, n, h, self.head_dim)).transpose((0, 2, 1, 3))
-        rn = ref.shape[1]
+        rn = paddle.shape(ref)[1]
         k = k.reshape((b, rn, h, self.head_dim)).transpose((0, 2, 1, 3))
         v = v.reshape((b, rn, h, self.head_dim)).transpose((0, 2, 1, 3))
 

paddlers/custom_models/cd/fc_ef.py

@@ -131,7 +131,8 @@ class FCEarlyFusion(nn.Layer):
 
         # Stage 4d
         x4d = self.upconv4(x4p)
-        pad4 = (0, x43.shape[3] - x4d.shape[3], 0, x43.shape[2] - x4d.shape[2])
+        pad4 = (0, paddle.shape(x43)[3] - paddle.shape(x4d)[3], 0,
+                paddle.shape(x43)[2] - paddle.shape(x4d)[2])
         x4d = paddle.concat([F.pad(x4d, pad=pad4, mode='replicate'), x43], 1)
         x43d = self.do43d(self.conv43d(x4d))
         x42d = self.do42d(self.conv42d(x43d))
@@ -139,7 +140,8 @@ class FCEarlyFusion(nn.Layer):
 
         # Stage 3d
         x3d = self.upconv3(x41d)
-        pad3 = (0, x33.shape[3] - x3d.shape[3], 0, x33.shape[2] - x3d.shape[2])
+        pad3 = (0, paddle.shape(x33)[3] - paddle.shape(x3d)[3], 0,
+                paddle.shape(x33)[2] - paddle.shape(x3d)[2])
         x3d = paddle.concat([F.pad(x3d, pad=pad3, mode='replicate'), x33], 1)
         x33d = self.do33d(self.conv33d(x3d))
         x32d = self.do32d(self.conv32d(x33d))
@@ -147,14 +149,16 @@ class FCEarlyFusion(nn.Layer):
 
         # Stage 2d
         x2d = self.upconv2(x31d)
-        pad2 = (0, x22.shape[3] - x2d.shape[3], 0, x22.shape[2] - x2d.shape[2])
+        pad2 = (0, paddle.shape(x22)[3] - paddle.shape(x2d)[3], 0,
+                paddle.shape(x22)[2] - paddle.shape(x2d)[2])
         x2d = paddle.concat([F.pad(x2d, pad=pad2, mode='replicate'), x22], 1)
         x22d = self.do22d(self.conv22d(x2d))
         x21d = self.do21d(self.conv21d(x22d))
 
         # Stage 1d
         x1d = self.upconv1(x21d)
-        pad1 = (0, x12.shape[3] - x1d.shape[3], 0, x12.shape[2] - x1d.shape[2])
+        pad1 = (0, paddle.shape(x12)[3] - paddle.shape(x1d)[3], 0,
+                paddle.shape(x12)[2] - paddle.shape(x1d)[2])
         x1d = paddle.concat([F.pad(x1d, pad=pad1, mode='replicate'), x12], 1)
         x12d = self.do12d(self.conv12d(x1d))
         x11d = self.conv11d(x12d)

paddlers/custom_models/cd/fc_siam_conc.py

@@ -154,8 +154,8 @@ class FCSiamConc(nn.Layer):
         # Decode
         # Stage 4d
         x4d = self.upconv4(x4p)
-        pad4 = (0, x43_1.shape[3] - x4d.shape[3], 0,
-                x43_1.shape[2] - x4d.shape[2])
+        pad4 = (0, paddle.shape(x43_1)[3] - paddle.shape(x4d)[3], 0,
+                paddle.shape(x43_1)[2] - paddle.shape(x4d)[2])
         x4d = paddle.concat(
             [F.pad(x4d, pad=pad4, mode='replicate'), x43_1, x43_2], 1)
         x43d = self.do43d(self.conv43d(x4d))
@@ -164,8 +164,8 @@ class FCSiamConc(nn.Layer):
 
         # Stage 3d
         x3d = self.upconv3(x41d)
-        pad3 = (0, x33_1.shape[3] - x3d.shape[3], 0,
-                x33_1.shape[2] - x3d.shape[2])
+        pad3 = (0, paddle.shape(x33_1)[3] - paddle.shape(x3d)[3], 0,
+                paddle.shape(x33_1)[2] - paddle.shape(x3d)[2])
         x3d = paddle.concat(
             [F.pad(x3d, pad=pad3, mode='replicate'), x33_1, x33_2], 1)
         x33d = self.do33d(self.conv33d(x3d))
@@ -174,8 +174,8 @@ class FCSiamConc(nn.Layer):
 
         # Stage 2d
         x2d = self.upconv2(x31d)
-        pad2 = (0, x22_1.shape[3] - x2d.shape[3], 0,
-                x22_1.shape[2] - x2d.shape[2])
+        pad2 = (0, paddle.shape(x22_1)[3] - paddle.shape(x2d)[3], 0,
+                paddle.shape(x22_1)[2] - paddle.shape(x2d)[2])
         x2d = paddle.concat(
             [F.pad(x2d, pad=pad2, mode='replicate'), x22_1, x22_2], 1)
         x22d = self.do22d(self.conv22d(x2d))
@@ -183,8 +183,8 @@ class FCSiamConc(nn.Layer):
 
         # Stage 1d
         x1d = self.upconv1(x21d)
-        pad1 = (0, x12_1.shape[3] - x1d.shape[3], 0,
-                x12_1.shape[2] - x1d.shape[2])
+        pad1 = (0, paddle.shape(x12_1)[3] - paddle.shape(x1d)[3], 0,
+                paddle.shape(x12_1)[2] - paddle.shape(x1d)[2])
         x1d = paddle.concat(
             [F.pad(x1d, pad=pad1, mode='replicate'), x12_1, x12_2], 1)
         x12d = self.do12d(self.conv12d(x1d))

paddlers/custom_models/cd/fc_siam_diff.py

@@ -154,8 +154,8 @@ class FCSiamDiff(nn.Layer):
         # Decode
         # Stage 4d
         x4d = self.upconv4(x4p)
-        pad4 = (0, x43_1.shape[3] - x4d.shape[3], 0,
-                x43_1.shape[2] - x4d.shape[2])
+        pad4 = (0, paddle.shape(x43_1)[3] - paddle.shape(x4d)[3], 0,
+                paddle.shape(x43_1)[2] - paddle.shape(x4d)[2])
         x4d = F.pad(x4d, pad=pad4, mode='replicate')
         x4d = paddle.concat([x4d, paddle.abs(x43_1 - x43_2)], 1)
         x43d = self.do43d(self.conv43d(x4d))
@@ -164,8 +164,8 @@ class FCSiamDiff(nn.Layer):
 
         # Stage 3d
         x3d = self.upconv3(x41d)
-        pad3 = (0, x33_1.shape[3] - x3d.shape[3], 0,
-                x33_1.shape[2] - x3d.shape[2])
+        pad3 = (0, paddle.shape(x33_1)[3] - paddle.shape(x3d)[3], 0,
+                paddle.shape(x33_1)[2] - paddle.shape(x3d)[2])
         x3d = F.pad(x3d, pad=pad3, mode='replicate')
         x3d = paddle.concat([x3d, paddle.abs(x33_1 - x33_2)], 1)
         x33d = self.do33d(self.conv33d(x3d))
@@ -174,8 +174,8 @@ class FCSiamDiff(nn.Layer):
 
         # Stage 2d
         x2d = self.upconv2(x31d)
-        pad2 = (0, x22_1.shape[3] - x2d.shape[3], 0,
-                x22_1.shape[2] - x2d.shape[2])
+        pad2 = (0, paddle.shape(x22_1)[3] - paddle.shape(x2d)[3], 0,
+                paddle.shape(x22_1)[2] - paddle.shape(x2d)[2])
         x2d = F.pad(x2d, pad=pad2, mode='replicate')
         x2d = paddle.concat([x2d, paddle.abs(x22_1 - x22_2)], 1)
         x22d = self.do22d(self.conv22d(x2d))
@@ -183,8 +183,8 @@ class FCSiamDiff(nn.Layer):
 
         # Stage 1d
         x1d = self.upconv1(x21d)
-        pad1 = (0, x12_1.shape[3] - x1d.shape[3], 0,
-                x12_1.shape[2] - x1d.shape[2])
+        pad1 = (0, paddle.shape(x12_1)[3] - paddle.shape(x1d)[3], 0,
+                paddle.shape(x12_1)[2] - paddle.shape(x1d)[2])
         x1d = F.pad(x1d, pad=pad1, mode='replicate')
         x1d = paddle.concat([x1d, paddle.abs(x12_1 - x12_2)], 1)
         x12d = self.do12d(self.conv12d(x1d))

paddlers/custom_models/cd/stanet.py

@@ -215,7 +215,8 @@ class BAM(nn.Layer):
 
         out = F.interpolate(out, scale_factor=self.ds)
         out = out + x
-        return out.reshape(tuple(out.shape[:-1]) + (out.shape[-1] // 2, 2))
+        return out.reshape(
+            tuple(paddle.shape(out)[:-1]) + (paddle.shape(out)[-1] // 2, 2))
 
 
 class PAMBlock(nn.Layer):
@@ -241,7 +242,7 @@ class PAMBlock(nn.Layer):
         value = self.conv_v(x_rs)
 
         # Split the whole image into subregions.
-        b, c, h, w = x_rs.shape
+        b, c, h, w = paddle.shape(x_rs)
 
         query = self._split_subregions(query)
         key = self._split_subregions(key)
@@ -264,7 +265,7 @@ class PAMBlock(nn.Layer):
         return out
 
     def _split_subregions(self, x):
-        b, c, h, w = x.shape
+        b, c, h, w = paddle.shape(x)
         assert h % self.scale == 0 and w % self.scale == 0
         x = x.reshape(
             (b, c, self.scale, h // self.scale, self.scale, w // self.scale))
@@ -296,7 +297,8 @@ class PAM(nn.Layer):
 
         out = self.conv_out(paddle.concat(res, axis=1))
 
-        return out.reshape(tuple(out.shape[:-1]) + (out.shape[-1] // 2, 2))
+        return out.reshape(
+            tuple(paddle.shape(out)[:-1]) + (paddle.shape(out)[-1] // 2, 2))
 
 
 class Attention(nn.Layer):

paddlers/custom_models/cls/condensenet_v2.py

@@ -36,10 +36,10 @@ class SELayer(nn.Layer):
             nn.Sigmoid(), )
 
     def forward(self, x):
-        b, c, _, _ = x.shape
+        b, c, _, _ = paddle.shape(x)
         y = self.avg_pool(x).reshape((b, c))
         y = self.fc(y).reshape((b, c, 1, 1))
-        return x * paddle.expand(y, shape=x.shape)
+        return x * paddle.expand(y, shape=paddle.shape(x))
 
 
 class HS(nn.Layer):
@@ -85,7 +85,7 @@ class Conv(nn.Sequential):
 
 
 def ShuffleLayer(x, groups):
-    batchsize, num_channels, height, width = x.shape
+    batchsize, num_channels, height, width = paddle.shape(x)
     channels_per_group = num_channels // groups
     # reshape
     x = x.reshape((batchsize, groups, channels_per_group, height, width))
@@ -97,7 +97,7 @@ def ShuffleLayer(x, groups):
 
 
 def ShuffleLayerTrans(x, groups):
-    batchsize, num_channels, height, width = x.shape
+    batchsize, num_channels, height, width = paddle.shape(x)
     channels_per_group = num_channels // groups
     # reshape
     x = x.reshape((batchsize, channels_per_group, groups, height, width))
@@ -188,7 +188,7 @@ class CondenseSFR(nn.Layer):
         x = self.activation(x)
         x = ShuffleLayerTrans(x, self.groups)
         x = self.conv(x)  # SIZE: N, C, H, W
-        N, C, H, W = x.shape
+        N, C, H, W = paddle.shape(x)
         x = x.reshape((N, C, H * W))
         x = x.transpose((0, 2, 1))  # SIZE: N, HW, C
         # x SIZE: N, HW, C; self.index SIZE: C, C; OUTPUT SIZE: N, HW, C
@@ -374,8 +374,8 @@ class CondenseNetV2(nn.Layer):
 
     def forward(self, x):
         features = self.features(x)
-        out = features.reshape((features.shape[0], features.shape[1] *
-                                features.shape[2] * features.shape[3]))
+        shape = paddle.shape(features)
+        out = features.reshape((shape[0], shape[1] * shape[2] * shape[3]))
         out = self.fc(out)
         out = self.fc_act(out)
 

paddlers/datasets/coco.py

@@ -336,7 +336,7 @@ class COCODetection(Dataset):
             max_img_id += 1
             im_fname = osp.join(image_dir, image)
             img_data = cv2.imread(im_fname, cv2.IMREAD_UNCHANGED)
-            im_h, im_w, im_c = img_data.shape
+            im_h, im_w, im_c = paddle.shape(img_data)
 
             im_info = {
                 'im_id': np.asarray([max_img_id]),

paddlers/datasets/voc.py

@@ -400,7 +400,7 @@ class VOCDetection(Dataset):
             max_img_id += 1
             im_fname = osp.join(image_dir, image)
             img_data = cv2.imread(im_fname, cv2.IMREAD_UNCHANGED)
-            im_h, im_w, im_c = img_data.shape
+            im_h, im_w, im_c = paddle.shape(img_data)
 
             im_info = {
                 'im_id': np.asarray([max_img_id]),