refine paddle.shape (#3703)

Author: wanghuancoder

paddleseg/models/decoupled_segnet.py

@@ -206,7 +206,7 @@ def __init__(self, inplane, align_corners=False):
                                    bias_attr=False)
 
     def forward(self, x):
-        size = x.shape[2:]
+        size = paddle.shape(x)[2:]
         seg_down = self.down(x)
         seg_down = F.interpolate(seg_down,
                                  size=size,

paddleseg/models/ginet.py

@@ -184,7 +184,7 @@ def forward(self, x, inp):
         sizeB = B.shape
         B = paddle.flatten(B, 2, 3)
 
-        sizex = x.shape
+        sizex = paddle.shape(x)
         x_reduce = self.conv_phi(x)
 
         x_reduce = paddle.flatten(x_reduce, 2, 3).transpose((0, 2, 1))

paddleseg/models/isanet.py

@@ -122,7 +122,7 @@ def __init__(self, num_classes, in_channels, isa_channels, down_factor,
     def forward(self, feat_list):
         C3, C4 = feat_list
         x = self.in_conv(C4)
-        x_shape = x.shape
+        x_shape = paddle.shape(x)
         P_h, P_w = self.down_factor
         Q_h, Q_w = paddle.ceil(x_shape[2:3] / P_h).astype('int32'), paddle.ceil(
             x_shape[3:4] / P_w).astype('int32')

paddleseg/models/losses/pixel_contrast_cross_entropy_loss.py

@@ -57,7 +57,7 @@ def _hard_anchor_sampling(self, X, y_hat, y):
             y_hat (Tensor): reshaped label, shape = [N, H * W]
             y (Tensor): reshaped predict, shape = [N, H * W]
         """
-        batch_size, feat_dim = x.shape[0], x.shape[-1]
+        batch_size, feat_dim = X.shape[0], X.shape[-1]
         classes = []
         total_classes = 0
         for i in range(batch_size):

paddleseg/models/pointrend.py

@@ -420,9 +420,9 @@ def get_points_test(self, seg_logits, uncertainty_func):  # finish
 
         num_points = self.subdivision_num_points
         uncertainty_map = uncertainty_func(seg_logits)
-        batch_size = uncertainty_map.shape[0:1]
-        height = uncertainty_map.shape[2:3]
-        width = uncertainty_map.shape[3:4]
+        batch_size = paddle.shape(uncertainty_map)[0:1]
+        height = paddle.shape(uncertainty_map)[2:3]
+        width = paddle.shape(uncertainty_map)[3:4]
         h_step = 1.0 / height
         w_step = 1.0 / width
 

paddleseg/models/sfnet.py

@@ -221,7 +221,7 @@ def flow_warp(self, input, flow, size):
     def forward(self, x):
         low_feature, h_feature = x
         h_feature_orign = h_feature
-        size = low_feature.shape[2:]
+        size = paddle.shape(low_feature)[2:]
         low_feature = self.down_l(low_feature)
         h_feature = self.down_h(h_feature)
         h_feature = F.interpolate(h_feature,