update *_batch_size_like ops (#258)

contrib/LaneNet/loss.py

@@ -19,8 +19,9 @@ from utils.config import cfg
 
 
 def unsorted_segment_sum(data, segment_ids, unique_labels, feature_dims):
-    zeros = fluid.layers.fill_constant_batch_size_like(unique_labels, shape=[1, feature_dims],
-                                                       dtype='float32', value=0)
+    unique_labels_shape = fluid.layers.shape(unique_labels)
+    zeros = fluid.layers.fill_constant(
+        shape=[unique_labels_shape[0], feature_dims], dtype='float32', value=0)
     segment_ids = fluid.layers.unsqueeze(segment_ids, axes=[1])
     segment_ids.stop_gradient = True
     segment_sum = fluid.layers.scatter_nd_add(zeros, segment_ids, data)
@@ -30,29 +31,23 @@ def unsorted_segment_sum(data, segment_ids, unique_labels, feature_dims):
 
 
 def norm(x, axis=-1):
-    distance = fluid.layers.reduce_sum(fluid.layers.abs(x), dim=axis, keep_dim=True)
+    distance = fluid.layers.reduce_sum(
+        fluid.layers.abs(x), dim=axis, keep_dim=True)
     return distance
 
-def discriminative_loss_single(
-        prediction,
-        correct_label,
-        feature_dim,
-        label_shape,
-        delta_v,
-        delta_d,
-        param_var,
-        param_dist,
-        param_reg):
-
-    correct_label = fluid.layers.reshape(
-        correct_label, [
-            label_shape[1] * label_shape[0]])
+
+def discriminative_loss_single(prediction, correct_label, feature_dim,
+                               label_shape, delta_v, delta_d, param_var,
+                               param_dist, param_reg):
+
+    correct_label = fluid.layers.reshape(correct_label,
+                                         [label_shape[1] * label_shape[0]])
     prediction = fluid.layers.transpose(prediction, [1, 2, 0])
     reshaped_pred = fluid.layers.reshape(
-        prediction, [
-            label_shape[1] * label_shape[0], feature_dim])
+        prediction, [label_shape[1] * label_shape[0], feature_dim])
 
-    unique_labels, unique_id, counts = fluid.layers.unique_with_counts(correct_label)
+    unique_labels, unique_id, counts = fluid.layers.unique_with_counts(
+        correct_label)
     correct_label.stop_gradient = True
     counts = fluid.layers.cast(counts, 'float32')
     num_instances = fluid.layers.shape(unique_labels)
@@ -69,24 +64,29 @@ def discriminative_loss_single(
     distance = norm(tmp)
     distance = distance - delta_v
 
-    distance_pos = fluid.layers.greater_equal(distance, fluid.layers.zeros_like(distance))
+    distance_pos = fluid.layers.greater_equal(distance,
+                                              fluid.layers.zeros_like(distance))
     distance_pos = fluid.layers.cast(distance_pos, 'float32')
     distance = distance * distance_pos
 
     distance = fluid.layers.square(distance)
 
-    l_var = unsorted_segment_sum(distance, unique_id, unique_labels, feature_dims=1)
+    l_var = unsorted_segment_sum(
+        distance, unique_id, unique_labels, feature_dims=1)
     l_var = fluid.layers.elementwise_div(l_var, counts_rsp)
     l_var = fluid.layers.reduce_sum(l_var)
-    l_var = l_var / fluid.layers.cast(num_instances * (num_instances - 1), 'float32')
+    l_var = l_var / fluid.layers.cast(num_instances * (num_instances - 1),
+                                      'float32')
 
     mu_interleaved_rep = fluid.layers.expand(mu, [num_instances, 1])
     mu_band_rep = fluid.layers.expand(mu, [1, num_instances])
-    mu_band_rep = fluid.layers.reshape(mu_band_rep, (num_instances * num_instances, feature_dim))
+    mu_band_rep = fluid.layers.reshape(
+        mu_band_rep, (num_instances * num_instances, feature_dim))
 
     mu_diff = fluid.layers.elementwise_sub(mu_band_rep, mu_interleaved_rep)
 
-    intermediate_tensor = fluid.layers.reduce_sum(fluid.layers.abs(mu_diff), dim=1)
+    intermediate_tensor = fluid.layers.reduce_sum(
+        fluid.layers.abs(mu_diff), dim=1)
     intermediate_tensor.stop_gradient = True
     zero_vector = fluid.layers.zeros([1], 'float32')
     bool_mask = fluid.layers.not_equal(intermediate_tensor, zero_vector)
@@ -95,7 +95,8 @@ def discriminative_loss_single(
 
     mu_norm = norm(mu_diff_bool)
     mu_norm = 2. * delta_d - mu_norm
-    mu_norm_pos = fluid.layers.greater_equal(mu_norm, fluid.layers.zeros_like(mu_norm))
+    mu_norm_pos = fluid.layers.greater_equal(mu_norm,
+                                             fluid.layers.zeros_like(mu_norm))
     mu_norm_pos = fluid.layers.cast(mu_norm_pos, 'float32')
     mu_norm = mu_norm * mu_norm_pos
     mu_norm_pos.stop_gradient = True
@@ -122,8 +123,8 @@ def discriminative_loss(prediction, correct_label, feature_dim, image_shape,
     output_ta_reg = 0.
     for i in range(batch_size):
         disc_loss_single, l_var_single, l_dist_single, l_reg_single = discriminative_loss_single(
-            prediction[i], correct_label[i], feature_dim, image_shape, delta_v, delta_d, param_var, param_dist,
-            param_reg)
+            prediction[i], correct_label[i], feature_dim, image_shape, delta_v,
+            delta_d, param_var, param_dist, param_reg)
         output_ta_loss += disc_loss_single
         output_ta_var += l_var_single
         output_ta_dist += l_dist_single
@@ -134,5 +135,3 @@ def discriminative_loss(prediction, correct_label, feature_dim, image_shape,
     l_dist = output_ta_dist / batch_size
     l_reg = output_ta_reg / batch_size
     return disc_loss, l_var, l_dist, l_reg
-
-

pdseg/models/model_builder.py

@@ -223,6 +223,7 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
                     raise Exception(
                         "softmax loss or lovasz softmax loss can not combine with bce loss or dice loss or lovasz hinge loss."
                     )
+            cfg.PHASE = phase
             logits = seg_model(image, class_num)
 
             # 根据选择的loss函数计算相应的损失函数

pdseg/models/modeling/fast_scnn.py

@@ -25,12 +25,15 @@ from models.libs.model_libs import separate_conv
 from utils.config import cfg
 
 
-def learning_to_downsample(x, dw_channels1=32, dw_channels2=48, out_channels=64):
+def learning_to_downsample(x, dw_channels1=32, dw_channels2=48,
+                           out_channels=64):
     x = relu(bn(conv(x, dw_channels1, 3, 2)))
     with scope('dsconv1'):
-        x = separate_conv(x, dw_channels2, stride=2, filter=3, act=fluid.layers.relu)
+        x = separate_conv(
+            x, dw_channels2, stride=2, filter=3, act=fluid.layers.relu)
     with scope('dsconv2'):
-        x = separate_conv(x, out_channels, stride=2, filter=3, act=fluid.layers.relu)
+        x = separate_conv(
+            x, out_channels, stride=2, filter=3, act=fluid.layers.relu)
     return x
 
 
@@ -43,7 +46,9 @@ def dropout2d(input, prob, is_train=False):
         return input
     channels = input.shape[1]
     keep_prob = 1.0 - prob
-    random_tensor = keep_prob + fluid.layers.uniform_random_batch_size_like(input, [-1, channels, 1, 1], min=0., max=1.)
+    shape = fluid.layers.shape(input)
+    random_tensor = keep_prob + fluid.layers.uniform_random(
+        [shape[0], channels, 1, 1], min=0., max=1.)
     binary_tensor = fluid.layers.floor(random_tensor)
     output = input / keep_prob * binary_tensor
     return output
@@ -136,18 +141,23 @@ def psp_module(input, out_features):
     for size in sizes:
         psp_name = "psp" + str(size)
         with scope(psp_name):
-            pool = fluid.layers.adaptive_pool2d(input,
-                                                pool_size=[size, size],
-                                                pool_type='avg',
-                                                name=psp_name + '_adapool')
-            data = conv(pool, out_features,
-                        filter_size=1,
-                        bias_attr=False,
-                        name=psp_name + '_conv')
+            pool = fluid.layers.adaptive_pool2d(
+                input,
+                pool_size=[size, size],
+                pool_type='avg',
+                name=psp_name + '_adapool')
+            data = conv(
+                pool,
+                out_features,
+                filter_size=1,
+                bias_attr=False,
+                name=psp_name + '_conv')
             data_bn = bn(data, act='relu')
-            interp = fluid.layers.resize_bilinear(data_bn,
-                                                  out_shape=input.shape[2:],
-                                                  name=psp_name + '_interp', align_mode=0)
+            interp = fluid.layers.resize_bilinear(
+                data_bn,
+                out_shape=input.shape[2:],
+                name=psp_name + '_interp',
+                align_mode=0)
         cat_layers.append(interp)
     cat_layers = [input] + cat_layers
     out = fluid.layers.concat(cat_layers, axis=1, name='psp_cat')
@@ -158,7 +168,11 @@ def psp_module(input, out_features):
 class FeatureFusionModule:
     """Feature fusion module"""
 
-    def __init__(self, higher_in_channels, lower_in_channels, out_channels, scale_factor=4):
+    def __init__(self,
+                 higher_in_channels,
+                 lower_in_channels,
+                 out_channels,
+                 scale_factor=4):
         self.higher_in_channels = higher_in_channels
         self.lower_in_channels = lower_in_channels
         self.out_channels = out_channels
@@ -166,14 +180,19 @@ class FeatureFusionModule:
 
     def net(self, higher_res_feature, lower_res_feature):
         h, w = higher_res_feature.shape[2:]
-        lower_res_feature = fluid.layers.resize_bilinear(lower_res_feature, [h, w], align_mode=0)
+        lower_res_feature = fluid.layers.resize_bilinear(
+            lower_res_feature, [h, w], align_mode=0)
 
         with scope('dwconv'):
-            lower_res_feature = relu(bn(conv(lower_res_feature, self.out_channels, 1)))#(lower_res_feature)
+            lower_res_feature = relu(
+                bn(conv(lower_res_feature, self.out_channels,
+                        1)))  #(lower_res_feature)
         with scope('conv_lower_res'):
-            lower_res_feature = bn(conv(lower_res_feature, self.out_channels, 1, bias_attr=True))
+            lower_res_feature = bn(
+                conv(lower_res_feature, self.out_channels, 1, bias_attr=True))
         with scope('conv_higher_res'):
-            higher_res_feature = bn(conv(higher_res_feature, self.out_channels, 1, bias_attr=True))
+            higher_res_feature = bn(
+                conv(higher_res_feature, self.out_channels, 1, bias_attr=True))
         out = higher_res_feature + lower_res_feature
 
         return relu(out)
@@ -182,8 +201,12 @@ class FeatureFusionModule:
 class GlobalFeatureExtractor():
     """Global feature extractor module"""
 
-    def __init__(self, in_channels=64, block_channels=(64, 96, 128), out_channels=128,
-                 t=6, num_blocks=(3, 3, 3)):
+    def __init__(self,
+                 in_channels=64,
+                 block_channels=(64, 96, 128),
+                 out_channels=128,
+                 t=6,
+                 num_blocks=(3, 3, 3)):
         self.in_channels = in_channels
         self.block_channels = block_channels
         self.out_channels = out_channels
@@ -191,12 +214,15 @@ class GlobalFeatureExtractor():
         self.num_blocks = num_blocks
 
     def net(self, x):
-        x, _ = inverted_blocks(x, self.in_channels, self.t, self.block_channels[0],
-                               self.num_blocks[0], 2, 'inverted_block_1')
-        x, _ = inverted_blocks(x, self.block_channels[0], self.t, self.block_channels[1],
-                               self.num_blocks[1], 2, 'inverted_block_2')
-        x, _ = inverted_blocks(x, self.block_channels[1], self.t, self.block_channels[2],
-                               self.num_blocks[2], 1, 'inverted_block_3')
+        x, _ = inverted_blocks(x, self.in_channels, self.t,
+                               self.block_channels[0], self.num_blocks[0], 2,
+                               'inverted_block_1')
+        x, _ = inverted_blocks(x, self.block_channels[0], self.t,
+                               self.block_channels[1], self.num_blocks[1], 2,
+                               'inverted_block_2')
+        x, _ = inverted_blocks(x, self.block_channels[1], self.t,
+                               self.block_channels[2], self.num_blocks[2], 1,
+                               'inverted_block_3')
         x = psp_module(x, self.block_channels[2] // 4)
         with scope('out'):
             x = relu(bn(conv(x, self.out_channels, 1)))
@@ -213,10 +239,21 @@ class Classifier:
 
     def net(self, x):
         with scope('dsconv1'):
-            x = separate_conv(x, self.dw_channels, stride=self.stride, filter=3, act=fluid.layers.relu)
+            x = separate_conv(
+                x,
+                self.dw_channels,
+                stride=self.stride,
+                filter=3,
+                act=fluid.layers.relu)
         with scope('dsconv2'):
-            x = separate_conv(x, self.dw_channels, stride=self.stride, filter=3, act=fluid.layers.relu)
-        x = dropout2d(x, 0.1, is_train=cfg.PHASE=='train')
+            x = separate_conv(
+                x,
+                self.dw_channels,
+                stride=self.stride,
+                filter=3,
+                act=fluid.layers.relu)
+
+        x = dropout2d(x, 0.1, is_train=cfg.PHASE == 'train')
         x = conv(x, self.num_classes, 1, bias_attr=True)
         return x
 
@@ -233,7 +270,8 @@ def fast_scnn(img, num_classes):
     size = img.shape[2:]
     classifier = Classifier(128, num_classes)
 
-    global_feature_extractor = GlobalFeatureExtractor(64, [64, 96, 128], 128, 6, [3, 3, 3])
+    global_feature_extractor = GlobalFeatureExtractor(64, [64, 96, 128], 128, 6,
+                                                      [3, 3, 3])
     feature_fusion = FeatureFusionModule(64, 128, 128)
 
     with scope('learning_to_downsample'):
@@ -249,15 +287,18 @@ def fast_scnn(img, num_classes):
     if len(cfg.MODEL.MULTI_LOSS_WEIGHT) == 3:
         with scope('aux_layer_higher'):
             higher_logit = aux_layer(higher_res_features, num_classes)
-            higher_logit = fluid.layers.resize_bilinear(higher_logit, size, align_mode=0)
+            higher_logit = fluid.layers.resize_bilinear(
+                higher_logit, size, align_mode=0)
         with scope('aux_layer_lower'):
             lower_logit = aux_layer(lower_res_feature, num_classes)
-            lower_logit = fluid.layers.resize_bilinear(lower_logit, size, align_mode=0)
+            lower_logit = fluid.layers.resize_bilinear(
+                lower_logit, size, align_mode=0)
         return logit, higher_logit, lower_logit
     elif len(cfg.MODEL.MULTI_LOSS_WEIGHT) == 2:
         with scope('aux_layer_higher'):
             higher_logit = aux_layer(higher_res_features, num_classes)
-            higher_logit = fluid.layers.resize_bilinear(higher_logit, size, align_mode=0)
+            higher_logit = fluid.layers.resize_bilinear(
+                higher_logit, size, align_mode=0)
         return logit, higher_logit
 
-    return logit
\ No newline at end of file
+    return logit