fix confict

Author: hanjr92

tensorlayerx/nn/layers/Transformer.py

@@ -513,15 +513,15 @@ def __init__(
         self._config.pop("self")
         self._config.pop("__class__", None)
         self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
-        self.linear1 = tlx.nn.layers.Dense(in_channels=d_model, n_units=dim_feedforward)
-        self.dropout1 = tlx.nn.layers.Dropout(float(1.0 - dropout))
-        self.linear2 = tlx.nn.layers.Dense(in_channels=dim_feedforward, n_units=d_model)
+        self.linear1 = tlx.nn.layers.Linear(in_features=d_model, out_features=dim_feedforward)
+        self.dropout1 = tlx.nn.layers.Dropout(float(dropout))
+        self.linear2 = tlx.nn.layers.Linear(in_features=dim_feedforward, out_features=d_model)
 
         self.norm1 = tlx.nn.layers.LayerNorm(d_model, epsilon=layer_norm_eps)
         self.norm2 = tlx.nn.layers.LayerNorm(d_model, epsilon=layer_norm_eps)
 
-        self.dropout2 = tlx.nn.layers.Dropout(float(1.0 - dropout))
-        self.dropout3 = tlx.nn.layers.Dropout(float(1.0 - dropout))
+        self.dropout2 = tlx.nn.layers.Dropout(float(dropout))
+        self.dropout3 = tlx.nn.layers.Dropout(float(dropout))
         if act == 'relu':
             self.act = tlx.relu
         elif act == 'gelu':
@@ -608,14 +608,14 @@ def __init__(
         self._config.pop("__class__", None)  # py3
         self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
         self.cross_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
-        self.dropout1 = tlx.nn.layers.Dropout(float(1 - dropout))
-        self.dropout2 = tlx.nn.layers.Dropout(float(1 - dropout))
-        self.dropout3 = tlx.nn.layers.Dropout(float(1 - dropout))
+        self.dropout1 = tlx.nn.layers.Dropout(float(dropout))
+        self.dropout2 = tlx.nn.layers.Dropout(float(dropout))
+        self.dropout3 = tlx.nn.layers.Dropout(float(dropout))
         self.norm1 = tlx.nn.layers.LayerNorm(d_model, epsilon=layer_norm_eps)
         self.norm2 = tlx.nn.layers.LayerNorm(d_model, epsilon=layer_norm_eps)
         self.norm3 = tlx.nn.layers.LayerNorm(d_model, epsilon=layer_norm_eps)
-        self.linear1 = tlx.nn.layers.Dense(in_channels=d_model, n_units=dim_feedforward)
-        self.linear2 = tlx.nn.layers.Dense(in_channels=dim_feedforward, n_units=d_model)
+        self.linear1 = tlx.nn.layers.Linear(in_features=d_model, out_features=dim_feedforward)
+        self.linear2 = tlx.nn.layers.Linear(in_features=dim_feedforward, out_features=d_model)
 
         if act == 'relu':
             self.act = tlx.relu

tests/layers/test_layers_core_basedense_dropout.py

@@ -23,7 +23,7 @@ def setUpClass(self):
         self.dense1 = tlx.nn.Linear(out_features=800, act=tlx.ReLU, in_features=784, name='test_dense')
         self.n1 = self.dense1(self.input)
 
-        self.dropout1 = tlx.nn.Dropout(keep=0.8)
+        self.dropout1 = tlx.nn.Dropout(p=0.2)
         self.n2 = self.dropout1(self.n1)
 
         self.dense2 = tlx.nn.Linear(out_features=10, act='relu', b_init=None, in_features=800)
@@ -39,7 +39,7 @@ class get_model(tensorlayerx.nn.Module):
             def __init__(self):
                 super(get_model, self).__init__()
                 self.layer1 = tlx.nn.Linear(out_features=800, act=tlx.ReLU, in_features=784, name='test_dense')
-                self.dp = tlx.nn.Dropout(p=0.8)
+                self.dp = tlx.nn.Dropout(p=0.2)
                 self.layer2 = tlx.nn.Linear(out_features=10, act='relu', b_init=None, in_features=800)
                 self.layer3 = tlx.nn.Linear(out_features=10, act='relu', b_init=None, in_features=10)
 

tests/layers/test_layers_core_nested.py

@@ -27,7 +27,7 @@ class MyLayer(tensorlayerx.nn.Module):
 
             def __init__(self, name=None):
                 super(MyLayer, self).__init__(name=name)
-                self.input_layer = tlx.nn.Dense(in_channels=50, n_units=20)
+                self.input_layer = tlx.nn.Linear(in_features=50, out_features=20)
                 self.build(None)
                 self._built = True
 

tests/layers/test_layers_extend.py

@@ -5,7 +5,6 @@
 import unittest
 
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-import tensorlayerx
 import tensorlayerx as tlx
 
 from tests.utils import CustomTestCase

tests/layers/test_layers_linear.py

@@ -21,7 +21,6 @@ def setUpClass(self):
 
         self.ni = tlx.nn.Input(self.inputs_shape, name='input_layer')
         self.layer1 = tlx.nn.BinaryLinear(out_features=5)
-
         self.layer2 = tlx.nn.BinaryLinear(out_features=5, in_features=10)
 
         self.n1 = self.layer1(self.ni)
@@ -76,9 +75,7 @@ def setUpClass(self):
 
         self.ni = tlx.nn.Input(self.inputs_shape, name='input_layer')
         self.layer1 = tlx.nn.DropconnectLinear(out_features=5, keep=1.0)
-
         self.layer2 = tlx.nn.DropconnectLinear(out_features=5, in_features=10, keep=0.01)
-
         self.n1 = self.layer1(self.ni)
         self.n2 = self.layer2(self.ni)
 
@@ -103,9 +100,7 @@ def setUpClass(self):
 
         self.ni = tlx.nn.Input(self.inputs_shape, name='input_layer')
         self.layer1 = tlx.nn.QuanLinear(out_features=5)
-
         self.layer2 = tlx.nn.QuanLinear(out_features=5, in_features=10)
-
         self.n1 = self.layer1(self.ni)
         self.n2 = self.layer2(self.ni)
 

tests/layers/test_layers_normalization.py

@@ -29,7 +29,7 @@ def setUpClass(cls):
 
         ## Base
         ni_1 = Input(x_1_input_shape, name='test_ni1')
-        nn_1 = Conv1d(out_channels=32, kernel_size=5, stride=2, name='test_conv1d')(ni_1)
+        nn_1 = Conv1d(out_channels=32, in_channels=1, stride=2, name='test_conv1d')(ni_1)
         n1_b = BatchNorm(name='test_conv')(nn_1)
         cls.n1_b = n1_b
 
@@ -47,7 +47,7 @@ class bn_0d_model(tlx.nn.Module):
 
             def __init__(self):
                 super(bn_0d_model, self).__init__()
-                self.fc = Dense(32, in_channels=10)
+                self.fc = Linear(32, in_features=10)
                 self.bn = BatchNorm(num_features=32, name='test_bn1d')
 
             def forward(self, x):
@@ -61,7 +61,7 @@ def forward(self, x):
 
         nin_0 = Input(x_0_input_shape, name='test_in1')
 
-        n0 = Dense(32)(nin_0)
+        n0 = Linear(32)(nin_0)
         n0 = BatchNorm1d(name='test_bn0d')(n0)
 
         cls.n0 = n0
@@ -70,7 +70,7 @@ class bn_0d_model(tlx.nn.Module):
 
             def __init__(self):
                 super(bn_0d_model, self).__init__(name='test_bn_0d_model')
-                self.fc = Dense(32, in_channels=10)
+                self.fc = Linear(32, in_features=10)
                 self.bn = BatchNorm1d(num_features=32, name='test_bn1d')
 
             def forward(self, x):
@@ -104,7 +104,6 @@ def forward(self, x):
         ## 2D ========================================================================
 
         nin_2 = Input(x_2_input_shape, name='test_in2')
-
         n2 = Conv2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), name='test_conv2d')(nin_2)
         n2 = BatchNorm2d(name='test_bn2d')(n2)
 
@@ -126,7 +125,6 @@ def forward(self, x):
         ## 3D ========================================================================
 
         nin_3 = Input(x_3_input_shape, name='test_in3')
-
         n3 = Conv3d(out_channels=32, kernel_size=(3, 3, 3), stride=(2, 2, 2), name='test_conv3d')(nin_3)
         n3 = BatchNorm3d(name='test_bn3d', act=tlx.ReLU)(n3)
 

tests/layers/test_layers_pooling.py

@@ -46,7 +46,6 @@ def setUpClass(cls):
 
         x_2_input_shape = [None, 100, 100, 3]
         nin_2 = Input(x_2_input_shape, name='test_in2')
-
         n6 = tlx.nn.Conv2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), name='test_conv2d')(nin_2)
         n7 = tlx.nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding='SAME',
                                            name='test_maxpool2d')(n6)

tests/layers/test_layers_resampling.py

@@ -26,6 +26,7 @@ def setUpClass(cls):
         x_2_input_shape = [None, 100, 100, 3]
         nin_2 = Input(x_2_input_shape)
 
+
         n6 = tlx.nn.Conv2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), name='test_conv2d')(nin_2)
 
         n7 = tlx.nn.UpSampling2d(scale=(2, 2), name='test_UpSampling2d_1')(n6)

tests/layers/test_mindspore.py

@@ -190,8 +190,8 @@ def test_pooling():
 
 def test_dense():
     input_layer = tlx.nn.Input([10, 30])
-    n1 = tlx.nn.Dense(n_units=100, in_channels=30, b_init=tlx.initializers.truncated_normal())(input_layer)
-    n2 = tlx.nn.Dense(n_units=10, name='none inchannels')(input_layer)
+    n1 = tlx.nn.Linear(out_features=100, in_features=30, b_init=tlx.initializers.truncated_normal())(input_layer)
+    n2 = tlx.nn.Linear(out_features=10, name='none inchannels')(input_layer)
     print("Dense :", n1.shape, n2.shape)
 
 def test_normalization():