New TensorFlow `TFDWConv()` module (#7824)

* New TensorFlow `TFDWConv()` module

Analog to DWConv() module:
8aa2085a7e/models/common.py (L53-L57)

* Fix and new activations() function

* Update tf.py

models/tf.py

@@ -70,25 +70,38 @@ class TFConv(keras.layers.Layer):
         # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
 
         conv = keras.layers.Conv2D(
-            c2,
-            k,
-            s,
-            'SAME' if s == 1 else 'VALID',
-            use_bias=False if hasattr(w, 'bn') else True,
+            filters=c2,
+            kernel_size=k,
+            strides=s,
+            padding='SAME' if s == 1 else 'VALID',
+            use_bias=not hasattr(w, 'bn'),
             kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
             bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
         self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
         self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
+        self.act = activations(w.act) if act else tf.identity
+
+    def call(self, inputs):
+        return self.act(self.bn(self.conv(inputs)))
 
-        # YOLOv5 activations
-        if isinstance(w.act, nn.LeakyReLU):
-            self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity
-        elif isinstance(w.act, nn.Hardswish):
-            self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity
-        elif isinstance(w.act, (nn.SiLU, SiLU)):
-            self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity
-        else:
-            raise Exception(f'no matching TensorFlow activation found for {w.act}')
+
+class TFDWConv(keras.layers.Layer):
+    # Depthwise convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
+        # ch_in, ch_out, weights, kernel, stride, padding, groups
+        super().__init__()
+        assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
+
+        conv = keras.layers.DepthwiseConv2D(
+            kernel_size=k,
+            strides=s,
+            padding='SAME' if s == 1 else 'VALID',
+            use_bias=not hasattr(w, 'bn'),
+            kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
+            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
+        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
+        self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
+        self.act = activations(w.act) if act else tf.identity
 
     def call(self, inputs):
         return self.act(self.bn(self.conv(inputs)))
@@ -103,10 +116,8 @@ class TFFocus(keras.layers.Layer):
 
     def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
         # inputs = inputs / 255  # normalize 0-255 to 0-1
-        return self.conv(
-            tf.concat(
-                [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]],
-                3))
+        inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
+        return self.conv(tf.concat(inputs, 3))
 
 
 class TFBottleneck(keras.layers.Layer):
@@ -439,6 +450,18 @@ class AgnosticNMS(keras.layers.Layer):
         return padded_boxes, padded_scores, padded_classes, valid_detections
 
 
+def activations(act=nn.SiLU):
+    # Returns TF activation from input PyTorch activation
+    if isinstance(act, nn.LeakyReLU):
+        return lambda x: keras.activations.relu(x, alpha=0.1)
+    elif isinstance(act, nn.Hardswish):
+        return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
+    elif isinstance(act, (nn.SiLU, SiLU)):
+        return lambda x: keras.activations.swish(x)
+    else:
+        raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}')
+
+
 def representative_dataset_gen(dataset, ncalib=100):
     # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
     for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):