Full-size proto code (optional) (#9980)

* Update tf.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update tf.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update tf.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

models/tf.py

@@ -333,6 +333,7 @@ def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w
 
     def call(self, x):
         p = self.proto(x[0])
+        # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0]))  # (optional) full-size protos
         p = tf.transpose(p, [0, 3, 1, 2])  # from shape(1,160,160,32) to shape(1,32,160,160)
         x = self.detect(self, x)
         return (x, p) if self.training else (x[0], p)
@@ -355,8 +356,8 @@ class TFUpsample(keras.layers.Layer):
     # TF version of torch.nn.Upsample()
     def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
         super().__init__()
-        assert scale_factor == 2, "scale_factor must be 2"
-        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
+        assert scale_factor % 2 == 0, "scale_factor must be multiple of 2"
+        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)
         # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
         # with default arguments: align_corners=False, half_pixel_centers=False
         # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,