Skip to content

Full-size proto code (optional) #9980

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
glenn-jocher merged 4 commits into from
Oct 30, 2022
Merged

Full-size proto code (optional) #9980

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
18d579c
Update tf.py
glenn-jocher Oct 30, 2022
9a3f636
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 30, 2022
8ba4333
Update tf.py
glenn-jocher Oct 30, 2022
f53c9cc
Update tf.py
glenn-jocher Oct 30, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 3 additions & 2 deletions models/tf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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,
Expand Down