Skip to content

space_to_depth #1017

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

Open
ayermolo wants to merge 2 commits into
base: master
Choose a base branch
from
Open

space_to_depth #1017

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4ebdee2
ONNX doesn't support extract image patch, but does support spaceToDep…
Apr 6, 2019
793f264
Changed imagePatch to SpaceToDepth, added fusedBatch.
Sep 25, 2019
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
17 changes: 12 additions & 5 deletions darkflow/net/ops/convolution.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from .baseop import BaseOp
import tensorflow as tf
import numpy as np
import math

class reorg(BaseOp):
def _forward(self):
Expand All @@ -24,6 +25,7 @@ def _forward(self):
def forward(self):
inp = self.inp.out
s = self.lay.stride
#working higher mAP
self.out = tf.extract_image_patches(
inp, [1,s,s,1], [1,s,s,1], [1,1,1,1], 'VALID')

Expand Down Expand Up @@ -75,17 +77,22 @@ def forward(self):

def batchnorm(self, layer, inp):
if not self.var:
temp = (inp - layer.w['moving_mean'])
temp /= (np.sqrt(layer.w['moving_variance']) + 1e-5)
temp *= layer.w['gamma']
return temp
tensor, mean, variance = tf.nn.fused_batch_norm(inp,
layer.w['gamma'],
np.zeros([np.size(layer.w['gamma'])], dtype=np.float32),
mean=layer.w['moving_mean'],
variance = layer.w['moving_variance'],
epsilon= 1e-5,
is_training=False)
return tensor;
else:
args = dict({
'center' : False, 'scale' : True,
'epsilon': 1e-5, 'scope' : self.scope,
'updates_collections' : None,
'is_training': layer.h['is_training'],
'param_initializers': layer.w
'param_initializers': layer.w,
'fused' : True
})
return slim.batch_norm(inp, **args)

Expand Down