gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[dnn] Adding batchnorm

Browse Source
This commit is contained in:
Philippe Tillet
2019-07-08 18:44:37 -07:00
parent b0cf3143c5
commit f9db0449b7
42 changed files with 682 additions and 1763 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
examples/python/tensorflow/run.py
View File

@@ -56,8 +56,8 @@ def blocksparse_matmul_grad(op, dy):
return (dx, dw)
def run_shift():
B, C, H, W = 1, 32, 8, 6
R, S, F = 3, 3, 16
B, C, H, W = 16, 1024, 8, 8
R, S, F = 3, 3, 1024
np.random.seed(2)
a = tf.placeholder(tf.float32, shape=[C, H, W, B])
b = tf.placeholder(tf.float32, shape=[C, F])
@@ -65,8 +65,6 @@ def run_shift():
hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32)
#hshift_h = np.ones(C, dtype=np.int32)
#hshift_w = np.ones(C, dtype=np.int32)
print(hshift_h)
print(hshift_w)
c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
# Reference
ha = np.random.rand(C, H, W, B)
@@ -74,16 +72,36 @@ def run_shift():
#ha = np.ones((C, H, W, B), dtype=np.int32)
#hb = np.ones((C, F), dtype=np.int32)
sess = tf.InteractiveSession()
grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (F, H, W, B),
extra_feed_dict={a: ha, b: hb})
dw_t, dw_n = grads[1]
dx_t, dx_n = grads[0]
print(np.max(np.abs(dw_t - dw_n)))
print(np.max(np.abs(dx_t - dx_n)))
#grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (F, H, W, B),
# extra_feed_dict = {a: ha, b: hb})
#dw_t, dw_n = grads[1]
#dx_t, dx_n = grads[0]
#print(np.max(np.abs(dw_t - dw_n)))
#print(np.max(np.abs(dx_t - dx_n)))
# Run
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {a: ha,
b: hb})[0]
#print(result)
run_shift()
def run_batchnorm():
C, H, W, B = 32, 16, 16, 16
np.random.seed(0)
# Placeholders
x = tf.placeholder(tf.float32, shape=[C, H, W, B])
g = tf.placeholder(tf.float32, shape=[C])
b = tf.placeholder(tf.float32, shape=[C])
# Feed values
hx = np.random.rand(C, H, W, B)
hg = np.random.rand(C)
hb = np.random.rand(C)
# batchnorm
y, m, v = module.batchnorm_forward(x, g, b, eps=1e-5)
# Run
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
result = sess.run([y, m, v], feed_dict = {x: hx, g: hg, b: hb})
print(hx.sum(axis=(1,2,3)))
print(result[1])
run_batchnorm()