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[dnn/shift] many bugfixes in strided shift-conv

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Philippe Tillet
2019-07-10 19:49:31 -07:00
parent 4ca83f1935
commit 75cf2df110
4 changed files with 84 additions and 73 deletions
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17
examples/python/tensorflow/run.py
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@@ -58,24 +58,29 @@ def blocksparse_matmul_grad(op, dy):
return (dx, dw)
def run_shift():
B, C, H, W = 16, 1, 4, 4
R, S, F = 3, 3, 32
B, C, H, W = 16, 16, 4, 4
R, S, F = 3, 3, 16
stride_h, stride_w = 2, 2
np.random.seed(2)
a = tf.placeholder(tf.float32, shape=[C, H, W, B])
b = tf.placeholder(tf.float32, shape=[C, F])
hshift_h = np.random.randint(- (R//2), R//2 + 1, size=C, dtype=np.int32)
hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32)
#hshift_h = np.random.randint(- (R//2), R//2 + 1, size=C, dtype=np.int32)
#hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32)
hshift_h = np.zeros(C, dtype=np.int32)
hshift_w = np.zeros(C, dtype=np.int32)
c = module.shift_conv(a, b, stride_h=stride_h, stride_w=stride_w, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
# feed values
ha = np.ones((C, H, W, B), dtype=np.float32)
hb = np.ones((C, F), dtype=np.float32)
ha = np.random.rand(C, H, W, B)
hb = np.random.rand(C, F)
#ha = np.ones((C, H, W, B), dtype=np.float32)
#hb = np.ones((C, F), dtype=np.float32)
sess = tf.InteractiveSession()
# test
grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (F, H//stride_h, W//stride_w, B),
extra_feed_dict = {a: ha, b: hb})
dw_t, dw_n = grads[1]
dx_t, dx_n = grads[0]
print(dw_t, dw_n)
print(np.max(np.abs(dw_t - dw_n)))
print(np.max(np.abs(dx_t - dx_n)))
# Run