98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
import os
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import tensorflow as tf
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from tensorflow.python.framework import ops
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import numpy as np
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from time import time
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data_files_path = tf.resource_loader.get_data_files_path()
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library_dir = os.path.dirname(os.path.realpath(__file__))
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module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
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def run_dot():
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M, N, K = 128,128,128
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a = tf.placeholder(tf.float16, shape=[M, K])
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b = tf.placeholder(tf.float16, shape=[N, K])
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locks = tf.placeholder(tf.int32, shape=[4096])
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# c = tf.matmul(a, b, transpose_a=True)
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c = module.dot(a, b, locks)
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# Reference
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ha = np.random.rand(M, K).astype(np.float16)
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hb = np.random.rand(N, K).astype(np.float16)
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# Run
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {locks: np.zeros(4096),
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a: ha,
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b: hb})[0]
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# Test
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hresult = np.dot(ha.T, hb).T
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dif = np.abs(result - hresult)
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print("dif: %f" % np.max(dif))
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def run_conv():
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B, C, H, W = 16, 32, 32, 32
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R, S, NF = 3, 3, 32
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a = tf.placeholder(tf.float32, shape=[B, C, H, W])
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b = tf.placeholder(tf.float32, shape=[C, R, S, NF])
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c = module.conv2d(a, b)
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# Reference
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ha = np.random.rand(B, C, H, W)
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hb = np.random.rand(C, R, S, NF)
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# Run
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {a: ha,
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b: hb})[0]
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@ops.RegisterGradient('ShiftConv')
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def blocksparse_matmul_grad(op, dy):
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shift_h = op.get_attr('shift_h')
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shift_w = op.get_attr('shift_w')
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x = op.inputs[0]
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w = op.inputs[1]
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dx = module.shift_conv_dx(dy, w, shift_h=shift_h, shift_w=shift_w)
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dw = module.shift_conv_dw(dy, x, shift_h=shift_h, shift_w=shift_w)
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return (dx, dw)
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def run_shift():
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B, C, H, W = 1, 16, 4, 4
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R, S, F = 3, 3, 16
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np.random.seed(2)
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a = tf.placeholder(tf.float32, shape=[C, H, W, B])
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b = tf.placeholder(tf.float32, shape=[C, F])
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hshift_h = np.random.randint(- (R//2), R//2 + 1, size=C, dtype=np.int32)
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hshift_w = np.random.randint(- (S//2), R//2 + 1, size=C, dtype=np.int32)
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print(hshift_h)
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print(hshift_w)
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#hshift_h = np.ones(C, dtype=np.int32)
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#hshift_w = np.ones(C, dtype=np.int32)
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c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
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# Reference
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ha = np.random.rand(C, H, W, B)
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hb = np.random.rand(C, F)
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#ha = np.ones((C, H, W, B), dtype=np.int32)
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#hb = np.ones((C, F), dtype=np.int32)
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sess = tf.InteractiveSession()
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grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (C, H, W, B),
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extra_feed_dict={a: ha, b: hb})
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dx_t, dx_n = grads[0]
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dw_t, dw_n = grads[1]
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print(dw_t)
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print(dw_n)
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print(np.max(dw_t - dw_n))
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#np.savetxt('diff.dat', dw_t - dw_n, fmt='%2.4f')
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#np.savetxt('theoretical.dat', dw_t, fmt='%2.4f')
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#np.savetxt('numerical.dat', dw_n, fmt='%2.4f')
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print(np.max(dx_t - dx_n))
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#np.savetxt('diff.dat', dx_t - dx_n, fmt='%2.4f')
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#np.savetxt('theoretical.dat', dx_t, fmt='%2.4f')
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#np.savetxt('numerical.dat', dx_n, fmt='%2.4f')
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# Run
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sess.run(tf.global_variables_initializer())
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result = sess.run([c], feed_dict = {a: ha,
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b: hb})[0]
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#print(result)
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run_shift()
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