[examples] added tensorflow dense convolution templates

examples/python/tensorflow/run.py

@@ -6,24 +6,40 @@ data_files_path = tf.resource_loader.get_data_files_path()
 library_dir = os.path.dirname(os.path.realpath(__file__))
 module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
 
-M, N, K = 128,128,128
-a = tf.placeholder(tf.float16, shape=[M, K])
-b = tf.placeholder(tf.float16, shape=[N, K])
-locks = tf.placeholder(tf.int32, shape=[4096])
-# c = tf.matmul(a, b, transpose_a=True)
-c = module.dot(a, b, locks)
+def run_dot():
+    M, N, K = 128,128,128
+    a = tf.placeholder(tf.float16, shape=[M, K])
+    b = tf.placeholder(tf.float16, shape=[N, K])
+    locks = tf.placeholder(tf.int32, shape=[4096])
+    # c = tf.matmul(a, b, transpose_a=True)
+    c = module.dot(a, b, locks)
+    # Reference
+    ha = np.random.rand(M, K).astype(np.float16)
+    hb = np.random.rand(N, K).astype(np.float16)
+    # Run
+    sess = tf.InteractiveSession()
+    sess.run(tf.global_variables_initializer())
+    result = sess.run([c], feed_dict = {locks: np.zeros(4096),
+                                        a: ha,
+                                        b: hb})[0]
+    # Test
+    hresult = np.dot(ha.T, hb).T
+    dif = np.abs(result - hresult)
+    print("dif: %f" % np.max(dif))
 
-# Reference
-ha = np.random.rand(M, K).astype(np.float16)
-hb = np.random.rand(N, K).astype(np.float16)
+def run_conv():
+    BS, C, H, W = 16, 32, 32, 32
+    R, S, NF = 3, 3, 32
+    a = tf.placeholder(tf.float32, shape=[BS, C, H, W])
+    b = tf.placeholder(tf.float32, shape=[C, R, S, NF])
+    c = module.dense_conv(a, b)
+    # Reference
+    ha = np.random.rand(BS, C, H, W)
+    hb = np.random.rand(C, R, S, NF)
+    # Run
+    sess = tf.InteractiveSession()
+    sess.run(tf.global_variables_initializer())
+    result = sess.run([c], feed_dict = {a: ha,
+                                        b: hb})[0]
 
-# Run
-sess = tf.InteractiveSession()
-sess.run(tf.global_variables_initializer())
-result = sess.run([c], feed_dict = {locks: np.zeros(4096),
-                                    a: ha,
-                                    b: hb})[0]
-
-hresult = np.dot(ha.T, hb).T
-dif = np.abs(result - hresult)
-print("dif: %f" % np.max(dif))
+run_conv()