def testShapeInference(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16] with self.test_session(use_gpu=True) as sess: for dtype in dtype_list: lhs = constant_op.constant([[0], [3], [5]], dtype=dtype) rhs = constant_op.constant([[1, 2, 4]], dtype=dtype) and_tensor = bitwise_ops.bitwise_and(lhs, rhs) or_tensor = bitwise_ops.bitwise_or(lhs, rhs) xor_tensor = bitwise_ops.bitwise_xor(lhs, rhs) ls_tensor = bitwise_ops.left_shift(lhs, rhs) rs_tensor = bitwise_ops.right_shift(lhs, rhs) and_result, or_result, xor_result, ls_result, rs_result = sess.run( [and_tensor, or_tensor, xor_tensor, ls_tensor, rs_tensor]) # Compare shape inference with result self.assertAllEqual(and_tensor.get_shape().as_list(), and_result.shape) self.assertAllEqual(and_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(or_tensor.get_shape().as_list(), or_result.shape) self.assertAllEqual(or_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(xor_tensor.get_shape().as_list(), xor_result.shape) self.assertAllEqual(xor_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(ls_tensor.get_shape().as_list(), ls_result.shape) self.assertAllEqual(ls_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(rs_tensor.get_shape().as_list(), rs_result.shape) self.assertAllEqual(rs_tensor.get_shape().as_list(), [3, 3])
def testInvertOp(self): dtype_list = [ dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64 ] inputs = [0, 5, 3, 14] with self.test_session(use_gpu=True) as sess: for dtype in dtype_list: # Because of issues with negative numbers, let's test this indirectly. # 1. invert(a) and a = 0 # 2. invert(a) or a = invert(0) input_tensor = constant_op.constant(inputs, dtype=dtype) not_a_and_a, not_a_or_a, not_0 = sess.run([ bitwise_ops.bitwise_and(input_tensor, bitwise_ops.invert(input_tensor)), bitwise_ops.bitwise_or(input_tensor, bitwise_ops.invert(input_tensor)), bitwise_ops.invert(constant_op.constant(0, dtype=dtype)) ]) self.assertAllEqual(not_a_and_a, [0, 0, 0, 0]) self.assertAllEqual(not_a_or_a, [not_0] * 4) # For unsigned dtypes let's also check the result directly. if dtype.is_unsigned: inverted = sess.run(bitwise_ops.invert(input_tensor)) expected = [dtype.max - x for x in inputs] self.assertAllEqual(inverted, expected)
def pack_uint8_r2_to_uint32(self, test_input): num_rows, num_columns = test_input.get_shape().as_list() num_output_columns = int(math.ceil(num_columns / 4.0)) padding_input = array_ops.pad( math_ops.cast(test_input, dtype=dtypes.uint8), constant_op.constant([[ 0, 0, ], [0, num_output_columns * 4 - num_columns]])) output = array_ops.zeros([num_rows, num_output_columns], dtype=dtypes.uint32) num_elements_per_pack = 4 shift_bits = 8 iota_r1 = math_ops.range(num_output_columns * num_elements_per_pack) for p in range(num_elements_per_pack): selected_index = math_ops.equal( math_ops.mod(iota_r1, num_elements_per_pack), p) gather_index = array_ops.boolean_mask(iota_r1, selected_index) gathered_input = array_ops.gather(padding_input, gather_index, axis=1) total_shift_bits = shift_bits * (num_elements_per_pack - p - 1) left_shift_input = bitwise_ops.left_shift( math_ops.cast(gathered_input, dtype=dtypes.uint32), total_shift_bits) output = bitwise_ops.bitwise_or(output, left_shift_input) return output
def testShapeInference(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16] with self.session(use_gpu=True) as sess: for dtype in dtype_list: lhs = constant_op.constant([[0], [3], [5]], dtype=dtype) rhs = constant_op.constant([[1, 2, 4]], dtype=dtype) and_tensor = bitwise_ops.bitwise_and(lhs, rhs) or_tensor = bitwise_ops.bitwise_or(lhs, rhs) xor_tensor = bitwise_ops.bitwise_xor(lhs, rhs) ls_tensor = bitwise_ops.left_shift(lhs, rhs) rs_tensor = bitwise_ops.right_shift(lhs, rhs) and_result, or_result, xor_result, ls_result, rs_result = sess.run( [and_tensor, or_tensor, xor_tensor, ls_tensor, rs_tensor]) # Compare shape inference with result self.assertAllEqual(and_tensor.get_shape().as_list(), and_result.shape) self.assertAllEqual(and_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(or_tensor.get_shape().as_list(), or_result.shape) self.assertAllEqual(or_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(xor_tensor.get_shape().as_list(), xor_result.shape) self.assertAllEqual(xor_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(ls_tensor.get_shape().as_list(), ls_result.shape) self.assertAllEqual(ls_tensor.get_shape().as_list(), [3, 3]) self.assertAllEqual(rs_tensor.get_shape().as_list(), rs_result.shape) self.assertAllEqual(rs_tensor.get_shape().as_list(), [3, 3])
def testBinaryOps(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16] with self.test_session(use_gpu=True) as sess: for dtype in dtype_list: lhs = constant_op.constant([0, 5, 3, 14], dtype=dtype) rhs = constant_op.constant([5, 0, 7, 11], dtype=dtype) and_result, or_result, xor_result = sess.run( [bitwise_ops.bitwise_and(lhs, rhs), bitwise_ops.bitwise_or(lhs, rhs), bitwise_ops.bitwise_xor(lhs, rhs)]) self.assertAllEqual(and_result, [0, 0, 3, 10]) self.assertAllEqual(or_result, [5, 5, 7, 15]) self.assertAllEqual(xor_result, [5, 5, 4, 5])
def testBinaryOps(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64] with self.session(use_gpu=True) as sess: for dtype in dtype_list: lhs = constant_op.constant([0, 5, 3, 14], dtype=dtype) rhs = constant_op.constant([5, 0, 7, 11], dtype=dtype) and_result, or_result, xor_result = sess.run( [bitwise_ops.bitwise_and(lhs, rhs), bitwise_ops.bitwise_or(lhs, rhs), bitwise_ops.bitwise_xor(lhs, rhs)]) self.assertAllEqual(and_result, [0, 0, 3, 10]) self.assertAllEqual(or_result, [5, 5, 7, 15]) self.assertAllEqual(xor_result, [5, 5, 4, 5])
def binarize_dense(x, transpose=False): if transpose: x = tf.transpose(x, [1, 0]) h, w = x.shape num_bins = int(w / 64) binary_x = tf.cast(x > 0, tf.int64) packed_x = [] for b in range(num_bins): packed_x.append(tf.zeros(shape=[h], dtype=tf.int64)) for k in range(num_bins): for b in range(64): packed_x[k] = bitwise_ops.bitwise_or( packed_x[k], bitwise_ops.left_shift(binary_x[:, 64 * k + b], b)) packed_x = tf.stack(packed_x, axis=-1) return packed_x
def testInvertOp(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16] inputs = [0, 5, 3, 14] with self.test_session(use_gpu=True) as sess: for dtype in dtype_list: # Because of issues with negative numbers, let's test this indirectly. # 1. invert(a) and a = 0 # 2. invert(a) or a = invert(0) input_tensor = constant_op.constant(inputs, dtype=dtype) not_a_and_a, not_a_or_a, not_0 = sess.run( [bitwise_ops.bitwise_and( input_tensor, bitwise_ops.invert(input_tensor)), bitwise_ops.bitwise_or( input_tensor, bitwise_ops.invert(input_tensor)), bitwise_ops.invert(constant_op.constant(0, dtype=dtype))]) self.assertAllEqual(not_a_and_a, [0, 0, 0, 0]) self.assertAllEqual(not_a_or_a, [not_0] * 4) # For unsigned dtypes let's also check the result directly. if dtype.is_unsigned: inverted = sess.run(bitwise_ops.invert(input_tensor)) expected = [dtype.max - x for x in inputs] self.assertAllEqual(inverted, expected)
def uint32s_to_uint64(x): return bitwise_ops.bitwise_or( math_ops.cast(x[0], dtypes.uint64), bitwise_ops.left_shift(math_ops.cast(x[1], dtypes.uint64), constant_op.constant(32, dtypes.uint64)))