[TOPI] Add GPU SSD

topi/python/topi/cuda/__init__.py

@@ -15,6 +15,8 @@
 from .pooling import schedule_pool, schedule_global_pool
 from .conv2d_transpose_nchw import schedule_conv2d_transpose_nchw
 from .extern import schedule_extern
-from .vision import schedule_region
-from .vision import schedule_reorg
 from .nn import schedule_lrn, schedule_l2_normalize
+from .vision import *
+from . import ssd
+from .ssd import *
+from .nms import *

topi/python/topi/cuda/nms.py

@@ -0,0 +1,354 @@
+# pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, too-many-statements, singleton-comparison
+"""Non-maximum suppression operator"""
+import math
+import tvm
+
+from tvm import api
+from topi.vision import nms
+
+
+def sort_ir(data, index, output, axis, is_descend):
+    """Low level IR to do sorting on the GPU, same usage as tvm.contrib.sort.argsort on the CPU.
+
+    Parameters
+    ----------
+    data: Buffer
+        2D Buffer of input boxes' score with shape [batch_size, num_anchors].
+
+    index : Buffer
+        Buffer of number of valid number of boxes.
+
+    output : Buffer
+        Output buffer of indicies of sorted tensor.
+
+    axis : int
+        The axis used for sorting.
+
+    is_descend : bool
+        If the sorted data is in descending order.
+
+    Returns
+    -------
+    stmt : Stmt
+        The result IR statement.
+    """
+
+    max_threads = int(
+        tvm.target.current_target(allow_none=False).max_num_threads)
+    tx = tvm.thread_axis("threadIdx.x")
+    bx = tvm.thread_axis("blockIdx.x")
+    ib = tvm.ir_builder.create()
+    p_data = ib.buffer_ptr(data)
+    p_index = ib.buffer_ptr(index)
+    p_out = ib.buffer_ptr(output)
+    ndim = len(data.shape)
+    assert data.dtype == "float32", "Currently only supports input dtype to be float32"
+    assert axis < ndim, "Axis out of boundary for input ndim %d" % ndim
+
+    axis_mul_before = 1
+    axis_mul_after = 1
+    if axis < 0:
+        axis = ndim + axis
+    for i in range(0, ndim):
+        if i < axis:
+            axis_mul_before *= data.shape[i]
+        elif i > axis:
+            axis_mul_after *= data.shape[i]
+
+    dshape = 0
+    for i in range(0, len(index.shape)):
+        dshape += index.shape[i]
+    dshape = tvm.select(dshape > axis_mul_before*axis_mul_after, dshape,
+                        axis_mul_before*axis_mul_after)
+
+    sizes_temp = ib.allocate(
+        "int32", dshape, name="sizes_temp", scope="global")
+    sizes = ib.allocate("int32", dshape, name="sizes", scope="global")
+    temp_index = ib.allocate("int32", dshape, name="temp_index", scope="local")
+    temp_data = ib.allocate("float32", dshape, name="temp_data", scope="local")
+    data_new = ib.allocate("float32", dshape, name="data_new", scope="global")
+    index_new = ib.allocate("int32", dshape, name="index_new", scope="global")
+    nthread_tx = max_threads
+    nthread_bx = dshape // max_threads + 1
+    ib.scope_attr(tx, "thread_extent", nthread_tx)
+    ib.scope_attr(bx, "thread_extent", nthread_bx)
+    tid = bx * max_threads + tx
+
+    with ib.if_scope(tid < axis_mul_before * axis_mul_after):
+        sizes[tid] = p_index[tid]
+        sizes_temp[tid] = p_index[tid]
+
+    with ib.if_scope(tid < axis_mul_before * axis_mul_after):
+        with ib.for_range(0, tvm.floor(tvm.sqrt((axis_mul_before * axis_mul_after) \
+             .astype("float32"))) + 1, name="k") as k:
+            with ib.if_scope(tid - (tvm.const(1, "int32") << k) >= 0):
+                with ib.if_scope(k % 2 == 0):
+                    sizes[tid] += sizes_temp[tid - (
+                        tvm.const(1, "int32") << k)]
+                    sizes_temp[tid] = sizes[tid]
+                with ib.else_scope():
+                    sizes_temp[tid] += sizes[tid - (
+                        tvm.const(1, "int32") << k)]
+                    sizes[tid] = sizes_temp[tid]
+
+    with ib.if_scope(tid < axis_mul_before * axis_mul_after):
+        i = tid / axis_mul_after
+        j = tid % axis_mul_after
+        current_sort_num = p_index[tid]
+        base_idx = i * data.shape[axis] * axis_mul_after + j
+        with ib.for_range(0, current_sort_num, name="k") as k:
+            full_idx = base_idx + k * axis_mul_after
+            with ib.if_scope(tid == 0):
+                start = 0
+            with ib.else_scope():
+                start = sizes[tid-1]
+            index_new[start + k] = k
+            data_new[start + k] = p_data[full_idx]
+
+    with ib.if_scope(tid < axis_mul_before * axis_mul_after):
+        with ib.if_scope(tid == 0):
+            start = 0
+        with ib.else_scope():
+            start = sizes[tid-1]
+        # OddEvenTransposeSort
+        with ib.for_range(0, p_index[tid], name="k") as k:
+            with ib.for_range(0, p_index[tid] - 1, name="i") as i:
+                with ib.if_scope(i % 2 == (k & 1)):
+                    with ib.if_scope(((data_new[i+start] < data_new[i+start+1]) ^
+                                      is_descend) == False):
+                        temp_data[tid] = data_new[i+start]
+                        data_new[i+start] = data_new[i+start+1]
+                        data_new[i+start+1] = temp_data[tid]
+                        temp_index[tid] = index_new[i+start]
+                        index_new[i+start] = index_new[i+start+1]
+                        index_new[i+start+1] = temp_index[tid]
+
+    with ib.if_scope(tid < axis_mul_before * axis_mul_after):
+        i = tid / axis_mul_after
+        j = tid % axis_mul_after
+        current_sort_num = p_index[tid]
+        base_idx = i * data.shape[axis] * axis_mul_after + j
+        with ib.for_range(0, data.shape[axis], name="k") as k:
+            with ib.if_scope(tid == 0):
+                start = 0
+            with ib.else_scope():
+                start = sizes[tid-1]
+            p_out[base_idx + k * axis_mul_after] = tvm.select(
+                k < current_sort_num,
+                index_new[k+start], k)
+    body = ib.get()
+    return body
+
+
+def nms_ir(data, sort_result, valid_count, out, nms_threshold, force_suppress, nms_topk):
+    """Low level IR routing for transform location in multibox_detection operator.
+
+    Parameters
+    ----------
+    data: Buffer
+        Buffer of output boxes with class and score.
+
+    sort_result : Buffer
+        Buffer of output box indexes sorted by score.
+
+    valid_count : Buffer
+        Buffer of number of valid output boxes.
+
+    out : Buffer
+        Output buffer.
+
+    nms_threshold : float
+        Non-maximum suppression threshold.
+
+    force_suppress : boolean
+        Whether to suppress all detections regardless of class_id.
+
+    nms_topk : int
+        Keep maximum top k detections before nms, -1 for no limit.
+
+    Returns
+    -------
+    stmt : Stmt
+        The result IR statement.
+    """
+    def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
+        """Calculate overlap of two boxes.
+        """
+        w = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 2], out_tensor[box_b_idx + 2])
+                         - tvm.make.Max(out_tensor[box_a_idx], out_tensor[box_b_idx]))
+        h = tvm.make.Max(0.0, tvm.make.Min(out_tensor[box_a_idx + 3], out_tensor[box_b_idx + 3])
+                         - tvm.make.Max(out_tensor[box_a_idx + 1], out_tensor[box_b_idx + 1]))
+        i = w * h
+        u = (out_tensor[box_a_idx + 2] - out_tensor[box_a_idx]) * \
+            (out_tensor[box_a_idx + 3] - out_tensor[box_a_idx + 1]) + \
+            (out_tensor[box_b_idx + 2] - out_tensor[box_b_idx]) * \
+            (out_tensor[box_b_idx + 3] - out_tensor[box_b_idx + 1]) - i
+        return tvm.select(u <= 0.0, 0.0, i / u)
+
+    max_threads = int(math.sqrt(
+        tvm.target.current_target(allow_none=False).max_num_threads))
+    tx = tvm.thread_axis("threadIdx.x")
+    ty = tvm.thread_axis("threadIdx.y")
+    bx = tvm.thread_axis("blockIdx.x")
+    by = tvm.thread_axis("blockIdx.y")
+    ib = tvm.ir_builder.create()
+    p_data = ib.buffer_ptr(data)
+    p_sort_result = ib.buffer_ptr(sort_result)
+    p_valid_count = ib.buffer_ptr(valid_count)
+    p_out = ib.buffer_ptr(out)
+    batch_size = out.shape[0]
+    num_anchors = out.shape[1]
+    nthread_tx = max_threads
+    nthread_bx = num_anchors // max_threads + 1
+    nthread_ty = max_threads
+    nthread_by = 6 // max_threads + 1
+    ib.scope_attr(tx, "thread_extent", nthread_tx)
+    ib.scope_attr(ty, "thread_extent", nthread_ty)
+    ib.scope_attr(bx, "thread_extent", nthread_bx)
+    ib.scope_attr(by, "thread_extent", nthread_by)
+    i = bx * max_threads + tx
+    j = by * max_threads + ty
+
+    nms_threshold_node = tvm.make.node(
+        "FloatImm", dtype="float32", value=nms_threshold)
+    nms_topk_node = tvm.make.node("IntImm", dtype="int32", value=nms_topk)
+    force_suppress_node = tvm.make.node(
+        "IntImm", dtype="int32", value=1 if force_suppress else 0)
+    with ib.for_range(0, batch_size, for_type="unroll", name="n") as n:
+        with ib.if_scope(
+            tvm.all(nms_threshold_node > 0, nms_threshold_node < 1,
+                    p_valid_count[0] > 0)):
+            # Reorder output
+            nkeep = tvm.select(
+                tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n]),
+                nms_topk, p_valid_count[n])
+            with ib.if_scope(i < nkeep):
+                with ib.if_scope(j < 6):
+                    p_out[(n * num_anchors * 6
+                           + i * 6 + j)] = p_data[(n * num_anchors * 6
+                                                   + p_sort_result[n * num_anchors + i] * 6 + j)]
+            with ib.if_scope(tvm.all(nms_topk_node > 0, nms_topk < p_valid_count[n])):
+                with ib.if_scope(i < p_valid_count[n] - nkeep):
+                    with ib.if_scope(j < 6):
+                        p_out[(n * num_anchors * 6
+                               + (i + nkeep) * 6 + j)] = p_data[(n * num_anchors * 6
+                                                                 + (i + nkeep) * 6 + j)]
+            # Apply nms
+            with ib.if_scope(i < p_valid_count[n]):
+                offset_i = i * 6
+                with ib.if_scope(p_out[n * num_anchors * 6 + offset_i] >= 0):
+                    with ib.if_scope(j < p_valid_count[n]):
+                        offset_j = j * 6
+                        with ib.if_scope(tvm.all(j > i, p_out[n * num_anchors * 6
+                                                              + offset_j] >= 0)):
+                            with ib.if_scope(tvm.any(force_suppress_node > 0,
+                                                     p_out[n * num_anchors * 6 + offset_i] ==
+                                                     p_out[n * num_anchors * 6 + offset_j])):
+                                # When force_suppress == True or class_id equals
+                                iou = calculate_overlap(
+                                    p_out, n * num_anchors * 6 + offset_i + 2,
+                                    n * num_anchors * 6 + offset_j + 2)
+                                with ib.if_scope(iou >= nms_threshold):
+                                    p_out[
+                                        n * num_anchors * 6 + offset_j] = -1.0
+        with ib.else_scope():
+            with ib.if_scope(i < p_valid_count[n]):
+                with ib.if_scope(j < 6):
+                    p_out[(n * num_anchors * 6
+                           + i * 6 + j)] = p_data[n * num_anchors * 6 + i * 6 + j]
+        # Set invalid entry to be -1
+        with ib.if_scope(i < num_anchors - p_valid_count[n]):
+            with ib.if_scope(j < 6):
+                p_out[n * num_anchors * 6 + (i +
+                                             p_valid_count[n]) * 6 + j] = -1.0
+    body = ib.get()
+    return body
+
+
+@nms.register(["cuda", "gpu"])
+def nms_gpu(data, valid_count, nms_threshold=0.5, force_suppress=False, nms_topk=-1):
+    """Non-maximum suppression operator for object detection.
+
+    Parameters
+    ----------
+    data: tvm.Tensor
+        3-D tensor with shape [batch_size, num_anchors, 6].
+        The last dimension should be in format of
+        [class_id, score, box_left, box_top, box_right, box_bottom].
+
+    valid_count : tvm.Tensor
+        1-D tensor for valid number of boxes.
+
+    nms_threshold : float
+        Non-maximum suppression threshold.
+
+    force_suppress : boolean
+        Whether to suppress all detections regardless of class_id.
+
+    nms_topk : int
+        Keep maximum top k detections before nms, -1 for no limit.
+
+    Returns
+    -------
+    out : tvm.Tensor
+        3-D tensor with shape [batch_size, num_anchors, 6].
+
+    Example
+    --------
+    .. code-block:: python
+
+        # An example to use nms
+        dshape = (1, 5, 6)
+        data = tvm.placeholder(dshape, name="data")
+        valid_count = tvm.placeholder(
+            (dshape[0],), dtype="int32", name="valid_count")
+        nms_threshold = 0.7
+        force_suppress = True
+        nms_topk = -1
+        out = nms(data, valid_count, nms_threshold, force_suppress, nms_topk)
+        np_data = np.random.uniform(dshape)
+        np_valid_count = np.array([4])
+        s = topi.generic.schedule_nms(out)
+        f = tvm.build(s, [data, valid_count, out], "llvm")
+        ctx = tvm.cpu()
+        tvm_data = tvm.nd.array(np_data, ctx)
+        tvm_valid_count = tvm.nd.array(np_valid_count, ctx)
+        tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data.dtype), ctx)
+        f(tvm_data, tvm_valid_count, tvm_out)
+    """
+    batch_size = data.shape[0]
+    num_anchors = data.shape[1]
+    valid_count_dtype = "int32"
+    valid_count_buf = api.decl_buffer(valid_count.shape, valid_count_dtype,
+                                      "valid_count_buf", data_alignment=4)
+    data_buf = api.decl_buffer(
+        data.shape, data.dtype, "data_buf", data_alignment=8)
+    score_axis = 1
+    score_shape = (batch_size, num_anchors)
+    score_tensor = tvm.compute(
+        score_shape, lambda i, j: data[i, j, score_axis], name="score_tensor")
+    score_tensor_buf = api.decl_buffer(score_tensor.shape, data.dtype,
+                                       "score_tensor_buf", data_alignment=8)
+    sort_tensor_dtype = "int32"
+    sort_tensor_buf = api.decl_buffer(score_shape, sort_tensor_dtype,
+                                      "sort_tensor_buf", data_alignment=8)
+
+    sort_tensor = \
+        tvm.extern(score_shape,
+                   [score_tensor, valid_count],
+                   lambda ins, outs: sort_ir(
+                       ins[0], ins[1], outs[0], score_axis, True),
+                   dtype=sort_tensor_dtype,
+                   in_buffers=[score_tensor_buf, valid_count_buf],
+                   out_buffers=sort_tensor_buf,
+                   name="nms_sort")
+    out = \
+        tvm.extern(data.shape,
+                   [data, sort_tensor, valid_count],
+                   lambda ins, outs: nms_ir(
+                       ins[0], ins[1], ins[2], outs[0], nms_threshold,
+                       force_suppress, nms_topk),
+                   dtype="float32",
+                   in_buffers=[data_buf, sort_tensor_buf, valid_count_buf],
+                   tag="nms")
+    return out

topi/python/topi/cuda/ssd/__init__.py

@@ -0,0 +1,5 @@
+# pylint: disable=wildcard-import
+"""VISION network operators"""
+from __future__ import absolute_import as _abs
+
+from .multibox import *