| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/topi/python/ |
| H A D | test_topi_conv3d_ndhwc_tensorcore.py | 39 in_channel, argument
57 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)
62 A = te.placeholder((batch, in_depth, in_height, in_width, in_channel), name="A")
63 W = te.placeholder((kernel, kernel, kernel, in_channel, num_filter), name="W")
111 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
120 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
|
| H A D | test_topi_conv2d_nhwc_winograd.py | 46 in_channel, argument
63 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)
68 A = te.placeholder((batch, in_height, in_width, in_channel), name="A")
69 W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W")
122 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
131 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
|
| H A D | test_topi_conv3d_ncdhw.py | 39 in_channel, argument
55 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)
60 A = te.placeholder((batch, in_channel, in_depth, in_height, in_width), name="A")
61 W = te.placeholder((num_filter, in_channel, kernel, kernel, kernel), name="W")
107 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
116 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
|
| H A D | test_topi_conv3d_winograd.py | 38 in_channel, argument
55 % (batch, in_channel, in_size, num_filter, space_kernel, stride, padding_sum, dilation)
60 A = te.placeholder((batch, in_channel, in_depth, in_height, in_width), name="A")
61 W = te.placeholder((num_filter, in_channel, depth_kernel, space_kernel, space_kernel), name="W")
113 in_channel,
131 in_channel,
|
| /dports/misc/py-tvm/incubator-tvm-0.6.1/topi/tests/python/ |
| H A D | test_topi_conv2d_winograd.py | 29 def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1,… argument
31 …print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kern…
35 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
36 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
79 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
82 …ld(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| H A D | test_topi_conv2d_int8.py | 33 def verify_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilat… argument
34 …print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kern…
38 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='int8')
39 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W', dtype='int8')
95 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
96 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
99 …ld(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| H A D | test_topi_conv2d_nchw.py | 29 def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1,… argument
30 …print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kern…
34 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
35 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
78 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
81 …ld(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| /dports/misc/tvm/incubator-tvm-0.6.1/topi/tests/python/ |
| H A D | test_topi_conv2d_int8.py | 33 def verify_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilat… argument
34 …print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kern…
38 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='int8')
39 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W', dtype='int8')
95 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
96 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
99 …ld(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| H A D | test_topi_conv2d_nchw.py | 29 def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1,… argument
30 …print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kern…
34 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
35 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
78 …[A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
81 …ld(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| /dports/lang/ocaml/ocaml-4.05.0/bytecomp/ |
| H A D | bytesections.mli | 33 val read_toc: in_channel -> unit
43 val seek_section: in_channel -> string -> int
48 val read_section_string: in_channel -> string -> string
51 val read_section_struct: in_channel -> string -> 'a
54 val pos_first_section: in_channel -> int
|
| /dports/misc/tvm/incubator-tvm-0.6.1/tests/webgl/ |
| H A D | test_local_topi_conv2d_nchw.py | 27 def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding): argument
30 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
31 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
64 …ld(s1, [A, W, B], device, name="conv2d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
65 …uild(s2, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| /dports/misc/py-tvm/incubator-tvm-0.6.1/tests/webgl/ |
| H A D | test_local_topi_conv2d_nchw.py | 27 def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding): argument
30 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
31 W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
64 …ld(s1, [A, W, B], device, name="conv2d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
65 …uild(s2, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_fi…
|
| /dports/lang/ocaml-nox11/ocaml-4.05.0/bytecomp/ |
| H A D | bytesections.mli | 33 val read_toc: in_channel -> unit
43 val seek_section: in_channel -> string -> int
48 val read_section_string: in_channel -> string -> string
51 val read_section_struct: in_channel -> string -> 'a
54 val pos_first_section: in_channel -> int
|
| /dports/misc/tvm/incubator-tvm-0.6.1/topi/python/topi/nn/ |
| H A D | conv2d.py | 207 batch, in_channel, in_height, in_width = Input.shape
221 rc = tvm.reduce_axis((0, in_channel), name='rc')
274 in_height, in_width, in_channel, batch = Input.shape
287 rc = tvm.reduce_axis((0, in_channel), name='rc')
338 batch, in_height, in_width, in_channel = Input.shape
351 rc = tvm.reduce_axis((0, in_channel), name='rc')
457 in_channel = ic_chunk * ic_bn
479 ic = tvm.reduce_axis((0, in_channel), name='ic')
597 in_channel = ic_chunk * ic_bn
618 ic = tvm.reduce_axis((0, in_channel), name='ic')
[all …]
|
| /dports/misc/py-tvm/incubator-tvm-0.6.1/topi/python/topi/nn/ |
| H A D | conv2d.py | 207 batch, in_channel, in_height, in_width = Input.shape
221 rc = tvm.reduce_axis((0, in_channel), name='rc')
274 in_height, in_width, in_channel, batch = Input.shape
287 rc = tvm.reduce_axis((0, in_channel), name='rc')
338 batch, in_height, in_width, in_channel = Input.shape
351 rc = tvm.reduce_axis((0, in_channel), name='rc')
457 in_channel = ic_chunk * ic_bn
479 ic = tvm.reduce_axis((0, in_channel), name='ic')
597 in_channel = ic_chunk * ic_bn
618 ic = tvm.reduce_axis((0, in_channel), name='ic')
[all …]
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/contrib/ |
| H A D | test_cudnn.py | 27 in_channel = 4
50 xshape = [batch, in_channel, height, width]
51 wshape = [out_channel, in_channel // groups, filter_h, filter_w]
53 xshape = [batch, height, width, in_channel]
54 wshape = [out_channel, filter_h, filter_w, in_channel // groups]
106 in_channel = 4
130 xshape = [batch, in_channel, depth, height, width]
131 wshape = [out_channel, in_channel // groups, filter_d, filter_h, filter_w]
|
| /dports/misc/tvm/incubator-tvm-0.6.1/topi/python/topi/testing/ |
| H A D | depthwise_conv2d_python.py | 44 batch, in_channel, in_height, in_width = input_np.shape
53 out_channel = in_channel * channel_multiplier
63 out_channel = in_channel * channel_multiplier
105 batch, in_height, in_width, in_channel = input_np.shape
114 out_channel = in_channel * channel_multiplier
124 out_channel = in_channel * channel_multiplier
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/testing/ |
| H A D | depthwise_conv2d_python.py | 45 batch, in_channel, in_height, in_width = input_np.shape
54 out_channel = in_channel * channel_multiplier
69 out_channel = in_channel * channel_multiplier
116 batch, in_height, in_width, in_channel = input_np.shape
125 out_channel = in_channel * channel_multiplier
140 out_channel = in_channel * channel_multiplier
|
| H A D | deformable_conv2d_nchw_python.py | 63 batch, in_channel, in_height, in_width = a_np.shape
67 ic_per_dgroup = in_channel // deformable_groups
93 a_deform = np.zeros((batch, in_channel, out_height, out_width, kernel_h, kernel_w), dtype=dtype)
105 for c, kh, kw in itertools.product(range(in_channel), range(kernel_h), range(kernel_w)):
117 range(batch), range(in_channel), range(out_channel), range(out_height), range(out_width)
|
| /dports/misc/py-tvm/incubator-tvm-0.6.1/topi/python/topi/testing/ |
| H A D | depthwise_conv2d_python.py | 44 batch, in_channel, in_height, in_width = input_np.shape
53 out_channel = in_channel * channel_multiplier
63 out_channel = in_channel * channel_multiplier
105 batch, in_height, in_width, in_channel = input_np.shape
114 out_channel = in_channel * channel_multiplier
124 out_channel = in_channel * channel_multiplier
|
| /dports/multimedia/lives/lives-3.2.0/lives-plugins/weed-plugins/ |
| H A D | alpha_means.c | 38 weed_plant_t *in_channel = weed_get_plantptr_value(inst, WEED_LEAF_IN_CHANNELS, NULL); in alpham_process() local
42 float *alpha = (float *)weed_get_voidptr_value(in_channel, WEED_LEAF_PIXEL_DATA, NULL); in alpham_process()
44 int width = weed_get_int_value(in_channel, WEED_LEAF_WIDTH, NULL); in alpham_process()
45 int height = weed_get_int_value(in_channel, WEED_LEAF_HEIGHT, NULL); in alpham_process()
47 int irow = weed_get_int_value(in_channel, WEED_LEAF_ROWSTRIDES, NULL) - width * sizeof(float); in alpham_process()
|
| H A D | negate.c | 31 weed_plant_t *in_channel = weed_get_in_channel(inst, 0), in negate_process() local
33 unsigned char *src = weed_channel_get_pixel_data(in_channel); in negate_process()
38 int pal = weed_channel_get_palette(in_channel); in negate_process()
39 int irowstride = weed_channel_get_stride(in_channel); in negate_process()
|
| H A D | blurzoom.c | 239 weed_plant_t *in_channel; in blurzoom_init() local
244 in_channel = weed_get_in_channel(inst, 0); in blurzoom_init()
246 video_width = weed_channel_get_width(in_channel); in blurzoom_init()
247 video_height = weed_channel_get_height(in_channel); in blurzoom_init()
315 makePalette(weed_get_int_value(in_channel, WEED_LEAF_CURRENT_PALETTE, NULL)); in blurzoom_init()
344 weed_plant_t *in_channel, *out_channel, **in_params; in blurzoom_process() local
354 in_channel = weed_get_in_channel(inst, 0); in blurzoom_process()
357 src = weed_channel_get_pixel_data(in_channel); in blurzoom_process()
360 video_width = weed_channel_get_width(in_channel); in blurzoom_process()
361 video_height = weed_channel_get_height(in_channel); in blurzoom_process()
[all …]
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/intel_graphics/ |
| H A D | conv2d_alter_op.py | 65 batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape)
79 (batch_size, in_channel // ic_bn, height, width, ic_bn), dtype=data_dtype
82 (out_channel // oc_bn, in_channel // ic_bn, kh, kw, ic_bn, oc_bn), dtype=kernel_dtype
106 batch_size, in_channel, in_height, in_width = data[1]
111 in_shape = (batch_size, in_channel // tile_ic, in_height, in_width, tile_ic)
|
| /dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/x86/ |
| H A D | conv2d.py | 67 batch_size, in_channel, in_height, in_width = data[1]
75 in_shape = (batch_size, idxdiv(in_channel, tile_ic), in_height, in_width, tile_ic)
178 in_channel = ic_chunk * ic_bn
181 n, in_channel, ih, iw = get_const_tuple(data.shape)
191 cfg.define_split("tile_ic", in_channel, num_outputs=2)
205 te.placeholder((n, in_channel, ih, iw), dtype=data.dtype),
207 (num_filter, in_channel, kernel_height, kernel_width), dtype=kernel.dtype
219 dshape = (n, in_channel // cfg["tile_ic"].size[-1], ih, iw, cfg["tile_ic"].size[-1])
223 in_channel // cfg["tile_ic"].size[-1],
|