/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/topi/python/ |
H A D | test_topi_upsampling.py | 30 in_channel, argument 41 A = te.placeholder((batch, in_channel, in_height, in_width), name="A") 45 in_channel, 57 in_channel, 64 size=(batch, in_channel, in_height, in_width, in_batch_block, in_channel_block) 67 A = te.placeholder((batch, in_height, in_width, in_channel), name="A") 73 in_channel, 170 in_channel, argument 181 A = te.placeholder((batch, in_channel, in_depth, in_height, in_width), name="A") 185 in_channel, [all …]
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H A D | test_topi_conv2d_int8.py | 37 in_channel, argument 51 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation) 55 A = te.placeholder((batch, in_height, in_width, in_channel), name="A", dtype="int8") 56 W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W", dtype="int8") 105 in_channel, argument 119 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation) 124 A = te.placeholder((batch, in_height, in_width, in_channel), name="A", dtype="int8") 125 W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W", dtype="int8") 206 in_channel, argument 225 A = te.placeholder((batch, in_channel, in_height, in_width), name="A", dtype="int8") [all …]
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H A D | test_topi_group_conv2d_NCHWc_int8.py | 42 out_channel, in_channel, kh, kw = kernel.shape 44 kernel, (out_channel // oc_bn, oc_bn, in_channel // ic_bn, ic_bn // 4, kh, kw, 4) 52 in_channel, argument 67 % (batch, in_channel, groups, in_size, num_filter, kernel, stride, padding) 84 (batch, in_channel // ic_block, in_height, in_width, ic_block), name="A", dtype="uint8" 89 in_channel // ic_block // groups, 102 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype("uint8") 103 w_np = np.random.uniform(size=(num_filter, in_channel // groups, kernel, kernel)).astype( 142 % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation),
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H A D | test_topi_group_conv2d.py | 45 in_channel, argument 63 A = te.placeholder((batch, in_channel, in_height, in_width), name="A") 120 in_channel, 139 in_channel, 161 in_channel, argument 246 in_channel, 265 in_channel, 284 in_channel, argument 302 A = te.placeholder((batch, in_height, in_width, in_channel), name="A") 359 in_channel, [all …]
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/dports/multimedia/lives/lives-3.2.0/lives-plugins/weed-plugins/ |
H A D | mirrors.c | 34 unsigned char *src = weed_get_voidptr_value(in_channel, WEED_LEAF_PIXEL_DATA, NULL); in mirrorx_process() 37 int pal = weed_get_int_value(in_channel, WEED_LEAF_CURRENT_PALETTE, NULL); in mirrorx_process() 38 int width = weed_get_int_value(in_channel, WEED_LEAF_WIDTH, NULL), hwidth; in mirrorx_process() 39 int height = weed_get_int_value(in_channel, WEED_LEAF_HEIGHT, NULL); in mirrorx_process() 40 int irowstride = weed_get_int_value(in_channel, WEED_LEAF_ROWSTRIDES, NULL); in mirrorx_process() 74 weed_plant_t *in_channel = weed_get_in_channel(inst, 0); in mirrory_process() local 76 int palette = weed_channel_get_palette(in_channel); in mirrory_process() 77 int width = weed_channel_get_width(in_channel); in mirrory_process() 78 int height = weed_channel_get_height(in_channel); in mirrory_process() 79 int irowstride = weed_channel_get_stride(in_channel); in mirrory_process() [all …]
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H A D | alien_overlay.c | 39 weed_plant_t *in_channel = weed_get_plantptr_value(inst, WEED_LEAF_IN_CHANNELS, NULL); in alien_over_init() local 40 int width = weed_channel_get_width(in_channel) * 3; in alien_over_init() 41 int height = weed_channel_get_height(in_channel); in alien_over_init() 78 …weed_plant_t *in_channel = weed_get_in_channel(inst, 0), *out_channel = weed_get_out_channel(inst,… in alien_over_process() local 79 unsigned char *src = weed_channel_get_pixel_data(in_channel); in alien_over_process() 84 int pal = weed_channel_get_palette(in_channel); in alien_over_process() 86 int width = weed_channel_get_width(in_channel) * psize; in alien_over_process() 87 int height = weed_channel_get_height(in_channel); in alien_over_process() 88 int irowstride = weed_channel_get_stride(in_channel); in alien_over_process()
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H A D | xeffect.c | 47 weed_plant_t *in_channel = weed_get_in_channel(inst, 0); in xeffect_init() local 48 int width = weed_channel_get_width(in_channel); in xeffect_init() 49 int height = weed_channel_get_height(in_channel); in xeffect_init() 67 weed_plant_t *in_channel = weed_get_in_channel(inst, 0), in xeffect_process() local 69 int palette = weed_channel_get_palette(in_channel); in xeffect_process() 72 int width = weed_channel_get_width(in_channel); in xeffect_process() 74 int height = weed_channel_get_height(in_channel); in xeffect_process() 76 int irowstride = weed_channel_get_stride(in_channel); in xeffect_process() 79 unsigned char *src = weed_channel_get_pixel_data(in_channel); in xeffect_process()
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H A D | revTV.c | 36 weed_plant_t *in_channel, *out_channel, **in_params; in revtv_process() local 49 in_channel = weed_get_plantptr_value(inst, WEED_LEAF_IN_CHANNELS, NULL); in revtv_process() 52 src = (unsigned char *)weed_get_voidptr_value(in_channel, WEED_LEAF_PIXEL_DATA, NULL); in revtv_process() 55 width = weed_get_int_value(in_channel, WEED_LEAF_WIDTH, NULL); in revtv_process() 56 height = weed_get_int_value(in_channel, WEED_LEAF_HEIGHT, NULL); in revtv_process() 58 pal = weed_get_int_value(in_channel, WEED_LEAF_CURRENT_PALETTE, NULL); in revtv_process() 60 irow = weed_get_int_value(in_channel, WEED_LEAF_ROWSTRIDES, NULL); in revtv_process()
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H A D | blank_frame_detector.c | 63 weed_plant_t *in_channel = weed_get_plantptr_value(inst, WEED_LEAF_IN_CHANNELS, NULL); in bfd_process() local 64 …unsigned char *src = (unsigned char *)weed_get_voidptr_value(in_channel, WEED_LEAF_PIXEL_DATA, NUL… in bfd_process() 68 int width = weed_get_int_value(in_channel, WEED_LEAF_WIDTH, NULL); in bfd_process() 69 int height = weed_get_int_value(in_channel, WEED_LEAF_HEIGHT, NULL); in bfd_process() 70 int pal = weed_get_int_value(in_channel, WEED_LEAF_CURRENT_PALETTE, NULL); in bfd_process() 71 int irowstride = weed_get_int_value(in_channel, WEED_LEAF_ROWSTRIDES, NULL); in bfd_process() 82 luma = calc_luma(&src[i], pal, weed_get_int_value(in_channel, WEED_LEAF_YUV_CLAMPING, NULL)); in bfd_process()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/topi/x86/ |
H A D | conv2d_alter_op.py | 78 batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape) 94 (out_channel // oc_bn, in_channel // ic_bn, kh, kw, ic_bn, oc_bn), 124 batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape) 150 (batch_size, in_channel // ic_bn, height, width, ic_bn), dtype=data_dtype 180 batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape) 313 in_channel = -1 316 in_channel = data_tensor.shape[3].value 319 in_channel = data_tensor.shape[1].value 324 if in_channel % 4 != 0: 325 new_in_channel = ((in_channel + 4) // 4) * 4 [all …]
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/dports/math/libxsmm/libxsmm-1.16.3/samples/deeplearning/tvm_cnnlayer/ |
H A D | mb1_tuned_latest.py | 61 def convert_input(a_np, batch, in_channel,input_height,input_width,pad_height,pad_width,vlen,A): argument 65 for j in range(math.ceil(in_channel/vlen)): 96 def convert_weight(w_np, in_channel, out_channel, kernel_height, kernel_width, vlen,W): argument 100 for j in range(math.ceil(in_channel/vlen)): 105 if i*vlen + n >= out_channel or j*vlen + m >= in_channel: 180 if math.ceil(in_channel/ifmblock) == rco: 228 if math.ceil(in_channel/ifmblock) == rco: 247 rco1 = tvm.reduce_axis((0, math.ceil(in_channel/ifmblock)), name='rco1') 273 A2 = tvm.compute((batch, math.ceil(in_channel/ifmblock),ofh,ofw,ifmblock), 301 cfg.add_flop(np.prod(get_const_tuple(B1.shape))*in_channel*filter_height*filter_width*2) [all …]
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/tests/python/ |
H A D | test_topi_group_conv2d_NCHWc_int8.py | 40 out_channel, in_channel, kh, kw = kernel.shape 41 kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn//4, kh, kw, 4)) 45 def verify_group_conv2d_NCHWc_int8(batch, in_channel, groups, in_size, num_filter, kernel, stride, argument 49 (batch, in_channel, groups, in_size, num_filter, kernel, stride, padding)) 64 …A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A', dtype=… 65 …W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block//groups, kernel, kernel, ic_block/… 69 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype("uint8") 70 … w_np = np.random.uniform(size=(num_filter, in_channel//groups, kernel, kernel)).astype("int8") 96 … (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
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H A D | test_topi_resize.py | 26 def verify_resize(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', ali… argument 28 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='float32') 30 out_shape = (batch, in_channel, out_height, out_width) 31 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 33 A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A', dtype='float32') 35 out_shape = (batch, out_height, out_width, in_channel) 36 a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
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H A D | test_topi_conv2d_NCHWc.py | 38 out_channel, in_channel, kh, kw = kernel.shape 39 kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn, kh, kw)) 50 def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride, argument 53 (batch, in_channel, in_size, num_filter, kernel, stride, padding)) 68 if in_channel % bn == 0: 72 A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A') 73 …W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block, kernel, kernel, ic_block, oc_bloc… 78 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 79 w_np = np.random.uniform(size=(num_filter, in_channel, kernel, kernel)).astype(dtype) 117 … (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) [all …]
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H A D | test_topi_upsampling.py | 26 def verify_upsampling(batch, in_channel, in_height, in_width, scale_h, scale_w, argument 29 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A') 31 out_shape = (batch, in_channel, int(round(in_height*scale_h)), int(round(in_width*scale_w))) 32 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 34 A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A') 36 out_shape = (batch, int(round(in_height*scale_h)), int(round(in_width*scale_w)), in_channel) 37 a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/tests/python/ |
H A D | test_topi_group_conv2d_NCHWc_int8.py | 40 out_channel, in_channel, kh, kw = kernel.shape 41 kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn//4, kh, kw, 4)) 45 def verify_group_conv2d_NCHWc_int8(batch, in_channel, groups, in_size, num_filter, kernel, stride, argument 49 (batch, in_channel, groups, in_size, num_filter, kernel, stride, padding)) 64 …A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A', dtype=… 65 …W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block//groups, kernel, kernel, ic_block/… 69 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype("uint8") 70 … w_np = np.random.uniform(size=(num_filter, in_channel//groups, kernel, kernel)).astype("int8") 96 … (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
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H A D | test_topi_resize.py | 26 def verify_resize(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', ali… argument 28 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='float32') 30 out_shape = (batch, in_channel, out_height, out_width) 31 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 33 A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A', dtype='float32') 35 out_shape = (batch, out_height, out_width, in_channel) 36 a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
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H A D | test_topi_conv2d_NCHWc.py | 38 out_channel, in_channel, kh, kw = kernel.shape 39 kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn, kh, kw)) 50 def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride, argument 53 (batch, in_channel, in_size, num_filter, kernel, stride, padding)) 68 if in_channel % bn == 0: 72 A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A') 73 …W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block, kernel, kernel, ic_block, oc_bloc… 78 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 79 w_np = np.random.uniform(size=(num_filter, in_channel, kernel, kernel)).astype(dtype) 117 … (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) [all …]
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H A D | test_topi_upsampling.py | 26 def verify_upsampling(batch, in_channel, in_height, in_width, scale_h, scale_w, argument 29 A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A') 31 out_shape = (batch, in_channel, int(round(in_height*scale_h)), int(round(in_width*scale_w))) 32 a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) 34 A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A') 36 out_shape = (batch, int(round(in_height*scale_h)), int(round(in_width*scale_w)), in_channel) 37 a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/recipe/conv/ |
H A D | depthwise_conv2d_test.py | 51 in_channel = 256 55 filter_channel = in_channel 66 Input = tvm.placeholder((batch, in_channel, in_height, in_width), name='Input') 69 Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') 70 Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') 122 for c in range(in_channel * channel_multiplier): 140 in_channel = 256 144 filter_channel = in_channel 158 Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') 159 Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') [all …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/apps/topi_recipe/conv/ |
H A D | depthwise_conv2d_test.py | 59 in_channel = 256 63 filter_channel = in_channel 74 Input = te.placeholder((batch, in_channel, in_height, in_width), name="Input") 79 Scale = te.placeholder((in_channel * channel_multiplier,), name="Scale") 80 Shift = te.placeholder((in_channel * channel_multiplier,), name="Shift") 144 for c in range(in_channel * channel_multiplier): 166 in_channel = 256 170 filter_channel = in_channel 186 Scale = te.placeholder((in_channel * channel_multiplier,), name="Scale") 187 Shift = te.placeholder((in_channel * channel_multiplier,), name="Shift") [all …]
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/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/recipe/conv/ |
H A D | depthwise_conv2d_test.py | 51 in_channel = 256 55 filter_channel = in_channel 66 Input = tvm.placeholder((batch, in_channel, in_height, in_width), name='Input') 69 Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') 70 Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') 122 for c in range(in_channel * channel_multiplier): 140 in_channel = 256 144 filter_channel = in_channel 158 Scale = tvm.placeholder((in_channel * channel_multiplier,), name='Scale') 159 Shift = tvm.placeholder((in_channel * channel_multiplier,), name='Shift') [all …]
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/dports/misc/py-gluoncv/gluon-cv-0.9.0/scripts/gan/stylegan/ |
H A D | modules.py | 25 def __init__(self, in_channel, out_channel, kernel_size, padding=0): argument 28 fan_in = in_channel * kernel_size * kernel_size 51 def __init__(self, in_channel, out_channel, kernel_size, padding=0): argument 57 fan_in = in_channel * kernel_size * kernel_size 150 def __init__(self, in_channel, style_dim): argument 153 self.norm = nn.InstanceNorm(in_channels=in_channel) 154 self.style = EqualLinear(style_dim, in_channel * 2) 160 mx_params[k].data()[:in_channel] = 1 161 mx_params[k].data()[in_channel:] = 0 251 self.conv1 = ConstantInput(in_channel) [all …]
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/dports/security/libssh/libssh-0.9.6/src/ |
H A D | connector.c | 55 ssh_channel in_channel; member 127 if (connector->in_channel != NULL) { in ssh_connector_free() 128 ssh_remove_channel_callbacks(connector->in_channel, in ssh_connector_free() 157 connector->in_channel = channel; in ssh_connector_set_in_channel() 189 connector->in_channel = NULL; in ssh_connector_set_in_fd() 335 if (connector->in_channel != NULL){ in ssh_connector_fd_out_cb() 526 if (connector->in_channel != NULL) { in ssh_connector_channel_write_wontblock_cb() 570 connector->in_channel == NULL) in ssh_connector_set_event() 605 if (connector->in_channel != NULL) { in ssh_connector_set_event() 607 ssh_channel_get_session(connector->in_channel)); in ssh_connector_set_event() [all …]
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/dports/misc/tvm/incubator-tvm-0.6.1/topi/python/topi/x86/ |
H A D | conv2d_alter_op.py | 48 batch_size, height, width, in_channel = get_const_tuple(data_tensor.shape) 51 batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape) 131 in_channel//ic_bn, ic_bn)) 139 in_channel // ic_bn, 164 new_kernel = tvm.placeholder((out_channel//oc_bn, in_channel//ic_bn, 267 in_channel = -1 270 in_channel = data_tensor.shape[3].value 273 in_channel = data_tensor.shape[1].value 278 if in_channel % 4 != 0: 279 new_in_channel = ((in_channel + 4) // 4) * 4 [all …]
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