| /aosp_15_r20/external/pytorch/torch/nn/modules/ |
| H A D | conv.py | 56 out_channels: int
70 out_channels: int,
88 if out_channels % groups != 0:
102 self.out_channels = out_channels
130 (in_channels, out_channels // groups, *kernel_size), **factory_kwargs))
133 (out_channels, in_channels // groups, *kernel_size), **factory_kwargs))
135 self.bias = Parameter(torch.empty(out_channels, **factory_kwargs))
277 out_channels: int,
296 in_channels, out_channels, kernel_size_, stride_, padding_, dilation_,
430 out_channels: int,
[all …]
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| /aosp_15_r20/external/executorch/backends/arm/test/ops/ |
| H A D | test_conv2d.py | 32 out_channels: Union[List, int, None] = None,
48 out_channels = [1 * nbr_conv] if out_channels is None else out_channels
61 if not isinstance(out_channels, List):
62 out_channels = [out_channels]
93 out_channels=out_channels[i],
116 out_channels=3,
128 out_channels=10,
139 out_channels=4,
150 out_channels=1,
161 out_channels=1,
[all …]
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| H A D | test_conv1d.py | 31 out_channels: Union[List, int, None] = None,
47 out_channels = [1 * nbr_conv] if out_channels is None else out_channels
60 if not isinstance(out_channels, List):
61 out_channels = [out_channels]
90 out_channels=out_channels[i],
113 out_channels=3,
124 out_channels=10,
134 out_channels=4,
144 out_channels=1,
154 out_channels=1,
[all …]
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| H A D | test_depthwise_conv.py | 33 out_channels=3,
44 out_channels=12,
55 out_channels=12,
67 out_channels=3,
78 out_channels=3,
90 out_channels=8,
102 out_channels=16,
114 out_channels=8,
128 out_channels=[8, 24],
142 out_channels=[8, 24],
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| /aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/nn_ops/ |
| H A D | cudnn_deterministic_base.py | 103 depth=3, height=3, width=3, in_channels=3, out_channels=2)
123 depth=3, height=3, width=3, in_channels=8, out_channels=1)
138 height=3, width=3, in_channels=8, out_channels=8)
163 height=7, width=7, in_channels=1, out_channels=3)
187 out_channels = 1
191 height=7, width=7, out_channels=out_channels, in_channels=in_channels)
198 channels=out_channels)
217 out_channels = 8
221 height=3, width=3, out_channels=out_channels, in_channels=in_channels)
228 channels=out_channels)
[all …]
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| /aosp_15_r20/external/pytorch/torch/ao/nn/intrinsic/qat/modules/ |
| H A D | conv_fused.py | 47 out_channels, argument
71 out_channels,
85 self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
88 self.bias = Parameter(torch.empty(out_channels))
156 self.out_channels, device=scaled_weight.device, dtype=input.dtype
201 self.out_channels, device=self.weight.device, dtype=input.dtype
371 conv.out_channels,
398 self.out_channels,
463 out_channels, argument
488 out_channels,
[all …]
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| /aosp_15_r20/external/executorch/backends/cadence/aot/ |
| H A D | ops_registrations.py | 349 out_channels, *kernel_size, _ = weight.shape
351 out_channels, _, *kernel_size = weight.shape
362 out_channels,
371 in_size, out_channels, stride, padding, dilation, kernel_size, channel_last
397 out_channels, *kernel_size, _ = weight.shape
399 out_channels, _, *kernel_size = weight.shape
410 out_channels,
419 in_size, out_channels, stride, padding, dilation, kernel_size, channel_last
625 out_channels, *kernel_size, _ = weight.shape
627 out_channels, _, *kernel_size = weight.shape
[all …]
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| /aosp_15_r20/external/pytorch/torch/ao/nn/quantized/modules/ |
| H A D | conv.py | 44 out_channels, argument
61 out_channels, argument
79 if out_channels % groups != 0:
82 self.out_channels = out_channels
97 weight_shape = [in_channels, out_channels // self.groups]
99 weight_shape = [out_channels, in_channels // self.groups]
109 out_channels,
172 self.out_channels,
221 self.out_channels = state[1]
258 mod.out_channels,
[all …]
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| /aosp_15_r20/external/libopus/dnn/torch/osce/utils/layers/ |
| H A D | limited_adaptive_conv1d.py | 44 out_channels, argument
90 self.out_channels = out_channels
108 self.conv_kernel = norm(nn.Linear(feature_dim, in_channels * out_channels * kernel_size))
114 self.filter_gain = norm(nn.Linear(feature_dim, out_channels))
136 … count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate)
140 …nt += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_c…
143 count += 3 * overlap * frame_rate * self.out_channels
173 …ls = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channe…
193 … conv_kernels = conv_kernels * conv_gains.view(batch_size, num_frames, self.out_channels, 1, 1)
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| H A D | limited_adaptive_comb1d.py | 88 self.out_channels = 1
159 …ls = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channe…
179 … overlap_mem = torch.zeros((batch_size, self.out_channels, self.overlap_size), device=x.device)
193 …pe((batch_size * self.out_channels, self.in_channels, self.kernel_size)), groups=batch_size).resha…
221 … count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate)
222 …nt += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_c…
225 …nt += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_c…
228 count += overlap * frame_rate * 3 * self.out_channels
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| /aosp_15_r20/external/pytorch/test/inductor/ |
| H A D | test_efficient_conv_bn_eval.py | 38 out_channels, argument
43 self.conv = conv_class(in_channels, out_channels, bias=use_bias, **kwargs).to(
46 self.bn = bn_class(out_channels).to(device)
62 out_channels, argument
67 self.conv1 = conv_class(in_channels, out_channels, bias=use_bias, **kwargs).to(
70 self.bn1 = bn_class(out_channels).to(device)
71 self.conv2 = conv_class(out_channels, out_channels, bias=use_bias, **kwargs).to(
74 self.bn2 = bn_class(out_channels).to(device)
75 self.conv3 = conv_class(out_channels, out_channels, bias=use_bias, **kwargs).to(
78 self.bn3 = bn_class(out_channels).to(device)
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| /aosp_15_r20/external/executorch/backends/xnnpack/test/ops/ |
| H A D | conv2d.py | 24 out_channels=1, argument
46 out_channels=out_channels,
72 out_channels=3,
79 out_channels=2,
228 self._test(Conv2d(groups=2, in_channels=2, out_channels=6))
232 Conv2d(groups=2, in_channels=2, out_channels=6),
270 def __init__(self, in_channels: int, out_channels: int, kernel_size):
274 out_channels=out_channels,
280 self.native_batchnorm = torch.nn.BatchNorm2d(out_channels)
294 Conv2dBatchNormHardTanh(in_channels=2, out_channels=1, kernel_size=(2, 2))
[all …]
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| H A D | conv1d.py | 29 out_channels = 1
36 out_channels=out_channels,
55 out_channels = 2
61 out_channels=out_channels,
72 out_channels=out_channels,
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| /aosp_15_r20/external/pytorch/torch/ao/nn/quantized/reference/modules/ |
| H A D | conv.py | 36 float_conv.out_channels,
58 out_channels: int,
73 out_channels,
121 out_channels, argument
136 out_channels,
184 out_channels, argument
199 out_channels,
254 float_conv.out_channels,
277 out_channels: int,
293 out_channels,
[all …]
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| /aosp_15_r20/external/pytorch/torch/ao/nn/quantized/dynamic/modules/ |
| H A D | conv.py | 57 out_channels: int,
80 out_channels,
142 out_channels, argument
165 out_channels,
226 out_channels, argument
248 out_channels,
316 out_channels, argument
334 out_channels,
398 out_channels, argument
416 out_channels,
[all …]
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| /aosp_15_r20/external/libopus/dnn/torch/osce/models/ |
| H A D | fd_discriminator.py | 375 layer.out_channels,
608 out_channels = self.num_channels
613 … norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)),
617 in_channels = out_channels + 2
618 out_channels = min(2 * out_channels, self.num_channels_max)
744 out_channels = self.num_channels
749 …norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=plan[i][0], dilation=plan[i][1], paddin…
753 in_channels = out_channels + 2
756 out_channels = min(channel_factor * out_channels, self.num_channels_max)
804 out_channels = self.num_channels
[all …]
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| /aosp_15_r20/external/libopus/dnn/ |
| H A D | nndsp.c | 84 int out_channels, in scale_kernel() argument
93 for (i_out_channels = 0; i_out_channels < out_channels; i_out_channels++) in scale_kernel()
143 int out_channels, in adaconv_process_frame() argument
192 … print_float_vector("adaconv_kernel_raw", kernel_buffer, in_channels * out_channels * kernel_size); in adaconv_process_frame()
193 print_float_vector("adaconv_gain_raw", gain_buffer, out_channels); in adaconv_process_frame()
195 transform_gains(gain_buffer, out_channels, filter_gain_a, filter_gain_b); in adaconv_process_frame()
196 scale_kernel(kernel_buffer, in_channels, out_channels, kernel_size, gain_buffer); in adaconv_process_frame()
199 print_float_vector("adaconv_kernel", kernel_buffer, in_channels * out_channels * kernel_size); in adaconv_process_frame()
200 print_float_vector("adaconv_gain", gain_buffer, out_channels); in adaconv_process_frame()
205 for (i_out_channels = 0; i_out_channels < out_channels; i_out_channels++) in adaconv_process_frame()
[all …]
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| H A D | nnet_arch.h | 159 static void conv2d_float(float *out, const float *weights, int in_channels, int out_channels, int k… in conv2d_float() argument
164 for (i=0;i<out_channels;i++) { in conv2d_float()
185 static void conv2d_3x3_float(float *out, const float *weights, int in_channels, int out_channels, c… in conv2d_3x3_float() argument
192 for (i=0;i<out_channels;i++) { in conv2d_3x3_float()
229 …conv2d_3x3_float(out, conv->float_weights, conv->in_channels, conv->out_channels, in_buf, height, … in RTCD_SUF()
231 …conv2d_float(out, conv->float_weights, conv->in_channels, conv->out_channels, conv->ktime, conv->k… in RTCD_SUF()
233 for (i=0;i<conv->out_channels;i++) { in RTCD_SUF()
238 for (i=0;i<conv->out_channels;i++) { in RTCD_SUF()
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| /aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/options/ |
| H A D | conv.h | 31 int64_t out_channels, in ConvNdOptions()
34 out_channels_(out_channels), in ConvNdOptions()
43 TORCH_ARG(int64_t, out_channels);
110 int64_t out_channels, in ConvOptions()
113 out_channels_(out_channels), in ConvOptions()
122 TORCH_ARG(int64_t, out_channels);
264 int64_t out_channels, in ConvTransposeOptions()
267 out_channels_(out_channels), in ConvTransposeOptions()
276 TORCH_ARG(int64_t, out_channels);
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| /aosp_15_r20/external/pytorch/test/ |
| H A D | test_mkldnn_fusion.py | 65 def __init__(self, in_channels, out_channels, bias, **kwargs): argument
67 self.conv = torch.nn.Conv2d(in_channels, out_channels, bias=bias, **kwargs)
104 def __init__(self, unary_fn, in_channels, out_channels, bias, **kwargs): argument
106 self.conv = torch.nn.Conv2d(in_channels, out_channels, bias=bias, **kwargs)
133 def __init__(self, m, in_channels, out_channels, bias, **kwargs): argument
135 self.conv = m(in_channels, out_channels, bias=bias, **kwargs)
196 def __init__(self, unary_fn, in_channels, out_channels, bias, **kwargs): argument
199 in_channels, out_channels, bias=bias, **kwargs
230 … def __init__(self, unary_fn, dim, in_channels, out_channels, dilation, groups, bias, **kwargs): argument
232 …self.conv = CONV_MODULES[dim](in_channels, out_channels, dilation=dilation, groups=groups, bias=bi…
[all …]
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| /aosp_15_r20/external/libaom/av1/encoder/ |
| H A D | cnn.c | 203 channels_per_branch[b] = layer_config->out_channels; in find_cnn_out_channels()
205 channels_per_branch[b] = layer_config->out_channels; in find_cnn_out_channels()
215 channels_per_branch[branch] = layer_config->out_channels; in find_cnn_out_channels()
240 int *out_height, int *out_channels) { in av1_find_cnn_output_size() argument
287 out_channels[output_num] = channels_per_branch[layer_config->branch]; in av1_find_cnn_output_size()
360 for (int i = 0; i < layer_config->out_channels; ++i) { in convolve_maxpool_padding_zero()
369 int off = k * layer_config->out_channels + i; in convolve_maxpool_padding_zero()
402 for (int i = 0; i < layer_config->out_channels; ++i) { in convolve_maxpool_padding_replicate()
411 int off = k * layer_config->out_channels + i; in convolve_maxpool_padding_replicate()
444 for (int i = 0; i < layer_config->out_channels; ++i) { in convolve_maxpool_padding_valid()
[all …]
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/ao_sparse/quantized/cpu/ |
| H A D | qlinear.cpp | 51 int64_t out_channels = static_cast<int64_t>(packW->R); in apply_impl() local
74 output_multiplier_float.resize(out_channels, 0.0); in apply_impl()
75 act_times_w_scale.resize(out_channels, 1.0f); in apply_impl()
76 for (const auto i : c10::irange(out_channels)) { in apply_impl()
91 bias.size(0) == out_channels, in apply_impl()
93 std::to_string(out_channels)); in apply_impl()
104 out_sizes.back() = out_channels; // NOLINT in apply_impl()
205 out_channels, in apply_impl()
210 out_channels); in apply_impl()
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| /aosp_15_r20/external/pytorch/torch/ao/nn/qat/modules/ |
| H A D | conv.py | 22 out_channels: int,
40 out_channels,
80 mod.out_channels,
101 self.out_channels,
145 out_channels: int,
163 out_channels,
206 out_channels: int,
224 out_channels,
270 out_channels: int,
288 out_channels,
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| /aosp_15_r20/external/executorch/backends/qualcomm/tests/ |
| H A D | models.py | 220 out_channels=3,
228 out_channels=2,
243 in_channels=2, out_channels=2, kernel_size=1, stride=1, padding=1
273 out_channels = 1
277 out_channels=out_channels,
286 self.native_batchnorm = torch.nn.BatchNorm2d(out_channels)
316 out_channels=2,
332 out_channels=3,
339 out_channels=2,
354 out_channels=3,
[all …]
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| /aosp_15_r20/external/executorch/backends/arm/operators/ |
| H A D | op_conv2d.py | 101 out_channels = weight.shape[0]
104 [out_channels],
106 [0] * out_channels,
120 out_channels = weight.shape[0]
121 if (in_channels == group.number) and (out_channels % in_channels) == 0:
125 m_length = int(out_channels / in_channels)
|