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/aosp_15_r20/external/mesa3d/src/etnaviv/ci/
H A Detnaviv-vipnano-fails.txt1 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_120_stride_2_padding_same_1…
2 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_128_stride_2_padding_same_1…
3 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_160_stride_2_padding_same_1…
4 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_1_stride_2_padding_same_1_i…
5 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_256_stride_2_padding_same_1…
6 Conv2D.Op/input_size_112_weight_size_5_input_channels_1_output_channels_32_stride_2_padding_same_1_…
7 Conv2D.Op/input_size_112_weight_size_5_input_channels_256_output_channels_120_stride_1_padding_same…
8 Conv2D.Op/input_size_112_weight_size_5_input_channels_256_output_channels_120_stride_1_padding_same…
9 Conv2D.Op/input_size_80_weight_size_5_input_channels_1_output_channels_120_stride_2_padding_same_1_…
10 Conv2D.Op/input_size_80_weight_size_5_input_channels_1_output_channels_128_stride_2_padding_same_1_…
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/aosp_15_r20/external/pytorch/torch/testing/_internal/
H A Dcommon_pruning.py150 r"""Model with only Conv2d layers, all without bias, some in a Sequential and some following.
151 Used to test pruned Conv2d-Conv2d fusion."""
156 nn.Conv2d(1, 32, 3, 1, bias=False),
157 nn.Conv2d(32, 64, 3, 1, bias=False),
159 self.conv2d1 = nn.Conv2d(64, 48, 3, 1, bias=False)
160 self.conv2d2 = nn.Conv2d(48, 52, 3, 1, bias=False)
170 r"""Model with only Conv2d layers, some with bias, some in a Sequential and some outside.
171 Used to test pruned Conv2d-Bias-Conv2d fusion."""
176 nn.Conv2d(1, 32, 3, 1, bias=True),
177 nn.Conv2d(32, 32, 3, 1, bias=True),
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/aosp_15_r20/external/tensorflow/tensorflow/core/grappler/optimizers/
H A Dgeneric_layout_optimizer_transposer_test.cc111 auto conv2d = ops::Conv2D( in SimpleConv2D() local
112 scope->WithOpName("conv2d").WithDevice("/device:GPU:0"), input, filter, in SimpleConv2D()
113 {1, kStride1, kStride2, 1}, "SAME", ops::Conv2D::DataFormat(kSrcFormat)); in SimpleConv2D()
115 return conv2d; in SimpleConv2D()
121 auto conv2d = SimpleConv2D(&scope, data_type); in CreateSimpleConv2DGraph() local
122 auto output = ops::Identity(scope.WithOpName("output"), conv2d); in CreateSimpleConv2DGraph()
296 Output conv2d = ops::Conv2D( in CreateSimpleAddN() local
297 scope.WithOpName("conv2d").WithDevice("/device:GPU:0"), input, filter, in CreateSimpleAddN()
298 {1, 2, 4, 1}, "SAME", ops::Conv2D::DataFormat(kSrcFormat)); in CreateSimpleAddN()
306 {a, b, c, conv2d}); in CreateSimpleAddN()
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/aosp_15_r20/external/pytorch/torch/ao/pruning/_experimental/pruner/
H A Dbase_structured_sparsifier.py33 nn.Conv2d,
100 …Returns the patterns for conv2d / linear conversion for each element in the activation functions/m…
109 # conv2d -> conv2d
110 (nn.Conv2d, "output"): prune_conv2d,
111 (nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
128 # conv2d -> activation -> conv2d
129 (nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
130 # conv2d -> activation -> pool -> conv2d
132 nn.Conv2d,
135 nn.Conv2d,
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H A Dprune_functions.py3 Collection of conversion functions for linear / conv2d structured pruning
43 if isinstance(next_layer, nn.Conv2d): # checking for Conv2d
45 # involves more steps since the Conv2d scaling weight has extra dimensions,
171 # CONV2D
172 def _prune_conv2d_helper(conv2d: nn.Conv2d) -> Tensor: argument
173 parametrization_dict = cast(nn.ModuleDict, conv2d.parametrizations)
180 parametrize.remove_parametrizations(conv2d, "weight", leave_parametrized=True)
181 conv2d.weight = nn.Parameter(conv2d.weight[mask]) # type: ignore[possibly-undefined]
182 conv2d.out_channels = conv2d.weight.shape[0]
184 _remove_bias_handles(conv2d)
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/aosp_15_r20/external/pytorch/test/inductor/
H A Dtest_mkldnn_pattern_matcher.py88 # while testing conv2d/3d/deconv2d
276 self.conv = torch.nn.Conv2d(3, 16, kernel_size=3, stride=1)
509 self.conv1 = torch.nn.Conv2d(3, 16, kernel_size=3, stride=1)
510 self.conv2 = torch.nn.Conv2d(3, 16, kernel_size=3, stride=1)
666 self.conv = torch.nn.Conv2d(3, 128, kernel_size=3, stride=1)
667 self.conv2 = torch.nn.Conv2d(128, 128, kernel_size=3, stride=1)
676 # 1. Dequant-Conv2D pattern matched in QConv2D weight prepack * 1
701 This testcase will quantize a single Conv2d module.
711 This testcase will quantize a single Conv2d module with int8_mixed_bf16 quantization.
727 self.conv = torch.nn.Conv2d(3, 128, kernel_size=3, stride=1)
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/aosp_15_r20/external/pytorch/test/quantization/jit/
H A Dtest_quantize_jit.py91 self.conv = torch.nn.Conv2d(3, 5, 3).float()
122 "aten::conv2d"
133 conv_module = {2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
176 conv_module = {2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
220 conv_module = {2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
263 conv_module = {2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
323 # This test case attempt to try combinations of conv2d/conv3d with bias/nobias
328 conv_module = {2: torch.nn.Conv2d, 3: torch.nn.Conv3d}
447 self.conv = torch.nn.Conv2d(3, 5, 3)
480 self.conv = torch.nn.Conv2d(3, 5, 3)
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/aosp_15_r20/external/executorch/backends/xnnpack/test/ops/
H A Dconv2d.py20 class Conv2d(torch.nn.Module): class
44 self.conv = torch.nn.Conv2d(
70 self.first = torch.nn.Conv2d(
77 self.second = torch.nn.Conv2d(
96 self.conv1 = torch.nn.Conv2d(
106 self.conv2 = torch.nn.Conv2d(
131 self.conv = torch.nn.Conv2d(
168 .check_count({"torch.ops.aten.conv2d": conv_count})
184 self._test(Conv2d(bias=has_bias, dtype=torch.float16))
188 self._test(Conv2d(bias=has_bias))
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/aosp_15_r20/external/ComputeLibrary/examples/
H A Dgraph_inception_v4.cpp86 .set_name("Conv2d_1a_3x3/Conv2D") in do_setup()
98 .set_name("Conv2d_2a_3x3/Conv2D") in do_setup()
110 .set_name("Conv2d_2b_3x3/Conv2D") in do_setup()
210 .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Conv2D") in get_mixed_3a()
230 .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Conv2D") in get_mixed_4a()
241 .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Conv2D") in get_mixed_4a()
254 .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Conv2D") in get_mixed_4a()
265 .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Conv2D") in get_mixed_4a()
276 .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Conv2D") in get_mixed_4a()
287 .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Conv2D") in get_mixed_4a()
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/aosp_15_r20/external/tensorflow/tensorflow/core/kernels/
H A Dconv_ops_benchmark_test.cc46 Node* conv2d; member
51 Node* conv2d; member
57 Node* conv2d; member
64 Node* conv2d; member
70 Node* conv2d; member
82 // Creates a simple Tensorflow graph with single Conv2D node.
84 static Conv2DGraph Conv2D(int batch, int height, int width, int in_depth, in Conv2D() function
100 Node* conv2d; in Conv2D() local
104 : NodeBuilder(graph->NewName("conv"), "Conv2D"); in Conv2D()
111 .Finalize(graph, &conv2d)); in Conv2D()
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/aosp_15_r20/external/pytorch/test/nn/
H A Dtest_convolution.py83 # m = nn.Conv2d(1, 1, 1)
132 r"Expected 3D \(unbatched\) or 4D \(batched\) input to conv2d, but got "
135 F.conv2d(x, w)
147 y = torch.nn.functional.conv2d(x, weight, None)
151 y_ = torch.nn.functional.conv2d(x, weight, None)
164 module = torch.nn.Conv2d(1, 1, kernel_size=3, dilation=2, stride=2).to(
170 module = nn.Conv2d(
182 module = nn.Conv2d(
192 module = nn.Conv2d(
228 torch.nn.Conv2d(1, 1, kernel_size=3, dilation=2, stride=2, groups=-1)
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/aosp_15_r20/external/pytorch/torch/ao/quantization/backend_config/
H A Donednn.py122 # (1) Conv2d + Add
124 # conv2d Y
129 # conv2d conv2d
151 # conv2d
192 (add_op, (nn.BatchNorm2d, nn.Conv2d), MatchAllNode)
205 (add_op, nn.Conv2d, MatchAllNode)
215 # Y conv2d
237 # conv2d
278 (add_op, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d))
291 (add_op, MatchAllNode, nn.Conv2d)
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/aosp_15_r20/external/pytorch/test/quantization/pt2e/
H A Dtest_duplicate_dq.py35 self.conv = torch.nn.Conv2d(3, 3, 3)
51 self.conv1 = torch.nn.Conv2d(3, 3, 3)
52 self.conv2 = torch.nn.Conv2d(3, 3, 1)
69 self.conv1 = torch.nn.Conv2d(3, 3, 3)
70 self.conv2 = torch.nn.Conv2d(3, 3, 1)
124 conv2d -> avgpool -> hardtanh -> linear
125 Check quantization tags on conv2d, avgpool and linear are correctly set
151 conv2d -> conv2d -> add
159 first conv2d is fed to next conv2d, add, and view_copy + linear.
187 conv2d -> conv2d -> add
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H A Dtest_x86inductor_quantizer.py46 self.conv = nn.Conv2d(3, 6, (2, 2), stride=(1, 1), padding=(1, 1))
59 self.conv = nn.Conv2d(
84 self.conv = torch.nn.Conv2d(
92 self.conv2 = torch.nn.Conv2d(
144 self.conv = torch.nn.Conv2d(
152 self.conv2 = torch.nn.Conv2d(
198 self.conv = nn.Conv2d(2, 2, 1)
207 """Serials of 2 Conv2d -> Add -> ReLU Pattern."""
213 self.conv = torch.nn.Conv2d(
221 self.conv2 = torch.nn.Conv2d(
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/aosp_15_r20/external/pytorch/test/fx/
H A Dtest_source_matcher_utils.py79 self.conv1 = torch.nn.Conv2d(
82 self.conv2 = torch.nn.Conv2d(
85 self.conv3 = torch.nn.Conv2d(
105 gm.graph, [torch.nn.Conv2d, torch.nn.ReLU, torch.nn.MaxPool2d]
109 self.assertEqual(len(module_partitions[torch.nn.Conv2d]), 3)
115 module_partitions[torch.nn.Conv2d][0],
121 module_partitions[torch.nn.Conv2d][1],
127 module_partitions[torch.nn.Conv2d][2],
155 return torch.nn.functional.conv2d(
182 gm.graph, [torch.nn.functional.conv2d]
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/aosp_15_r20/external/pytorch/torch/ao/quantization/quantizer/
H A Dx86_inductor_quantizer.py99 torch.ops.aten.conv2d.default,
200 ([torch.nn.Conv2d, F.conv2d], torch.ops.aten.conv2d.default),
284 "conv2d": [
285 [torch.nn.Conv2d],
286 [F.conv2d],
292 [torch.nn.Conv2d, F.conv2d],
299 supported_operators["conv2d"].append([conv_op, relu_op]) # type: ignore[list-item]
302 supported_operators["conv2d"].append([conv_op, add_op]) # type: ignore[list-item]
305 … supported_operators["conv2d"].append([conv_op, add_op, relu_op]) # type: ignore[list-item]
817 gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add, torch.nn.ReLU]
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/aosp_15_r20/external/pytorch/torch/ao/quantization/
H A Dfuser_method_mappings.py27 conv: Module instance of type conv2d/conv3d
32 >>> m1 = nn.Conv2d(10, 20, 3)
43 nn.Conv2d: nni.ConvBn2d,
50 ), "Output channel of Conv2d must match num_features of BatchNorm2d"
72 conv: Module instance of type conv2d/conv3d
77 >>> m1 = nn.Conv2d(10, 20, 3)
90 nn.Conv2d: nni.ConvBnReLU2d,
108 nn.Conv2d: nni.ConvReLU2d,
197 (nn.Conv2d, nn.BatchNorm2d): fuse_conv_bn,
198 (nn.Conv2d, nn.BatchNorm2d, nn.ReLU): fuse_conv_bn_relu,
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/aosp_15_r20/external/executorch/backends/arm/test/ops/
H A Dtest_conv_combos.py40 # 1. 1x1 CONV2d + ReLU6 (Pointwise)
41 self.pointwise_conv2d = torch.nn.Conv2d(
48 self.depthwise_conv2d = torch.nn.Conv2d(
57 # 3. Linear 1x1 Conv2d
58 self.pointwise_conv2d_linear = torch.nn.Conv2d(
67 # 1x1 CONV2d + ReLU6 (Pointwise)
77 # Linear 1x1 Conv2d
94 self.conv2d = torch.nn.Conv2d(
104 x = self.conv2d(x)
117 self.conv2d = torch.nn.Conv2d(
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/quantization/tensorflow/tests/
H A Dprepare_lifting.mlir45 …%0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings =…
52 // CHECK: %[[CONV2D:.*]] = "tf.Conv2D"(%arg0, %[[CONST]]) {data_format = "NHWC", dilations = [1, 1,…
53 // CHECK-NEXT: %[[BIASADD:.*]] = "tf.BiasAdd"(%[[CONV2D]], %[[CONST_0]]) {data_format = "NHWC"} : (…
59 …%0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings =…
66 // CHECK-NEXT: %[[CONV2D:.*]] = "tf.Conv2D"(%arg0, %[[CONST]]) {data_format = "NHWC", dilations = […
67 // CHECK-NEXT: %[[ADD:.*]] = "tf.Add"(%[[CONV2D]], %[[CONST_0]]) : (tensor<1x3x2x3xf32>, tensor<1x3…
73 …%0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings =…
79 // CHECK-NEXT: %[[CONV2D:.*]] = "tf.Conv2D"(%arg0, %[[CONST]]) {data_format = "NHWC", dilations = […
80 // CHECK-NEXT: return %[[CONV2D]] : tensor<1x3x2x2xf32>
85 …%0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings =…
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/tensorflow/transforms/
H A Dtpu_space_to_depth_pass.cc150 void HandleConv2DStride(TF::Conv2DOp conv2d) { in HandleConv2DStride() argument
151 MLIRContext* context = conv2d.getContext(); in HandleConv2DStride()
158 conv2d->setAttr("strides", strides); in HandleConv2DStride()
162 void HandleConv2DInput(TF::Conv2DOp conv2d, int64_t block_size) { in HandleConv2DInput() argument
163 auto input = conv2d.input(); in HandleConv2DInput()
217 void HandleConv2DFilter(TF::Conv2DOp conv2d, int64_t block_size) { in HandleConv2DFilter() argument
224 auto filter = conv2d.filter(); in HandleConv2DFilter()
225 OpBuilder builder(conv2d); in HandleConv2DFilter()
226 builder.setInsertionPoint(conv2d); in HandleConv2DFilter()
263 // Update filter of Conv2D. in HandleConv2DFilter()
[all …]
/aosp_15_r20/external/pytorch/test/quantization/fx/
H A Dtest_numeric_suite_fx.py167 # conv2d
168 self.conv2d_0 = nn.Conv2d(1, 1, 1)
169 # conv2d - relu
170 self.conv2d_1 = nn.Conv2d(1, 1, 1)
172 # conv2d - bn (qat only)
173 self.conv2d_2 = nn.Conv2d(1, 1, 1)
175 # conv2d - bn - relu (qat only)
176 self.conv2d_3 = nn.Conv2d(1, 1, 1)
207 # conv2d
263 x = F.conv2d(
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H A Dtest_quantize_fx.py213 self.conv1 = torch.nn.Conv2d(1, 1, 1).float()
214 self.conv2 = torch.nn.Conv2d(1, 1, 1).float()
246 self.conv1 = torch.nn.Conv2d(1, 1, 1).float()
247 self.conv2 = torch.nn.Conv2d(1, 1, 1).float()
275 self.conv2d = nn.Conv2d(1, 1, 1)
281 self.conv2d2 = nn.Conv2d(1, 1, 1)
291 x = self.conv2d(x)
335 ns.call_module(nn.Conv2d),
504 ns.call_module(nn.Conv2d),
523 """ Test fusion and lowering of Conv2d - (bn -) ReLU
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/jit/tests/
H A Dkeras_imagenet_main.pbtxt18194 name: "res5a_branch1_1/Conv2D/ReadVariableOp"
18242 name: "res4a_branch1_1/Conv2D/ReadVariableOp"
18290 name: "res3a_branch1_1/Conv2D/ReadVariableOp"
18338 name: "res2a_branch1_1/Conv2D/ReadVariableOp"
18386 name: "conv1_1/Conv2D/ReadVariableOp"
18453 name: "res2a_branch2c_1/Conv2D/ReadVariableOp"
18501 name: "res2a_branch2b_1/Conv2D/ReadVariableOp"
18549 name: "res2a_branch2a_1/Conv2D/ReadVariableOp"
18597 name: "res2b_branch2c_1/Conv2D/ReadVariableOp"
18645 name: "res2b_branch2b_1/Conv2D/ReadVariableOp"
[all …]
/aosp_15_r20/external/armnn/src/profiling/test/
H A DProfilingTestUtils.cpp428 IConnectableLayer* conv2d = net->AddConvolution2dLayer(conv2dDesc); in VerifyPostOptimisationStructureTestImpl() local
434 weightsLayer->GetOutputSlot(0).Connect(conv2d->GetInputSlot(1u)); in VerifyPostOptimisationStructureTestImpl()
437 biasLayer->GetOutputSlot(0).Connect(conv2d->GetInputSlot(2u)); in VerifyPostOptimisationStructureTestImpl()
446 input->GetOutputSlot(0).Connect(conv2d->GetInputSlot(0)); in VerifyPostOptimisationStructureTestImpl()
447 conv2d->GetOutputSlot(0).Connect(abs->GetInputSlot(0)); in VerifyPostOptimisationStructureTestImpl()
451 conv2d->GetOutputSlot(0).SetTensorInfo(outputInfo); in VerifyPostOptimisationStructureTestImpl()
712 // Conv2d layer in VerifyPostOptimisationStructureTestImpl()
713 // Conv2d layer entity in VerifyPostOptimisationStructureTestImpl()
714 VerifyTimelineEntityBinaryPacketData(conv2d->GetGuid(), readableData, offset); in VerifyPostOptimisationStructureTestImpl()
723 conv2d->GetGuid(), in VerifyPostOptimisationStructureTestImpl()
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/aosp_15_r20/external/tensorflow/tensorflow/compiler/mlir/lite/tests/
H A Ddilated-conv.mlir7 …%1 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, …
13 …// CHECK-NEXT: [[RESULT:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) {dilations = [1, 2, 2, 1], padd…
21 …%1 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x64x64x3xf32>, …
27 …// CHECK-NEXT: [[RESULT:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) {dilations = [1, 2, 2, 1], padd…
36 …%1 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, …
42 …// CHECK-NEXT: [[RESULT:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) {dilations = [1, 2, 2, 1], padd…
51 …%1 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", dilations = [1, 2, 2, 1], strides = [1, 1, 1, 1]} …
57 // CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"
83 …%1 = "tf.Conv2D"(%0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, …
91 …// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) {dilations = [1, 2, 2, 1], paddin…
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