[PT FE]: support conv transpose (#15191)

* [PT FE]: support conv transpose

* apply comments

tests/layer_tests/pytorch_tests/test_conv_transposend.py

@@ -0,0 +1,196 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestConvTranspose2D(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 10, 10).astype(np.float32),)
+
+    def create_model(self, weights_shape, strides, pads, dilations, groups, bias, output_padding):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_conv_transpose2d(torch.nn.Module):
+            def __init__(self):
+                super(aten_conv_transpose2d, self).__init__()
+                self.weight = torch.randn(weights_shape)
+                self.bias = None
+                if bias:
+                    self.bias = torch.randn(groups)
+                self.strides = strides
+                self.pads = pads
+                self.dilations = dilations
+                self.groups = groups
+                self.output_padding = output_padding
+
+            def forward(self, x):
+                return F.conv_transpose2d(x, weight=self.weight, bias=self.bias, stride=self.strides, padding=self.pads, output_padding=self.output_padding, dilation=self.dilations, groups=self.groups)
+
+        ref_net = None
+
+        return aten_conv_transpose2d(), ref_net, "aten::conv_transpose2d"
+
+    @pytest.mark.parametrize("params",
+                             [{'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [0, 0],
+                               'dilations': [2, 2], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  0, 0], 'dilations': [1, 1], 'groups': 3, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  1, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  3, 1], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  1, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  1, 0], 'dilations': [1, 1], 'groups': 3, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [1, 1], 'pads': [
+                                  1, 0], 'dilations': [2, 2], 'groups': 3, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [2, 1], 'pads': [
+                                  1, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'pads': [
+                                  0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'pads': [
+                                  0, 0], 'dilations': [1, 1], 'groups': 1, 'output_padding': [0, 0]},
+                              {'weights_shape': [3, 1, 1, 1], 'strides': [2, 2], 'pads': [
+                                  1, 1], 'dilations': [2, 2], 'groups': 1, 'output_padding': [1, 1]},
+                              ])
+    @pytest.mark.parametrize("bias", [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_conv_transpose2d(self, params, bias, ie_device, precision, ir_version):
+        self._test(*self.create_model(**params, bias=bias),
+                   ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1)
+
+
+class TestConvTranspose1D(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 10).astype(np.float32),)
+
+    def create_model(self, weights_shape, strides, pads, dilations, groups, bias, output_padding):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_conv_transpose1d(torch.nn.Module):
+            def __init__(self):
+                super(aten_conv_transpose1d, self).__init__()
+                self.weight = torch.randn(weights_shape)
+                self.bias = None
+                if bias:
+                    self.bias = torch.randn(groups)
+                self.strides = strides
+                self.pads = pads
+                self.dilations = dilations
+                self.groups = groups
+                self.output_padding = output_padding
+
+            def forward(self, x):
+                return F.conv_transpose1d(
+                    x,
+                    weight=self.weight,
+                    bias=self.bias,
+                    stride=self.strides,
+                    padding=self.pads,
+                    output_padding=self.output_padding,
+                    dilation=self.dilations,
+                    groups=self.groups
+                )
+
+        ref_net = None
+
+        return aten_conv_transpose1d(), ref_net, "aten::conv_transpose1d"
+
+    @pytest.mark.parametrize("params",
+                             [{'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 0, 'dilations': 1, 'groups': 1, 'output_padding': 0},
+                              {'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 0,
+                                  'dilations': 1, 'groups': 3, 'output_padding': 0},
+                              {'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 1,
+                                  'dilations': 1, 'groups': 1, 'output_padding': 0},
+                              {'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 1,
+                                  'dilations': 1, 'groups': 3, 'output_padding': 0},
+                              {'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 3,
+                                  'dilations': 2, 'groups': 1, 'output_padding': 1},
+                              {'weights_shape': [3, 1, 1], 'strides': 1, 'pads': 3,
+                                  'dilations': 2, 'groups': 3, 'output_padding': 1},
+                              ])
+    @pytest.mark.parametrize("bias", [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_conv_transpose1d(self, params, bias, ie_device, precision, ir_version):
+        self._test(*self.create_model(**params, bias=bias),
+                   ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1)
+
+
+class TestConvTranspose3D(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 10, 10, 4).astype(np.float32),)
+
+    def create_model(self, weights_shape, strides, pads, dilations, groups, bias, output_padding):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_conv_transpose3d(torch.nn.Module):
+            def __init__(self):
+                super(aten_conv_transpose3d, self).__init__()
+                self.weight = torch.randn(weights_shape)
+                self.bias = None
+                if bias:
+                    self.bias = torch.randn(groups)
+                self.strides = strides
+                self.pads = pads
+                self.dilations = dilations
+                self.groups = groups
+                self.output_padding = output_padding
+
+            def forward(self, x):
+                return F.conv_transpose3d(
+                    x,
+                    weight=self.weight,
+                    bias=self.bias,
+                    stride=self.strides,
+                    padding=self.pads,
+                    output_padding=self.output_padding,
+                    dilation=self.dilations,
+                    groups=self.groups
+                )
+
+        ref_net = None
+
+        return aten_conv_transpose3d(), ref_net, "aten::conv_transpose3d"
+
+    @pytest.mark.parametrize("params",
+                             [{'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [0, 0, 0],
+                               'dilations': [2, 2, 2], 'groups': 1, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
+                                  0, 0, 0], 'dilations': [1, 1, 1], 'groups': 3, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
+                                  1, 1, 0], 'dilations': [1, 1, 2], 'groups': 1, 'output_padding': [0, 0, 1]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 2], 'pads': [
+                                  3, 1, 0], 'dilations': [4, 4, 4], 'groups': 1, 'output_padding': [1, 1, 1]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
+                                  1, 0, 1], 'dilations': [1, 2, 1], 'groups': 1, 'output_padding': [0, 1, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
+                                  1, 0, 0], 'dilations': [1, 1, 2], 'groups': 3, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [1, 1, 1], 'pads': [
+                                  1, 0, 0], 'dilations': [2, 2, 1], 'groups': 3, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 1, 2], 'pads': [
+                                  1, 0, 0], 'dilations': [3, 4, 2], 'groups': 1, 'output_padding': [2, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'pads': [
+                                  0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'pads': [
+                                  0, 0, 0], 'dilations': [1, 1, 1], 'groups': 1, 'output_padding': [0, 0, 0]},
+                              {'weights_shape': [3, 1, 1, 1, 1], 'strides': [2, 2, 2], 'pads': [
+                                  1, 1, 2], 'dilations': [2, 2, 2], 'groups': 1, 'output_padding': [1, 1, 0]},
+                              ])
+    @pytest.mark.parametrize("bias", [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_conv_transpose3d(self, params, bias, ie_device, precision, ir_version):
+        self._test(*self.create_model(**params, bias=bias),
+                   ie_device, precision, ir_version, dynamic_shapes=params['groups'] == 1)