feat: add support for ExtractImagePatches

Signed-off-by: Nanoskript <96655713+nanoskript@users.noreply.github.com>

Author: nanoskript

tests/test_backend.py

@@ -72,6 +72,7 @@
     matrix_diag_part = tf.compat.v1.matrix_diag_part
     fake_quant_with_min_max_args = tf.quantization.fake_quant_with_min_max_args
     fake_quant_with_min_max_vars = tf.quantization.fake_quant_with_min_max_vars
+    extract_image_patches = tf.image.extract_patches
 elif Version(tf.__version__) >= Version("1.13"):
     conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
     conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
@@ -94,6 +95,7 @@
     matrix_diag_part = tf.compat.v1.matrix_diag_part
     fake_quant_with_min_max_args = tf.compat.v1.quantization.fake_quant_with_min_max_args
     fake_quant_with_min_max_vars = tf.compat.v1.quantization.fake_quant_with_min_max_vars
+    extract_image_patches = tf.compat.v1.extract_image_patches
 else:
     conv2d_backprop_input = tf.nn.conv2d_backprop_input
     conv3d_transpose = tf.nn.conv3d_transpose
@@ -111,6 +113,7 @@
     is_inf = tf.is_inf
     floormod = tf.floormod
     matrix_diag_part = tf.matrix_diag_part
+    extract_image_patches = tf.extract_image_patches
 
 
 def make_xval(shape):
@@ -6283,5 +6286,21 @@ def func(tensor, indices, updates):
         self._run_test_case(func, [_OUTPUT], {_INPUT: tensor_val, _INPUT1: indices_val, _INPUT2: updates_val})
         self._run_test_case(func, [_OUTPUT], {_INPUT: tensor_val, _INPUT1: indices64_val, _INPUT2: updates_val})
 
+    def test_extract_image_patches(self):
+        for rates in [[1, 1], [1, 4], [4, 1], [3, 3]]:
+            for _, padding, x_shape, sizes, strides in get_conv_getdata():
+                def func(x):
+                    return extract_image_patches(
+                        x,
+                        sizes=sizes,
+                        strides=strides,
+                        rates=[1] + rates + [1],
+                        padding=padding,
+                        name=_TFOUTPUT
+                    )
+
+                x_val = make_xval(x_shape)
+                self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
 if __name__ == '__main__':
     unittest_main()

tf2onnx/rewriter/__init__.py

@@ -24,6 +24,7 @@
 from tf2onnx.rewriter.lstm_tf2_rewriter import rewriter_lstm_tf2
 from tf2onnx.rewriter.gru_tf2_rewriter import rewrite_gru_tf2
 from tf2onnx.rewriter.fused_op_rewriter import rewrite_fused_ops
+from tf2onnx.rewriter.extract_image_patches_rewriter import rewrite_extract_image_patches
 
 
 __all__ = [
@@ -53,4 +54,5 @@
     "rewriter_lstm_tf2",
     "rewrite_gru_tf2",
     "rewrite_fused_ops",
+    "rewrite_extract_image_patches",
 ]

tf2onnx/rewriter/extract_image_patches_rewriter.py

@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: Apache-2.0
+
+
+"""
+tf2onnx.rewriter.extract_image_patches_rewriter - Rewrites ExtractImagePatches into supported operations.
+"""
+
+import numpy as np
+from tf2onnx import utils
+from tf2onnx.graph_matcher import OpTypePattern, GraphMatcher
+
+
+# pylint: disable=missing-docstring
+
+def rewrite_extract_image_patches(g, ops):
+    pattern = OpTypePattern("ExtractImagePatches", name="extract_image_patches")
+    matcher = GraphMatcher(pattern)
+    match_results = list(matcher.match_ops(ops))
+    for match_result in match_results:
+        operation = match_result.get_op("extract_image_patches")
+        input_shape = g.get_shape(operation.input[0])
+        output_shape = operation.output_shapes[0]
+
+        sizes = operation.get_attr_value("ksizes")
+        strides = operation.get_attr_value("strides")
+        rates = operation.get_attr_value("rates")
+        padding = operation.get_attr_str("padding")
+
+        # Our constraints.
+        utils.make_sure(0 not in output_shape, "Empty ExtractImagePatches output is unsupported.")
+        [_, size_rows, size_cols, _] = sizes
+
+        # Transform input into [N * C, H, W, 1].
+        transformed_input = g.make_node("Reshape", inputs=[
+            g.make_node("Transpose", inputs=operation.input, attr=dict(perm=[0, 3, 1, 2])).output[0],
+            g.make_const(utils.make_name("new_shape"), np.int64([
+                input_shape[0] * input_shape[3],
+                input_shape[1],
+                input_shape[2],
+                1,
+            ])).output[0],
+        ])
+
+        # Create identity kernel.
+        k = size_rows * size_cols
+        identity_kernel = g.make_node("Reshape", inputs=[
+            g.make_node("EyeLike", inputs=[
+                g.make_node("ConstantOfShape", inputs=[
+                    g.make_const(utils.make_name("eye_size"), np.array([k, k], dtype=np.int64)).output[0],
+                ]).output[0],
+            ]).output[0],
+            g.make_const(utils.make_name("new_shape"), np.array([
+                size_rows,
+                size_cols,
+                1,
+                k,
+            ], dtype=np.int64)).output[0],
+        ])
+
+        # Convolve into [N * C, ?H, ?W, K].
+        convolution = g.make_node("Conv2D", inputs=[transformed_input.output[0], identity_kernel.output[0]],
+                                  attr=dict(strides=strides, dilations=rates, padding=padding, data_format="NHWC"),
+                                  shapes=[[input_shape[0] * input_shape[3], output_shape[1], output_shape[2], k]],
+                                  dtypes=operation.output_dtypes, skip_conversion=False)
+
+        # Transform into [N, ?H, ?W, C * K].
+        output_node = g.make_node("Reshape", inputs=[
+            g.make_node("Transpose", inputs=[
+                g.make_node("Reshape", inputs=[
+                    convolution.output[0],
+                    g.make_const(utils.make_name("new_shape"), np.array([
+                        input_shape[0],
+                        input_shape[3],
+                        output_shape[1],
+                        output_shape[2],
+                        k,
+                    ], dtype=np.int64)).output[0],
+                ]).output[0],
+            ], attr=dict(perm=[0, 2, 3, 4, 1])).output[0],
+            g.make_const(utils.make_name("new_shape"), np.array(output_shape, dtype=np.int64)).output[0],
+        ])
+
+        # Replace node.
+        g.replace_all_inputs(operation.output[0], output_node.output[0])
+        g.remove_node(operation.name)
+    return g.get_nodes()

tf2onnx/tfonnx.py

@@ -598,6 +598,7 @@ def compat_handler(ctx, node, **kwargs):
         rewriter_lstm_tf2,
         rewrite_gru_tf2,
         rewrite_single_direction_lstm,
+        rewrite_extract_image_patches,
         # bi-directional
         rewrite_bi_direction_lstm,
         rewrite_single_direction_gru,