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Aug 7, 2020
Merged

Added support for Conv3DBackpropInputV2 #1041

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100 changes: 81 additions & 19 deletions tests/test_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@

if is_tf2():
conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
multinomial = tf.compat.v1.random.multinomial
space_to_batch_nd = tf.compat.v1.space_to_batch_nd
batch_to_space_nd = tf.compat.v1.batch_to_space_nd
Expand All @@ -73,6 +74,7 @@
fake_quant_with_min_max_args = tf.quantization.fake_quant_with_min_max_args
elif LooseVersion(tf.__version__) >= "1.13":
conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
multinomial = tf.compat.v1.random.multinomial
space_to_batch_nd = tf.compat.v1.space_to_batch_nd
batch_to_space_nd = tf.compat.v1.batch_to_space_nd
Expand All @@ -93,6 +95,7 @@
fake_quant_with_min_max_args = tf.compat.v1.quantization.fake_quant_with_min_max_args
else:
conv2d_backprop_input = tf.nn.conv2d_backprop_input
conv3d_transpose = tf.nn.conv3d_transpose
multinomial = tf.multinomial
space_to_batch_nd = tf.space_to_batch_nd
batch_to_space_nd = tf.batch_to_space_nd
Expand Down Expand Up @@ -3136,45 +3139,38 @@ def func(x):
@check_opset_min_version(10, "Conv2DBackpropInput")
def test_Conv2DBackpropInput_const(self):
input_sizes_val_ = np.array([1, 10, 10, 3], dtype=np.int32)
filter_val_ = np.random.randint(low=0, high=256, size=[3, 3, 3, 5])
out_backprop_val_ = np.random.randint(low=0, high=256, size=[1, 10, 10, 5])
def func():
def func(filter_val, out_backprop_val):
input_sizes_val = tf.constant(input_sizes_val_, dtype=tf.int32)
filter_val = tf.constant(filter_val_, dtype=tf.float32)
out_backprop_val = tf.constant(out_backprop_val_, dtype=tf.float32)
return conv2d_backprop_input(input_sizes=input_sizes_val, filter=filter_val,
out_backprop=out_backprop_val, strides=[1, 1, 1, 1],
padding='SAME', name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {})
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
out_backprop_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: filters_val, _INPUT1: out_backprop_val})

@check_opset_min_version(10, "Conv2DBackpropInput")
def test_Conv2DBackpropInput_const_strided(self):
input_sizes_val_ = np.array([1, 10, 10, 3], dtype=np.int32)
filter_val_ = np.random.randint(low=0, high=256, size=[3, 3, 3, 5])
out_backprop_val_ = np.random.randint(low=0, high=256, size=[1, 5, 5, 5])

def func():
def func(filter_val, out_backprop_val):
input_sizes_val = tf.constant(input_sizes_val_, dtype=tf.int32)
filter_val = tf.constant(filter_val_, dtype=tf.float32)
out_backprop_val = tf.constant(out_backprop_val_, dtype=tf.float32)
return conv2d_backprop_input(input_sizes=input_sizes_val, filter=filter_val,
out_backprop=out_backprop_val, strides=[1, 2, 2, 1],
padding='SAME', name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {})
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
out_backprop_val = np.random.randint(low=0, high=256, size=[1, 5, 5, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: filters_val, _INPUT1: out_backprop_val})

@check_opset_min_version(10, "Conv2DBackpropInput")
def test_Conv2DBackpropInput_const_valid(self):
input_sizes_val_ = np.array([1, 12, 12, 3], dtype=np.int32)
filter_val_ = np.random.randint(low=0, high=256, size=[3, 3, 3, 5])
out_backprop_val_ = np.random.randint(low=0, high=256, size=[1, 10, 10, 5])
def func():
def func(filter_val, out_backprop_val):
input_sizes_val = tf.constant(input_sizes_val_, dtype=tf.int32)
filter_val = tf.constant(filter_val_, dtype=tf.float32)
out_backprop_val = tf.constant(out_backprop_val_, dtype=tf.float32)
return conv2d_backprop_input(input_sizes=input_sizes_val, filter=filter_val,
out_backprop=out_backprop_val, strides=[1, 1, 1, 1],
padding='VALID', name=_TFOUTPUT)
self._run_test_case(func, [_OUTPUT], {})
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
out_backprop_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: filters_val, _INPUT1: out_backprop_val})

@check_opset_min_version(10, "Conv2DBackpropInput")
def test_Conv2DBackpropInput(self):
Expand Down Expand Up @@ -3206,6 +3202,72 @@ def func(input_sizes, filters, out_backprop):
out_backprop_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filters_val, _INPUT2: out_backprop_val})

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2_const(self):
output_shape_val_ = np.array([1, 10, 10, 10, 3], dtype=np.int32)
def func(value, filters):
output_shape_val = tf.constant(output_shape_val_, dtype=tf.int32)
return conv3d_transpose(value, filters, output_shape_val, strides=[1, 1, 1, 1, 1],
padding='SAME', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 3, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 10, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val}, rtol=1e-6)

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2_const_strided(self):
output_shape_val_ = np.array([1, 10, 10, 10, 3], dtype=np.int32)
def func(value, filters):
output_shape_val = tf.constant(output_shape_val_, dtype=tf.int32)
return conv3d_transpose(value, filters, output_shape_val, strides=[1, 2, 2, 2, 1],
padding='SAME', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 3, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[1, 5, 5, 5, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val}, rtol=1e-6)

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2_const_valid(self):
output_shape_val_ = np.array([1, 12, 12, 12, 3], dtype=np.int32)
def func(value, filters):
output_shape_val = tf.constant(output_shape_val_, dtype=tf.int32)
return conv3d_transpose(value, filters, output_shape_val, strides=[1, 1, 1, 1, 1],
padding='VALID', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 3, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 10, 5]).astype(np.float32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val}, rtol=1e-6)

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2(self):
def func(value, filters, output_shape):
return conv3d_transpose(value, filters, output_shape, strides=[1, 1, 1, 1, 1],
padding='SAME', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[2, 3, 4, 4, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[2, 7, 8, 9, 5]).astype(np.float32)
output_shape_val = np.array([2, 7, 8, 9, 4], dtype=np.int32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val, _INPUT2: output_shape_val},
rtol=1e-6)

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2_strided(self):
def func(value, filters, output_shape):
return conv3d_transpose(value, filters, output_shape, strides=[1, 2, 2, 2, 1],
padding='SAME', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 3, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[1, 5, 5, 5, 5]).astype(np.float32)
output_shape_val = np.array([1, 10, 10, 10, 3], dtype=np.int32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val, _INPUT2: output_shape_val},
rtol=1e-6)

@check_opset_min_version(10, "Conv3DBackpropInputV2")
def test_Conv3DBackpropInputV2_valid(self):
def func(value, filters, output_shape):
return conv3d_transpose(value, filters, output_shape, strides=[1, 1, 1, 1, 1],
padding='VALID', data_format="NDHWC", name=_TFOUTPUT)
filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 3, 5]).astype(np.float32)
value_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 10, 5]).astype(np.float32)
output_shape_val = np.array([1, 12, 12, 12, 3], dtype=np.int32)
self._run_test_case(func, [_OUTPUT], {_INPUT: value_val, _INPUT1: filters_val, _INPUT2: output_shape_val},
rtol=1e-6)

@check_opset_min_version(8, "CategoryMapper")
@skip_tf2()
def test_hashtable_lookup(self):
Expand Down
2 changes: 2 additions & 0 deletions tf2onnx/graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -146,6 +146,8 @@ def data_format(self, val):

def is_nhwc(self):
"""Return True if node is in NHWC format."""
utils.make_sure('D' not in self.data_format, "is_nhwc called on %s with spatial=2 but data_format=%s",
self.name, self.data_format)
return self.data_format == "NHWC"

def is_const(self):
Expand Down
62 changes: 45 additions & 17 deletions tf2onnx/onnx_opset/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -362,8 +362,7 @@ def version_11(cls, ctx, node, **kwargs):
# No change.
cls.version_1(ctx, node, **kwargs)


@tf_op("Conv2DBackpropInput")
@tf_op(["Conv2DBackpropInput", "Conv3DBackpropInputV2"])
class ConvTranspose:
@classmethod
def version_1(cls, ctx, node, **kwargs):
Expand All @@ -372,24 +371,36 @@ def version_1(cls, ctx, node, **kwargs):
# T Y = ConvTranspose(T X, T W, T B, @STRING auto_pad, @INTS dilations,
# @INT group, @INTS kernel_shape, @INTS output_shape, @INTS pads, @INTS strides)

if node.type == "Conv3DBackpropInputV2":
spatial = 3
else:
spatial = 2
node.type = "ConvTranspose"
# Note: inputs are reversed from what one would expect.
conv_kernel_shape(ctx, node, 1)
conv_kernel_shape(ctx, node, 1, spatial=spatial)
input_shape = ctx.get_shape(node.input[2])
output_shape_orig = node.output_shapes

# ouput_shape is explicitly specified here, in this case pads values are auto generated/calculated.
if node.inputs[0].is_const():
output_shape = ctx.get_shape(node.output[0])
if node.is_nhwc():
if is_channels_last(node):
new_output_shape = [output_shape[1], output_shape[2]]
input_hw = [input_shape[1], input_shape[2]]
input_dims = [input_shape[1], input_shape[2]]
if spatial == 3:
new_output_shape.append(output_shape[3])
input_dims.append(input_shape[3])
else:
new_output_shape = [output_shape[2], output_shape[3]]
input_hw = [input_shape[2], input_shape[3]]
utils.make_sure(new_output_shape.count(-1) <= 0, "output h and w need to be known")
utils.make_sure(new_output_shape[0] >= input_hw[0] and new_output_shape[1] >= input_hw[1],
"output h and w cannot be smaller than input h and w.")
input_dims = [input_shape[2], input_shape[3]]
if spatial == 3:
new_output_shape.append(output_shape[4])
input_dims.append(input_shape[4])

utils.make_sure(new_output_shape.count(-1) <= 0, "output dims need to be known")
utils.make_sure(all(new_output_shape[i] >= input_dims[i] for i in range(spatial)),
"output dims cannot be smaller than input dims.")

node.set_attr("output_shape", new_output_shape)
else:
input_shape = ctx.make_node("Cast", [node.input[0]], attr={'to': TensorProto.INT64})
Expand All @@ -409,20 +420,37 @@ def version_1(cls, ctx, node, **kwargs):
start_w = ctx.make_node("Div", [diff_w.output[0], const_two.output[0]])
end_h = ctx.make_node("Add", [start_h.output[0], expect_h])
end_w = ctx.make_node("Add", [start_w.output[0], expect_w])
starts = ctx.make_node("Concat", [start_h.output[0], start_w.output[0]], attr={"axis": 0})
ends = ctx.make_node("Concat", [end_h.output[0], end_w.output[0]], attr={"axis": 0})
const_one_two = ctx.make_const(utils.make_name(node.name + "_const_one_two"),
np.array([1, 2], dtype=np.int64))
if spatial == 3:
output_d = GraphBuilder(ctx).make_slice(
{"data": output_shape.output[0], "ends": [4], "starts": [3], "axes": [0]})
expect_d = GraphBuilder(ctx).make_slice(
{"data": input_shape.output[0], "ends": [4], "starts": [3], "axes": [0]})
diff_d = ctx.make_node("Sub", [output_d, expect_d])
start_d = ctx.make_node("Div", [diff_d.output[0], const_two.output[0]])
end_d = ctx.make_node("Add", [start_d.output[0], expect_d])

starts = ctx.make_node("Concat", [start_h.output[0], start_w.output[0], start_d.output[0]],
attr={"axis": 0})
ends = ctx.make_node("Concat", [end_h.output[0], end_w.output[0], end_d.output[0]], attr={"axis": 0})
slice_axes = ctx.make_const(utils.make_name(node.name + "_const_slice_axes"),
np.array([1, 2, 3], dtype=np.int64))
else:
starts = ctx.make_node("Concat", [start_h.output[0], start_w.output[0]], attr={"axis": 0})
ends = ctx.make_node("Concat", [end_h.output[0], end_w.output[0]], attr={"axis": 0})
slice_axes = ctx.make_const(utils.make_name(node.name + "_const_slice_axes"),
np.array([1, 2], dtype=np.int64))

slice_node = ctx.make_node("Slice",
[node.output[0], starts.output[0], ends.output[0], const_one_two.output[0]],
[node.output[0], starts.output[0], ends.output[0], slice_axes.output[0]],
shapes=output_shape_orig)

downstream_nodes = ctx.find_output_consumers(node.output[0])
downstream_nodes.remove(output_shape)
downstream_nodes.remove(slice_node)
ctx.replace_all_inputs(downstream_nodes, node.output[0], slice_node.output[0])

conv_dims_attr(node, "strides")
conv_dims_attr(node, "dilations")
conv_dims_attr(node, "strides", spatial=spatial)
conv_dims_attr(node, "dilations", spatial=spatial)

# remove output_shapes input
ctx.remove_input(node, node.input[0])
Expand All @@ -431,7 +459,7 @@ def version_1(cls, ctx, node, **kwargs):
node.input[0] = node.input[1]
node.input[1] = t

conv_convert_inputs(ctx, node, with_kernel=True)
conv_convert_inputs(ctx, node, with_kernel=True, spatial=spatial)

@classmethod
def version_11(cls, ctx, node, **kwargs):
Expand Down