def _pool_helper(name, tf_node, inputs, uff_graph, **kwargs):
func = kwargs["func"]
window_size = tf2uff.get_tf_int_list(tf_node.attr['ksize'])
strides = tf2uff.get_tf_int_list(tf_node.attr['strides'])
fmt = convert_to_str(tf_node.attr['data_format'].s)
fmt = fmt if fmt else "NHWC"
inputs, padding, fields = tf2uff.apply_fused_padding(
tf_node, inputs, kwargs["tf_nodes"])
data_format = tf2uff.convert_tf2uff_data_format(fmt)
if fmt == 'NCHW':
window_size = window_size[2:]
strides = strides[2:]
if padding is not None:
padding = padding[2:]
elif fmt == 'NHWC':
window_size = [window_size[1], window_size[2]]
strides = [strides[1], strides[2]]
if padding is not None:
padding = [padding[1], padding[2]]
else:
raise ValueError("Unsupported data format: " + fmt)
uff_graph.pool(
inputs[0], func, window_size, strides, padding,
data_format=data_format, name=name, fields=fields)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def _conv2d_transpose_helper(name, tf_node, inputs, uff_graph, **kwargs):
kwargs.pop("func") # FIXME support depthwise transpose
fmt = convert_to_str(tf_node.attr['data_format'].s)
fmt = fmt if fmt else "NHWC"
strides = tf2uff.get_tf_int_list(tf_node.attr['strides'])
fields = {}
padding = None
number_groups = None
tf_padding = convert_to_str(tf_node.attr['padding'].s)
if tf_padding == "SAME":
fields['implicit_padding'] = "same"
elif tf_padding != "VALID":
raise ValueError("Padding mode %s not supported" % tf_padding)
lhs_fmt = tf2uff.convert_tf2uff_data_format(fmt)
rhs_fmt = '+KC'
if fmt == 'NCHW':
strides = strides[2:]
elif fmt == 'NHWC':
strides = [strides[1], strides[2]]
else:
raise ValueError("Unsupported data format: " + fmt)
uff_graph.conv_transpose(
inputs[2], inputs[1], inputs[0],
strides, padding,
dilation=None, number_groups=number_groups,
left_format=lhs_fmt, right_format=rhs_fmt,
name=name, fields=fields)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def convert_squeeze(name, tf_node, inputs, uff_graph, **kwargs):
axis = tf2uff.get_tf_int_list(tf_node.attr['squeeze_dims'])
uff_graph.squeeze(inputs[0], name=name, axis=axis)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]
def _conv2d_helper(name, tf_node, inputs, uff_graph, **kwargs):
func = kwargs["func"]
fmt = convert_to_str(tf_node.attr['data_format'].s)
fmt = fmt if fmt else "NHWC"
strides = tf2uff.get_tf_int_list(tf_node.attr['strides'])
inputs, padding, fields = tf2uff.apply_fused_padding(
tf_node, inputs, kwargs["tf_nodes"])
lhs_fmt = tf2uff.convert_tf2uff_data_format(fmt)
rhs_fmt = '+CK'
if fmt == 'NCHW':
strides = strides[2:]
if padding is not None:
padding = padding[2:]
elif fmt == 'NHWC':
strides = [strides[1], strides[2]]
if padding is not None:
padding = [padding[1], padding[2]]
else:
raise ValueError("Unsupported data format: " + fmt)
if func == "depthwise":
wt = kwargs["tf_nodes"][inputs[1]]
number_groups = int(wt.attr['value'].tensor.tensor_shape.dim[2].size)
else:
number_groups = None
# If this node represents a dilated conv, pull in the dilations.
dilation = None
if "dilations" in tf_node.attr:
if fmt == "NCHW":
dilation = tf2uff.get_tf_int_list(tf_node.attr['dilations'])[2:]
else:
dilation = tf2uff.get_tf_int_list(tf_node.attr['dilations'])[1:3]
# FIXME: Need a better way to check for dilated convs. This just checks if the block_shape input is as expected.
# Ideally we should have a 'get_input_by_name' function. Maybe we can leverage GS here.
# Another possibility is that GS can add these as attributes to the node rather than maintaining them as
# separate const nodes.
tf_block_shape_node = kwargs["tf_nodes"][inputs[1]]
if "block_shape" in tf_block_shape_node.name.split('/')[-1] and tf_block_shape_node.op == "Const":
# Get the second input (block_shape) - of the form [1, dilation_value, dilation_value]
dilation = np.frombuffer(tf_block_shape_node.attr["value"].tensor.tensor_content, dtype=np.int32).tolist()
if len(dilation) > 2:
dilation = [dilation[1], dilation[2]]
inputs.pop(1)
tf_paddings_node = kwargs["tf_nodes"][inputs[1]]
if "paddings" in tf_paddings_node.name.split('/')[-1] and tf_paddings_node.op == "Const":
# Get the second input (paddings, since block_shape is already removed)
paddings_temp = np.frombuffer(tf_paddings_node.attr["value"].tensor.tensor_content, dtype=np.int32).tolist()
inputs.pop(1)
# Get cropping information, but only if paddings is also present.
tf_crops_node = kwargs["tf_nodes"][inputs[1]]
if "crops" in tf_crops_node.name.split('/')[-1] and tf_crops_node.op == "Const":
# Get the second input (crops, since block_shape is already removed)
crops = np.frombuffer(tf_crops_node.attr["value"].tensor.tensor_content, dtype=np.int32)
inputs.pop(1)
paddings_temp = (np.array(paddings_temp) - crops).tolist()
# TF paddings are [[top,bottom], [left,right]], so we need to rearrange.
perm = [0, 2, 1, 3]
# HACK: Sometimes paddings has [0, 0] at the front.
if len(paddings_temp) == 6:
paddings_temp = paddings_temp[2:]
paddings_temp = [paddings_temp[p] for p in perm]
# Symmetric padding ("same")
if paddings_temp[0] == paddings_temp[2] and paddings_temp[1] == paddings_temp[3]:
paddings_temp = paddings_temp[0:2]
padding = paddings_temp if not padding else [p + pt for p, pt in zip(padding, paddings_temp)]
else:
print("Asymmetric padding for dilated convolutions is currently unsupported in the UFF converter.")
uff_graph.conv(
inputs[0], inputs[-1], strides, padding,
dilation=dilation, number_groups=number_groups,
left_format=lhs_fmt, right_format=rhs_fmt,
name=name, fields=fields)
return [tf2uff.split_node_name_and_output(inp)[0] for inp in inputs]