def test_conf2d_q2(caplog):
caplog.set_level(logging.INFO)
weights_q = QType(16, 1, True)
weights = weights_q.quantize(np.full([1, 1, 2, 2], 1.0))
filt = Conv2DFilterDim(2, 2, 1, 1)
stride = StrideDim(1)
pad = PadDim.valid()
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
in_q = QType(16, 0, True)
calc_q = QType(weights_q.bits + in_q.bits, weights_q.q + in_q.q, True)
qrec = FilterQuantizationRecord(in_qs=[in_q],
out_qs=[in_q],
weights_q=weights_q,
acc_q=calc_q,
calc_q=calc_q)
input_ = in_q.quantize(np.full([1, 2, 2], 1.0))
in_dims = Dim.named(c=1, h=2, w=2).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = conv2d(params,
in_dims,
out_dims[0],
input_,
weights,
None,
qrec=qrec)
output_ = in_q.dequantize(output_)
assert np.array_equal(output_, [[[4.]]])
def test_conf2d_depth():
# TF Lite depthwise convolution
weights = np.arange(9).reshape([3, 3])
weights = np.repeat(weights, 2).reshape([1, 3, 3, 2])
filt = Conv2DFilterDim(3, 3, 2,
1).impose_order(["in_c", "h", "w", "out_c"])
stride = StrideDim(1)
pad = PadDim(0)
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
groups=1,
multiplier=2,
tf_depthwise=True,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
input_ = np.arange(16).reshape([1, 4, 4])
in_dims = Dim.named(c=1, h=4, w=4).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output1 = conv2d(params, in_dims, out_dims[0], input_, weights, None)
assert np.array_equal(output1,
[[[258, 294], [402, 438]], [[258, 294], [402, 438]]])
output2 = conv2d(params,
in_dims,
out_dims[0],
input_,
weights,
None,
allow_faster=False)
assert np.array_equal(output1, output2)
def test_conf2d_normal():
weights = np.arange(9).reshape([1, 1, 3, 3])
filt = Conv2DFilterDim(3, 3, 1, 1)
stride = StrideDim(1)
pad = PadDim(0)
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
input_ = np.arange(16).reshape([1, 4, 4])
in_dims = Dim.named(c=1, h=4, w=4).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
details = {}
output_ = conv2d(params,
in_dims,
out_dims[0],
input_,
weights,
None,
details=details)
# assert details['max_acc'] == 438.0 and details['min_acc'] == 258.0
assert np.array_equal(output_, [[[258, 294], [402, 438]]])
def conv(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
# input N x C x H x W
x = inputs[0]
x_rank = len(x[2].shape)
x_shape = x[2].shape
spatial_size = x_rank - 2
assert spatial_size <= 2, "only 1D and 2D convolutions supported"
# M x C/group x kH x kW
weights = cls.get_constant(inputs[1])
out_c = weights.shape[0]
group = node.attrs.get("group", 1)
in_c = x_shape[1]
filt_in_c = in_c // group
filt_h = weights.shape[2]
filt_w = weights.shape[2]
h = 1 if spatial_size <= 1 else x_shape[2]
w = 1 if spatial_size == 0 else (x_shape[2] if spatial_size == 1 else x_shape[3])
filt_dim = Conv2DFilterDim(filt_h, filt_w,
out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.ONNX_FILTER_ORDER)
if len(inputs) > 2:
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([out_c])
dilations = cls.pad_start_with(node.attrs.get("dilations", [1] * spatial_size), [1], 2)
strides = cls.pad_start_with(node.attrs.get("strides", [1] * spatial_size), [1], 2)
pad_dim = cls.calc_pad_dim(node, spatial_size)
params = Conv2DParameters(valid_name,
filt=filt_dim,
stride=StrideDim(strides[0],
strides[1]),
dilation=DilationDim(dilations[0],
dilations[1]),
groups=group,
padding=pad_dim,
has_bias=True,
in_dims_hint=SparseList([['c', 'h', 'w']]),
out_dims_hint=SparseList([['c', 'h', 'w']]),
constant_store=G.constant_store)
params.weights = weights
params.biases = biases
in_dim = Dim.named_ordered(c=in_c, h=h, w=w)
out_dims = params.get_output_size([in_dim])
pout_dims = ProvisionalDim([x_shape[0]] + out_dims[0].shape)
G.add_edge(NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def test_conf2d_depth_q():
calc_q = QType(32, 9, True)
biases_q = acc_q = out_q = QType(16, 4, True)
weights_q = QType(16, 4, True)
in_q = QType(16, 5, True)
# TF Lite depthwise convolution
biases = np.full([2], 0.5)
qbiases = biases_q.quantize(biases)
weights = np.full([3, 3], 0.5)
weights = np.repeat(weights, 2).reshape([1, 3, 3, 2])
qweights = weights_q.quantize(weights)
filt = Conv2DFilterDim(3, 3, 2,
1).impose_order(["in_c", "h", "w", "out_c"])
stride = StrideDim(1)
pad = PadDim(0)
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
groups=1,
multiplier=2,
tf_depthwise=True,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
qrec = FilterQuantizationRecord(in_qs=[in_q],
out_qs=[out_q],
weights_q=weights_q,
biases_q=biases_q,
acc_q=acc_q,
calc_q=calc_q)
input_ = np.full([1, 4, 4], 2)
qinput_ = in_q.quantize(input_)
in_dims = Dim.named(c=1, h=4, w=4).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = conv2d(params, in_dims, out_dims[0], input_, weights, biases)
qoutput_ = conv2d(params,
in_dims,
out_dims[0],
qinput_,
qweights,
qbiases,
qrec=qrec)
dqoutput_ = out_q.dequantize(qoutput_)
assert np.array_equal(output_, dqoutput_)
def test_conf2d_pad_dilate():
weights = np.arange(9).reshape([1, 1, 3, 3])
filt = Conv2DFilterDim(3, 3, 1, 1)
stride = StrideDim(1)
pad = PadDim.same()
dilation = DilationDim(2)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
input_ = np.arange(16).reshape([1, 4, 4])
in_dims = Dim.named(c=1, h=4, w=4).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = conv2d(params, in_dims, out_dims[0], input_, weights, None, None)
assert np.array_equal(output_, [[[266., 206.], [98., 66.]]])
def test_conf2d_pad():
weights = np.arange(9).reshape([1, 1, 3, 3])
filt = Conv2DFilterDim(3, 3, 1, 1)
stride = StrideDim(1)
pad = PadDim.same()
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
input_ = np.arange(16).reshape([1, 4, 4])
in_dims = Dim.named(c=1, h=4, w=4).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = conv2d(params, in_dims, out_dims[0], input_, weights, None)
assert np.array_equal(output_, [[[73, 121, 154, 103], [171, 258, 294, 186],\
[279, 402, 438, 270], [139, 187, 202, 113]]])
def calc_shapes(node, spatial_size, input_size, kernel_shape):
padding = expand_dim(node.attrs.get('pads', None), 4 - spatial_size * 2, 0)
auto_pad = node.attrs.get('auto_pad', 'NOTSET')
output_shape = expand_dim(node.attrs.get('output_shape', None), 2 - spatial_size, 1)
output_padding = Dim2D(*expand_dim(node.attrs.get('output_padding', None), 2 - spatial_size, 0))
dilations = DilationDim(*expand_dim(node.attrs.get('dilations', None), 2 - spatial_size, 1))
strides = StrideDim(*expand_dim(node.attrs.get('strides', None), 2 - spatial_size, 1))
if output_shape:
total_padding = strides * (input_size - 1) + output_padding + ((kernel_shape - 1) * dilations + 1) - output_shape
if auto_pad == 'SAME_UPPER':
pad_start = total_padding // 2
pad_end = total_padding - pad_start
else:
pad_end = total_padding // 2
pad_start = total_padding - pad_end
padding = PadDim(pad_start.h, pad_end.h, pad_start.w, pad_end.w)
elif auto_pad == 'NOTSET':
assert padding, 'pads not set and auto_pad is NOTSET'
padding = PadDim(*padding)
elif auto_pad == 'VALID':
padding = PadDim.valid()
return padding, dilations, strides, output_padding
def test_conf2d_2_in_2_out_c():
weights = np.arange(4).reshape([1, 2, 2])
weights = np.append(weights, weights, axis=0)
weights = np.append(weights, weights, axis=0)
weights = weights.reshape([2, 2, 2, 2])
filt = Conv2DFilterDim(2, 2, 2, 2)
stride = StrideDim(1)
pad = PadDim.valid()
dilation = DilationDim(1)
params = Conv2DParameters("test",
filt=filt,
stride=stride,
padding=pad,
dilation=dilation,
in_dims_hint=[['c', 'h', 'w']],
out_dims_hint=[['c', 'h', 'w']])
input_ = np.arange(9).reshape([1, 3, 3])
input_ = np.append(input_, input_, axis=0)
in_dims = Dim.named(c=2, h=3, w=3).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = conv2d(params, in_dims, out_dims[0], input_, weights, None, None)
assert np.array_equal(output_, [[[38., 50.], [74., 86.]],\
[[38., 50.], [74., 86.]]])
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(DepthwiseConv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x = cls.remove_known_batch_dimension(G, x, node)
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_in_c, filt_h, filt_w, filt_out_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
# multiplier should match filter
check(filt_dim.out_c == node_opts.DepthMultiplier() * in_c,
"invalid multiplier")
groups = filt_dim.out_c // node_opts.DepthMultiplier()
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
# TFLITE produces single channel input DW convolutions with the
# multiplier equal to the number of out channels. This is just
# a normal convolution and since we don't handle the channel
# multiplier at present (but can) just convert them to normal
# convolutions
convert_to_conv = in_c == 1 and groups == 1
if convert_to_conv:
filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
else:
filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
groups=groups,
multiplier=node_opts.DepthMultiplier(),
has_bias=True,
tf_depthwise=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_DW_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G,
params,
params.filter.actual_shape,
params.filter.get_order_idx('out_c'),
node.input[0],
weights_node,
bias_node,
node.output[0],
opts,
dw_to_pw=convert_to_conv)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=True,
in_dims_hint=SparseList([['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(),
['out_c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G, params, node.input[0], weights_node,
bias_node, node.output[0], opts)
# if opts.get('load_dequantized'):
# weights_node.value, bias_node.value = cls.load_dequantized_filter_parameters(
# node.input, bias_node.value)
# else:
# qrec, weights_node.value, bias_node.value = cls.load_filter_parameters(G, params, node.input, bias_node.value,
# node.output, opts)
# if qrec:
# G.quantization[NodeId(weights_node)].out_qs[0] = qrec.in_qs[1]
# G.quantization[NodeId(bias_node)].out_qs[0] = qrec.in_qs[2]
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
filt_tensor = node.input[1]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
filt_tensor.used = True
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
has_bias = len(inputs) > 2
if has_bias:
node.input[2].used = True
params = Conv2DParameters(node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(),
node_opts.StrideW()),
dilation=DilationDim(
node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
if opts.get('load_dequantized'):
cls.load_dequantized_filter_parameters(params, node.input)
else:
cls.load_filter_parameters(G, params, node.input, node.output,
opts)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size([in_dim])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def conv(cls, node, quantized=False, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
# input N x C x H x W
x = inputs[0]
x_rank = len(x[2].shape)
x_shape = x[2].shape
if x_shape[0] is not None:
real_in_shape = tuple(x_shape.copy())
if x_shape[0] > 1:
# support for multi batch is very limited
batch = x_shape[0]
logger.warning(
f"{valid_name} has a non 1 batch dimension of {batch} -"
" this is not supported by nntool or autotiler kernels")
else:
# if the batch is specified but is 1 then the input will be reshaped
# and the output will have the batch dim set as unknown.
batch = None
else:
real_in_shape = tuple(x_shape[1:])
batch = None
spatial_size = x_rank - 2
assert spatial_size == 2 or spatial_size == 1, "only 1D and 2D convolutions supported"
# Input error checking
undefined = []
if x_shape[1] is None:
# cope with swapped batch and channel due to bad initial reshape
if x_shape[0] == 1:
batch = None
x_shape = [x_shape[1], x_shape[0]] + list(x_shape[2:])
real_in_shape = x_shape[1:]
else:
undefined.append(f"input channel size of filter {valid_name} must be defined.")
if not all(dim is not None for dim in x_shape[-spatial_size:]):
undefined.append(f"input spatial size {x_shape} of filter {valid_name} must be defined.")
if undefined:
raise ValueError(f"{' '.join(undefined)}. You may need to override input dimensions.")
# M x C/group x kH x kW
weights_idx = 3 if quantized else 1
weights_node = inputs[weights_idx][0]
weights_node.name = f'{valid_name}_weights'
weights = cls.get_constant(inputs[weights_idx])
out_c = weights.shape[0]
group = node.attrs.get("group", 1)
in_c = x_shape[1]
filt_in_c = in_c // group
if in_c != weights.shape[1] * group:
raise ValueError(f'node {valid_name} has incorrect input channel '
f'dimension {in_c} expecting {weights.shape[1] * group}')
if spatial_size == 1:
filt_w = weights.shape[-1]
filt_h = h = 1
w = x_shape[-1]
# create a new constant node since we are changing the shape
weights = np.reshape(weights, (out_c, filt_in_c, filt_h, filt_w))
weights_node = ConstantInputParameters(f'{valid_name}_weights', value=weights,
dims=Dim.unnamed(
weights.shape))
cls.record_constant_qrec(inputs[1], weights_node, **kwargs)
else:
filt_h = weights.shape[-2]
filt_w = weights.shape[-1]
h = x_shape[-2]
w = x_shape[-1]
conv_in_shape = (in_c, h, w)
# h = 1 if spatial_size == 1 else (
# x_shape[-2] if x_shape[-2] is not None else 1)
# w = x_shape[-1] if x_shape[-1] is not None else 1
filt_dim = Conv2DFilterDim(filt_h, filt_w,
out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.ONNX_FILTER_ORDER)
biases_idx = 8 if quantized else 2
if len(inputs) > biases_idx:
biases_node = inputs[biases_idx][0]
biases = cls.get_constant(inputs[biases_idx])
else:
biases = np.zeros([out_c], dtype=np.float32)
biases_node = ConstantInputParameters(f'{valid_name}_biases', value=biases,
dims=Dim.unnamed(
biases.shape))
dilations = cls.pad_start_with(node.attrs.get("dilations", []), [1], 2)
strides = cls.pad_start_with(node.attrs.get("strides", []), [1], 2)
pad_dim = cls.calc_pad_dim(node, 4)
if batch is not None:
in_hint = ['n', 'c', 'h', 'w']
out_hint = ['n', 'c', 'h', 'w']
in_dim = Dim.named_ordered(n=batch, c=in_c, h=h, w=w)
ker_in_order = [
['n', 'c', 'h', 'w'],
['out_c', 'in_c', 'h', 'w'],
['out_c']]
ker_out_order = [['n', 'c', 'h', 'w']]
else:
in_hint = ['c', 'h', 'w']
out_hint = ['c', 'h', 'w']
in_dim = Dim.named_ordered(c=in_c, h=h, w=w)
ker_in_order = [
['c', 'h', 'w'],
['out_c', 'in_c', 'h', 'w'],
['out_c']]
ker_out_order = [['c', 'h', 'w']]
params = Conv2DParameters(valid_name,
filt=filt_dim,
stride=StrideDim(strides[0],
strides[1]),
dilation=DilationDim(dilations[0],
dilations[1]),
batch=batch,
groups=group,
padding=pad_dim,
ker_in_order=ker_in_order,
ker_out_order=ker_out_order,
has_bias=True,
in_dims_hint=[in_hint,
cls.ONNX_FILTER_ORDER, ['c']],
out_dims_hint=[out_hint])
if quantized:
qrecs = kwargs['qrecs']
x_zp = cls.get_constant(inputs[2])
x_scale = cls.get_constant(inputs[1])
x_qtype = QType(dtype=x_zp.dtype, scale=x_scale, zero_point=x_zp)
w_zp = cls.get_constant(inputs[5])
w_scale = cls.get_constant(inputs[4])
weights_node.qtype = w_qtype = QType(
dtype=w_zp.dtype, scale=w_scale,
zero_point=w_zp, quantized_dimension=0 if len(w_scale) > 1 else None)
o_zp = cls.get_constant(inputs[7])
o_scale = cls.get_constant(inputs[6])
o_qtype = QType(dtype=o_zp.dtype, scale=o_scale, zero_point=o_zp)
biases_node.qtype = b_qtype = QType(
dtype=biases.dtype, scale=w_scale*x_scale)
qrecs[NodeId(params)] = QRec.scaled(
in_qs=[x_qtype, w_qtype, b_qtype],
out_qs=[o_qtype],
)
else:
o_qtype = None
w_dim = Dim.named_ordered(
out_c=out_c, in_c=filt_in_c, h=filt_h, w=filt_w)
b_dim = Dim.named_ordered(c=out_c)
out_dims = params.get_output_size([in_dim, w_dim, b_dim])
G.add_edge(NNEdge(from_node=weights_node,
to_node=params, from_idx=0, to_idx=1))
G.add_edge(NNEdge(from_node=biases_node,
to_node=params, from_idx=0, to_idx=2))
# check if input needs a reshape
if conv_in_shape != real_in_shape:
r1_params = ReshapeParameters(f'{valid_name}_reshape_in',
old_shape=Dim.unnamed(real_in_shape),
shape=Dim.unnamed(conv_in_shape))
G.add_edge(
NNEdge(from_node=x[0], to_node=r1_params, from_idx=x[1], to_idx=0))
G.add_edge(NNEdge(from_node=r1_params,
to_node=params, from_idx=0, to_idx=0))
else:
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
# check if output needs a reshape
if spatial_size == 1:
if batch is not None:
oned_out_shape = [batch, out_dims[0].c, out_dims[0].w]
pout_dims = ProvisionalDim(oned_out_shape)
else:
oned_out_shape = [out_dims[0].c, out_dims[0].w]
pout_dims = ProvisionalDim([None] + oned_out_shape)
r2_params = ReshapeParameters(f'{valid_name}_reshape_out',
old_shape=out_dims[0],
shape=Dim.unnamed(oned_out_shape))
G.add_edge(NNEdge(from_node=params,
to_node=r2_params, from_idx=0, to_idx=0))
params = r2_params
else:
pout_dims = ProvisionalDim([batch] + out_dims[0].shape)
all_nodes[node.output[0]] = (params, 0, pout_dims, o_qtype)
return params
def version_1(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(Conv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x = cls.remove_known_batch_dimension(G, x, node)
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
weights_node = filt[0]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_out_c, filt_h, filt_w, filt_in_c = tuple(filt_shape)
# get filter dimensions
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([filt_out_c]),
value=np.zeros([filt_out_c], dtype=np.float32)) # TODO - check
groups = in_c // filt_in_c
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
groups=groups,
padding=pad,
has_bias=True,
in_dims_hint=[['h', 'w', 'c'],
cls.TF_LITE_FILTER_ORDER.copy(), ['out_c']],
out_dims_hint=[['h', 'w', 'c']])
G.add_edge(NNEdge(from_node=weights_node, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
cls.new_load_filter_parameters(G, params, params.filter.actual_shape,
params.filter.get_order_idx('out_c'),
node.input[0], weights_node, bias_node,
node.output[0], opts)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size(
[in_dim,
Dim.unnamed(filt_dim.shape),
Dim.unnamed([filt_out_c])])
pout_dims = ProvisionalDim([None] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
oparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (oparams, 0, pout_dims)
return oparams
def _common(cls, node: TFLiteNode, **kwargs):
node_opts = node.get_options(DepthwiseConv2DOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
inputs = [all_nodes[t] for t in node.input]
x = inputs[0]
x_shape = x[2].shape
in_b, h, w, in_c = tuple(x_shape)
filt = inputs[1]
filt_tensor = node.input[1]
filt_shape = filt[2].shape
# ['in_c', 'h', 'w', 'out_c']
filt_in_c, filt_h, filt_w, filt_out_c = tuple(filt_shape)
# get filter dimensions
filt_tensor.used = True
if filt_h > h or filt_w > w:
LOG.warning(
"Filter %s of shape [%dx%d] is bigger than input of shape [%dx%d]",
node.name, filt_h, filt_w, h, w)
filt_dim = Conv2DFilterDim(filt_h, filt_w, filt_out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
# multiplier should match filter
check(filt_dim.out_c == node_opts.DepthMultiplier() * in_c,
"invalid multiplier")
groups = filt_dim.out_c // node_opts.DepthMultiplier()
# compute padding
pad = cls.get_tf_padding(node_opts.Padding())
# does it have biases
has_bias = len(inputs) > 2
if has_bias:
node.input[2].used = True
# TFLITE produces single channel input DW convolutions with the
# multiplier equal to the number of out channels. This is just
# a normal convolution and since we don't handle the channel
# multiplier at present (but can) just convert them to normal
# convolutions
convert_to_conv = in_c == 1 and groups == 1
if convert_to_conv:
filt_dim.impose_order(cls.TF_LITE_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
else:
filt_dim.impose_order(cls.TF_LITE_DW_FILTER_ORDER)
params = Conv2DParameters(
node.name,
filt=filt_dim,
stride=StrideDim(node_opts.StrideH(), node_opts.StrideW()),
dilation=DilationDim(node_opts.DilationHFactor(),
node_opts.DilationWFactor()),
padding=pad,
groups=groups,
multiplier=node_opts.DepthMultiplier(),
has_bias=has_bias,
tf_depthwise=True,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]),
constant_store=G.constant_store)
if opts.get('load_dequantized'):
cls.load_dequantized_filter_parameters(params,
node.input,
convert_to_conv,
is_dw=True)
else:
cls.load_filter_parameters(G,
params,
node.input,
node.output,
opts,
converted_to_conv=convert_to_conv)
in_dim = Dim.named_ordered(h=h, w=w, c=in_c)
out_dims = params.get_output_size([in_dim])
pout_dims = ProvisionalDim([in_b] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
params = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def conv(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
inputs = [all_nodes[inp] for inp in node.input]
# input N x C x H x W
x = inputs[0]
x_rank = len(x[2].shape)
x_shape = x[2].shape
spatial_size = x_rank - 2
assert spatial_size == 2 or spatial_size == 1, "only 1D and 2D convolutions supported"
# M x C/group x kH x kW
weights_node = inputs[1][0]
weights_node.name = f'{valid_name}_weights'
weights = cls.get_constant(inputs[1])
out_c = weights.shape[0]
group = node.attrs.get("group", 1)
in_c = x_shape[1]
filt_in_c = in_c // group
if in_c != weights.shape[1] * group:
raise ValueError(
f'node {valid_name} has incorrect input channel '
f'dimension {in_c} expecting {weights.shape[1] * group}')
if spatial_size == 1:
filt_w = weights.shape[-1]
filt_h = 1
# create a new constant node since we are changing the shape
weights = np.reshape(weights, (out_c, filt_in_c, filt_h, filt_w))
weights_node = ConstantInputParameters(
f'{valid_name}_weights',
value=weights,
dims=Dim.unnamed(weights.shape),
constant_store=G.constant_store)
else:
filt_h = weights.shape[-2]
filt_w = weights.shape[-1]
h = 1 if spatial_size == 1 else x_shape[-2]
w = x_shape[-1]
filt_dim = Conv2DFilterDim(filt_h, filt_w, out_c, in_c=filt_in_c)
filt_dim = filt_dim.impose_order(cls.ONNX_FILTER_ORDER)
if len(inputs) > 2:
biases_node = inputs[2][0]
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([out_c], dtype=np.float32)
biases_node = ConstantInputParameters(
f'{valid_name}_biases',
value=biases,
dims=Dim.unnamed(biases.shape),
constant_store=G.constant_store)
dilations = cls.pad_start_with(node.attrs.get("dilations", []), [1], 2)
strides = cls.pad_start_with(node.attrs.get("strides", []), [1], 2)
pad_dim = cls.calc_pad_dim(node, 4)
params = Conv2DParameters(valid_name,
filt=filt_dim,
stride=StrideDim(strides[0], strides[1]),
dilation=DilationDim(dilations[0],
dilations[1]),
groups=group,
padding=pad_dim,
has_bias=True,
in_dims_hint=[['c', 'h', 'w'],
cls.ONNX_FILTER_ORDER, ['c']],
out_dims_hint=[['c', 'h', 'w']],
constant_store=G.constant_store)
in_dim = Dim.named_ordered(c=in_c, h=h, w=w)
w_dim = Dim.named_ordered(out_c=out_c,
in_c=filt_in_c,
h=filt_h,
w=filt_w)
b_dim = Dim.named_ordered(c=out_c)
out_dims = params.get_output_size([in_dim, w_dim, b_dim])
G.add_edge(
NNEdge(from_node=weights_node,
to_node=params,
from_idx=0,
to_idx=1))
G.add_edge(
NNEdge(from_node=biases_node, to_node=params, from_idx=0,
to_idx=2))
if spatial_size == 1:
oned_in_shape = [in_c, w]
twod_in_shape = [in_c, 1, w]
oned_out_shape = [out_dims[0].c, out_dims[0].w]
r1_params = ReshapeParameters(f'{valid_name}_reshape2d',
old_shape=Dim.unnamed(oned_in_shape),
shape=Dim.unnamed(twod_in_shape))
r2_params = ReshapeParameters(f'{valid_name}_reshape1d',
old_shape=out_dims[0],
shape=Dim.unnamed(oned_out_shape))
G.add_edge(
NNEdge(from_node=x[0],
to_node=r1_params,
from_idx=x[1],
to_idx=0))
G.add_edge(
NNEdge(from_node=r1_params,
to_node=params,
from_idx=0,
to_idx=0))
G.add_edge(
NNEdge(from_node=params,
to_node=r2_params,
from_idx=0,
to_idx=0))
pout_dims = ProvisionalDim([x_shape[0]] + oned_out_shape)
all_nodes[node.output[0]] = (r2_params, 0, pout_dims)
return r2_params
else:
pout_dims = ProvisionalDim([x_shape[0]] + out_dims[0].shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1],
to_idx=0))
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params