def test_fc():
filt = FcFilterDim(3, 3, 3, 1)
params = FcParameters("test", filt=filt)
weights_q = QType(16, 2, True)
in_q = QType(16, 2, True)
acc_q = QType(16, 4, True)
calc_q = QType(16, 4, True)
qrec = FilterQuantizationRecord(in_qs=[in_q],
out_qs=[in_q],
calc_q=calc_q,
acc_q=acc_q,
biases_q=None,
weights_q=weights_q)
weights = weights_q.quantize(np.full([3, 1, 3, 3], 1.0))
input_ = in_q.quantize(np.arange(9)).reshape([1, 3, 3])
in_dims = Dim.named(c=1, h=3, w=3).impose_order(['c', 'h', 'w'])
out_dims = params.get_output_size([in_dims])
output_ = linear(params,
in_dims,
out_dims[0],
input_,
weights,
None,
qrec=qrec)
output_ = in_q.dequantize(output_)
assert np.array_equal(output_, [[[36]], [[36]], [[36]]])
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
trans_a = node.attrs.get('transA', 0)
trans_b = node.attrs.get('transB', 0)
alpha = node.attrs.get('alpha', 1.0)
beta = node.attrs.get('beta', 1.0)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
y_shape = y[2].shape
real_x_shape = cls._get_real_dim(x_shape)
real_y_shape = cls._get_real_dim(y_shape)
real_x_shape = [
real_x_shape[1], real_x_shape[0]
] if len(real_x_shape) == 2 and trans_a else real_x_shape
real_y_shape = [
real_y_shape[1], real_y_shape[0]
] if len(real_y_shape) == 2 and trans_b else real_y_shape
if not cls.is_linear(y, real_x_shape, real_y_shape) or trans_a:
raise ValueError(
"GEMM is only currently supported for operations that map onto a linear kernel"
)
if len(inputs) > 2:
has_bias = True
biases = cls.get_constant(inputs[2])
else:
biases = None
has_bias = False
filt_dim = FcFilterDim(real_y_shape[1], real_x_shape[0])
weights = cls.get_constant(y) * alpha
if not trans_b:
weights = np.transpose(weights, [1, 0])
params = FcParameters(valid_name,
filt=filt_dim,
has_bias=has_bias,
in_dims_hint=SparseList([['c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
params.weights = weights
params.biases = biases * beta
out_dims = params.get_output_size([Dim.unnamed(real_x_shape)])
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if isinstance(x[2], ProvisionalDim):
out_dim = x[2].infer_mapping(out_dims[0].shape)
else:
out_dim = out_dims[0]
all_nodes[node.output[0]] = (params, 0, out_dim)
return params
def add_fully_connected(G,
tensors,
name,
subgraph,
_,
op,
load_tensors=False,
dequantize=False):
fc_opts = FullyConnectedOptions.FullyConnectedOptions()
fc_opts.Init(op.BuiltinOptions().Bytes, op.BuiltinOptions().Pos)
# get filter dimensions
inp = get_input_size(tensors, subgraph, op, 0)
check(inp[0] == 1, "Multi batch not supported")
filt = get_input_size(tensors, subgraph, op, 1, order=TF_LITE_FC_ORDER)
check(filt['sz'] == reduce(lambda i, j: i * j, inp, 1),
"filter doesn't match input size")
# in the case we get an input of 1 batch with everything flattened fill h and w with 1
if len(inp) == 2:
inp = {'h': 1, 'w': 1, 'c': inp[1]}
elif len(inp) == 4:
inp = {'h': inp[1], 'w': inp[2], 'c': inp[3]}
else:
raise NotImplementedError('FC input size not implemented')
filt_dim = FcFilterDim(inp['h'],
inp['w'],
filt['out_c'],
in_c=inp['c'],
order=TF_LITE_FC_EXP_ORDER)
# does it have biases
has_bias = op.InputsLength() > 2
node = FcParameters(name,
filt=filt_dim,
has_bias=has_bias,
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
if load_tensors:
node.weights = get_tensor(G.model,
tensors,
subgraph,
op,
1,
dequantize=dequantize)
if has_bias:
node.biases = get_tensor(G.model,
tensors,
subgraph,
op,
2,
dequantize=dequantize)
return fuse_activation(G, fc_opts, name, node)
def _common(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]
x = inputs[0]
x_shape = cls._get_real_dim(x[2].shape)
y = inputs[1]
y_shape = cls._get_real_dim(y[2].shape)
if cls.is_linear(y, x_shape, y_shape):
filt_dim = FcFilterDim(y_shape[1], x_shape[0])
weights = np.transpose(cls.get_constant(y), [1, 0])
weights_params = ConstantInputParameters(
f'{valid_name}_weights',
dims=Dim.unnamed([y_shape[1], x_shape[0]]),
value=weights)
params = FcParameters(valid_name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList([['c'],
['out_c', 'in_c'],
['out_c']]),
out_dims_hint=SparseList([['c']]),
constant_store=G.constant_store)
out_dims = params.get_output_size([Dim.unnamed(x_shape)])
biases_params = ConstantInputParameters(
f'{valid_name}_biases',
dims=Dim.unnamed([y_shape[1]]),
value=np.zeros((y_shape[1]), dtype=np.float32))
G.add_edge(
NNEdge(from_node=weights_params, to_node=params, to_idx=1))
G.add_edge(
NNEdge(from_node=biases_params, to_node=params, to_idx=2))
else:
params = MatMulOpParameters(valid_name)
out_dims = params.get_output_size(
[Dim.unnamed(x_shape),
Dim.unnamed(y_shape)])
G.add_edge(
NNEdge(from_node=y[0], to_node=params, from_idx=y[1],
to_idx=1))
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
pout_dims = x[2].infer_mapping(out_dims[0].shape)
all_nodes[node.output[0]] = (params, 0, pout_dims)
return params
def add_fully_connected(out_graph, routes, idx, l):
activation = get_str(l, 'activation', default="logistic")
filter_c = get_int(l, 'output')
node_name = "{}_{}".format(l['type'], idx)
if activation is None:
routes['in'][idx], routes['out'][idx] =\
out_graph.add_operator(node_name, FcParameters(FcFilterDim(filter_c), has_bias=True))
else:
activation = DARKNET_ACTIVATION_TYPES[activation]
routes['in'][idx], routes['out'][idx] =\
out_graph.add_operators(
node_name,
[FcParameters(FcFilterDim(filter_c), has_bias=True),\
ActivationParameters(activation)]
)
return True
def _common(cls, node, **kwargs):
node_opts = node.get_options(FullyConnectedOptions)
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
x_known_shape = x[2].known_shape
inp_sz = np.prod(np.array(x_known_shape))
weights = inputs[1]
weights_shape = weights[2].shape
out_c = weights_shape[0]
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz == inp_sz, "filter doesn't match input size")
if len(node.input) > 2:
node.input[2].used = True
keep_dims = node_opts.KeepNumDims()
in_hint = [str(i) for i in range(len(x_known_shape) - 1)] + ['c']
out_hint = in_hint.copy() if keep_dims else ['c']
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList([in_hint]),
out_dims_hint=SparseList([out_hint]),
constant_store=G.constant_store,
keep_dims=keep_dims)
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)
if x_shape[0] is None:
out_shape = x_shape[:-1:] + [out_c] if keep_dims else [
x_shape[0], out_c
]
else:
out_shape = x_known_shape[:-1:] + [out_c] if keep_dims else [out_c]
pout_dims = ProvisionalDim(out_shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def _handle(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]
x = inputs[0]
x_shape = cls._get_real_dim(x[2].shape)
y_idx = 3 if quantized else 1
y = inputs[y_idx]
y_shape = cls._get_real_dim(y[2].shape)
if quantized:
qrecs = kwargs['qrecs']
x_zp = cls.get_constant(inputs[2])
x_scale = cls.get_constant(inputs[1])
if len(x_scale) > 1:
raise NotImplementedError('QMatMul scales must be scalar')
x_qtype = QType(dtype=x_zp.dtype, scale=x_scale, zero_point=x_zp)
y_zp = cls.get_constant(inputs[5])
y_scale = cls.get_constant(inputs[4])
if len(y_scale) > 1:
raise NotImplementedError('QMatMul scales must be scalar')
y_qtype = QType(dtype=y_zp.dtype, scale=y_scale, zero_point=y_zp)
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)
else:
o_qtype = None
if cls.is_linear(y, x_shape, y_shape):
filt_dim = FcFilterDim(y_shape[1], x_shape[0])
weights = np.transpose(cls.get_constant(y), [1, 0])
weights_params = ConstantInputParameters(
f'{valid_name}_weights',
dims=Dim.unnamed([y_shape[1], x_shape[0]]),
value=weights)
cls.record_constant_qrec(y, weights_params, **kwargs)
params = FcParameters(
valid_name,
filt=filt_dim,
has_bias=True,
# in_dims_hint=[
# ['c'], ['out_c', 'in_c'], ['out_c']],
in_dims_hint=[None, ['out_c', 'in_c'], ['out_c']],
out_dims_hint=[['c']])
out_dims = params.get_output_size([Dim.unnamed(x_shape)])
biases_params = ConstantInputParameters(
f'{valid_name}_biases',
dims=Dim.unnamed([y_shape[1]]),
value=np.zeros((y_shape[1]), dtype=np.float32))
G.add_edge(
NNEdge(from_node=weights_params, to_node=params, to_idx=1))
G.add_edge(
NNEdge(from_node=biases_params, to_node=params, to_idx=2))
if quantized:
weights_params.qtype = y_qtype
qrecs[NodeId(params)] = QRec.scaled(
in_qs=[x_qtype, y_qtype, None],
out_qs=[o_qtype],
)
else:
params = MatMulTransposedParameters(valid_name)
trans_shape = [i for i in range(len(y_shape))]
temp = trans_shape[-1]
trans_shape[-1] = trans_shape[-2]
trans_shape[-2] = temp
trans2 = TransposeParameters(f'{valid_name}_tin2',
transpose=tuple(trans_shape))
out_dims = params.get_output_size([
Dim.unnamed(x_shape),
Dim.unnamed(y_shape[:-2] + y_shape[-2:][::-1])
])
G.add_edge(
NNEdge(from_node=y[0], to_node=trans2, from_idx=y[1],
to_idx=0))
G.add_edge(
NNEdge(from_node=trans2, to_node=params, from_idx=0, to_idx=1))
biases_params = ConstantInputParameters(
f'{valid_name}_biases',
dims=Dim.unnamed([out_dims[0].shape[1]]),
value=np.zeros((out_dims[0].shape[1]), dtype=np.float32))
G.add_edge(
NNEdge(from_node=biases_params, to_node=params, to_idx=2))
if quantized:
qrecs[NodeId(trans2)] = QRec.scaled(
in_qs=[y_qtype],
out_qs=[y_qtype],
)
qrecs[NodeId(params)] = QRec.scaled(
in_qs=[x_qtype, y_qtype],
out_qs=[o_qtype],
)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
pout_dims = x[2].infer_mapping(out_dims[0].shape)
all_nodes[node.output[0]] = (params, 0, pout_dims, o_qtype)
return params
def _common(cls, node, **kwargs):
node_opts = node.get_options(FullyConnectedOptions)
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
x_known_shape = x[2].known_shape
inp_sz = np.prod(np.array(x_known_shape))
weights = inputs[1]
weights_node = weights[0]
weights_shape = weights[2].shape
out_c = weights_shape[0]
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz == inp_sz, "filter doesn't match input size")
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([out_c]),
value=np.zeros([out_c], dtype=np.float32)) # TODO - check
keep_dims = node_opts.KeepNumDims()
in_hint = [str(i) for i in range(len(x_known_shape) - 1)] + ['c']
out_hint = in_hint.copy() if keep_dims else ['c']
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=SparseList(
[in_hint, ['out_c', 'in_c'], ['out_c']]),
out_dims_hint=SparseList([out_hint]),
constant_store=G.constant_store,
keep_dims=keep_dims)
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]
if x_shape[0] is None:
out_shape = x_shape[:-1:] + [out_c] if keep_dims else [
x_shape[0], out_c
]
else:
out_shape = x_known_shape[:-1:] + [out_c] if keep_dims else [out_c]
pout_dims = ProvisionalDim(out_shape)
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
G = kwargs['G']
valid_name = kwargs['valid_name']
trans_a = node.attrs.get('transA', 0)
trans_b = node.attrs.get('transB', 0)
alpha = node.attrs.get('alpha', 1.0)
beta = node.attrs.get('beta', 1.0)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
y_shape = y[2].shape
real_x_shape = cls._get_real_dim(x_shape)
real_y_shape = cls._get_real_dim(y_shape)
real_x_shape = [
real_x_shape[1], real_x_shape[0]
] if len(real_x_shape) == 2 and trans_a else real_x_shape
real_y_shape = [
real_y_shape[1], real_y_shape[0]
] if len(real_y_shape) == 2 and trans_b else real_y_shape
if not cls.is_linear(y, real_x_shape, real_y_shape) or trans_a:
raise ValueError(
"GEMM is only currently supported for operations that map onto a linear kernel"
)
if len(inputs) > 2:
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([real_y_shape[1]], dtype=np.float32)
filt_dim = FcFilterDim(real_y_shape[1], real_x_shape[0])
# always create new constants since they may be modified by this not and could be linked elsewhere
weights = cls.get_constant(y) * alpha
if not trans_b:
weights = np.transpose(weights, [1, 0])
weights_params = ConstantInputParameters(f'{valid_name}_weights',
dims=Dim.unnamed(
weights.shape),
value=weights)
biases = biases * beta
biases_params = ConstantInputParameters(f'{valid_name}_biases',
dims=Dim.unnamed(biases.shape),
value=biases)
params = FcParameters(
valid_name,
filt=filt_dim,
has_bias=True,
# in_dims_hint=[['c']],
in_dims_hint=[None, ['out_c', 'in_c'], ['out_c']],
out_dims_hint=[['c']],
constant_store=G.constant_store)
G.add_edge(NNEdge(from_node=weights_params, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=biases_params, to_node=params, to_idx=2))
out_dims = params.get_output_size([Dim.unnamed(real_x_shape)])
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if isinstance(x[2], ProvisionalDim):
out_dim = x[2].infer_mapping(out_dims[0].shape)
else:
out_dim = out_dims[0]
all_nodes[node.output[0]] = (params, 0, out_dim)
return params
def _common(cls, node, **kwargs):
all_nodes = kwargs['all_nodes']
trans_a = node.attrs.get('transA', 0)
trans_b = node.attrs.get('transB', 0)
alpha = node.attrs.get('alpha', 1.0)
beta = node.attrs.get('beta', 1.0)
inputs = [all_nodes[inp] for inp in node.input]
x = inputs[0]
x_shape = x[2].shape
y = inputs[1]
y_shape = y[2].shape
real_x_shape = cls._get_real_dim(x_shape)
real_y_shape = cls._get_real_dim(y_shape)
real_x_shape = [real_x_shape[1], real_x_shape[0]] if len(
real_x_shape) == 2 and trans_a else real_x_shape
real_y_shape = [real_y_shape[1], real_y_shape[0]] if len(
real_y_shape) == 2 and trans_b else real_y_shape
if not cls.is_linear(y, real_x_shape, real_y_shape) or trans_a:
if alpha != 1.0 or beta != 1.0:
raise NotImplementedError('Alpha and Beta not implemented on pure matmul GEMM')
return cls._import_as_matmul(node, inputs, x, y, real_x_shape, real_y_shape,
trans_a, trans_b, alpha, beta, **kwargs)
G = kwargs['G']
valid_name = kwargs['valid_name']
if len(inputs) > 2:
biases = cls.get_constant(inputs[2])
else:
biases = np.zeros([real_y_shape[1]], dtype=np.float32)
filt_dim = FcFilterDim(real_y_shape[1], real_x_shape[0])
# always create new constants since they may be modified by this not and could be linked elsewhere
weights = cls.get_constant(y) * alpha
if not trans_b:
weights = np.transpose(weights, [1, 0])
weights_params = ConstantInputParameters(
f'{valid_name}_weights', dims=Dim.unnamed(weights.shape), value=weights)
if y[3]:
if alpha == 1.0:
cls.record_constant_qrec(y, weights_params, **kwargs)
else:
raise NotImplementedError("qtype on Gemm with alpha != 1.0")
biases = biases * beta
biases_params = ConstantInputParameters(
f'{valid_name}_biases', dims=Dim.unnamed(biases.shape), value=biases)
if len(inputs) > 2 and inputs[2][3]:
if beta == 1.0:
cls.record_constant_qrec(inputs[2], biases_params, **kwargs)
else:
raise NotImplementedError("qtype on Gemm with beta != 1.0")
params = FcParameters(valid_name, filt=filt_dim, has_bias=True,
# in_dims_hint=[['c']],
in_dims_hint=[
None, ['out_c', 'in_c'], ['out_c']],
out_dims_hint=[['c']])
G.add_edge(NNEdge(from_node=weights_params, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=biases_params, to_node=params, to_idx=2))
out_dims = params.get_output_size([Dim.unnamed(real_x_shape)])
G.add_edge(
NNEdge(from_node=x[0], to_node=params, from_idx=x[1], to_idx=0))
if isinstance(x[2], ProvisionalDim):
out_dim = x[2].infer_mapping(out_dims[0].shape)
else:
out_dim = out_dims[0]
all_nodes[node.output[0]] = (params, 0, out_dim, None)
return params
def _common(cls, node, **kwargs):
node_opts = node.get_options(FullyConnectedOptions)
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
x_known_shape = x[2].known_shape
inp_sz = np.prod(np.array(x_known_shape))
weights = inputs[1]
weights_node = weights[0]
weights_shape = weights[2].shape
assert len(weights_shape
) == 2, f'bad filter shape {weights_shape} in {node.name}'
out_c = weights_shape[0]
batch_size = inp_sz // weights_shape[1]
if batch_size > 1:
filt_dim = FcFilterDim(weights_shape[0], weights_shape[1])
else:
filt_dim = FcFilterDim(weights_shape[0], *x_known_shape)
node.input[1].used = True
check(filt_dim.sz * batch_size == inp_sz,
"filter doesn't match input size")
if len(inputs) > 2:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(
f'{node.name}_bias',
dims=Dim.unnamed([out_c]),
value=np.zeros([out_c], dtype=np.float32)) # TODO - check
keep_dims = node_opts.KeepNumDims()
if batch_size > 1:
if keep_dims:
raise ValueError(
f'keep dims on Fully Connected {node.name} with batch size > 1 is not supported'
)
# add a reshape to force the size of the input to batch * in_c
input_shape = (batch_size, weights_shape[1])
if x_known_shape != input_shape:
rparams = ReshapeParameters(
G.unique_name(f'{node.name}_batch'),
old_shape=Dim.unnamed(x_known_shape),
shape=Dim.unnamed(input_shape))
G.add_edge(
NNEdge(from_node=x[0],
to_node=rparams,
from_idx=x[1],
to_idx=0))
link = (rparams, 0)
else:
link = x
# the batched linear is transpose(weights . transpose(input))
params = MatMulOpParameters(node.name)
params.transpose_in = [None, (1, 0), None]
params.transpose_out = [(1, 0)]
cls.new_load_filter_parameters(G, params, weights_shape, 0,
node.input[0], weights_node,
bias_node, node.output[0], opts)
G.add_edge(
NNEdge(from_node=link[0],
to_node=params,
from_idx=link[1],
to_idx=1))
G.add_edge(NNEdge(from_node=weights_node, to_node=params,
to_idx=0))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
out_shape = [batch_size, out_c]
else:
# in_hint = [[str(i) for i in range(len(x_known_shape) - 1)] + ['c'],
# ['out_c', 'in_c'], ['out_c']]
in_hint = [None, ['out_c', 'in_c'], ['out_c']]
out_hint = in_hint.copy() if keep_dims else ['c']
ker_in_order = None
ker_out_order = None
link = (x[0], x[1])
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
in_dims_hint=in_hint,
out_dims_hint=[out_hint],
ker_in_order=ker_in_order,
ker_out_order=ker_out_order,
batch_size=batch_size,
constant_store=G.constant_store,
keep_dims=keep_dims)
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)
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))
G.add_edge(
NNEdge(from_node=link[0],
to_node=params,
from_idx=link[1],
to_idx=0))
# handle keep_dims
if x_shape[0] is None:
if keep_dims:
out_shape = x_shape[:-1:] + [out_c]
else:
out_shape = [None, out_c]
else:
if keep_dims:
out_shape = [None] + x_shape[1:-1:] + [out_c]
else:
out_shape = [None, out_c]
pout_dims = ProvisionalDim(out_shape)
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
def _common(cls, node, **kwargs):
node_opts = node.get_options(FullyConnectedOptions)
G = kwargs['G']
opts = kwargs['opts']
all_nodes = kwargs['all_nodes']
keep_dims = node_opts.KeepNumDims()
# check(not keep_dims,
# f'keep dims on Fully Connected {node.name} is not supported')
inputs = [all_nodes[t] if t is not None else None for t in node.input]
x = inputs[0]
x_shape = x[2]
x_known_shape = x_shape.known_shape
inp_sz = np.prod(np.array(x_known_shape))
weights = inputs[1]
weights_node = weights[0]
weights_shape = weights[2].shape
check(
len(weights_shape) == 2,
f'bad filter shape {weights_shape} in {node.name}')
out_c = weights_shape[0]
batch_size = inp_sz // weights_shape[1]
keep_dims = node_opts.KeepNumDims()
if keep_dims:
if x_shape.shape[-1] != weights_shape[1]:
raise ValueError(
f'Keep dims set on {node.name} but last input dimension does not match weights'
)
out_shape = x_shape.shape.copy()
out_shape[-1] = out_c
elif batch_size > 1:
out_shape = (batch_size, out_c)
else:
out_shape = (None, out_c)
real_out_shape = tuple(dim for dim in out_shape if dim is not None)
filt_dim = FcFilterDim(weights_shape[0], weights_shape[1])
node.input[1].used = True
check(filt_dim.sz * batch_size == inp_sz,
"filter doesn't match input size")
if len(inputs) > 2 and inputs[2] is not None:
bias = inputs[2]
bias_node = bias[0]
else:
bias_node = ConstantInputParameters(f'{node.name}_bias',
dims=Dim.unnamed([out_c]),
value=np.zeros(
[out_c], dtype=np.float32))
if batch_size > 1:
# add a reshape to force the size of the input to batch * in_c
input_shape = (batch_size, weights_shape[1])
if x_known_shape != input_shape:
rparams = ReshapeParameters(
G.unique_name(f'{node.name}_batch'),
old_shape=Dim.unnamed(x_known_shape),
shape=Dim.unnamed(input_shape))
G.add_edge(
NNEdge(from_node=x[0],
to_node=rparams,
from_idx=x[1],
to_idx=0))
link = (rparams, 0)
else:
link = x
# the batched linear is ([NxM] . [MxK]) + [K]
params = MatMulTransposedParameters(node.name)
cls.new_load_filter_parameters(G, params, weights_shape, 0,
node.input[0], weights_node,
bias_node, node.output[0], opts)
trans2 = TransposeParameters(G.unique_name(f'{node.name}_tin2'),
transpose=(1, 0))
G.add_edge(
NNEdge(from_node=link[0], to_node=params, from_idx=link[1]))
G.add_edge(NNEdge(from_node=weights_node, to_node=params,
to_idx=1))
#G.add_edge(NNEdge(from_node=trans2, to_node=params, to_idx=1))
G.add_edge(NNEdge(from_node=bias_node, to_node=params, to_idx=2))
fc_shape = (batch_size, out_c)
else:
ker_in_order = None
ker_out_order = None
link = (x[0], x[1])
params = FcParameters(node.name,
filt=filt_dim,
has_bias=True,
ker_in_order=ker_in_order,
ker_out_order=ker_out_order,
batch_size=batch_size,
keep_dims=keep_dims)
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)
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))
G.add_edge(
NNEdge(from_node=link[0],
to_node=params,
from_idx=link[1],
to_idx=0))
fc_shape = (out_c, )
pout_dims = ProvisionalDim(out_shape)
aparams = cls.fuse_activation(node_opts, node.name, params, **kwargs)
if real_out_shape != fc_shape:
rparams = ReshapeParameters(G.unique_name(f'{node.name}_keepdims'),
old_shape=fc_shape,
shape=real_out_shape)
G.add_edge(NNEdge(from_node=aparams, to_node=rparams))
aparams = rparams
all_nodes[node.output[0]] = (aparams, 0, pout_dims)
return params
