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 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_activation():
in_q = QType(16, 13, True)
input_ = in_q.quantize(np.array([-1.2, 0.5, 0.5, -0.6])).reshape([4, 1, 1])
in_dims = Dim.named(c=4, h=1, w=1).impose_order(['c', 'h', 'w'])
params = ActivationParameters("test")
qrec = QuantizationRecord([in_q], [in_q])
out_dims = params.get_output_size([in_dims])
output_ = activation(params, in_dims, out_dims[0], input_, qrec=qrec)
output_ = in_q.dequantize(output_)
assert np.array_equal(output_, [[[0]], [[0.5]], [[0.5]], [[0]]])
def test_max_pool_q():
filt = PoolFilterDim(2, 2)
stride = StrideDim(1)
pad = PadDim(0)
params = PoolingParameters("test",
filt=filt,
stride=stride,
padding=pad,
pool_type="max")
in_q = QType(16, 0, True)
qrec = QuantizationRecord([in_q], [in_q])
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_ = max_pool(params, in_dims, out_dims[0], input_)
output_ = in_q.dequantize(output_)
assert np.array_equal(output_, [[[4, 5], [7, 8]]])
