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_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_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 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 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.]]])
