def __init__(self):
super().__init__()
self.act = QuantIdentity(bit_width=7,
act_quant=ShiftedUint8ActPerTensorFloat,
return_quant_tensor=True)
self.pool = QuantMaxPool2d(kernel_size=KERNEL_SIZE,
stride=KERNEL_SIZE,
return_quant_tensor=False)
def __init__(self):
super().__init__()
self.conv1 = QuantConv2d(
out_channels=OUT_CH,
in_channels=IN_CH,
kernel_size=KERNEL_SIZE,
bias=False,
weight_quant=Int8WeightPerTensorFixedPoint,
input_quant=Int8ActPerTensorFixedPoint,
output_quant=Int8ActPerTensorFixedPoint,
return_quant_tensor=True)
self.max_pool = QuantMaxPool2d(kernel_size=2, stride=2, return_quant_tensor=False)
self.conv1.weight.data.uniform_(-0.01, 0.01)
def __init__(self):
super().__init__()
self.conv1 = QuantConv2d(kernel_size=KERNEL_SIZE,
in_channels=CHANNELS,
out_channels=CHANNELS,
weight_quant=DPUv1WeightQuantInjector,
bias_quant=None,
output_quant=DPUv1OutputQuantInjector,
bias=False,
return_quant_tensor=True)
self.act1 = QuantReLU(act_quant=DPUv1ActQuantInjector,
return_quant_tensor=True)
self.conv2 = QuantConv2d(kernel_size=KERNEL_SIZE,
in_channels=CHANNELS,
out_channels=CHANNELS,
weight_quant=DPUv1WeightQuantInjector,
bias_quant=None,
output_quant=DPUv1OutputQuantInjector,
bias=False,
return_quant_tensor=True)
self.act2 = QuantReLU(act_quant=DPUv1ActQuantInjector,
return_quant_tensor=True)
self.conv3 = QuantConv2d(kernel_size=KERNEL_SIZE,
in_channels=CHANNELS,
out_channels=CHANNELS,
weight_quant=DPUv1WeightQuantInjector,
bias_quant=None,
output_quant=DPUv1OutputQuantInjector,
bias=False,
return_quant_tensor=True)
self.act3 = QuantReLU(act_quant=DPUv1ActQuantInjector,
return_quant_tensor=False)
self.max_pool = QuantMaxPool2d(kernel_size=KERNEL_SIZE,
stride=1,
return_quant_tensor=True)
self.eltwise_add = QuantEltwiseAdd(
input_quant=DPUv1OutputQuantInjector,
output_quant=DPUv1ActQuantInjector,
return_quant_tensor=True)
self.linear = nn.Linear(FC_IN_SIZE, CHANNELS)
def __init__(self):
super().__init__()
self.act = QuantIdentity(return_quant_tensor=True)
self.pool = QuantMaxPool2d(kernel_size=2,
return_quant_tensor=False)