def dequantize(self, apot_tensor) -> Tensor:
apot_tensor_data = apot_tensor.data
# map apot_to_float over tensor2quantize elements
result = apot_tensor_data.apply_(lambda x: float(apot_to_float(x, self.quantization_levels, self.level_indices)))
return result
def dequantize(self, float2apot: Tensor): # type: ignore[override]
float2apot = float2apot.float()
quantization_levels = self.quantization_levels
level_indices = self.level_indices
# map apot_to_float over tensor2quantize elements
result = float2apot.apply_(lambda x: float(
apot_to_float(x, quantization_levels, level_indices)))
return result
def quant_levels_visualization(self, obs_result, filename):
xs = [float(x) / 1000.0 for x in range(1000)]
ys = [apot_to_float(float_to_apot(x, obs_result[1], obs_result[2]),
obs_result[1], obs_result[2]).item() for x in xs]
f = plt.figure(figsize=(15, 10))
plt.plot(xs, ys)
plt.title("APoT Quantization Plot")
plt.xlabel("Full Precision")
plt.ylabel("Quantized")
plt.show()
def dequantize(self, apot_tensor) -> Tensor:
apot_tensor_data = apot_tensor.data
# map apot_to_float over tensor2quantize elements
result_temp = np.empty(apot_tensor_data.size())
for ele in apot_tensor_data:
new_ele = apot_to_float(ele, self.quantization_levels,
self.level_indices)
np.append(result_temp, new_ele)
result = torch.from_numpy(result_temp).int()
return result
def quant_levels_visualization(self, signed=False):
alpha, gamma, quantization_levels, level_indices = self.calculate_qparams(signed)
xs = [float(x) / 1000.0 for x in range(1000)]
ys = [apot_to_float(float_to_apot(x, quantization_levels, level_indices, alpha),
quantization_levels, level_indices).item() for x in xs]
f = plt.figure(figsize=(15, 10))
plt.plot(xs, ys)
plt.title("APoT Quantization Plot")
plt.xlabel("Full Precision")
plt.ylabel("Quantized")
plt.show()
def dequantize(self, apot_tensor) -> Tensor:
orig_size = apot_tensor.data.size()
apot_tensor_data = apot_tensor.data.flatten()
print(apot_tensor_data)
# map apot_to_float over tensor2quantize elements
result_temp = np.empty(shape=apot_tensor_data.size())
for i in range(len(apot_tensor_data)):
new_ele = apot_to_float(apot_tensor_data[i],
self.quantization_levels,
self.level_indices)
result_temp[i] = new_ele
result = torch.from_numpy(result_temp).reshape(orig_size)
return result