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extract_conv.py
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139 lines (136 loc) · 8.09 KB
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import numpy as np
from utils.utils import *
def extract_conv(op, kwargs, interpreter, unknown_config):
# print(op['inputs'][2])
# load the stride
try:
stride_w = op['builtin_options']['stride_w']
except:
kwargs['stride_width='] = 'stride_width=1'
stride_w = 1
else:
kwargs['stride_width='] = 'stride_width=' + str(stride_w)
try:
stride_h = op['builtin_options']['stride_h']
except:
kwargs['stride_height='] = 'stride_height=1'
stride_h = 1
else:
kwargs['stride_height='] = 'stride_height=' + str(stride_h)
# load the dilation_
try:
dilation_w_factor = op['builtin_options']['dilation_w_factor']
except:
kwargs['dilation_width_factor='] = 'dilation_width_factor=1'
else:
kwargs['dilation_width_factor='] = 'dilation_width_factor=' + str(dilation_w_factor)
try:
dilation_h_factor = op['builtin_options']['dilation_h_factor']
except:
kwargs['dilation_height_factor='] = 'dilation_height_factor=1'
else:
kwargs['dilation_height_factor='] = 'dilation_height_factor=' + str(dilation_h_factor)
# load the activation
try:
fused_activation_function = op['builtin_options']['fused_activation_function']
except:
kwargs['activation='] = 'activation=kTfLiteActNone'
print("Warning: no activation function found")
else:
kwargs['activation='] = 'activation=' + conv_activation_parser(fused_activation_function)
# load the padding
try:
padding = op['builtin_options']['padding']
except:
kwargs['paddings='] = 'paddings=kTfLitePaddingSame'
print("Warning: no paddings found and using default padding SAME")
else:
kwargs['paddings='] = 'paddings=' + conv_padding_parser(padding)
if len(op['inputs']) > 2:
kwargs['has_conv_bias='] = 'has_conv_bias=true'
else:
kwargs['has_conv_bias='] = 'has_conv_bias=false'
kwargs['const TfLiteType bias_type=;'] = ' '
kwargs['const int bias_dims_size=;'] = ' '
kwargs['const int32_t bias_dims_raw=;'] = ' '
kwargs['float bias_raw=;'] = ' '
for tensor_details in interpreter.get_tensor_details():
if tensor_details['index'] == op['inputs'][1]:
for tensor_details_lower in interpreter.get_tensor_details():
if tensor_details_lower['index'] == op['inputs'][0]:
tflite_type_in, _ = conv_data_type_parser(tensor_details_lower['dtype'])
if tflite_type_in == 'kTfLiteFloat32':
need_hwcn = True
filter_tensor = interpreter.get_tensor(tensor_details["index"])
filter_item_num = filter_tensor.size
filter_input_channel = filter_tensor.shape[3]
filter_output_channel = filter_tensor.shape[0]
filter_height = filter_tensor.shape[1]
filter_width = filter_tensor.shape[2]
filter_dims_raw = '{' + str(filter_output_channel) + ',' + str(filter_height) + ',' + str(filter_width) + ',' + str(filter_input_channel) +'}'
filter_dims_size = len(filter_tensor.shape)
tflite_type, type_str = conv_data_type_parser(filter_tensor.dtype)
quantization_filter = tensor_details['quantization']
kwargs['filter_dims_size='] = 'filter_dims_size=' + str(filter_dims_size)
kwargs['filter_dims_raw='] = 'filter_dims_raw[' + str(filter_dims_size) + ']=' + filter_dims_raw
kwargs['filter_type='] = 'filter_type=' + tflite_type
if need_hwcn:
kwargs['filter_raw='] = type_str + ' filter_raw[' + str(filter_item_num) + ']=' + '{' + str(np.transpose(filter_tensor, (1,2,3,0)).flatten('C').tolist()).strip('[').strip(']') + '}'
else:
kwargs['filter_raw='] = type_str + ' filter_raw[' + str(filter_item_num) + ']=' + '{' + str(filter_tensor.flatten('C').tolist()).strip('[').strip(']') + '}'
kwargs['scale_filter='] = 'scale_filter=' + str(quantization_filter[0])
kwargs['zero_point_filter='] = 'zero_point_filter=' + str(quantization_filter[1])
kwargs['filter_tensor_data=filter_raw'] = type_str + '* filter_tensor_data=filter_raw'
elif tensor_details['index'] == op['inputs'][2]:
bias_tensor = interpreter.get_tensor(tensor_details["index"])
bias_item_num = bias_tensor.size
bias_channel = bias_tensor.shape[0]
bias_dims_raw = '{' + str(bias_channel) + '}'
bias_dims_size = len(bias_tensor.shape)
tflite_type, type_str = conv_data_type_parser(bias_tensor.dtype)
quantization_bias = tensor_details['quantization']
kwargs['bias_type='] = 'bias_type=' + tflite_type
kwargs['bias_dims_size='] = 'bias_dims_size=' + str(bias_dims_size)
kwargs['bias_dims_raw='] = 'bias_dims_raw[' + str(bias_dims_size) + ']=' + bias_dims_raw
kwargs['bias_raw='] = type_str + ' bias_raw[' + str(bias_item_num) + ']=' + '{' + str(bias_tensor.tolist()).strip('[').strip(']') + '}'
kwargs['scale_bias='] = 'scale_bias=' + str(quantization_bias[0])
kwargs['zero_point_bias='] = 'zero_point_bias=' + str(quantization_bias[1])
kwargs['bias_tensor_data=bias_raw'] = type_str + '* bias_tensor_data=bias_raw'
elif tensor_details['index'] == op['outputs'][0]:
# output_tensor = interpreter.get_tensor(tensor_details["index"])
output_channel = tensor_details['shape'][3]
output_height = tensor_details['shape'][1]
output_width = tensor_details['shape'][2]
output_num = output_channel*output_height*output_width
output_dims_raw = '{' + '1,' + str(output_height) + ',' + str(output_width) + ',' + str(output_channel) + '}'
output_dims_size = len(tensor_details['shape'])
tflite_type, type_str = conv_data_type_parser(tensor_details['dtype'])
quantization_output = tensor_details['quantization']
kwargs['output_dims_size='] = 'output_dims_size=' + str(output_dims_size)
kwargs['output_dims_raw='] = 'output_dims_raw[' + str(output_dims_size) + ']=' + output_dims_raw
kwargs['output_num='] = 'output_num=' + str(output_num)
kwargs['output_type='] = 'output_type=' + tflite_type
kwargs['scale_output='] = 'scale_output=' + str(quantization_output[0])
kwargs['zero_point_output='] = 'zero_point_output=' + str(quantization_output[1])
elif tensor_details['index'] == op['inputs'][0]:
# input_tensor = interpreter.get_tensor(tensor_details["index"])
input_channel = tensor_details['shape'][3]
input_height = tensor_details['shape'][1]
input_width = tensor_details['shape'][2]
input_dims_raw = '{' + '1,' + str(input_height) + ',' + str(input_width) + ',' + str(input_channel) + '}'
input_dims_size = len(tensor_details['shape'])
tflite_type, type_str = conv_data_type_parser(tensor_details['dtype'])
quantization_input = tensor_details['quantization']
kwargs['input_dims_size='] = 'input_dims_size=' + str(input_dims_size)
kwargs['input_dims_raw='] = 'input_dims_raw[' + str(input_dims_size) + ']=' + input_dims_raw
kwargs['input_type='] = 'input_type=' + tflite_type
kwargs['scale_input='] = 'scale_input=' + str(quantization_input[0])
kwargs['zero_point_input='] = 'zero_point_input=' + str(quantization_input[1])
# kwargs['input_tensor_data=input_raw'] = type_str + '* input_tensor_data=input_raw'
if kwargs['paddings='] == 'paddings=kTfLitePaddingValid':
kwargs['padding_values_width='] = 'padding_values_width=0'
kwargs['padding_values_height='] = 'padding_values_height=0'
elif kwargs['paddings='] == 'paddings=kTfLitePaddingSame':
kwargs['padding_values_width='] = 'padding_values_width=' + str(np.floor((filter_width - stride_w) / 2.0))
kwargs['padding_values_height='] = 'padding_values_height=' + str(np.floor((filter_height - stride_h) / 2.0))
return kwargs, unknown_config