Lines Matching refs:np

21 import numpy as np
179         kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
181         kernel = np.transpose(kernel, [3, 0, 1, 2])
184         np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
201         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
231             kernel = np.float32(ktensor.float_val[0])
233             kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
235         kernel = np.transpose(kernel, [1, 0])
237         np.array([self.op2code[node.op], self.conv_activations[activation], in_channels, out_channels, has_bias], dtype=np.uint32).tofile(f)
252         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
275             kernel = np.float32(ktensor.float_val[0])
277             kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
279         kernel = np.transpose(kernel, [3, 0, 1, 2])
284         np.array([self.op2code[node.op], dilation, self.conv_paddings[padding], self.conv_activations['None'],
285                   in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
290         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
297         np.array([self.op2code[node.op], block_size], dtype=np.uint32).tofile(f)
301         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
309         np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f)
317         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
325         np.array([self.op2code[node.op]], dtype=np.uint32).tofile(f)
326         np.array([y], dtype=np.float32).tofile(f)
330         np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
338         np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f)
341             np.array([1], dtype=np.uint32).tofile(f)            # broadcast: 1
342             np.array([scalar], dtype=np.float32).tofile(f)
343             np.array([0], dtype=np.uint32).tofile(f)            # broadcast: 0
345             np.array([input_operand_index], dtype=np.uint32).tofile(f)
348             np.array([0], dtype=np.uint32).tofile(f)
350             np.array([input_operand_index], dtype=np.uint32).tofile(f)
351             np.array([1], dtype=np.uint32).tofile(f)
352             np.array([scalar], dtype=np.float32).tofile(f)
354             np.array([0], dtype=np.uint32).tofile(f)
356             np.array([input_operand_index], dtype=np.uint32).tofile(f)
357             np.array([0], dtype=np.uint32).tofile(f)
359             np.array([input_operand_index], dtype=np.uint32).tofile(f)
361         np.array([output_operand_index], dtype=np.uint32).tofile(f)
368         np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f)
370         np.array([input_operand_index], dtype=np.uint32).tofile(f)
372         np.array([output_operand_index],dtype=np.uint32).tofile(f)
398         np.array([self.op2code[node.op], strides, self.pool_paddings[padding], filter_height],
399                  dtype=np.uint32).tofile(f)
403         np.array([input_operand_index, output_operand_index],dtype=np.uint32).tofile(f)
447                 np.array([operand.index, len(operand.name)], dtype=np.uint32).tofile(f)
449                 np.array([operand.iotype, operand.dtype], dtype=np.uint32).tofile(f)
450                 np.array(operand.dims, dtype=np.uint32).tofile(f)
456             np.array([header.major, header.minor], dtype=np.uint32).tofile(f)
459             np.array([self.layer_number, len(self.name_operand_dict)], dtype=np.uint32).tofile(f)