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calibration.py
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#endoced_output: Tensor("nmt/encoder/layer_preprocess/layer_norm/add_1:0", shape=(1, 6, 1024), dtype=float32)
#output Tensor("nmt/while/Exit_3:0", shape=(?, 25), dtype=int64)
#k_encdec_tensor: Tensor("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/strided_slice:0", shape=(1, 6, 1024), dtype=float32)
#v_encdec_tensor: Tensor("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/strided_slice_1:0", shape=(1, 6, 1024), dtype=float32)
import tensorflow as tf
import numpy as np
from tensorflow.python import pywrap_tensorflow
model_dir = './checkpoint/'
ckpt = tf.train.get_checkpoint_state(model_dir)
ckpt_path = ckpt.model_checkpoint_path
reader = pywrap_tensorflow.NewCheckpointReader(ckpt_path)
param_dict = reader.get_variable_to_shape_map()
for tmp in param_dict:
if "encoder" in tmp:
print(tmp)
# ckpt = tf.train.get_checkpoint_state('./checkpoint/') # 通过检查点文件锁定最新的模型
# saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta') # 载入图结构，保存在.meta文件中
#
#
# #############################################self_attention###########################################
# emb = tf.get_default_graph().get_tensor_by_name("nmt/modality/decoder/embedding_shard_0:0")
# position = tf.get_default_graph().get_tensor_by_name("nmt/Reshape_6:0")
# test = tf.nn.embedding_lookup(emb, [0])
# input_0 = test + position[:, 0:1]
#
# bias = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/self_attention/layer_preprocess/layer_norm/layer_norm_bias:0')
# scale = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/self_attention/layer_preprocess/layer_norm/layer_norm_scale:0')
# mean = tf.reduce_mean(input_0, axis=[-1], keepdims=True)
# variance = tf.reduce_mean(tf.square(input_0 - mean), axis=[-1], keepdims=True)
# norm_x = (input_0 - mean) * tf.rsqrt(variance + tf.constant(1e-6))
# input_1 = norm_x * scale + bias
#
# weight_1 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/self_attention/multihead_attention/q/weight:0")
# q = tf.tensordot(input_1, weight_1, [[-1], [0]])
# weight_2 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/self_attention/multihead_attention/k/weight:0")
# k = tf.tensordot(input_1, weight_2, [[-1], [0]])
# weight_3 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/self_attention/multihead_attention/v/weight:0")
# v = tf.tensordot(input_1, weight_3, [[-1], [0]])
#
#
# q = tf.reshape(q, tf.concat([[1, 1], [16, 64]], 0))
# q = tf.transpose(q, [0, 2, 1, 3])
# k = tf.reshape(k, tf.concat([[1, 1], [16, 64]], 0))
# k = tf.transpose(k, [0, 2, 1, 3])
#
# v = tf.reshape(v, tf.concat([[1, 1], [16, 64]], 0))
# v = tf.transpose(v, [0, 2, 1, 3])
#
# tem_q_k = tf.matmul(q, k, transpose_b=True)
# weight_v = tf.nn.softmax(tem_q_k)
# input_2 = tf.matmul(weight_v, v)
#
# x = tf.transpose(input_2, [0, 2, 1, 3])
# x = tf.reshape(x, [1, 1] + [1024])
# weight_last = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/self_attention/multihead_attention/output_transform/weight:0")
# input_3 = tf.tensordot(x, weight_last, [[-1], [0]])
#
# output = input_0 + input_3
#
# #############################################encdec_attention###########################################
#
#
# bias = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/encdec_attention/layer_preprocess/layer_norm/layer_norm_bias:0')
# scale = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/encdec_attention/layer_preprocess/layer_norm/layer_norm_scale:0')
# mean = tf.reduce_mean(output, axis=[-1], keepdims=True)
# variance = tf.reduce_mean(tf.square(output - mean), axis=[-1], keepdims=True)
# norm_x = (output - mean) * tf.rsqrt(variance + tf.constant(1e-6))
# input_1 = norm_x * scale + bias
#
# weight_1 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/q/weight:0")
# q = tf.tensordot(input_1, weight_1, [[-1], [0]])
# k = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/strided_slice:0")
# v = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/strided_slice_1:0")
#
# q = tf.reshape(q, tf.concat([[1, 1], [16, 64]], 0))
# q = tf.transpose(q, [0, 2, 1, 3])
# k = tf.reshape(k, tf.concat([[1, 6], [16, 64]], 0))
# k = tf.transpose(k, [0, 2, 1, 3])
#
# v = tf.reshape(v, tf.concat([[1, 6], [16, 64]], 0))
# v = tf.transpose(v, [0, 2, 1, 3])
# tem_q_k = tf.matmul(q, k, transpose_b=True)
# weight_v = tf.nn.softmax(tem_q_k)
# input_2 = tf.matmul(weight_v, v)
#
# x = tf.transpose(input_2, [0, 2, 1, 3])
# x = tf.reshape(x, [1, 1] + [1024])
# weight_last = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/encdec_attention/multihead_attention/output_transform/weight:0")
# input_3 = tf.tensordot(x, weight_last, [[-1], [0]])
#
# output = output + input_3
#
# ########################################fnn_result#####################################################
# bias = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/ffn/layer_preprocess/layer_norm/layer_norm_bias:0')
# scale = tf.get_default_graph().get_tensor_by_name('nmt/body/decoder/layer_0/ffn/layer_preprocess/layer_norm/layer_norm_scale:0')
# mean = tf.reduce_mean(output, axis=[-1], keepdims=True)
# variance = tf.reduce_mean(tf.square(output - mean), axis=[-1], keepdims=True)
# norm_x = (output - mean) * tf.rsqrt(variance + tf.constant(1e-6))
# input_1 = norm_x * scale + bias
#
# weight_1 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/ffn/conv1/weight:0")
# bias_1 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/ffn/conv1/bias:0")
# ffn_1 = tf.tensordot(input_1, weight_1, [[-1], [0]])
# ffn_1 = tf.nn.bias_add(ffn_1, bias_1)
# ffn_1 = tf.nn.relu(ffn_1)
#
# weight_2 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/ffn/conv2/weight:0")
# bias_2 = tf.get_default_graph().get_tensor_by_name("nmt/body/decoder/layer_0/ffn/conv2/bias:0")
# ffn_2 = tf.tensordot(ffn_1, weight_2, [[-1], [0]])
# input_4 = tf.nn.bias_add(ffn_2, bias_2)
#
# output = output + input_4
#
# ##############################################################################################
#
#
# with tf.Session() as sess:
# saver.restore(sess, ckpt.model_checkpoint_path)
# result = sess.run(tf.get_default_graph().get_tensor_by_name("nmt/while/Exit_3:0"))
# print(result)