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      1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #include "tensorflow/core/util/util.h"
     17 
     18 #include "tensorflow/core/lib/gtl/inlined_vector.h"
     19 #include "tensorflow/core/lib/strings/strcat.h"
     20 #include "tensorflow/core/platform/logging.h"
     21 
     22 namespace tensorflow {
     23 
     24 StringPiece NodeNamePrefix(const StringPiece& op_name) {
     25   StringPiece sp(op_name);
     26   auto p = sp.find('/');
     27   if (p == StringPiece::npos || p == 0) {
     28     return "";
     29   } else {
     30     return StringPiece(sp.data(), p);
     31   }
     32 }
     33 
     34 StringPiece NodeNameFullPrefix(const StringPiece& op_name) {
     35   StringPiece sp(op_name);
     36   auto p = sp.rfind('/');
     37   if (p == StringPiece::npos || p == 0) {
     38     return "";
     39   } else {
     40     return StringPiece(sp.data(), p);
     41   }
     42 }
     43 
     44 MovingAverage::MovingAverage(int window)
     45     : window_(window),
     46       sum_(0.0),
     47       data_(new double[window_]),
     48       head_(0),
     49       count_(0) {
     50   CHECK_GE(window, 1);
     51 }
     52 
     53 MovingAverage::~MovingAverage() { delete[] data_; }
     54 
     55 void MovingAverage::Clear() {
     56   count_ = 0;
     57   head_ = 0;
     58   sum_ = 0;
     59 }
     60 
     61 double MovingAverage::GetAverage() const {
     62   if (count_ == 0) {
     63     return 0;
     64   } else {
     65     return static_cast<double>(sum_) / count_;
     66   }
     67 }
     68 
     69 void MovingAverage::AddValue(double v) {
     70   if (count_ < window_) {
     71     // This is the warmup phase. We don't have a full window's worth of data.
     72     head_ = count_;
     73     data_[count_++] = v;
     74   } else {
     75     if (window_ == ++head_) {
     76       head_ = 0;
     77     }
     78     // Toss the oldest element
     79     sum_ -= data_[head_];
     80     // Add the newest element
     81     data_[head_] = v;
     82   }
     83   sum_ += v;
     84 }
     85 
     86 static char hex_char[] = "0123456789abcdef";
     87 
     88 string PrintMemory(const char* ptr, size_t n) {
     89   string ret;
     90   ret.resize(n * 3);
     91   for (int i = 0; i < n; ++i) {
     92     ret[i * 3] = ' ';
     93     ret[i * 3 + 1] = hex_char[ptr[i] >> 4];
     94     ret[i * 3 + 2] = hex_char[ptr[i] & 0xf];
     95   }
     96   return ret;
     97 }
     98 
     99 string SliceDebugString(const TensorShape& shape, const int64 flat) {
    100   // Special case rank 0 and 1
    101   const int dims = shape.dims();
    102   if (dims == 0) return "";
    103   if (dims == 1) return strings::StrCat("[", flat, "]");
    104 
    105   // Compute strides
    106   gtl::InlinedVector<int64, 32> strides(dims);
    107   strides.back() = 1;
    108   for (int i = dims - 2; i >= 0; i--) {
    109     strides[i] = strides[i + 1] * shape.dim_size(i + 1);
    110   }
    111 
    112   // Unflatten index
    113   int64 left = flat;
    114   string result;
    115   for (int i = 0; i < dims; i++) {
    116     strings::StrAppend(&result, i ? "," : "[", left / strides[i]);
    117     left %= strides[i];
    118   }
    119   strings::StrAppend(&result, "]");
    120   return result;
    121 }
    122 
    123 }  // namespace tensorflow
    124