| /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
| |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| |
| #include <iostream> |
| #include "tensorflow/core/framework/op_kernel.h" |
| #include "tensorflow/core/lib/core/status.h" |
| #include "tensorflow/core/lib/strings/str_util.h" |
| #include "tensorflow/core/platform/logging.h" |
| |
| namespace tensorflow { |
| |
| class AssertOp : public OpKernel { |
| public: |
| explicit AssertOp(OpKernelConstruction* ctx) : OpKernel(ctx) { |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("summarize", &summarize_)); |
| } |
| |
| void Compute(OpKernelContext* ctx) override { |
| const Tensor& cond = ctx->input(0); |
| OP_REQUIRES(ctx, IsLegacyScalar(cond.shape()), |
| errors::InvalidArgument("In[0] should be a scalar: ", |
| cond.shape().DebugString())); |
| |
| if (cond.scalar<bool>()()) { |
| return; |
| } |
| string msg = "assertion failed: "; |
| for (int i = 1; i < ctx->num_inputs(); ++i) { |
| strings::StrAppend(&msg, "[", ctx->input(i).SummarizeValue(summarize_), |
| "]"); |
| if (i < ctx->num_inputs() - 1) strings::StrAppend(&msg, " "); |
| } |
| ctx->SetStatus(errors::InvalidArgument(msg)); |
| } |
| |
| private: |
| int32 summarize_ = 0; |
| }; |
| |
| REGISTER_KERNEL_BUILDER(Name("Assert").Device(DEVICE_CPU), AssertOp); |
| |
| class PrintOp : public OpKernel { |
| public: |
| explicit PrintOp(OpKernelConstruction* ctx) |
| : OpKernel(ctx), call_counter_(0) { |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("message", &message_)); |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("first_n", &first_n_)); |
| OP_REQUIRES_OK(ctx, ctx->GetAttr("summarize", &summarize_)); |
| } |
| |
| void Compute(OpKernelContext* ctx) override { |
| if (IsRefType(ctx->input_dtype(0))) { |
| ctx->forward_ref_input_to_ref_output(0, 0); |
| } else { |
| ctx->set_output(0, ctx->input(0)); |
| } |
| if (first_n_ >= 0) { |
| mutex_lock l(mu_); |
| if (call_counter_ >= first_n_) return; |
| call_counter_++; |
| } |
| string msg; |
| strings::StrAppend(&msg, message_); |
| for (int i = 1; i < ctx->num_inputs(); ++i) { |
| strings::StrAppend(&msg, "[", ctx->input(i).SummarizeValue(summarize_), |
| "]"); |
| } |
| std::cerr << msg << std::endl; |
| } |
| |
| private: |
| mutex mu_; |
| int64 call_counter_ GUARDED_BY(mu_) = 0; |
| int64 first_n_ = 0; |
| int32 summarize_ = 0; |
| string message_; |
| }; |
| |
| REGISTER_KERNEL_BUILDER(Name("Print").Device(DEVICE_CPU), PrintOp); |
| |
| class TimestampOp : public OpKernel { |
| public: |
| explicit TimestampOp(OpKernelConstruction* context) : OpKernel(context) {} |
| |
| void Compute(OpKernelContext* context) override { |
| TensorShape output_shape; // Default shape is 0 dim, 1 element |
| Tensor* output_tensor = nullptr; |
| OP_REQUIRES_OK(context, |
| context->allocate_output(0, output_shape, &output_tensor)); |
| |
| auto output_scalar = output_tensor->scalar<double>(); |
| double now_us = static_cast<double>(Env::Default()->NowMicros()); |
| double now_s = now_us / 1000000; |
| output_scalar() = now_s; |
| } |
| }; |
| |
| REGISTER_KERNEL_BUILDER(Name("Timestamp").Device(DEVICE_CPU), TimestampOp); |
| |
| } // end namespace tensorflow |