[GitHub] zxy110 opened a new issue #12247: 【C++】throw exception when use MXDataIter("CSVIter") to train

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zxy110 opened a new issue #12247: 【C++】throw exception when use MXDataIter("CSVIter") to train
URL: https://github.com/apache/incubator-mxnet/issues/12247
 
 
   Hello, I want to user CSVIter in my cpp program, but I got an exception at
    `MXDataIterBeforeFirst(blob_ptr_->handle_);` 
   in io.hpp
   I don't know whether it's caused by the shapes I set.
   I've use the mnist datasets, Here is my code.
   
   	#include <chrono>
   	#include "utils.h"
   	#include "mxnet-cpp/MxNetCpp.h"
   	using namespace mxnet::cpp;
   
   	Symbol mlp(const std::vector<int> &layers) {
   		auto x = Symbol::Variable("X");
   		auto label = Symbol::Variable("label");
   	
   		std::vector<Symbol> weights(layers.size());
   		std::vector<Symbol> biases(layers.size());
   		std::vector<Symbol> outputs(layers.size());
   		for (size_t i = 0; i < layers.size(); ++i) {
   			weights[i] = Symbol::Variable("w" + std::to_string(i));
   			biases[i] = Symbol::Variable("b" + std::to_string(i));
   			Symbol fc = FullyConnected(
   				i == 0 ? x : outputs[i - 1],  // data
   				weights[i],
   				biases[i],
   				layers[i]);
   			outputs[i] = i == layers.size() - 1 ? fc : Activation(fc, ActivationActType::kRelu);
   		}
   	return SoftmaxOutput(outputs.back(), label);
   	}
   
   	int main(int argc, char** argv) {
   		const int image_size = 28;
   		const std::vector<int> layers{ 128, 64, 10 };
   		const int batch_size = 100;
   		const int max_epoch = 10;
   		const float learning_rate = 0.1;
   		const float weight_decay = 1e-2;
   	
   		std::vector<std::string> data_files = { "D:/mxnet/test3/some_test/mxnet_test/mxnet-	cvsiter/data/mnist/mnist_iter_train_data.csv",
   		"D:/mxnet/test3/some_test/mxnet_test/mxnet-cvsiter/data/mnist/mnist_iter_train_label.csv",
   		"D:/mxnet/test3/some_test/mxnet_test/mxnet-cvsiter/data/mnist/mnist_iter_test_data.csv",
   		"D:/mxnet/test3/some_test/mxnet_test/mxnet-cvsiter/data/mnist/mnist_iter_test_label.csv"
   	};
   
   	auto train_iter = MXDataIter("CSVIter");
   	setDataIter(&train_iter, "Train", data_files, batch_size, 0);			//shuffle = 1
   
   	auto val_iter = MXDataIter("CSVIter");
   	setDataIter(&val_iter, "Label", data_files, batch_size, 0);				//shuffle = 0
   	auto net = mlp(layers);
   
   	Context ctx = Context::cpu();  // Use CPU for training
   
   
   	std::map<std::string, NDArray> args;
   	args["X"] = NDArray(Shape(batch_size, image_size*image_size), ctx);
   	args["label"] = NDArray(Shape(batch_size), ctx);
   	// Let MXNet infer shapes other parameters such as weights
   	net.InferArgsMap(ctx, &args, args);
   
   	// Initialize all parameters with uniform distribution U(-0.01, 0.01)
   	auto initializer = Uniform(0.01);
   	for (auto& arg : args) {
   		// arg.first is parameter name, and arg.second is the value
   		initializer(arg.first, &arg.second);
   	}
   
   	// Create sgd optimizer
   	Optimizer* opt = OptimizerRegistry::Find("sgd");
   	opt->SetParam("rescale_grad", 1.0 / batch_size)
   		->SetParam("lr", learning_rate)
   		->SetParam("wd", weight_decay);
   
   	// Create executor by binding parameters to the model
   	auto *exec = net.SimpleBind(ctx, args);
   	auto arg_names = net.ListArguments();
   
   	// Start training
   	for (int iter = 0; iter < max_epoch; ++iter) {
   		int samples = 0;
   		train_iter.Reset();
   
   		auto tic = std::chrono::system_clock::now();
   		while (train_iter.Next()) {
   			samples += batch_size;
   			auto data_batch = train_iter.GetDataBatch();
   			// Set data and label
   			data_batch.data.CopyTo(&args["X"]);
   			data_batch.label.CopyTo(&args["label"]);
   
   			// Compute gradients
   			exec->Forward(true);
   			exec->Backward();
   			// Update parameters
   			for (size_t i = 0; i < arg_names.size(); ++i) {
   				if (arg_names[i] == "X" || arg_names[i] == "label") continue;
   				opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
   			}
   		}
   		auto toc = std::chrono::system_clock::now();
   
   		Accuracy acc;
   		val_iter.Reset();
   		while (val_iter.Next()) {
   			auto data_batch = val_iter.GetDataBatch();
   			data_batch.data.CopyTo(&args["X"]);
   			data_batch.label.CopyTo(&args["label"]);
   			// Forward pass is enough as no gradient is needed when evaluating
   			exec->Forward(false);
   			acc.Update(data_batch.label, exec->outputs[0]);
   		}
   		float duration = std::chrono::duration_cast<std::chrono::milliseconds>
   			(toc - tic).count() / 1000.0;
   	LG << "Epoch: " << iter << " " << samples / duration << " samples/sec Accuracy: " << acc.Get();
   	}
   
   	delete exec;
   	MXNotifyShutdown();
   	return 0;
   	}
   
   
   

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