diff --git a/README.md b/README.md index 4cb56684..2fd46c6b 100644 --- a/README.md +++ b/README.md @@ -26,12 +26,12 @@ In comparison to other projects, like for instance [TensorFlowSharp](https://www ### How to use -| TensorFlow | tf native1.14 | tf native 1.15 | tf native 2.3 | -| -------------------------- | ------------- | -------------- | ------------- | -| tf.net 0.3x, tf.keras 0.2 | | | x | -| tf.net 0.2x | | x | x | -| tf.net 0.15 | x | x | | -| tf.net 0.14 | x | | | +| TensorFlow | tf native1.14, cuda 10.0 | tf native 1.15, cuda 10.0 | tf native 2.3, cuda 10.1 | tf native 2.4, cuda 11 | +| -------------------------- | ------------- | -------------- | ------------- | ------------- | +| tf.net 0.3x, tf.keras 0.2 | | | x | not compatible | +| tf.net 0.2x | | x | x | | +| tf.net 0.15 | x | x | | | +| tf.net 0.14 | x | | | | Troubleshooting of running example or installation, please refer [here](tensorflowlib/README.md). diff --git a/src/SciSharp.TensorFlow.Redist/README.md b/src/SciSharp.TensorFlow.Redist/README.md index 26eec870..141bba35 100644 --- a/src/SciSharp.TensorFlow.Redist/README.md +++ b/src/SciSharp.TensorFlow.Redist/README.md @@ -22,11 +22,19 @@ https://www.nuget.org/packages/SciSharp.TensorFlow.Redist Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5ba61ad0e400623821236bd117cc24c6cb77). + + +#### Download pre-build package + +[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.4.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.4.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.4.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.4.0.tar.gz), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.4.0.zip) + + + #### Pack and Deploy #### On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries. 1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux. -2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.3.1.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600` +2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.4.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600` diff --git a/src/TensorFlowNET.Core/Data/DatasetV2.cs b/src/TensorFlowNET.Core/Data/DatasetV2.cs index 104789df..763baa31 100644 --- a/src/TensorFlowNET.Core/Data/DatasetV2.cs +++ b/src/TensorFlowNET.Core/Data/DatasetV2.cs @@ -3,6 +3,7 @@ using System.Collections; using System.Collections.Generic; using System.Linq; using Tensorflow.Framework.Models; +using static Tensorflow.Binding; namespace Tensorflow { @@ -98,6 +99,20 @@ namespace Tensorflow return dataset; } + public Tensor dataset_cardinality(string name = null) + { + if (tf.Context.executing_eagerly()) + { + var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName, + "DatasetCardinality", name, + null, + variant_tensor); + return results[0]; + } + + throw new NotImplementedException(""); + } + public override string ToString() => $"{GetType().Name} shapes: {string.Join(", ", structure.Select(x => x.shape))}, types: {string.Join(", ", structure.Select(x => "tf." + x.dtype.as_numpy_name()))}"; @@ -117,7 +132,9 @@ namespace Tensorflow break; } - yield return (results[0], results.Length == 1 ? null : results[1]); + yield return results.Length == 2 + ? (results[0], results[1]) + : (null, results[0]); } } diff --git a/src/TensorFlowNET.Core/Data/IDatasetV2.cs b/src/TensorFlowNET.Core/Data/IDatasetV2.cs index fc47c832..9a31ff51 100644 --- a/src/TensorFlowNET.Core/Data/IDatasetV2.cs +++ b/src/TensorFlowNET.Core/Data/IDatasetV2.cs @@ -74,5 +74,7 @@ namespace Tensorflow /// /// IDatasetV2 apply_options(); + + Tensor dataset_cardinality(string name = null); } } diff --git a/src/TensorFlowNET.Core/Data/MapDataset.cs b/src/TensorFlowNET.Core/Data/MapDataset.cs index 231e613e..c593322b 100644 --- a/src/TensorFlowNET.Core/Data/MapDataset.cs +++ b/src/TensorFlowNET.Core/Data/MapDataset.cs @@ -1,5 +1,6 @@ using System; using Tensorflow.Functions; +using static Tensorflow.Binding; namespace Tensorflow { @@ -14,7 +15,12 @@ namespace Tensorflow bool preserve_cardinality = false, bool use_legacy_function = false) : base(input_dataset) { - var func = new ConcreteFunction(map_func, input_dataset.element_spec[0].dtype); + using var func = new ConcreteFunction($"autograph_{map_func.Method.Name}"); + var input = tf.placeholder(input_dataset.element_spec[0].dtype); + var output = map_func(input); + func.ToGraph(input, output); + + structure = func.OutputStructure; variant_tensor = ops.map_dataset(input_dataset.variant_tensor, func, diff --git a/src/TensorFlowNET.Core/Functions/ConcreteFunction.cs b/src/TensorFlowNET.Core/Functions/ConcreteFunction.cs index a3067182..90cb0494 100644 --- a/src/TensorFlowNET.Core/Functions/ConcreteFunction.cs +++ b/src/TensorFlowNET.Core/Functions/ConcreteFunction.cs @@ -109,6 +109,8 @@ namespace Tensorflow.Functions inputs, outputs, null); + + OutputStructure = outputs.Select(x => x.ToTensorSpec()).ToArray(); } public Tensors Invoke(Tensors inputs) @@ -128,6 +130,9 @@ namespace Tensorflow.Functions return new ForwardBackwardCall(functions, args, tape_watching: true); } + public override string ToString() + => Name; + public void Dispose() { c_api.TFE_ContextRemoveFunction(tf.Context.Handle, Name, tf.Status.Handle); diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/TensorLikeDataAdapterArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataAdapterArgs.cs similarity index 86% rename from src/TensorFlowNET.Core/Keras/ArgsDefinition/TensorLikeDataAdapterArgs.cs rename to src/TensorFlowNET.Core/Keras/ArgsDefinition/DataAdapterArgs.cs index 921a4726..f3cca438 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/TensorLikeDataAdapterArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataAdapterArgs.cs @@ -2,10 +2,11 @@ namespace Tensorflow.Keras.ArgsDefinition { - public class TensorLikeDataAdapterArgs + public class DataAdapterArgs { public Tensor X { get; set; } public Tensor Y { get; set; } + public IDatasetV2 Dataset { get; set; } public int BatchSize { get; set; } = 32; public int Steps { get; set; } public int Epochs { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataHandlerArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataHandlerArgs.cs index 63de54ad..b6e6849b 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataHandlerArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/DataHandlerArgs.cs @@ -6,6 +6,7 @@ namespace Tensorflow.Keras.ArgsDefinition { public Tensor X { get; set; } public Tensor Y { get; set; } + public IDatasetV2 Dataset { get; set; } public int BatchSize { get; set; } = 32; public int StepsPerEpoch { get; set; } = -1; public int InitialEpoch { get; set; } = 0; diff --git a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs index 014e376a..e48cb031 100644 --- a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs +++ b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs @@ -1702,74 +1702,79 @@ new_height, new_width"); public static Tensor decode_image(Tensor contents, int channels = 0, TF_DataType dtype = TF_DataType.TF_UINT8, string name = null, bool expand_animations = true) { - Func _jpeg = () => + return tf_with(ops.name_scope(name, "decode_image"), scope => { - int jpeg_channels = channels; - var good_channels = math_ops.not_equal(jpeg_channels, 4, name: "check_jpeg_channels"); - string channels_msg = "Channels must be in (None, 0, 1, 3) when decoding JPEG 'images'"; - var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); - return tf_with(ops.control_dependencies(new[] { assert_channels }), delegate + var substr = tf.strings.substr(contents, 0, 3); + + Func _jpeg = () => { - return convert_image_dtype(gen_image_ops.decode_jpeg(contents, channels), dtype); - }); - }; + int jpeg_channels = channels; + var good_channels = math_ops.not_equal(jpeg_channels, 4, name: "check_jpeg_channels"); + string channels_msg = "Channels must be in (None, 0, 1, 3) when decoding JPEG 'images'"; + var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); + return tf_with(ops.control_dependencies(new[] { assert_channels }), delegate + { + return convert_image_dtype(gen_image_ops.decode_jpeg(contents, channels), dtype); + }); + }; - Func _gif = () => - { - int gif_channels = channels; - var good_channels = math_ops.logical_and( - math_ops.not_equal(gif_channels, 1, name: "check_gif_channels"), - math_ops.not_equal(gif_channels, 4, name: "check_gif_channels")); - - string channels_msg = "Channels must be in (None, 0, 3) when decoding GIF images"; - var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); - return tf_with(ops.control_dependencies(new[] { assert_channels }), delegate + /*Func _gif = () => { - var result = convert_image_dtype(gen_image_ops.decode_gif(contents), dtype); - if (!expand_animations) - result = array_ops.gather(result, 0); - return result; - }); - }; + int gif_channels = channels; + var good_channels = math_ops.logical_and( + math_ops.not_equal(gif_channels, 1, name: "check_gif_channels"), + math_ops.not_equal(gif_channels, 4, name: "check_gif_channels")); + + string channels_msg = "Channels must be in (None, 0, 3) when decoding GIF images"; + var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); + return tf_with(ops.control_dependencies(new[] { assert_channels }), delegate + { + var result = convert_image_dtype(gen_image_ops.decode_gif(contents), dtype); + if (!expand_animations) + result = array_ops.gather(result, 0); + return result; + }); + }; - Func _bmp = () => - { - int bmp_channels = channels; - var signature = tf.strings.substr(contents, 0, 2); - var is_bmp = math_ops.equal(signature, "BM", name: "is_bmp"); - string decode_msg = "Unable to decode bytes as JPEG, PNG, GIF, or BMP"; - var assert_decode = control_flow_ops.Assert(is_bmp, new string[] { decode_msg }); - var good_channels = math_ops.not_equal(bmp_channels, 1, name: "check_channels"); - string channels_msg = "Channels must be in (None, 0, 3) when decoding BMP images"; - var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); - return tf_with(ops.control_dependencies(new[] { assert_decode, assert_channels }), delegate + Func _bmp = () => { - return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype); - }); - }; + int bmp_channels = channels; + var signature = tf.strings.substr(contents, 0, 2); + var is_bmp = math_ops.equal(signature, "BM", name: "is_bmp"); + string decode_msg = "Unable to decode bytes as JPEG, PNG, GIF, or BMP"; + var assert_decode = control_flow_ops.Assert(is_bmp, new string[] { decode_msg }); + var good_channels = math_ops.not_equal(bmp_channels, 1, name: "check_channels"); + string channels_msg = "Channels must be in (None, 0, 3) when decoding BMP images"; + var assert_channels = control_flow_ops.Assert(good_channels, new string[] { channels_msg }); + return tf_with(ops.control_dependencies(new[] { assert_decode, assert_channels }), delegate + { + return convert_image_dtype(gen_image_ops.decode_bmp(contents), dtype); + }); + }; - Func _png = () => - { - return convert_image_dtype(gen_image_ops.decode_png( - contents, - channels, - dtype: dtype), - dtype); - }; + Func _png = () => + { + return convert_image_dtype(gen_image_ops.decode_png( + contents, + channels, + dtype: dtype), + dtype); + }; - Func check_gif = () => - { - return control_flow_ops.cond(is_gif(contents), _gif, _bmp, name: "cond_gif"); - }; + Func check_gif = () => + { + var gif = tf.constant(new byte[] { 0x47, 0x49, 0x46 }, TF_DataType.TF_STRING); + var is_gif = math_ops.equal(substr, gif, name: name); + return control_flow_ops.cond(is_gif, _gif, _bmp, name: "cond_gif"); + }; - Func check_png = () => - { - return control_flow_ops.cond(is_png(contents), _png, check_gif, name: "cond_png"); - }; + Func check_png = () => + { + return control_flow_ops.cond(is_png(contents), _png, check_gif, name: "cond_png"); + };*/ - return tf_with(ops.name_scope(name, "decode_image"), scope => - { - return control_flow_ops.cond(is_jpeg(contents), _jpeg, check_png, name: "cond_jpeg"); + // return control_flow_ops.cond(is_jpeg(contents), _jpeg, check_png, name: "cond_jpeg"); + return _jpeg() as Tensor; }); } diff --git a/src/TensorFlowNET.Core/Tensorflow.Binding.csproj b/src/TensorFlowNET.Core/Tensorflow.Binding.csproj index fa292679..507f260c 100644 --- a/src/TensorFlowNET.Core/Tensorflow.Binding.csproj +++ b/src/TensorFlowNET.Core/Tensorflow.Binding.csproj @@ -5,7 +5,7 @@ TensorFlow.NET Tensorflow 2.2.0 - 0.31.1 + 0.31.2 8.0 Haiping Chen, Meinrad Recheis, Eli Belash SciSharp STACK @@ -19,7 +19,7 @@ Google's TensorFlow full binding in .NET Standard. Building, training and infering deep learning models. https://tensorflownet.readthedocs.io - 0.31.1.0 + 0.31.2.0 tf.net 0.20.x and above are based on tensorflow native 2.x. * Eager Mode is added finally. @@ -30,7 +30,7 @@ https://tensorflownet.readthedocs.io TensorFlow .NET v0.30 is focused on making more Keras API work including: * tf.keras.datasets * Building keras model in subclass, functional and sequential api - 0.31.1.0 + 0.31.2.0 LICENSE true true diff --git a/src/TensorFlowNET.Core/Tensors/tensor_util.cs b/src/TensorFlowNET.Core/Tensors/tensor_util.cs index 7a665c23..e2697aeb 100644 --- a/src/TensorFlowNET.Core/Tensors/tensor_util.cs +++ b/src/TensorFlowNET.Core/Tensors/tensor_util.cs @@ -20,6 +20,7 @@ using System.Collections.Generic; using System.Linq; using System.Text; using Tensorflow.Eager; +using static Tensorflow.Binding; namespace Tensorflow { @@ -410,14 +411,10 @@ would not be rank 1.", tensor.op.get_attr("axis"))); var value = constant_value(tensor); if (!(value is null)) { - int[] d_ = { }; - foreach (int d in value) - { - if (d >= 0) - d_[d_.Length] = d; - else - d_[d_.Length] = -1; // None - } + var d_ = new int[value.size]; + foreach (var (index, d) in enumerate(value.ToArray())) + d_[index] = d >= 0 ? d : -1; + ret = ret.merge_with(new TensorShape(d_)); } return ret; @@ -577,7 +574,7 @@ would not be rank 1.", tensor.op.get_attr("axis"))); return string.Join(string.Empty, nd.ToArray() .Select(x => x < 32 || x > 127 ? "\\x" + x.ToString("x") : Convert.ToChar(x).ToString())); case TF_DataType.TF_BOOL: - return (nd.GetByte(0) > 0).ToString(); + return nd.GetBoolean(0).ToString(); case TF_DataType.TF_VARIANT: case TF_DataType.TF_RESOURCE: return ""; diff --git a/src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs b/src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs index de5f4a8c..1bcb9c1d 100644 --- a/src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs +++ b/src/TensorFlowNET.Keras/Engine/DataAdapters/DataHandler.cs @@ -37,19 +37,38 @@ namespace Tensorflow.Keras.Engine.DataAdapters _steps_per_execution_value = args.StepsPerExecution.numpy(); } - _adapter = new TensorLikeDataAdapter(new TensorLikeDataAdapterArgs + if(args.Dataset == null) { - X = args.X, - Y = args.Y, - BatchSize = args.BatchSize, - Steps = args.StepsPerEpoch, - Epochs = args.Epochs - args.InitialEpoch, - Shuffle = args.Shuffle, - MaxQueueSize = args.MaxQueueSize, - Worker = args.Workers, - UseMultiprocessing = args.UseMultiprocessing, - Model = args.Model - }); + _adapter = new TensorLikeDataAdapter(new DataAdapterArgs + { + X = args.X, + Y = args.Y, + BatchSize = args.BatchSize, + Steps = args.StepsPerEpoch, + Epochs = args.Epochs - args.InitialEpoch, + Shuffle = args.Shuffle, + MaxQueueSize = args.MaxQueueSize, + Worker = args.Workers, + UseMultiprocessing = args.UseMultiprocessing, + Model = args.Model + }); + } + else + { + _adapter = new DatasetAdapter(new DataAdapterArgs + { + Dataset = args.Dataset, + BatchSize = args.BatchSize, + Steps = args.StepsPerEpoch, + Epochs = args.Epochs - args.InitialEpoch, + Shuffle = args.Shuffle, + MaxQueueSize = args.MaxQueueSize, + Worker = args.Workers, + UseMultiprocessing = args.UseMultiprocessing, + Model = args.Model + }); + } + _dataset = _adapter.GetDataset(); _inferred_steps = _infer_steps(args.StepsPerEpoch, _dataset); _current_step = 0; @@ -66,7 +85,8 @@ namespace Tensorflow.Keras.Engine.DataAdapters if (adapter_steps > -1) return adapter_steps; - throw new NotImplementedException(""); + var size = dataset.dataset_cardinality(); + return size.numpy(); } public IEnumerable<(int, OwnedIterator)> enumerate_epochs() diff --git a/src/TensorFlowNET.Keras/Engine/DataAdapters/DatasetAdapter.cs b/src/TensorFlowNET.Keras/Engine/DataAdapters/DatasetAdapter.cs new file mode 100644 index 00000000..d5f9613f --- /dev/null +++ b/src/TensorFlowNET.Keras/Engine/DataAdapters/DatasetAdapter.cs @@ -0,0 +1,35 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Tensorflow.Keras.ArgsDefinition; + +namespace Tensorflow.Keras.Engine.DataAdapters +{ + public class DatasetAdapter : IDataAdapter + { + DataAdapterArgs args; + IDatasetV2 _dataset => args.Dataset; + public DatasetAdapter(DataAdapterArgs args) + { + this.args = args; + } + + public bool CanHandle(Tensor x, Tensor y = null) + { + throw new NotImplementedException(); + } + + public IDatasetV2 GetDataset() + => _dataset; + + public int GetSize() + => -1; + + public (Tensor, Tensor) Expand1d(Tensor x, Tensor y) + { + if (y.TensorShape.ndim == 1) + y = array_ops.expand_dims(y, axis: -1); + return (x, y); + } + } +} diff --git a/src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs b/src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs index 13f634dd..ecf5cbf9 100644 --- a/src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs +++ b/src/TensorFlowNET.Keras/Engine/DataAdapters/TensorLikeDataAdapter.cs @@ -9,14 +9,14 @@ namespace Tensorflow.Keras.Engine.DataAdapters /// public class TensorLikeDataAdapter : IDataAdapter { - TensorLikeDataAdapterArgs args; + DataAdapterArgs args; int _size; int _batch_size; int num_samples; int num_full_batches; IDatasetV2 _dataset; - public TensorLikeDataAdapter(TensorLikeDataAdapterArgs args) + public TensorLikeDataAdapter(DataAdapterArgs args) { this.args = args; _process_tensorlike(); diff --git a/src/TensorFlowNET.Keras/Engine/Functional.cs b/src/TensorFlowNET.Keras/Engine/Functional.cs index 56d0863a..b2b109ba 100644 --- a/src/TensorFlowNET.Keras/Engine/Functional.cs +++ b/src/TensorFlowNET.Keras/Engine/Functional.cs @@ -39,10 +39,12 @@ namespace Tensorflow.Keras.Engine _input_coordinates = new List(); _output_coordinates = new List(); tensor_usage_count = new Dictionary(); + if (this is Sequential) + return; _init_graph_network(inputs, outputs); } - void _init_graph_network(Tensors inputs, Tensors outputs) + protected void _init_graph_network(Tensors inputs, Tensors outputs) { _is_graph_network = true; this.inputs = inputs; diff --git a/src/TensorFlowNET.Keras/Engine/Model.Compile.cs b/src/TensorFlowNET.Keras/Engine/Model.Compile.cs index 003262d9..dd91a5de 100644 --- a/src/TensorFlowNET.Keras/Engine/Model.Compile.cs +++ b/src/TensorFlowNET.Keras/Engine/Model.Compile.cs @@ -9,10 +9,6 @@ namespace Tensorflow.Keras.Engine { LossesContainer compiled_loss; MetricsContainer compiled_metrics; - public void compile(string optimizerName, ILossFunc lossName) - { - throw new NotImplementedException(""); - } public void compile(ILossFunc loss, OptimizerV2 optimizer, string[] metrics) { @@ -29,12 +25,12 @@ namespace Tensorflow.Keras.Engine this.loss = loss; } - public void compile(string optimizerName, string lossName) + public void compile(string optimizer, string loss, string[] metrics) { - switch (optimizerName) + switch (optimizer) { case "rmsprop": - optimizer = new RMSprop(new RMSpropArgs + this.optimizer = new RMSprop(new RMSpropArgs { }); diff --git a/src/TensorFlowNET.Keras/Engine/Model.Fit.cs b/src/TensorFlowNET.Keras/Engine/Model.Fit.cs index 951908f5..3c4960e7 100644 --- a/src/TensorFlowNET.Keras/Engine/Model.Fit.cs +++ b/src/TensorFlowNET.Keras/Engine/Model.Fit.cs @@ -68,5 +68,49 @@ namespace Tensorflow.Keras.Engine Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}"))); } } + + public void fit(IDatasetV2 dataset, + IDatasetV2 validation_data = null, + int batch_size = -1, + int epochs = 1, + int verbose = 1, + float validation_split = 0f, + bool shuffle = true, + int initial_epoch = 0, + int max_queue_size = 10, + int workers = 1, + bool use_multiprocessing = false) + { + data_handler = new DataHandler(new DataHandlerArgs + { + Dataset = dataset, + BatchSize = batch_size, + InitialEpoch = initial_epoch, + Epochs = epochs, + Shuffle = shuffle, + MaxQueueSize = max_queue_size, + Workers = workers, + UseMultiprocessing = use_multiprocessing, + Model = this, + StepsPerExecution = _steps_per_execution + }); + + stop_training = false; + _train_counter.assign(0); + Console.WriteLine($"Training..."); + foreach (var (epoch, iterator) in data_handler.enumerate_epochs()) + { + // reset_metrics(); + // callbacks.on_epoch_begin(epoch) + // data_handler.catch_stop_iteration(); + IEnumerable<(string, Tensor)> results = null; + foreach (var step in data_handler.steps()) + { + // callbacks.on_train_batch_begin(step) + results = step_function(iterator); + } + Console.WriteLine($"epoch: {epoch + 1}, " + string.Join(", ", results.Select(x => $"{x.Item1}: {(float)x.Item2}"))); + } + } } } diff --git a/src/TensorFlowNET.Keras/Engine/Node.cs b/src/TensorFlowNET.Keras/Engine/Node.cs index d78e5533..fad58534 100644 --- a/src/TensorFlowNET.Keras/Engine/Node.cs +++ b/src/TensorFlowNET.Keras/Engine/Node.cs @@ -35,7 +35,7 @@ namespace Tensorflow.Keras.Engine public int[] node_indices; public int[] tensor_indices; - public Tensors input_tensors => args.InputTensors; + public Tensors input_tensors => is_input ? Outputs : args.InputTensors; public Tensors Outputs => args.Outputs; public TensorShape[] input_shapes; public TensorShape[] output_shapes; diff --git a/src/TensorFlowNET.Keras/Engine/Sequential.cs b/src/TensorFlowNET.Keras/Engine/Sequential.cs index 50974cf7..e2267d53 100644 --- a/src/TensorFlowNET.Keras/Engine/Sequential.cs +++ b/src/TensorFlowNET.Keras/Engine/Sequential.cs @@ -17,6 +17,7 @@ using System.Collections.Generic; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Layers; +using Tensorflow.Keras.Utils; using static Tensorflow.KerasApi; namespace Tensorflow.Keras.Engine @@ -25,36 +26,40 @@ namespace Tensorflow.Keras.Engine /// `Sequential` groups a linear stack of layers into a `tf.keras.Model`. /// `Sequential` provides training and inference features on this model. /// - public class Sequential : Model + public class Sequential : Functional { SequentialArgs args; bool _is_graph_network; - Tensor inputs; - Tensor outputs; - - bool computeOutputAndMaskJointly; - bool autoTrackSubLayers; - TensorShape inferredInputShape; - bool hasExplicitInputShape; - TF_DataType inputDType; - List layers => args.Layers; - public TensorShape output_shape => outputs.TensorShape; + Tensors inputs; + Tensors outputs; + + bool _compute_output_and_mask_jointly; + bool _auto_track_sub_layers; + TensorShape _inferred_input_shape; + bool _has_explicit_input_shape; + TF_DataType _input_dtype; + + public TensorShape output_shape => outputs[0].TensorShape; bool built = false; public Sequential(SequentialArgs args) - : base(new ModelArgs - { - Name = args.Name - }) + : base(args.Inputs, args.Outputs, name: args.Name) { this.args = args; if (args.Layers == null) args.Layers = new List(); // SupportsMasking = true; - computeOutputAndMaskJointly = true; - autoTrackSubLayers = false; - hasExplicitInputShape = false; + _compute_output_and_mask_jointly = true; + _auto_track_sub_layers = false; + _has_explicit_input_shape = false; _is_graph_network = false; + + // Add to the model any layers passed to the constructor. + if (args.Layers != null) + { + foreach (var layer in args.Layers) + add(layer as Layer); + } } public void add(Tensor tensor) @@ -71,7 +76,7 @@ namespace Tensorflow.Keras.Engine { built = false; var set_inputs = false; - if (layers.Count == 0) + if (_layers.Count == 0) { if (layer is InputLayer) { @@ -83,7 +88,7 @@ namespace Tensorflow.Keras.Engine { // Instantiate an input layer. var x = keras.Input( - shape: layer.BatchInputShape, + batch_input_shape: layer.BatchInputShape, dtype: layer.DType, name: layer.Name + "_input"); @@ -99,36 +104,26 @@ namespace Tensorflow.Keras.Engine { // If an input layer (placeholder) is available. outputs = layer.InboundNodes[^1].Outputs; + inputs = layer_utils.get_source_inputs(outputs[0]); + built = true; + _has_explicit_input_shape = true; } - } else if (outputs != null) { outputs = layer.Apply(outputs); + built = true; } if (set_inputs || _is_graph_network) { _init_graph_network(inputs, outputs); + _is_graph_network = true; } else { } } - - void _init_graph_network(Tensor inputs, Tensor outputs) - { - _is_graph_network = true; - this.inputs = inputs; - this.outputs = outputs; - built = true; - _map_graph_network(inputs, outputs); - } - - void _map_graph_network(Tensor inputs, Tensor outputs) - { - layers.add(outputs.KerasHistory.Layer); - } } } diff --git a/src/TensorFlowNET.Keras/KerasInterface.cs b/src/TensorFlowNET.Keras/KerasInterface.cs index 6cb733d3..40519ac4 100644 --- a/src/TensorFlowNET.Keras/KerasInterface.cs +++ b/src/TensorFlowNET.Keras/KerasInterface.cs @@ -62,16 +62,21 @@ namespace Tensorflow.Keras /// public Tensor Input(TensorShape shape = null, int batch_size = -1, + TensorShape batch_input_shape = null, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool sparse = false, bool ragged = false, Tensor tensor = null) { + if (batch_input_shape != null) + shape = batch_input_shape.dims[1..]; + var args = new InputLayerArgs { Name = name, InputShape = shape, + BatchInputShape = batch_input_shape, BatchSize = batch_size, DType = dtype, Sparse = sparse, diff --git a/src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs b/src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs index f81ee161..7466685f 100644 --- a/src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs +++ b/src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs @@ -23,5 +23,10 @@ namespace Tensorflow.Keras.Layers offset = math_ops.cast(args.Offset, args.DType); return math_ops.cast(inputs, args.DType) * scale + offset; } + + public override TensorShape ComputeOutputShape(TensorShape input_shape) + { + return input_shape; + } } } diff --git a/src/TensorFlowNET.Keras/Layers/Reshaping/Flatten.cs b/src/TensorFlowNET.Keras/Layers/Reshaping/Flatten.cs index 316cab8c..66235198 100644 --- a/src/TensorFlowNET.Keras/Layers/Reshaping/Flatten.cs +++ b/src/TensorFlowNET.Keras/Layers/Reshaping/Flatten.cs @@ -1,4 +1,5 @@ using System; +using Tensorflow.Framework; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; using Tensorflow.Keras.Utils; @@ -15,6 +16,7 @@ namespace Tensorflow.Keras.Layers public Flatten(FlattenArgs args) : base(args) { + this.args = args; args.DataFormat = conv_utils.normalize_data_format(args.DataFormat); input_spec = new InputSpec(min_ndim: 1); _channels_first = args.DataFormat == "channels_first"; @@ -31,8 +33,29 @@ namespace Tensorflow.Keras.Layers { return array_ops.reshape(inputs, new[] { inputs.shape[0], -1 }); } + else + { + var input_shape = inputs.shape; + var rank = inputs.shape.rank; + if (rank == 1) + return array_ops.expand_dims(inputs, axis: 1); + var batch_dim = tensor_shape.dimension_value(input_shape[0]); + if (batch_dim != -1) + { + return array_ops.reshape(inputs, new[] { batch_dim, -1 }); + } - throw new NotImplementedException(""); + var non_batch_dims = ((int[])input_shape)[1..]; + var num = 1; + if (non_batch_dims.Length > 0) + { + for (var i = 0; i < non_batch_dims.Length; i++) + { + num *= non_batch_dims[i]; + } + } + return array_ops.reshape(inputs, new[] { inputs.shape[0], num }); + } } } } diff --git a/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs b/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs index 33754b00..cf7ef12c 100644 --- a/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs +++ b/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs @@ -40,8 +40,8 @@ namespace Tensorflow.Keras.Preprocessings labels.AddRange(Enumerable.Range(0, files.Length).Select(x => label)); } - var return_labels = new int[labels.Count]; - var return_file_paths = new string[file_paths.Count]; + var return_labels = labels.Select(x => x).ToArray(); + var return_file_paths = file_paths.Select(x => x).ToArray(); if (shuffle) { diff --git a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs index c9af1915..a57ac73e 100644 --- a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs +++ b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs @@ -41,7 +41,7 @@ namespace Tensorflow.Keras int num_channels = 0; if (color_mode == "rgb") num_channels = 3; - // C:/Users/haipi/.keras/datasets/flower_photos + var (image_paths, label_list, class_name_list) = keras.preprocessing.dataset_utils.index_directory(directory, formats: WHITELIST_FORMATS, class_names: class_names, diff --git a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.paths_and_labels_to_dataset.cs b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.paths_and_labels_to_dataset.cs index ad950fc9..abf07735 100644 --- a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.paths_and_labels_to_dataset.cs +++ b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.paths_and_labels_to_dataset.cs @@ -16,27 +16,11 @@ namespace Tensorflow.Keras var path_ds = tf.data.Dataset.from_tensor_slices(image_paths); var img_ds = path_ds.map(x => path_to_image(x, image_size, num_channels, interpolation)); - /*Shape shape = (image_paths.Length, image_size.dims[0], image_size.dims[1], num_channels); - Console.WriteLine($"Allocating memory for shape{shape}, {NPTypeCode.Float}"); - var data = np.zeros(shape, NPTypeCode.Float); - - for (var i = 0; i < image_paths.Length; i++) - { - var image = path_to_image(image_paths[i], image_size, num_channels, interpolation); - data[i] = image.numpy(); - if (i % 100 == 0) - Console.WriteLine($"Filled {i}/{image_paths.Length} data into ndarray."); - } - - var img_ds = tf.data.Dataset.from_tensor_slices(data); - if (label_mode == "int") { - var label_ds = tf.keras.preprocessing.dataset_utils.labels_to_dataset(labels, label_mode, num_classes); + var label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes); img_ds = tf.data.Dataset.zip(img_ds, label_ds); } - else*/ - throw new NotImplementedException(""); return img_ds; } @@ -47,6 +31,7 @@ namespace Tensorflow.Keras img = tf.image.decode_image( img, channels: num_channels, expand_animations: false); img = tf.image.resize_images_v2(img, image_size, method: interpolation); + // img.set_shape((image_size[0], image_size[1], num_channels)); return img; } } diff --git a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj index aec973ad..b97fdb8d 100644 --- a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj +++ b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj @@ -6,7 +6,7 @@ 8.0 Tensorflow.Keras AnyCPU;x64 - 0.2.1 + 0.3.0 Haiping Chen Keras for .NET Apache 2.0, Haiping Chen 2020 @@ -25,11 +25,13 @@ Keras is an API designed for human beings, not machines. Keras follows best prac SciSharp STACK true tensorflow, keras, deep learning, machine learning - false + true Git true Open.snk - 0.2.1.0 + 0.3.0.0 + 0.3.0.0 + LICENSE @@ -55,4 +57,11 @@ Keras is an API designed for human beings, not machines. Keras follows best prac + + + True + + + + diff --git a/src/TensorFlowNET.Keras/Utils/layer_utils.cs b/src/TensorFlowNET.Keras/Utils/layer_utils.cs index 34f553d4..4166fd51 100644 --- a/src/TensorFlowNET.Keras/Utils/layer_utils.cs +++ b/src/TensorFlowNET.Keras/Utils/layer_utils.cs @@ -187,5 +187,34 @@ namespace Tensorflow.Keras.Utils var total = weight_shapes.Select(p => (int)np.prod(p.dims)).Sum(); return total; } + + public static Tensors get_source_inputs(Tensor tensor, ILayer layer = null, int node_index = -1) + { + if (layer == null) + (layer, node_index, _) = tensor.KerasHistory; + if (layer.InboundNodes == null || layer.InboundNodes.Count == 0) + return tensor; + else + { + var node = layer.InboundNodes[node_index]; + if (node.is_input) + return node.input_tensors; + else + { + var source_tensors = new List(); + foreach (var _layer in node.iterate_inbound()) + { + (layer, node_index, tensor) = (_layer.Item1, _layer.Item2, _layer.Item4); + var previous_sources = get_source_inputs(tensor, layer, node_index); + foreach(var x in previous_sources) + { + // should be check if exist? + source_tensors.append(x); + } + } + return source_tensors; + } + } + } } } diff --git a/tensorflowlib/README.md b/tensorflowlib/README.md index eb957b46..20d30f6f 100644 --- a/tensorflowlib/README.md +++ b/tensorflowlib/README.md @@ -24,7 +24,7 @@ More information about [System.Drawing on Linux]( Install-Package SciSharp.TensorFlow.Redist-Windows-GPU PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU ``` +Since NuGet limits file size for 250M, we can't ship Linux GPU version as NuGet, you can download the library from [Google TensorFlow Storage](https://storage.googleapis.com/tensorflow). + ### Download prebuild binary manually -Tensorflow packages are built nightly and uploaded to GCS for all supported platforms. They are uploaded to the [libtensorflow-nightly](https://www.tensorflow.org/install/lang_c) GCS bucket and are indexed by operating system and date built. +TensorFlow packages are built nightly and uploaded to GCS for all supported platforms. They are uploaded to the [libtensorflow-nightly](https://www.tensorflow.org/install/lang_c) GCS bucket and are indexed by operating system and date built. ### Build from source for Windows diff --git a/test/TensorFlowNET.UnitTest/ImageTest.cs b/test/TensorFlowNET.UnitTest/ImageTest.cs index 48794650..b32c659e 100644 --- a/test/TensorFlowNET.UnitTest/ImageTest.cs +++ b/test/TensorFlowNET.UnitTest/ImageTest.cs @@ -28,7 +28,7 @@ namespace TensorFlowNET.UnitTest.Basics public void decode_image() { var img = tf.image.decode_image(contents); - Assert.AreEqual(img.name, "decode_image/cond_jpeg/Merge:0"); + Assert.AreEqual(img.name, "decode_image/Identity:0"); } [TestMethod] diff --git a/test/TensorFlowNET.UnitTest/Keras/LayersTest.cs b/test/TensorFlowNET.UnitTest/Keras/LayersTest.cs index 279a5db5..c6858ba0 100644 --- a/test/TensorFlowNET.UnitTest/Keras/LayersTest.cs +++ b/test/TensorFlowNET.UnitTest/Keras/LayersTest.cs @@ -57,7 +57,7 @@ namespace TensorFlowNET.UnitTest.Keras { 2, 3, 4, 5 }, { 3, 4, 5, 6 } }); - model.compile("rmsprop", "mse"); + // model.compile("rmsprop", "mse"); var output_array = model.predict(input_array); Assert.AreEqual((32, 10, 64), output_array.TensorShape); }