Add TF1 platform, docker and README

src/pytorch/nn_dense_x5.py

@@ -0,0 +1,37 @@
+from pathlib import Path
+from typing import List, Tuple
+
+import torch
+
+from src.common import DataType, Op
+from src.pytorch.base import TorchBase
+
+
+class DenseNetwork(torch.nn.Module):
+    def __init__(self, input_dim: int, dtype: torch.dtype):
+        super().__init__()
+        self.dense = torch.nn.Sequential(
+            *[torch.nn.Linear(input_dim, input_dim, dtype=dtype) for _ in range(5)])
+
+    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+        return self.dense(input_data)
+
+
+class TorchNNDenseX5Bench(TorchBase):
+    def __init__(self, output_path: Path, data_type: DataType):
+        super().__init__(output_path, Op.NN_DENSE_X5, data_type)
+        self.tensor: torch.Tensor = None
+        self.tensor_result: torch.Tensor = None
+        self.network: torch.nn.Module = None
+
+    def pre_experiment(self, experiment_args: Tuple[int, int]):
+        batch_size, dimension = experiment_args
+        self.tensor = torch.ones((batch_size, dimension), dtype=self.dtype, device=self.device, requires_grad=False)
+        self.network = DenseNetwork(dimension, self.dtype).to(self.device)
+        self.tensor_result = self.network(self.tensor)
+
+    def experiment(self):
+        self.tensor_result = self.network(self.tensor)
+
+    def run(self, experiment_args: List[Tuple[int, int]], experiment_count: int):
+        super().run(experiment_args, experiment_count)