drop torchvision, tests only (#797)

* drop torchvision, tests only

* manifest

* move test utils

tests/models/debug.py

@@ -0,0 +1,54 @@
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
+
+import pytorch_lightning as pl
+
+
+# from test_models import assert_ok_test_acc, load_model, \
+#     clear_save_dir, get_test_tube_logger, get_hparams, init_save_dir, \
+#     init_checkpoint_callback, reset_seed, set_random_master_port
+
+
+class CoolModel(pl.LightningModule):
+
+    def __init(self):
+        super(CoolModel, self).__init__()
+        # not the best model...
+        self.l1 = torch.nn.Linear(28 * 28, 10)
+
+    def forward(self, x):
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'training_loss': self.my_loss(y_hat, y)}
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        y_hat = self.forward(x)
+        return {'val_loss': self.my_loss(y_hat, y)}
+
+    def validation_end(self, outputs):
+        avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
+        return avg_loss
+
+    def configure_optimizers(self):
+        return [torch.optim.Adam(self.parameters(), lr=0.02)]
+
+    @pl.data_loader
+    def train_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
+
+    @pl.data_loader
+    def val_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
+
+    @pl.data_loader
+    def test_dataloader(self):
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)