Use device to disable pin_memory behaviour

Closes #77 (closed)

dan/manager/dataset.py

@@ -15,12 +15,14 @@ from dan.transforms import apply_data_augmentation
 
 
 class DatasetManager:
-    def __init__(self, params):
+    def __init__(self, params, device: str):
         self.params = params
         self.dataset_class = None
         self.img_padding_value = params["config"]["padding_value"]
 
         self.my_collate_function = None
+        # Whether data should be copied on GPU via https://pytorch.org/docs/stable/generated/torch.Tensor.pin_memory.html
+        self.pin_memory = device != "cpu"
 
         self.train_dataset = None
         self.valid_datasets = dict()
@@ -115,7 +117,7 @@ class DatasetManager:
             batch_sampler=self.train_sampler,
             sampler=self.train_sampler,
             num_workers=self.params["num_gpu"] * self.params["worker_per_gpu"],
-            pin_memory=True,
+            pin_memory=self.pin_memory,
             collate_fn=self.my_collate_function,
             worker_init_fn=self.seed_worker,
             generator=self.generator,
@@ -129,7 +131,7 @@ class DatasetManager:
                 batch_sampler=self.valid_samplers[key],
                 shuffle=False,
                 num_workers=self.params["num_gpu"] * self.params["worker_per_gpu"],
-                pin_memory=True,
+                pin_memory=self.pin_memory,
                 drop_last=False,
                 collate_fn=self.my_collate_function,
                 worker_init_fn=self.seed_worker,
@@ -174,7 +176,7 @@ class DatasetManager:
             sampler=self.test_samplers[custom_name],
             shuffle=False,
             num_workers=self.params["num_gpu"] * self.params["worker_per_gpu"],
-            pin_memory=True,
+            pin_memory=self.pin_memory,
             drop_last=False,
             collate_fn=self.my_collate_function,
             worker_init_fn=self.seed_worker,