[REF] Improve MWE

experiments/visual_abstract/mwe.py

@@ -1,27 +1,29 @@
 """PyTorch's convolutions store their input when `weight.requires_grad=False`."""
 
 from collections import OrderedDict
-from time import sleep
 
 from memory_profiler import memory_usage
 from torch import rand
 from torch.nn import Conv2d, Sequential
 
-X = rand(256, 8, 256, 256)
-NUM_LAYERS = 3
+SHAPE_X = (256, 8, 256, 256)  # shape of the input
+MEM_X = 512  # requires 512 MiB storage
+NUM_LAYERS = 5
 
-# Create a deep linear CNN with size-preserving convolutions.
-"""Create a deep linear CNN with size-preserving convolutions."""
-layers = OrderedDict()
-for i in range(NUM_LAYERS):
-    layers[f"conv{i}"] = Conv2d(8, 8, 3, padding=1, bias=False)
-net = Sequential(layers)
+
+def setup():
+    """Create a deep linear CNN with size-preserving convolutions and an input."""
+    layers = OrderedDict()
+    for i in range(NUM_LAYERS):
+        layers[f"conv{i}"] = Conv2d(8, 8, 3, padding=1, bias=False)
+    return rand(*SHAPE_X), Sequential(layers)
 
 
 # Consider three different scenarios: 1) no parameters are trainable, 2) all
 # layers are trainable, 3) only the first layer is trainable
 def non_trainable():
     """Forward pass through the CNN with all layers non-trainable."""
+    X, net = setup()
     for i in range(NUM_LAYERS):
         getattr(net, f"conv{i}").weight.requires_grad = False
 
@@ -33,6 +35,7 @@ def non_trainable():
 
 def all_trainable():
     """Forward pass through the CNN with all layers trainable."""
+    X, net = setup()
     for i in range(NUM_LAYERS):
         getattr(net, f"conv{i}").weight.requires_grad = True
 
@@ -44,6 +47,7 @@ def all_trainable():
 
 def first_trainable():
     """Forward pass through the CNN with first layer trainable."""
+    X, net = setup()
     for i in range(NUM_LAYERS):
         getattr(net, f"conv{i}").weight.requires_grad = i == 1
 
@@ -57,14 +61,20 @@ def first_trainable():
     kwargs = {"interval": 1e-4, "max_usage": True}  # memory profiler settingss
 
     # measure memory and print
-    mem_offset = memory_usage(lambda: sleep(0.1), **kwargs)
-    print(f"Memory weights+input: {mem_offset:.1f} MiB.")
-
-    mem_non = memory_usage(non_trainable, **kwargs) - mem_offset
-    print(f"Memory non-trainable: {mem_non:.1f} MiB.")
-
-    mem_all = memory_usage(all_trainable, **kwargs) - mem_offset
-    print(f"Memory all-trainable: {mem_all:.1f} MiB.")
-
-    mem_first = memory_usage(first_trainable, **kwargs) - mem_offset
-    print(f"Memory first-trainable: {mem_first:.1f} MiB.")
+    mem_setup = memory_usage(setup, **kwargs)
+    print(f"Weights+input: {mem_setup:.1f} MiB.")
+
+    mem_non = memory_usage(non_trainable, **kwargs) - mem_setup
+    print(
+        f"Non-trainable: {mem_non:.1f} MiB (≈{mem_non / MEM_X:.1f} hidden activations)."
+    )
+
+    mem_all = memory_usage(all_trainable, **kwargs) - mem_setup
+    print(
+        f"All-trainable: {mem_all:.1f} MiB (≈{mem_all / MEM_X:.1f} hidden activations)."
+    )
+
+    mem_first = memory_usage(first_trainable, **kwargs) - mem_setup
+    print(
+        f"First-trainable: {mem_first:.1f} MiB (≈{mem_first / MEM_X:.1f} hidden activations)."
+    )