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train_it.py

@@ -4,10 +4,17 @@ import os
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 import sys
+
+if len(sys.argv) < 2:
+    order_id: float | int | None = None
+else:
+    order_id = float(sys.argv[1])
+
 import torch
 import dataconf
 import logging
 from datetime import datetime
+import math
 
 from network.Parameter import Config
 
@@ -24,10 +31,16 @@ from network.loop_train_test import (
     loop_test_reconstruction,
 )
 from network.SbSReconstruction import SbSReconstruction
+from network.InputSpikeImage import InputSpikeImage
 
 from torch.utils.tensorboard import SummaryWriter
 
 
+if order_id is None:
+    order_id_string: str = ""
+else:
+    order_id_string = f"_{order_id}"
+
 # ######################################################################
 # We want to log what is going on into a file and screen
 # ######################################################################
@@ -35,7 +48,7 @@ from torch.utils.tensorboard import SummaryWriter
 now = datetime.now()
 dt_string_filename = now.strftime("%Y_%m_%d_%H_%M_%S")
 logging.basicConfig(
-    filename="log_" + dt_string_filename + ".txt",
+    filename=f"log_{dt_string_filename}{order_id_string}.txt",
     filemode="w",
     level=logging.INFO,
     format="%(asctime)s %(message)s",
@@ -57,7 +70,9 @@ if os.path.exists("dataset.json") is False:
     raise Exception("Config file not found! dataset.json")
 
 
-cfg = dataconf.multi.file("network.json").file("dataset.json").file("def.json").on(Config)
+cfg = (
+    dataconf.multi.file("network.json").file("dataset.json").file("def.json").on(Config)
+)
 logging.info(cfg)
 
 logging.info(f"Number of spikes: {cfg.number_of_spikes}")
@@ -71,7 +86,7 @@ logging.info("")
 logging.info("*** Config loaded.")
 logging.info("")
 
-tb = SummaryWriter(log_dir=cfg.log_path)
+tb = SummaryWriter(log_dir=f"{cfg.log_path}{order_id_string}")
 
 # ###########################################
 # GPU Yes / NO ?
@@ -114,10 +129,33 @@ load_previous_weights(
     logging=logging,
     device=device,
     default_dtype=default_dtype,
+    order_id=order_id,
 )
 
 logging.info("")
 
+# Fiddling around with the amount of spikes in the input layer
+if order_id is not None:
+
+    image_size_x = (
+        the_dataset_train.initial_size[0] - 2 * cfg.augmentation.crop_width_in_pixel
+    )
+    image_size_y = (
+        the_dataset_train.initial_size[1] - 2 * cfg.augmentation.crop_width_in_pixel
+    )
+    number_of_spikes_in_input_layer = int(
+        math.ceil(
+            order_id * the_dataset_train.channel_size * image_size_x * image_size_y
+        )
+    )
+
+    assert len(cfg.number_of_spikes) > 0
+    cfg.number_of_spikes[0] = number_of_spikes_in_input_layer
+
+    if isinstance(network[0], InputSpikeImage) is True:
+        network[0].number_of_spikes = number_of_spikes_in_input_layer
+
+
 last_test_performance: float = -1.0
 with torch.no_grad():
     if cfg.learning_parameters.learning_active is True:
@@ -145,6 +183,7 @@ with torch.no_grad():
                 adapt_learning_rate=cfg.learning_parameters.adapt_learning_rate_after_minibatch,
                 lr_scheduler=lr_scheduler,
                 last_test_performance=last_test_performance,
+                order_id=order_id,
             )
 
             # Let the torch learning rate scheduler update the