zarr dataloader mitocondria

scripts/training_loop_VaeResnet18_ac.py

@@ -21,10 +21,10 @@
 import yaml
 
 # Parameters
-model_name = "test_linear_ac_latent_128"
-run_name= "Linear_dataset_split_17_latent_128"
+model_name = "test_linear_ac_latent_128_b5e-6"
+run_name= "Linear_dataset_split_17_latent_128_b5e-6"
 latent_space_dim = 128
-beta = 1e-4
+beta = 5e-6
 n_epochs = 15
 find_port = False #change to false if you already have tensorboard running
 

src/embed_time/launch_tensorboard_ac.py

@@ -0,0 +1,25 @@
+import os
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+tensorboard_process = launch_tensorboard("embed_time_static_runs")

src/embed_time/zarr_dataloader_ac.py

@@ -67,16 +67,16 @@ def __getitem__(self, idx):
 
         # transformation
         transform_array = np.max(array, axis=2).squeeze(0)
-        print(transform_array.shape)
-        transform_array = scipy.ndimage.zoom(transform_array, zoom=(1, 0.5, 0.5))
-        print(transform_array.shape)
-        flip_prob = np.random.rand(1)
+        # print(transform_array.shape)
+        # transform_array = scipy.ndimage.zoom(transform_array, zoom=(1, 0.5, 0.5))
+        # print(transform_array.shape)
+        # flip_prob = np.random.rand(1)
 
-        if flip_prob >0.5:
-            transform_array = np.flip(transform_array, axis=(1,2)) #(2,2024,2024) 
-            print(transform_array.shape)
-        transform_array = np.rot90(transform_array, k=np.random.randint(4), axes=(1,2))
-        print(transform_array.shape)
+        # if flip_prob >0.5:
+        #     transform_array = np.flip(transform_array, axis=(1,2)) #(2,2024,2024) 
+        #     print(transform_array.shape)
+        # transform_array = np.rot90(transform_array, k=np.random.randint(4), axes=(1,2))
+        # print(transform_array.shape)
         return transform_array
 
 dataset = ZarrDataset("/home/S-ac/embed_time/zarrdata/mitochondria.zarr")