Update LiveCELL inference scripts

finetuning/livecell/evaluation/evaluate_amg.py

@@ -3,13 +3,10 @@
 
 from micro_sam.evaluation.evaluation import run_evaluation
 from micro_sam.evaluation.livecell import run_livecell_amg
-from util import DATA_ROOT, get_checkpoint, get_experiment_folder, get_pred_and_gt_paths
+from util import DATA_ROOT, get_pred_and_gt_paths
 
 
-def run_amg(name, model_type, checkpoint):
-    if checkpoint is None:
-        checkpoint, model_type = get_checkpoint(name)
-    experiment_folder = get_experiment_folder(name)
+def run_amg(model_type, checkpoint, experiment_folder):
     input_folder = DATA_ROOT
     prediction_folder = run_livecell_amg(
         checkpoint,
@@ -21,28 +18,26 @@ def run_amg(name, model_type, checkpoint):
     return prediction_folder
 
 
-def eval_amg(name, prediction_folder):
+def eval_amg(prediction_folder, experiment_folder):
     print("Evaluating", prediction_folder)
     pred_paths, gt_paths = get_pred_and_gt_paths(prediction_folder)
-    save_path = os.path.join(get_experiment_folder(name), "results", "amg.csv")
+    save_path = os.path.join(experiment_folder, "results", "amg.csv")
     res = run_evaluation(gt_paths, pred_paths, save_path=save_path)
     print(res)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("-n", "--name", required=True)
     parser.add_argument(
-        "-m", "--model", type=str,  # options: "vit_h", "vit_h_generalist", "vit_h_specialist"
+        "-m", "--model", type=str, required=True,
         help="Provide the model type to initialize the predictor"
     )
-    parser.add_argument("-c", "--checkpoint", type=str, default=None)
+    parser.add_argument("-c", "--checkpoint", type=str, required=True)
+    parser.add_argument("-e", "--experiment_folder", type=str, required=True)
     args = parser.parse_args()
 
-    name = args.name
-
-    prediction_folder = run_amg(name, args.model, args.checkpoint)
-    eval_amg(name, prediction_folder)
+    prediction_folder = run_amg(args.model, args.checkpoint, args.experiment_folder)
+    eval_amg(prediction_folder, args.experiment_folder)
 
 
 if __name__ == "__main__":

finetuning/livecell/evaluation/evaluate_amg.sbatch

@@ -6,6 +6,7 @@
 #SBATCH -G A100:1
 #SBATCH -A nim00007
 
-source ~/.bashrc
-micromamba activate main
-python evaluate_amg.py $@
+source activate sam
+python evaluate_amg.py -c /scratch/usr/nimanwai/micro-sam/checkpoints/vit_b/livecell_sam/best.pt \
+                       -m vit_b \
+                       -e /scratch/projects/nim00007/sam/experiments/new_models/specialists/livecell/vit_b/

finetuning/livecell/evaluation/evaluate_instance_segmentation.py

@@ -3,13 +3,10 @@
 
 from micro_sam.evaluation.evaluation import run_evaluation
 from micro_sam.evaluation.livecell import run_livecell_instance_segmentation_with_decoder
-from util import DATA_ROOT, get_checkpoint, get_experiment_folder, get_pred_and_gt_paths
+from util import DATA_ROOT, get_pred_and_gt_paths
 
 
-def run_instance_segmentation_with_decoder(name, model_type, checkpoint):
-    if checkpoint is None:
-        checkpoint, model_type = get_checkpoint(name)
-    experiment_folder = get_experiment_folder(name)
+def run_instance_segmentation_with_decoder(model_type, checkpoint, experiment_folder):
     input_folder = DATA_ROOT
     prediction_folder = run_livecell_instance_segmentation_with_decoder(
         checkpoint,
@@ -21,28 +18,28 @@ def run_instance_segmentation_with_decoder(name, model_type, checkpoint):
     return prediction_folder
 
 
-def eval_instance_segmentation_with_decoder(name, prediction_folder):
+def eval_instance_segmentation_with_decoder(prediction_folder, experiment_folder):
     print("Evaluating", prediction_folder)
     pred_paths, gt_paths = get_pred_and_gt_paths(prediction_folder)
-    save_path = os.path.join(get_experiment_folder(name), "results", "instance_segmentation_with_decoder.csv")
+    save_path = os.path.join(experiment_folder, "results", "instance_segmentation_with_decoder.csv")
     res = run_evaluation(gt_paths, pred_paths, save_path=save_path)
     print(res)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("-n", "--name", required=True)
     parser.add_argument(
-        "-m", "--model", type=str,  # options: "vit_h", "vit_h_generalist", "vit_h_specialist"
+        "-m", "--model", type=str, required=True,
         help="Provide the model type to initialize the predictor"
     )
-    parser.add_argument("-c", "--checkpoint", type=str, default=None)
+    parser.add_argument("-c", "--checkpoint", type=str, required=True,)
+    parser.add_argument("-e", "--experiment_folder", type=str, required=True)
     args = parser.parse_args()
 
-    name = args.name
-
-    prediction_folder = run_instance_segmentation_with_decoder(name, args.model, args.checkpoint)
-    eval_instance_segmentation_with_decoder(name, prediction_folder)
+    prediction_folder = run_instance_segmentation_with_decoder(
+        args.model, args.checkpoint, args.experiment_folder
+    )
+    eval_instance_segmentation_with_decoder(prediction_folder, args.experiment_folder)
 
 
 if __name__ == "__main__":

finetuning/livecell/evaluation/evaluate_instance_segmentation.sbatch

@@ -6,6 +6,7 @@
 #SBATCH -G A100:1
 #SBATCH -A nim00007
 
-source ~/.bashrc
-micromamba activate main
-python evaluate_instance_segmentation.py $@
+source activate sam
+python evaluate_instance_segmentation.py -c /scratch/usr/nimanwai/micro-sam/checkpoints/vit_h/livecell_sam/best.pt \
+                                         -m vit_h \
+                                         -e /scratch/projects/nim00007/sam/experiments/new_models/specialists/livecell/vit_h/

finetuning/livecell/evaluation/iterative_prompting.py

@@ -6,7 +6,7 @@
 
 from micro_sam.evaluation import inference
 from micro_sam.evaluation.evaluation import run_evaluation
-from util import get_paths, get_experiment_folder, get_model, get_pred_and_gt_paths
+from util import get_paths, get_model, get_pred_and_gt_paths
 
 
 def run_interactive_prompting(exp_folder, predictor, start_with_box_prompt):
@@ -26,16 +26,9 @@ def run_interactive_prompting(exp_folder, predictor, start_with_box_prompt):
     return prediction_root
 
 
-def evaluate_interactive_prompting(prediction_root, start_with_box_prompt, name):
+def evaluate_interactive_prompting(prediction_root, start_with_box_prompt, exp_folder):
     assert os.path.exists(prediction_root), prediction_root
 
-    csv_save_dir = f"./iterative_prompting_results/{name}"
-    os.makedirs(csv_save_dir, exist_ok=True)
-    csv_path = os.path.join(csv_save_dir, "start_with_box.csv" if start_with_box_prompt else "start_with_point.csv")
-    if os.path.exists(csv_path):
-        print("The evaluated results for the expected setting already exist here:", csv_path)
-        return
-
     prediction_folders = sorted(glob(os.path.join(prediction_root, "iteration*")))
     list_of_results = []
     for pred_folder in prediction_folders:
@@ -46,10 +39,9 @@ def evaluate_interactive_prompting(prediction_root, start_with_box_prompt, name)
         print(res)
 
     df = pd.concat(list_of_results, ignore_index=True)
-    df.to_csv(csv_path)
 
-    # Also save the results in the experiment folder.
-    result_folder = os.path.join(get_experiment_folder(name), "results")
+    # Save the results in the experiment folder.
+    result_folder = os.path.join(exp_folder, "results")
     os.makedirs(result_folder, exist_ok=True)
     csv_path = os.path.join(
         result_folder,
@@ -60,25 +52,21 @@ def evaluate_interactive_prompting(prediction_root, start_with_box_prompt, name)
 
 def main():
     parser = argparse.ArgumentParser()
-
-    parser.add_argument("-n", "--name", required=True)
     parser.add_argument(
-        "-m", "--model", type=str,  # options: "vit_h", "vit_h_generalist", "vit_h_specialist"
-        help="Provide the model type to initialize the predictor"
+        "-m", "--model", type=str, required=True, help="Provide the model type to initialize the predictor"
     )
-    parser.add_argument("-c", "--checkpoint", type=str, default=None)
+    parser.add_argument("-c", "--checkpoint", type=str, required=True)
+    parser.add_argument("-e", "--experiment_folder", type=str, required=True)
     parser.add_argument("--box", action="store_true", help="If passed, starts with first prompt as box")
     args = parser.parse_args()
 
-    name = args.name
     start_with_box_prompt = args.box  # overwrite to start first iters' prompt with box instead of single point
 
     # get the predictor to perform inference
-    predictor = get_model(name, model_type=args.model, ckpt=args.checkpoint)
+    predictor = get_model(model_type=args.model, ckpt=args.checkpoint)
 
-    exp_folder = get_experiment_folder(name)
-    prediction_root = run_interactive_prompting(exp_folder, predictor, start_with_box_prompt)
-    evaluate_interactive_prompting(prediction_root, start_with_box_prompt, name)
+    prediction_root = run_interactive_prompting(args.experiment_folder, predictor, start_with_box_prompt)
+    evaluate_interactive_prompting(prediction_root, start_with_box_prompt)
 
 
 if __name__ == "__main__":

finetuning/livecell/evaluation/iterative_prompting.sbatch

@@ -6,6 +6,7 @@
 #SBATCH -G A100:1
 #SBATCH -A nim00007
 
-source ~/.bashrc
-micromamba activate main
-python iterative_prompting.py $@
+source activate sam
+python iterative_prompting.py -c /scratch/usr/nimanwai/micro-sam/checkpoints/vit_h/livecell_sam/best.pt \
+                              -m vit_h \
+                              -e /scratch/projects/nim00007/sam/experiments/new_models/specialists/livecell/vit_h/

finetuning/livecell/evaluation/submit_evaluation.py

@@ -0,0 +1,85 @@
+import os
+import shutil
+import subprocess
+from glob import glob
+from datetime import datetime
+
+
+def write_batch_script(env_name, out_path, inference_setup, checkpoint, model_type, experiment_folder):
+    """Writing scripts with different fold-trainings for micro-sam evaluation
+    """
+    batch_script = f"""#!/bin/bash
+#SBATCH -c 8
+#SBATCH --mem 128G
+#SBATCH -t 6:00:00
+#SBATCH -p grete:shared
+#SBATCH -G A100:1
+#SBATCH -A nim00007
+#SBATCH --job-name={inference_setup}
+
+source ~/.bashrc 
+mamba activate {env_name}
+python {inference_setup}.py """
+
+    _op = out_path[:-3] + f"_{inference_setup}.sh"
+
+    # add the finetuned checkpoint
+    batch_script += f"-c {checkpoint} "
+
+    # name of the model configuration
+    batch_script += f"-m {model_type} "
+
+    # experiment folder
+    batch_script += f"-e {experiment_folder} "
+
+    with open(_op, "w") as f:
+        f.write(batch_script)
+
+
+def get_batch_script_names(tmp_folder):
+    tmp_folder = os.path.expanduser(tmp_folder)
+    os.makedirs(tmp_folder, exist_ok=True)
+
+    script_name = "livecell-inference"
+
+    dt = datetime.now().strftime("%Y_%m_%d_%H_%M_%S_%f")
+    tmp_name = script_name + dt
+    batch_script = os.path.join(tmp_folder, f"{tmp_name}.sh")
+
+    return batch_script
+
+
+def submit_slurm():
+    """Submit python script that needs gpus with given inputs on a slurm node.
+    """
+    tmp_folder = "./gpu_jobs"
+
+    # parameters to run the inference scripts
+    env_name = "sam"
+    checkpoint = "/scratch/usr/nimanwai/micro-sam/checkpoints/vit_h/livecell_sam/best.pt"
+    model_type = "vit_h"
+    experiment_folder = "/scratch/projects/nim00007/sam/experiments/new_models/specialists/livecell/vit_h/"
+
+    all_setups = ["evaluate_amg", "evaluate_instance_segmentation", "iterative_prompting"]
+    for current_setup in all_setups:
+        write_batch_script(
+            env_name=env_name,
+            out_path=get_batch_script_names(tmp_folder),
+            inference_setup=current_setup,
+            checkpoint=checkpoint,
+            model_type=model_type,
+            experiment_folder=experiment_folder,
+            )
+
+    for my_script in glob(tmp_folder + "/*"):
+        cmd = ["sbatch", my_script]
+        subprocess.run(cmd)
+
+
+if __name__ == "__main__":
+    try:
+        shutil.rmtree("./gpu_jobs")
+    except FileNotFoundError:
+        pass
+
+    submit_slurm()

finetuning/livecell/evaluation/util.py

@@ -32,7 +32,7 @@ def get_checkpoint(name):
     return ckpt, model_type
 
 
-def get_model(name, model_type=None, ckpt=None):
+def get_model(name=None, model_type=None, ckpt=None):
     if ckpt is None:
         ckpt, model_type = get_checkpoint(name)
     assert (ckpt is not None) and (model_type is not None)