config fix & code clean

.gitignore

@@ -152,4 +152,7 @@ ggg_data/
 ggg_utils/
 saved_models
 src/analysis/orca/orca
-src/analysis/orca/tmp*
+src/analysis/orca/tmp*
+sync.sh
+.vscode
+venv

configs/experiment/comm20.yaml

@@ -13,7 +13,7 @@ general:
     log_every_steps: 50
     number_chain_steps: 50        # Number of frames in each gif
     final_model_samples_to_generate: 20
-    final_model_samples_to_save:
+    final_model_samples_to_save: 20
     final_model_chains_to_save: 10
 
 train:

src/analysis/orca/orca.cpp

@@ -1354,6 +1354,7 @@ int writeResults(int g, const char* output_filename) {
         fout << endl;
     }
     fout.close();
+    return 0;
 }
 
 string writeResultsString(int g) {
@@ -1385,6 +1386,7 @@ int writeEdgeResults(int g, const char* output_filename) {
         fout << endl;
     }
     fout.close();
+    return 0;
 }
 
 string writeEdgeResultsString(int g) {
@@ -1401,7 +1403,7 @@ string writeEdgeResultsString(int g) {
 }
 
 int motif_counts(const char* orbit_type, int graphlet_size, 
-        const char* input_filename, const char* output_filename, string &out_str) {
+        const char* input_filename, const char* output_filename, int &out_code) {
     fstream fin; // input and output files
     // open input, output files
     if (strcmp(orbit_type, "node")!=0 && strcmp(orbit_type, "edge")!=0) {
@@ -1487,7 +1489,7 @@ int motif_counts(const char* orbit_type, int graphlet_size,
         if (strcmp(output_filename, "std") == 0) {
             cout << "orbit counts: \n" << writeResultsString(graphlet_size) << endl;
         } else {
-            out_str = writeResults(graphlet_size, output_filename);
+            out_code = writeResults(graphlet_size, output_filename);
         }
     } else {
         printf("Counting EDGE orbits of graphlets on %d nodes.\n\n",graphlet_size);
@@ -1496,7 +1498,7 @@ int motif_counts(const char* orbit_type, int graphlet_size,
         if (strcmp(output_filename, "std") == 0) {
             cout << "orbit counts: \n" << writeEdgeResultsString(graphlet_size) << endl;
         } else {
-            out_str = writeEdgeResults(graphlet_size, output_filename);
+            out_code = writeEdgeResults(graphlet_size, output_filename);
         }
     }
 
@@ -1511,8 +1513,8 @@ int init(int argc, char *argv[]) {
     }
     int graphlet_size;
     sscanf(argv[2],"%d", &graphlet_size);
-    string out;
-    motif_counts(argv[1], graphlet_size, argv[3], argv[4], out);
+    int result;
+    motif_counts(argv[1], graphlet_size, argv[3], argv[4], result);
 
     return 1;
 }

src/analysis/spectre_utils.py

@@ -772,11 +772,15 @@ def forward(self, generated_graphs: list, name, current_epoch, val_counter, loca
 
         np.savez('generated_adjs.npz', *adjacency_matrices)
 
+        to_log = {}
+
         if 'degree' in self.metrics_list:
             if local_rank == 0:
                 print("Computing degree stats..")
             degree = degree_stats(reference_graphs, networkx_graphs, is_parallel=True,
                                   compute_emd=self.compute_emd)
+
+            to_log['degree'] = degree
             if wandb.run:
                 wandb.run.summary['degree'] = degree
 
@@ -786,8 +790,6 @@ def forward(self, generated_graphs: list, name, current_epoch, val_counter, loca
         # eigval_stats(eig_ref_list, eig_pred_list, max_eig=20, is_parallel=True, compute_emd=False)
         # spectral_filter_stats(eigvec_ref_list, eigval_ref_list, eigvec_pred_list, eigval_pred_list, is_parallel=False,
         #                       compute_emd=False)          # This is the one called wavelet
-        to_log = {}
-
         if 'spectre' in self.metrics_list:
             if local_rank == 0:
                 print("Computing spectre stats...")
@@ -796,7 +798,7 @@ def forward(self, generated_graphs: list, name, current_epoch, val_counter, loca
 
             to_log['spectre'] = spectre
             if wandb.run:
-              wandb.run.summary['spectre'] = spectre
+                wandb.run.summary['spectre'] = spectre
 
         if 'clustering' in self.metrics_list:
             if local_rank == 0:

src/main.py

@@ -23,45 +23,10 @@
 
 
 def get_resume(cfg, model_kwargs):
-    """ Resumes a run. It loads previous config without allowing to update keys (used for testing). """
-    saved_cfg = cfg.copy()
-    name = cfg.general.name + '_resume'
-    resume = cfg.general.test_only
-    if cfg.model.type == 'discrete':
-        model = DiscreteDenoisingDiffusion.load_from_checkpoint(resume, **model_kwargs)
-    else:
-        model = LiftedDenoisingDiffusion.load_from_checkpoint(resume, **model_kwargs)
-    cfg = model.cfg
-    cfg.general.test_only = resume
-    cfg.general.name = name
-    cfg = utils.update_config_with_new_keys(cfg, saved_cfg)
-    return cfg, model
-
-
-def get_resume_adaptive(cfg, model_kwargs):
-    """ Resumes a run. It loads previous config but allows to make some changes (used for resuming training)."""
-    saved_cfg = cfg.copy()
-    # Fetch path to this file to get base path
-    current_path = os.path.dirname(os.path.realpath(__file__))
-    root_dir = current_path.split('outputs')[0]
-
-    resume_path = os.path.join(root_dir, cfg.general.resume)
-
-    if cfg.model.type == 'discrete':
-        model = DiscreteDenoisingDiffusion.load_from_checkpoint(resume_path, **model_kwargs)
-    else:
-        model = LiftedDenoisingDiffusion.load_from_checkpoint(resume_path, **model_kwargs)
-    new_cfg = model.cfg
-
-    for category in cfg:
-        for arg in cfg[category]:
-            new_cfg[category][arg] = cfg[category][arg]
-
-    new_cfg.general.resume = resume_path
-    new_cfg.general.name = new_cfg.general.name + '_resume'
-
-    new_cfg = utils.update_config_with_new_keys(new_cfg, saved_cfg)
-    return new_cfg, model
+    """ Resumes a run from current config (assume the core configs like model arch keep the same). """
+    cfg.general.name += "_resume"
+    model_class = DiscreteDenoisingDiffusion if cfg.model.type == 'discrete' else LiftedDenoisingDiffusion
+    return cfg, model_class(cfg, **model_kwargs)
 
 
 
@@ -150,22 +115,17 @@ def main(cfg: DictConfig):
     else:
         raise NotImplementedError("Unknown dataset {}".format(cfg["dataset"]))
 
-    if cfg.general.test_only:
-        # When testing, previous configuration is fully loaded
-        cfg, _ = get_resume(cfg, model_kwargs)
-        os.chdir(cfg.general.test_only.split('checkpoints')[0])
-    elif cfg.general.resume is not None:
-        # When resuming, we can override some parts of previous configuration
-        cfg, _ = get_resume_adaptive(cfg, model_kwargs)
-        os.chdir(cfg.general.resume.split('checkpoints')[0])
+    current_ckpt = cfg.general.test_only or cfg.general.resume
+    if current_ckpt:
+        cfg, model = get_resume(cfg, model_kwargs)
+        ckpt_dir = current_ckpt.split('checkpoints')[0]
+        os.chdir(ckpt_dir)
+    else:
+        model_class = DiscreteDenoisingDiffusion if cfg.model.type == 'discrete' else LiftedDenoisingDiffusion
+        model = model_class(cfg=cfg, **model_kwargs)
 
     utils.create_folders(cfg)
 
-    if cfg.model.type == 'discrete':
-        model = DiscreteDenoisingDiffusion(cfg=cfg, **model_kwargs)
-    else:
-        model = LiftedDenoisingDiffusion(cfg=cfg, **model_kwargs)
-
     callbacks = []
     if cfg.train.save_model:
         checkpoint_callback = ModelCheckpoint(dirpath=f"checkpoints/{cfg.general.name}",
@@ -188,7 +148,7 @@ def main(cfg: DictConfig):
 
     use_gpu = cfg.general.gpus > 0 and torch.cuda.is_available()
     trainer = Trainer(gradient_clip_val=cfg.train.clip_grad,
-                      strategy="ddp_find_unused_parameters_true",  # Needed to load old checkpoints
+                    #   strategy="ddp" if cfg.general.gpus > 1 else "auto",
                       accelerator='gpu' if use_gpu else 'cpu',
                       devices=cfg.general.gpus if use_gpu else 1,
                       max_epochs=cfg.train.n_epochs,