linting

Author: declanmcnamara

case_studies/redshift/evaluation/evaluate_cts.py

@@ -7,8 +7,6 @@
 from hydra.utils import instantiate
 from omegaconf import DictConfig
 from tqdm import tqdm
-from hydra import compose, initialize
-from case_studies.redshift.redshift_from_img.encoder.encoder import RedshiftsEncoder
 
 
 def get_best_ckpt(ckpt_dir: str):
@@ -21,26 +19,23 @@ def get_best_ckpt(ckpt_dir: str):
     raise FileExistsError("No ckpt files found in the directory")
 
 
-# @hydra.main(config_path=".", config_name="continuous_eval")
+@hydra.main(config_path=".", config_name="continuous_eval")
 def main(cfg: DictConfig):
-    with initialize(config_path=".", job_name="continuous_eval"):
-        cfg = compose(config_name="continuous_eval")
-
     output_dir = cfg.paths.plot_dir
     ckpt_dir = cfg.paths.ckpt_dir
 
     output_dir = Path(output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
     ckpt_path = get_best_ckpt(ckpt_dir)
-    device = torch.device('cpu')
+    device = torch.device("cpu")
 
     # set up testing dataset
     dataset = instantiate(cfg.train.data_source)
     dataset.setup("test")
 
     # load bliss trained model - continuous version
-    bliss_encoder: RedshiftsEncoder = instantiate(cfg.encoder).to(device=device)
+    bliss_encoder = instantiate(cfg.encoder).to(device=device)
     pretrained_weights = torch.load(ckpt_path, device)["state_dict"]
     bliss_encoder.load_state_dict(pretrained_weights)
     bliss_encoder.eval()
@@ -52,7 +47,6 @@ def main(cfg: DictConfig):
     if not bliss_output_path.exists():
         test_loader = dataset.test_dataloader()
         for batch_idx, batch in tqdm(enumerate(test_loader), total=len(test_loader)):
-            batch["images"] = batch["images"].to(device)
             bliss_encoder.update_metrics(batch, batch_idx)
         bliss_out_dict = bliss_encoder.mode_metrics.compute()
 

case_studies/redshift/evaluation/evaluate_discrete.py

@@ -27,7 +27,7 @@ def main(cfg: DictConfig):
     output_dir.mkdir(parents=True, exist_ok=True)
 
     ckpt_path = get_best_ckpt(ckpt_dir)
-    device = torch.device("cpu") # torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cpu")
 
     # set up testing dataset
     dataset = instantiate(cfg.train.data_source)

case_studies/redshift/evaluation/plots_bliss.py

@@ -6,12 +6,10 @@
 from matplotlib import pyplot as plt
 from matplotlib.ticker import FormatStrFormatter
 from omegaconf import DictConfig
-from hydra import compose, initialize
+
 
 @hydra.main(config_path=".", config_name="discrete_eval")
 def main(cfg: DictConfig):
-    with initialize(config_path=".", job_name="discrete_eval"):
-        cfg = compose(config_name="discrete_eval")
     output_dir = Path(cfg.paths.plot_dir)
 
     # Load metric results

case_studies/redshift/redshift_from_img/encoder/metrics.py

@@ -10,6 +10,26 @@
 from bliss.encoder.metrics import CatalogMatcher
 
 
+def convert_nmgy_to_njymag(nmgy):
+    """Convert from flux (nano-maggie) to mag (nano-jansky), which is the format used by DC2.
+
+    For the difference between mag (Pogson magnitude) and njymag (AB magnitude), please view
+    the "Flux units: maggies and nanomaggies" part of
+    https://www.sdss3.org/dr8/algorithms/magnitudes.php#nmgy
+    When we change the standard source to AB sources, we need to do the conversion
+    described in "2.10 AB magnitudes" at
+    https://pstn-001.lsst.io/fluxunits.pdf
+
+    Args:
+        nmgy: the fluxes in nanomaggies
+
+    Returns:
+        Tensor indicating fluxes in AB magnitude
+    """
+
+    return 22.5 - 2.5 * torch.log10(nmgy / 3631)
+
+
 class MetricBin(Metric):
     def __init__(
         self,
@@ -67,6 +87,7 @@ def __init__(
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -80,7 +101,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             red_err = (true_red - est_red).abs() ** 2
@@ -238,6 +259,7 @@ def __init__(
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -251,7 +273,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             metric_outlier = torch.abs(true_red - est_red) / (1 + true_red)
@@ -322,6 +344,7 @@ def __init__(
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -335,7 +358,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             metric_outlier_cata = torch.abs(true_red - est_red)
@@ -399,6 +422,7 @@ def __init__(self, **kwargs):
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -412,7 +436,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             metrics = (true_red - est_red) / (1 + true_red)
@@ -492,6 +516,7 @@ def __init__(self, **kwargs):
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -505,7 +530,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             metrics = (true_red - est_red) / (1 + true_red)
@@ -577,6 +602,7 @@ def __init__(self, **kwargs):
 
     def update(self, true_cat, est_cat, matching):
         cutoffs = torch.tensor(self.bin_cutoffs, device=self.device)
+        on_fluxes = convert_nmgy_to_njymag(true_cat.on_fluxes)
         for i in range(true_cat.batch_size):
             tcat_matches, ecat_matches = matching[i]
 
@@ -590,7 +616,7 @@ def update(self, true_cat, est_cat, matching):
                 true_red = true_cat["redshifts"][i, tcat_matches, :].to(self.device)
                 est_red = est_cat["redshifts"][i, ecat_matches, :].to(self.device)
 
-            true_mag = true_cat.on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
+            true_mag = on_fluxes[i][..., self.mag_band][tcat_matches].to(self.device)
             bin_indices = torch.bucketize(true_mag, cutoffs)
 
             metrics = torch.abs(true_red - est_red) / (1 + true_red)

case_studies/redshift/runner.sh

@@ -6,32 +6,32 @@ export MKL_NUM_THREADS="16"
 export NUMEXPR_NUM_THREADS="16"
 
 # Produce data artifacts
-# echo "producing data artifacts for BLISS and RAIL from DC2"
-# python artifacts/data_generation.py
+echo "producing data artifacts for BLISS and RAIL from DC2"
+python artifacts/data_generation.py
 
-# # Run BLISS (discrete variational distribution)
-# DIRNAME="$OUT_DIR/discrete"
+# Run BLISS (discrete variational distribution)
+DIRNAME="$OUT_DIR/discrete"
 
-# if [ ! -d "$DIRNAME" ]; then
-#   mkdir -p "$DIRNAME"
-#   echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
-# else
-#   echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
-# fi
+if [ ! -d "$DIRNAME" ]; then
+  mkdir -p "$DIRNAME"
+  echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
+else
+  echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
+fi
 
-# nohup python bliss/main.py -cp ~/bliss/case_studies/redshift/redshift_from_img -cn discrete > "$DIRNAME/output.out" 2>&1 &
+nohup python bliss/main.py -cp ~/bliss/case_studies/redshift/redshift_from_img -cn discrete > "$DIRNAME/output.out" 2>&1 &
 
 # Run BLISS (continuous variational distribution)
-# DIRNAME="$OUT_DIR/continuous"
+DIRNAME="$OUT_DIR/continuous"
 
-# if [ ! -d "$DIRNAME" ]; then
-#   mkdir -p "$DIRNAME"
-#   echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
-# else
-#   echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
-# fi
+if [ ! -d "$DIRNAME" ]; then
+  mkdir -p "$DIRNAME"
+  echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
+else
+  echo "BLISS training logs/checkpoints will be saved to $DIRNAME"
+fi
 
-# nohup python bliss/main.py -cp ~/bliss/case_studies/redshift/redshift_from_img -cn continuous > "$DIRNAME/output.out" 2>&1 &
+nohup python bliss/main.py -cp ~/bliss/case_studies/redshift/redshift_from_img -cn continuous > "$DIRNAME/output.out" 2>&1 &
 
 # # Run RAIL
 # # TODO