Don't compute FID clean and FID clip if one channel image, fixes #6

giulio98 · May 5, 2024 · c1aa1f6 · c1aa1f6
1 parent cb13fb0
commit c1aa1f6
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Showing 4 changed files with 24 additions and 17 deletions.
diff --git a/README.md b/README.md
@@ -60,7 +60,7 @@ Before you begin with any experiments, ensure to create a `.env` file with the f
 export WANDB_API_KEY=<your wandb api key>
 export HOME=<your_home_directory>  # e.g., /home/username
 export CUDA_HOME=/usr/local/cuda
-export PROJECT_ROOT=<your_project_directory>  # /home/username/functional_diffusion_processes
+export PROJECT_ROOT=<your_project_directory>  # /home/username/functional-diffusion-processes
 export DATA_ROOT=${PROJECT_ROOT}/data
 export LOGS_ROOT=${PROJECT_ROOT}/logs
 export TFDS_DATA_DIR=${DATA_ROOT}/tensorflow_datasets

diff --git a/conf/experiments_maml/eval_mnist.yaml b/conf/experiments_maml/eval_mnist.yaml
@@ -1,3 +1,6 @@
+
+
+
  # @package _global_
 defaults:
   - override /trainers: trainer_maml
@@ -24,7 +27,7 @@ trainers:
   evaluation_config:
     seed: 43 # random seed for reproducibility
     eval_dir: ${oc.env:LOGS_ROOT}/inr_mnist # directory where evaluation results are saved
-    num_samples: 10000 # number of samples to be generated for evaluation
+    num_samples: 16000 # number of samples to be generated for evaluation
 
 sdes:
   sde_config:
@@ -65,4 +68,6 @@ models:
 datasets:
   test:
     data_config:
-      batch_size: 1
+      image_height_size: 32
+      image_width_size: 32
+      batch_size: 512
diff --git a/conf/trainers/trainer_maml.yaml b/conf/trainers/trainer_maml.yaml
@@ -56,6 +56,6 @@ trainer_logging:
   use_wandb: True # if True, wandb is used for logging
   wandb_init:
     name: ${trainers.training_config.save_dir}
-    project: "your_project"
+    project: "fdp"
     entity: "your_entity"
     save_code: False
diff --git a/src/functional_diffusion_processes/trainers/trainer.py b/src/functional_diffusion_processes/trainers/trainer.py
@@ -687,22 +687,24 @@ class EvalMeta:
             fid_score, inception_score = fid_metric.compute_fid(self.eval_dir, num_sampling_rounds)
             pylogger.info("FID: %.6e" % fid_score)
             pylogger.info("Inception score %.6e" % inception_score)
+            if not ds_test.data_config.output_size == 1:
+                # Compute FID clean
+                clean_dataset = os.path.join(
+                    fid_metric.metric_config.real_features_path,
+                    f"{fid_metric.metric_config.dataset_name.lower()}_clean",
+                )
+                clean_fake_dataset = os.path.join(self.eval_dir, "clean")
+                fid_clean = fid.compute_fid(clean_fake_dataset, clean_dataset, mode="clean")
+                pylogger.info("FID clean: %.6e" % fid_clean)
 
-            # Compute FID clean
-            clean_dataset = os.path.join(
-                fid_metric.metric_config.real_features_path, f"{fid_metric.metric_config.dataset_name.lower()}_clean"
-            )
-            clean_fake_dataset = os.path.join(self.eval_dir, "clean")
-            fid_clean = fid.compute_fid(clean_fake_dataset, clean_dataset, mode="clean")
-            pylogger.info("FID clean: %.6e" % fid_clean)
-
-            # Compute FID-CLIP
-            fid_clip = fid.compute_fid(clean_fake_dataset, clean_dataset, mode="clean", model_name="clip_vit_b_32")
-            pylogger.info("FID-CLIP: %.6e" % fid_clip)
+                # Compute FID-CLIP
+                fid_clip = fid.compute_fid(clean_fake_dataset, clean_dataset, mode="clean", model_name="clip_vit_b_32")
+                pylogger.info("FID-CLIP: %.6e" % fid_clip)
 
             if self.logging.use_wandb:
                 wandb.log({"FID": float(fid_score)})
                 wandb.log({"inception score": float(inception_score)})
-                wandb.log({"FID clean": float(fid_clean)})
-                wandb.log({"FID-CLIP": float(fid_clip)})
+                if not ds_test.data_config.output_size == 1:
+                    wandb.log({"FID clean": float(fid_clean)})
+                    wandb.log({"FID-CLIP": float(fid_clip)})
         wandb.finish()