Keep 915 inference viewer images after 100 results

NN_server/Models/ensemble_1200_v44.py

@@ -10,6 +10,20 @@ import os
 import re
 
 
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
 def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
@@ -232,7 +246,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
 
         if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -241,7 +255,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()

NN_server/Models/ensemble_2400_v44.py

@@ -10,6 +10,20 @@ import os
 import re
 
 
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
 def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
@@ -232,7 +246,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
 
         if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -241,7 +255,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()

NN_server/Models/ensemble_915_v44.py

@@ -6,6 +6,58 @@ import torch
 import cv2
 import gc
 import io
+import os
+import re
+
+
+def _as_display_image(image):
+    arr = np.asarray(image)
+    if arr.ndim == 3 and arr.shape[0] in {1, 3, 4}:
+        arr = np.moveaxis(arr, 0, -1)
+
+    if np.issubdtype(arr.dtype, np.floating):
+        arr = np.nan_to_num(arr, nan=0.0, posinf=255.0, neginf=0.0)
+        if arr.size and (arr.max() > 1.0 or arr.min() < 0.0):
+            return np.clip(arr, 0, 255).astype(np.uint8)
+        return np.clip(arr, 0.0, 1.0)
+
+    return arr
+
+
+def _prune_old_inference_images(src, model_type, model_id, keep_last=200):
+    try:
+        keep_last = int(os.getenv("INFERENCE_IMAGE_KEEP_LAST", str(keep_last)))
+    except ValueError:
+        keep_last = keep_last
+
+    if keep_last <= 0 or not src or not os.path.isdir(src):
+        return
+
+    pattern = re.compile(
+        r"_inference_(\d+)_.*_"
+        + re.escape(str(model_id))
+        + "_"
+        + re.escape(str(model_type))
+        + r"\.png$"
+    )
+    grouped = {}
+    for name in os.listdir(src):
+        match = pattern.match(name)
+        if match is None:
+            continue
+        grouped.setdefault(int(match.group(1)), []).append(name)
+
+    if len(grouped) <= keep_last:
+        return
+
+    for old_result_id in sorted(grouped)[: len(grouped) - keep_last]:
+        for name in grouped[old_result_id]:
+            try:
+                os.remove(os.path.join(src, name))
+            except FileNotFoundError:
+                pass
+            except OSError as exc:
+                print(f"failed to remove old inference image {name}: {exc}")
 
 
 def _render_plot(values, figsize=(16, 16), dpi=16):
@@ -164,9 +216,9 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
         matplotlib.use("Agg")
         plt.ioff()
 
-        if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
+        if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[0], 0, -1))
+            ax.imshow(_as_display_image(data[0]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -175,7 +227,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             gc.collect()
 
             fig, ax = plt.subplots()
-            ax.imshow(np.moveaxis(data[1], 0, -1))
+            ax.imshow(_as_display_image(data[1]))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
             plt.clf()
             plt.cla()
@@ -183,6 +235,8 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             cv2.destroyAllWindows()
             gc.collect()
 
+            _prune_old_inference_images(src, model_type, model_id)
+
         plt.clf()
         plt.cla()
         plt.close()