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Fixed command in the error message for template not found #313

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Fixed command in the error message for template not found #313

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b859249
Fixed command in the error message for template not found to the new …
sdata07 Aug 12, 2026
a0d8f05
fix: [#312] Changed instances of .max to np.max to fix typecheck issues
sdata07 Aug 21, 2026
5add842
Fix rgb_min calculation for normalization
sdata07 Aug 21, 2026
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2 changes: 1 addition & 1 deletion fenn/cli/pull.py
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Original file line number Diff line number Diff line change
Expand Up @@ -285,7 +285,7 @@ def _download_template(template_name: str, target_dir: Path, force: bool) -> Non
if e.response.status_code == 404: # ty: ignore[unresolved-attribute]
raise TemplateNotFoundError(
f"Template {Fore.LIGHTYELLOW_EX}{template_name}{Fore.RED} not found. "
f"Use {Fore.LIGHTYELLOW_EX}fenn pull --list{Fore.RED} to see available templates, "
f"Use {Fore.LIGHTYELLOW_EX}fenn list{Fore.RED} to see available templates, "
f"or visit {Fore.CYAN}https://github.com/{TEMPLATES_REPO}{Style.RESET_ALL}"
)
raise NetworkError(f"Failed to check template existence: {e}")
Expand Down
24 changes: 12 additions & 12 deletions fenn/experimental/vision/normalize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,7 @@ def _normalize_0_1(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already normalized
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = alpha_channel / 255.0
else:
normalized_alpha = alpha_channel
Expand All @@ -130,7 +130,7 @@ def _normalize_0_1(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already normalized
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = alpha_channel / 255.0
else:
normalized_alpha = alpha_channel
Expand Down Expand Up @@ -198,7 +198,7 @@ def _normalize_minus1_1(array: np.ndarray) -> np.ndarray:
normalized_alpha = 2.0 * (alpha_channel / dtype_max) - 1.0
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = 2.0 * (alpha_channel / 255.0) - 1.0
else:
# Already in [0, 1], map to [-1, 1]
Expand All @@ -223,7 +223,7 @@ def _normalize_minus1_1(array: np.ndarray) -> np.ndarray:
normalized_alpha = 2.0 * (alpha_channel / dtype_max) - 1.0
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = 2.0 * (alpha_channel / 255.0) - 1.0
else:
# Already in [0, 1], map to [-1, 1]
Expand Down Expand Up @@ -290,8 +290,8 @@ def _normalize_imagenet_stats(array: np.ndarray) -> np.ndarray:
alpha_channel_original = array_float[:, 3:4, ...].copy()

# Auto-normalize only RGB channels to [0, 1] if needed
rgb_max = rgb_channels_float.max()
rgb_min = rgb_channels_float.min()
rgb_max = np.max(rgb_channels_float)
rgb_min = np.min(rgb_channels_float)

if rgb_min < 0.0:
if rgb_max <= 1.0:
Expand All @@ -306,8 +306,8 @@ def _normalize_imagenet_stats(array: np.ndarray) -> np.ndarray:
rgb_channels_float = rgb_channels_float / 255.0
else:
# Auto-normalize entire array to [0, 1] if values are outside expected [0, 1] range
array_max = array_float.max()
array_min = array_float.min()
array_max = np.max(array_float)
array_min = np.min(array_float)

if array_min < 0.0:
# Values are < 0, likely in [-1, 1] range
Expand Down Expand Up @@ -341,7 +341,7 @@ def _normalize_imagenet_stats(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel_original / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel_original.max() > 1.0:
if np.max(alpha_channel_original) > 1.0:
normalized_alpha = alpha_channel_original / 255.0
else:
# Already in [0, 1], preserve as-is
Expand All @@ -359,7 +359,7 @@ def _normalize_imagenet_stats(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel_original / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel_original.max() > 1.0:
if np.max(alpha_channel_original) > 1.0:
normalized_alpha = alpha_channel_original / 255.0
else:
# Already in [0, 1], preserve as-is
Expand Down Expand Up @@ -435,7 +435,7 @@ def _normalize_zscore(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = alpha_channel / 255.0
else:
# Already in [0, 1], preserve as-is
Expand All @@ -459,7 +459,7 @@ def _normalize_zscore(array: np.ndarray) -> np.ndarray:
normalized_alpha = alpha_channel / dtype_max
else:
# Float type: if > 1, assume [0, 255] range, else already in [0, 1]
if alpha_channel.max() > 1.0:
if np.max(alpha_channel) > 1.0:
normalized_alpha = alpha_channel / 255.0
else:
# Already in [0, 1], preserve as-is
Expand Down