From 20114f51381801e44e0cae073a5d87defc7cdea5 Mon Sep 17 00:00:00 2001
From: Elm Forest <timbrightland@gmail.com>
Date: Fri, 7 Apr 2023 00:50:18 +0800
Subject: [PATCH] Fixed some typos in comments
---
scripts/export_onnx_model.py | 2 +-
segment_anything/automatic_mask_generator.py | 4 ++--
segment_anything/modeling/image_encoder.py | 4 ++--
segment_anything/modeling/sam.py | 4 ++--
segment_anything/modeling/transformer.py | 2 +-
segment_anything/predictor.py | 2 +-
segment_anything/utils/amg.py | 2 +-
segment_anything/utils/transforms.py | 2 +-
8 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/scripts/export_onnx_model.py b/scripts/export_onnx_model.py
index 8ec5c2e..15d51f0 100644
--- a/scripts/export_onnx_model.py
+++ b/scripts/export_onnx_model.py
@@ -149,7 +149,7 @@ def run_export(
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
warnings.filterwarnings("ignore", category=UserWarning)
with open(output, "wb") as f:
- print(f"Exporing onnx model to {output}...")
+ print(f"Exporting onnx model to {output}...")
torch.onnx.export(
onnx_model,
tuple(dummy_inputs.values()),
diff --git a/segment_anything/automatic_mask_generator.py b/segment_anything/automatic_mask_generator.py
index 2326497..da944ed 100644
--- a/segment_anything/automatic_mask_generator.py
+++ b/segment_anything/automatic_mask_generator.py
@@ -73,10 +73,10 @@ class SamAutomaticMaskGenerator:
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
- crops_n_layers (int): If >0, mask prediction will be run again on
+ crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
- crops_nms_thresh (float): The box IoU cutoff used by non-maximal
+ crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
diff --git a/segment_anything/modeling/image_encoder.py b/segment_anything/modeling/image_encoder.py
index a6ad9ad..3e77373 100644
--- a/segment_anything/modeling/image_encoder.py
+++ b/segment_anything/modeling/image_encoder.py
@@ -198,7 +198,7 @@ class Attention(nn.Module):
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
- qkv_bias (bool: If True, add a learnable bias to query, key, value.
+ qkv_bias (bool): If True, add a learnable bias to query, key, value.
rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
input_size (int or None): Input resolution for calculating the relative positional
@@ -270,7 +270,7 @@ def window_unpartition(
"""
Window unpartition into original sequences and removing padding.
Args:
- x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+ windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
window_size (int): window size.
pad_hw (Tuple): padded height and width (Hp, Wp).
hw (Tuple): original height and width (H, W) before padding.
diff --git a/segment_anything/modeling/sam.py b/segment_anything/modeling/sam.py
index 303bc2f..8074cff 100644
--- a/segment_anything/modeling/sam.py
+++ b/segment_anything/modeling/sam.py
@@ -85,8 +85,8 @@ class Sam(nn.Module):
(list(dict)): A list over input images, where each element is
as dictionary with the following keys.
'masks': (torch.Tensor) Batched binary mask predictions,
- with shape BxCxHxW, where B is the number of input promts,
- C is determiend by multimask_output, and (H, W) is the
+ with shape BxCxHxW, where B is the number of input prompts,
+ C is determined by multimask_output, and (H, W) is the
original size of the image.
'iou_predictions': (torch.Tensor) The model's predictions
of mask quality, in shape BxC.
diff --git a/segment_anything/modeling/transformer.py b/segment_anything/modeling/transformer.py
index f1a2812..28fafea 100644
--- a/segment_anything/modeling/transformer.py
+++ b/segment_anything/modeling/transformer.py
@@ -96,7 +96,7 @@ class TwoWayTransformer(nn.Module):
key_pe=image_pe,
)
- # Apply the final attenion layer from the points to the image
+ # Apply the final attention layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
diff --git a/segment_anything/predictor.py b/segment_anything/predictor.py
index 57c089d..91b2ed3 100644
--- a/segment_anything/predictor.py
+++ b/segment_anything/predictor.py
@@ -186,7 +186,7 @@ class SamPredictor:
point_labels (torch.Tensor or None): A BxN array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
- box (np.ndarray or None): A Bx4 array given a box prompt to the
+ boxes (np.ndarray or None): A Bx4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form Bx1xHxW, where
diff --git a/segment_anything/utils/amg.py b/segment_anything/utils/amg.py
index 3a13777..be06407 100644
--- a/segment_anything/utils/amg.py
+++ b/segment_anything/utils/amg.py
@@ -162,7 +162,7 @@ def calculate_stability_score(
the predicted mask logits at high and low values.
"""
# One mask is always contained inside the other.
- # Save memory by preventing unnecesary cast to torch.int64
+ # Save memory by preventing unnecessary cast to torch.int64
intersections = (
(masks > (mask_threshold + threshold_offset))
.sum(-1, dtype=torch.int16)
diff --git a/segment_anything/utils/transforms.py b/segment_anything/utils/transforms.py
index 3ad3466..97a682a 100644
--- a/segment_anything/utils/transforms.py
+++ b/segment_anything/utils/transforms.py
@@ -15,7 +15,7 @@ from typing import Tuple
class ResizeLongestSide:
"""
- Resizes images to longest side 'target_length', as well as provides
+ Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""