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ultralytics 8.0.90 actions and docs improvements (#2326)

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This commit is contained in:
Glenn Jocher
2023-04-29 20:16:56 +02:00
committed by GitHub
parent 243fc4b1fe
commit 44c7c3514d
39 changed files with 783 additions and 143 deletions
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11
ultralytics/vit/sam/modules/encoders.py
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@ -233,10 +233,10 @@ class PromptEncoder(nn.Module):
embeddings.
Arguments:
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
points (tuple(torch.Tensor, torch.Tensor), None): point coordinates
and labels to embed.
boxes (torch.Tensor or none): boxes to embed
masks (torch.Tensor or none): masks to embed
boxes (torch.Tensor, None): boxes to embed
masks (torch.Tensor, None): masks to embed
Returns:
torch.Tensor: sparse embeddings for the points and boxes, with shape
@ -337,7 +337,7 @@ class Block(nn.Module):
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
window_size (int): Window size for window attention blocks. If it equals 0, then
use global attention.
input_size (tuple(int, int) or None): Input resolution for calculating the relative
input_size (tuple(int, int), None): Input resolution for calculating the relative
positional parameter size.
"""
super().__init__()
@ -392,9 +392,8 @@ class Attention(nn.Module):
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.
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 (tuple(int, int) or None): Input resolution for calculating the relative
input_size (tuple(int, int), None): Input resolution for calculating the relative
positional parameter size.
"""
super().__init__()

9
ultralytics/vit/sam/modules/mask_generator.py
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@ -45,7 +45,7 @@ class SamAutomaticMaskGenerator:
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
points_per_side (int, None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
@ -70,7 +70,7 @@ class SamAutomaticMaskGenerator:
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
point_grids (list(np.ndarray), None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
@ -128,9 +128,8 @@ class SamAutomaticMaskGenerator:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
list(dict(str, any)): A list over records for masks. Each record is a dict containing the following keys:
segmentation (dict(str, any), np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.

12
ultralytics/vit/sam/modules/prompt_predictor.py
View File

@ -81,12 +81,12 @@ class PromptPredictor:
Predict masks for the given input prompts, using the currently set image.
Arguments:
point_coords (np.ndarray or None): A Nx2 array of point prompts to the
point_coords (np.ndarray, None): A Nx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (np.ndarray or None): A length N array of labels for the
point_labels (np.ndarray, None): A length N array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A length 4 array given a box prompt to the
box (np.ndarray, None): A length 4 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 1xHxW, where
@ -158,12 +158,12 @@ class PromptPredictor:
transformed to the input frame using ResizeLongestSide.
Arguments:
point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
point_coords (torch.Tensor, None): A BxNx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (torch.Tensor or None): A BxN array of labels for the
point_labels (torch.Tensor, None): A BxN array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
boxes (np.ndarray or None): A Bx4 array given a box prompt to the
boxes (np.ndarray, 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