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Modified object detection SSD priors #34

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Modified object detection SSD priors #34

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bf15850
Modified Object detection SSD to encorporate [4,6,6,6,4,4] priors for…
aiwithshekhar May 12, 2020
ddf91fa
minor updates in cx, cy denominator values.
aiwithshekhar May 13, 2020
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50 changes: 26 additions & 24 deletions model/detection/generate_priors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,14 +9,16 @@
import torch.nn as nn
from math import sqrt


class PriorBox(nn.Module):
def __init__(self, cfg):
super(PriorBox, self).__init__()
self.image_size = cfg.image_size
self.feature_maps = cfg.feature_maps
self.min_sizes = cfg.min_sizes
self.max_sizes = cfg.max_sizes
self.strides = cfg.strides
# self.min_sizes = cfg.min_sizes
# self.max_sizes = cfg.max_sizes
# self.strides = cfg.strides
self.scales = cfg.scales
self.aspect_ratios = cfg.aspect_ratio
self.clip = cfg.clip

Expand All @@ -29,40 +31,40 @@ def forward(self):
"""
priors = []
for k, f in enumerate(self.feature_maps):
scale = self.image_size / self.strides[k]
for i, j in product(range(f), repeat=2):
# unit center x,y
cx = (j + 0.5) / scale
cy = (i + 0.5) / scale

# small sized square box
size = self.min_sizes[k]
h = w = size / self.image_size
priors.append([cx, cy, w, h])

# big sized square box
size = sqrt(self.min_sizes[k] * self.max_sizes[k])
h = w = size / self.image_size
priors.append([cx, cy, w, h])
cx = (j + 0.5) / self.feature_maps[k]
cy = (i + 0.5) / self.feature_maps[k]

# change h/w ratio of the small sized box
size = self.min_sizes[k]
h = w = size / self.image_size
for ratio in self.aspect_ratios[k]:
ratio = sqrt(ratio)
priors.append([cx, cy, w * ratio, h / ratio])
priors.append([cx, cy, w / ratio, h * ratio])
priors.append(
[
cx,
cy,
self.scales[k] * sqrt(ratio),
self.scales[k] / sqrt(ratio),
]
)
# If aspect ratio is 1 prior scale is geometric_mean of (scale of the current feature map, scale of the next feature map)
if ratio == 1:
try:
extra_scale = sqrt(self.scales[k] * self.scales[k + 1])
# last feature map scale is 1
except IndexError:
extra_scale = 1
priors.append([cx, cy, extra_scale, extra_scale])

priors = torch.Tensor(priors)
if self.clip:
priors.clamp_(max=1.0, min=0)
return priors


if __name__ == '__main__':
if __name__ == "__main__":
from model.detection.ssd_config import SSD300Configuration as cfg

center_form_priors = PriorBox(cfg)()
from utilities import box_utils

corner_form_priors = box_utils.center_form_to_corner_form(center_form_priors)
print(corner_form_priors)

74 changes: 47 additions & 27 deletions model/detection/ssd_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,28 +28,38 @@ class SSD300Configuration(object):
feature_maps.append(temp)
#feature_maps = [38, 19, 10, 5, 3, 1]


s_max_size = int(math.ceil(1.05 * image_size))
s_min_size = int(math.ceil(0.1 * image_size))
sizes = [int(k) for k in np.linspace(s_min_size, s_max_size, m+1)]
min_sizes = sizes[:m]
max_sizes = sizes[1:]
scales = [0.1, 0.2, 0.375, 0.55, 0.725, 0.9] #priors scales
# s_max_size = int(math.ceil(1.05 * image_size))
# s_min_size = int(math.ceil(0.1 * image_size))
# sizes = [int(k) for k in np.linspace(s_min_size, s_max_size, m+1)]
# min_sizes = sizes[:m]
# max_sizes = sizes[1:]
#min_sizes = [30, 60, 111, 162, 213, 264]
#max_sizes = [60, 111, 162, 213, 264, 315]

# aspect ratio contains a list of pair (e.g. [2, 2] or [2,3] or single valued list e.g. [2,]
# This has a relationship with # of boxes per location. For example, [2,] means that 4 (=2*2) boxes per location
# [2, 3] means that 6=(2*2) boxes per location
aspect_ratio = [[2, 3]] * m

box_per_location = [] # number of boxes per feature map location
for pair in aspect_ratio:
if len(pair) == 1:
box_per_location.append(pair[0] * pair[0])
# aspect_ratio = [[2, 3]] * m

# Aspects ratios for diffrent feature maps
aspect_ratios = [
[1.0, 2.0, 0.5],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5],
]

box_per_location = [] # number of boxes per feature map location [4,6,6,6,4,4]
for pair in aspect_ratios:
if len(pair) == 3:
box_per_location.append(4)
else:
box_per_location.append(np.prod(pair))
box_per_location.append(6)

assert len(feature_maps) == len(strides) == len(min_sizes) == len(max_sizes) == len(aspect_ratio)
assert len(feature_maps) == len(scales) == len(aspect_ratio)

clip = True

Expand All @@ -76,28 +86,38 @@ class SSD512Configuration(object):
for stride in strides:
temp = int(math.ceil(image_size / stride))
feature_maps.append(temp)


scales = [0.1, 0.2, 0.375, 0.55, 0.725, 0.9] #priors scales
#min_sizes = [36, 77, 154, 230, 307, 461]
#max_sizes = [77, 154, 230, 307, 384, 538]
s_max_size = int(math.ceil(1.05 * image_size))
s_min_size = int(math.ceil(0.1 * image_size))
sizes = [int(k) for k in np.linspace(s_min_size, s_max_size, m + 1)]
min_sizes = sizes[:m]
max_sizes = sizes[1:]
# s_max_size = int(math.ceil(1.05 * image_size))
# s_min_size = int(math.ceil(0.1 * image_size))
# sizes = [int(k) for k in np.linspace(s_min_size, s_max_size, m + 1)]
# min_sizes = sizes[:m]
# max_sizes = sizes[1:]

# aspect ratio contains a list of pair (e.g. [2, 2] or [2,3] or single valued list e.g. [2,]
# This has a relationship with # of boxes per location. For example, [2,] means that 4 (=2*2) boxes per location
# [2, 3] means that 6=(2*2) boxes per location
aspect_ratio = [[2, 3]] * m
box_per_location = [] # number of boxes per feature map location
for pair in aspect_ratio:
if len(pair) == 1:
box_per_location.append(pair[0] * pair[0])

# Aspects ratios for diffrent feature maps
aspect_ratios = [
[1.0, 2.0, 0.5],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 3.0, 0.5, 0.333],
[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5],
]
box_per_location = [] # number of boxes per feature map location [4,6,6,6,4,4]
for pair in aspect_ratios:
if len(pair) == 3:
box_per_location.append(4)
else:
box_per_location.append(np.prod(pair))
box_per_location.append(6)
clip = True

assert len(feature_maps) == len(strides) == len(min_sizes) == len(max_sizes) == len(aspect_ratio)
assert len(feature_maps) == len(scales) == len(aspect_ratio)

# test specific options
nms_threshold = 0.45
Expand Down