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AlessandroMondin/adaptive_shape.py

Last active December 26, 2022 15:42
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adaptive_shape.py
def adaptive_shape(self, annotations):
name = "train" if self.train else "val"
path = os.path.join(
self.root_directory, "labels",
"adaptive_ann_{}_{}_br_{}.csv".format(name, self.len_ann, int(self.batch_range))
)
if os.path.isfile(path):
print(f"==> Loading cached annotations for rectangular training on {self.annot_folder}")
parsed_annot = pd.read_csv(path, index_col=0)
else:
print("...Running adaptive_shape for 'rectangular training' on training set...")
annotations["w_h_ratio"] = annotations.iloc[:, 2] / annotations.iloc[:, 1]
annotations.sort_values(["w_h_ratio"], ascending=True, inplace=True)
for i in range(0, len(annotations), self.batch_range):
size = [annotations.iloc[i, 2], annotations.iloc[i, 1]] # [width, height]
max_dim = max(size)
max_idx = size.index(max_dim)
size[~max_idx] += 32
sz = random.randrange(int(self.default_size * 0.9), int(self.default_size * 1.1)) // 32 * 32
size[~max_idx] = ((sz/size[max_idx])*(size[~max_idx]) // 32) * 32
size[max_idx] = sz
if i + self.batch_range <= len(annotations):
bs = self.batch_range
else:
bs = len(annotations) - i
annotations.iloc[i:bs, 2] = size[0]
annotations.iloc[i:bs, 1] = size[1]
# sample annotation to avoid having pseudo-equal images in the same batch
annotations.iloc[i:i+bs, :] = annotations.iloc[i:i+bs, :].sample(frac=1, axis=0)
parsed_annot = pd.DataFrame(annotations.iloc[:,:3])
parsed_annot.to_csv(path)
return parsed_annot
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