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support set shuffle by config

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gaotingquan 2022-10-13 04:28:34 +00:00 committed by Tingquan Gao
parent d7014129f1
commit b83da15d27
1 changed files with 3 additions and 3 deletions
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6
ppcls/data/dataloader/multi_scale_sampler.py
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@ -15,6 +15,7 @@ class MultiScaleSampler(Sampler):
first_bs,
divided_factor=32,
is_training=True,
shuffle=True,
seed=None):
"""
multi scale samper
@ -23,7 +24,7 @@ class MultiScaleSampler(Sampler):
scales(list): several scales for image resolution
first_bs(int): batch size for the first scale in scales
divided_factor(int): ImageNet models down-sample images by a factor, ensure that width and height dimensions are multiples are multiple of devided_factor.
is_training(boolean): mode
is_training(boolean): mode
"""
# min. and max. spatial dimensions
self.data_source = data_source
@ -47,7 +48,7 @@ class MultiScaleSampler(Sampler):
math.ceil(self.n_data_samples * 1.0 / num_replicas))
img_indices = [idx for idx in range(self.n_data_samples)]
self.shuffle = False
self.shuffle = shuffle
if is_training:
# compute the spatial dimensions and corresponding batch size
# ImageNet models down-sample images by a factor of 32.
@ -66,7 +67,6 @@ class MultiScaleSampler(Sampler):
batch_size = int(max(1, (base_elements / (h * w))))
img_batch_pairs.append((w, h, batch_size))
self.img_batch_pairs = img_batch_pairs
self.shuffle = True
else:
self.img_batch_pairs = [(base_im_w, base_im_h, base_batch_size)]