mirrors/PaddleClas
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/PaddleClas.git synced 2025-06-03 21:55:06 +08:00
Code Issues Projects Releases Wiki Activity

support set shuffle by config

Browse Source
This commit is contained in:
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
ppcls/data/dataloader/multi_scale_sampler.py
View File

@ -15,6 +15,7 @@ class MultiScaleSampler(Sampler):
first_bs, first_bs,
divided_factor=32, divided_factor=32,
is_training=True, is_training=True,
shuffle=True,
seed=None): seed=None):
""" """
multi scale samper multi scale samper
@ -23,7 +24,7 @@ class MultiScaleSampler(Sampler):
scales(list): several scales for image resolution scales(list): several scales for image resolution
first_bs(int): batch size for the first scale in scales 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. 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 # min. and max. spatial dimensions
self.data_source = data_source self.data_source = data_source
@ -47,7 +48,7 @@ class MultiScaleSampler(Sampler):
math.ceil(self.n_data_samples * 1.0 / num_replicas)) math.ceil(self.n_data_samples * 1.0 / num_replicas))
img_indices = [idx for idx in range(self.n_data_samples)] img_indices = [idx for idx in range(self.n_data_samples)]
self.shuffle = False self.shuffle = shuffle
if is_training: if is_training:
# compute the spatial dimensions and corresponding batch size # compute the spatial dimensions and corresponding batch size
# ImageNet models down-sample images by a factor of 32. # 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)))) batch_size = int(max(1, (base_elements / (h * w))))
img_batch_pairs.append((w, h, batch_size)) img_batch_pairs.append((w, h, batch_size))
self.img_batch_pairs = img_batch_pairs self.img_batch_pairs = img_batch_pairs
self.shuffle = True
else: else:
self.img_batch_pairs = [(base_im_w, base_im_h, base_batch_size)] self.img_batch_pairs = [(base_im_w, base_im_h, base_batch_size)]