def __iter__(self):
samplers_list = []
sampler_iterators = []
datasets_length = []
for dataset_idx in range(self.number_of_datasets):
cur_dataset = self.dataset.datasets[dataset_idx]
print("The length for dataset + " + str(cur_dataset.batch_layers) +
" is " + str(len(cur_dataset.data)))
sampler = RandomSampler(cur_dataset)
samplers_list.append(sampler)
cur_sampler_iterator = sampler.__iter__()
sampler_iterators.append(cur_sampler_iterator)
datasets_length.append(len(cur_dataset))
push_index_val = [0] + self.dataset.cumulative_sizes[:-1]
step = self.batch_size # * self.number_of_datasets
samples_to_grab = self.batch_size
largest_dataset_index = torch.argmax(
torch.as_tensor(datasets_length)).item()
# for this case we want to get all samples in dataset, this force us to resample from the smaller datasets
# epoch_samples = datasets_length[largest_dataset_index] * self.number_of_datasets
# iterate over the total length of the combined datasets (slightly oversampling some datasets, and undersampling others)
epoch_samples = self.total_length
final_samples_list = [
] # this is a list of indexes from the combined dataset
for _ in range(0, epoch_samples, step):
#TODO instead of alternating between datasets, flip a (weighted) coin every time
coin_toss = torch.rand(1).item()
for threshold in self.partitions:
if threshold > coin_toss:
i = self.partitions.index(threshold)
break
# for i in range(self.number_of_datasets):
cur_batch_sampler = sampler_iterators[i]
cur_samples = []
for _ in range(samples_to_grab):
try:
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
except StopIteration:
if i == largest_dataset_index:
# largest dataset iterator is done we can break
samples_to_grab = len(
cur_samples) # adjusting the samples_to_grab
# got to the end of iterator - extend final list and continue to next task if possible
break
else:
# restart the iterator - we want more samples until finishing with the largest dataset
sampler_iterators[i] = samplers_list[i].__iter__()
cur_batch_sampler = sampler_iterators[i]
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
final_samples_list.extend(cur_samples)
return iter(final_samples_list)
def __iter__(self):
samplers_list = []
sampler_iterators = []
datasets_length = []
for dataset_idx in range(self.number_of_datasets):
cur_dataset = self.dataset.datasets[dataset_idx]
sampler = RandomSampler(cur_dataset)
samplers_list.append(sampler)
cur_sampler_iterator = sampler.__iter__()
sampler_iterators.append(cur_sampler_iterator)
datasets_length.append(len(cur_dataset))
push_index_val = [0] + self.dataset.cumulative_sizes[:-1]
step = self.batch_size * self.number_of_datasets
samples_to_grab = self.batch_size
largest_dataset_index = torch.argmax(
torch.as_tensor(datasets_length)).item()
# for this case we want to get all samples in dataset, this force us to resample from the smaller datasets
epoch_samples = datasets_length[
largest_dataset_index] * self.number_of_datasets
final_samples_list = [
] # this is a list of indexes from the combined dataset
for _ in range(0, epoch_samples, step):
for i in range(self.number_of_datasets):
cur_batch_sampler = sampler_iterators[i]
cur_samples = []
if i == 0:
samples_to_grab *= 2
else:
samples_to_grab = self.batch_size
for _ in range(samples_to_grab):
try:
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
except StopIteration:
if i == largest_dataset_index:
# largest dataset iterator is done we can break
samples_to_grab = len(
cur_samples) # adjusting the samples_to_grab
# got to the end of iterator - extend final list and continue to next task if possible
break
else:
# restart the iterator - we want more samples until finishing with the largest dataset
sampler_iterators[i] = samplers_list[i].__iter__()
cur_batch_sampler = sampler_iterators[i]
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
final_samples_list.extend(cur_samples)
return iter(final_samples_list)
def __iter__(self):
samplers_list = []
sampler_iterators = []
for dataset_idx in range(self.number_of_datasets):
cur_dataset = self.dataset.datasets[dataset_idx]
sampler = RandomSampler(cur_dataset)
samplers_list.append(sampler)
cur_sampler_iterator = sampler.__iter__()
sampler_iterators.append(cur_sampler_iterator)
push_index_val = [0] + self.dataset.cumulative_sizes[:-1]
step = self.batch_size * self.number_of_datasets
samples_to_grab = self.batch_size
# for this case we want to get all samples in dataset, this force us to resample from the smaller datasets
epoch_samples = self.largest_dataset_size * self.number_of_datasets
final_samples_list = [
] # this is a list of indexes from the combined dataset
for _ in range(0, epoch_samples, step):
for i in range(self.number_of_datasets):
cur_batch_sampler = sampler_iterators[i]
cur_samples = []
for _ in range(samples_to_grab):
try:
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
except StopIteration:
# got to the end of iterator - restart the iterator and continue to get samples
# until reaching "epoch_samples"
sampler_iterators[i] = samplers_list[i].__iter__()
cur_batch_sampler = sampler_iterators[i]
cur_sample_org = cur_batch_sampler.__next__()
cur_sample = cur_sample_org + push_index_val[i]
cur_samples.append(cur_sample)
final_samples_list.extend(cur_samples)
return iter(final_samples_list)
