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samplers.py
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import torch
import numpy as np
from IPython import embed
class CategoriesSampler():
def __init__(self, label, n_batch, n_cls, n_per):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_per = n_per
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
for c in classes:
l = self.m_ind[c]
pos = torch.randperm(len(l))[:self.n_per]
batch.append(l[pos])
batch = torch.stack(batch).t().reshape(-1)
yield batch
class CategoriesSampler_train():
def __init__(self, label, n_batch, n_cls, n_shot,n_query, n_base_class):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_shot = n_shot
self.n_query = n_query
self.n_base_class = n_base_class
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
query_batch = []
shot_batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
classes,order =classes.sort()
for c in classes:
if c < self.n_base_class:
l = self.m_ind[c]
tmp = torch.randperm(len(l))
batch.append(l[tmp[:self.n_shot+self.n_query]])
else:
l = self.m_ind[c]
tmp = torch.randperm(self.n_shot)
batch.append(torch.cat((l[tmp],torch.zeros(self.n_query).type(torch.LongTensor))))
batch = torch.stack(batch).t().reshape(-1)
yield batch
class CategoriesSampler_train_repeat():
def __init__(self, label, n_batch, n_cls, n_shot,n_query, n_base_class):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_shot = n_shot
self.n_query = n_query
self.n_base_class = n_base_class
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
query_batch = []
shot_batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
classes,order =classes.sort()
for c in classes:
if c < self.n_base_class:
l = self.m_ind[c]
tmp = torch.randperm(len(l))
batch.append(l[tmp[:self.n_shot+self.n_query]])
else:
l = self.m_ind[c]
tmp = torch.randperm(self.n_shot)
novel_query = torch.randperm(self.n_shot-1)[0]+1
a = tmp[:self.n_shot-novel_query]
b = tmp[self.n_shot-novel_query:]
batch.append(torch.cat((l[a.repeat(15)[:self.n_shot]],l[b.repeat(15)[:self.n_query]])))
batch = torch.stack(batch).t().reshape(-1)
yield batch
class CategoriesSampler_train_100way():
def __init__(self, label, n_batch, n_cls, n_shot,n_query, n_base_class):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_shot = n_shot
self.n_query = n_query
self.n_base_class = n_base_class
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
query_batch = []
shot_batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
classes,order =classes.sort()
for c in classes:
if c < self.n_base_class:
l = self.m_ind[c]
tmp = torch.randperm(len(l)-100)
batch.append(l[tmp[:self.n_shot+self.n_query]])
else:
l = self.m_ind[c]
tmp = torch.randperm(self.n_shot)
novel_query = torch.randperm(self.n_shot-1)[0]+1
a = tmp[:self.n_shot-novel_query]
b = tmp[self.n_shot-novel_query:]
batch.append(torch.cat((l[a.repeat(15)[:self.n_shot]],l[b.repeat(15)[:self.n_query]])))
batch = torch.stack(batch).t().reshape(-1)
yield batch
class CategoriesSampler_val_100way():
def __init__(self, label, n_batch, n_cls, n_shot,n_query):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_shot = n_shot
self.n_query = n_query
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
for c in classes:
l = self.m_ind[c]
#pos = torch.cat([torch.Tensor(range(0,self.n_shot)).type(torch.LongTensor), self.n_shot+torch.randperm(len(l)-self.n_shot)[:self.n_query]])
tmp = torch.randperm(100)+500
batch.append(l[tmp])
batch = torch.stack(batch).t().reshape(-1)
yield batch
class CategoriesSampler_val():
def __init__(self, label, n_batch, n_cls, n_shot,n_query):
self.n_batch = n_batch
self.n_cls = n_cls
self.n_shot = n_shot
self.n_query = n_query
label = np.array(label)
self.m_ind = []
for i in range(max(label) + 1):
ind = np.argwhere(label == i).reshape(-1)
ind = torch.from_numpy(ind)
self.m_ind.append(ind)
def __len__(self):
return self.n_batch
def __iter__(self):
for i_batch in range(self.n_batch):
batch = []
classes = torch.randperm(len(self.m_ind))[:self.n_cls]
for c in classes:
l = self.m_ind[c]
pos = torch.cat([torch.Tensor(range(0,self.n_shot)).type(torch.LongTensor),self.n_shot+torch.randperm(len(l)-self.n_shot)[:self.n_query]])
batch.append(l[pos])
batch = torch.stack(batch).t().reshape(-1)
yield batch