IndexError: tensors used as indices must be long, byte or bool tensors - Pytorch
IndexError: tensors used as indices must be long, byte or bool tensors - Pytorch
我正在使用来自此 Repository 的预训练图像字幕模型，但我收到此错误，尽管我将类型更改为 long!!
错误：
File "caption.py", line 213, in
seq, alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size) File "caption.py", line 111, in
caption_image_beam_search
seqs = torch.cat([seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) IndexError:
tensors used as indices must be long, byte or bool tensors
代码：
def caption_image_beam_search(encoder, decoder, image_path, word_map, beam_size=3):
k = beam_size
vocab_size = len(word_map)
# Read image and process
img = imageio.imread(image_path)
if len(img.shape) == 2:
img = img[:, :, np.newaxis]
img = np.concatenate([img, img, img], axis=2)
img = skimage.transform.resize(img, (256, 256))
img = img.transpose(2, 0, 1)
img = img / 255.
img = torch.FloatTensor(img).to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([normalize])
image = transform(img) # (3, 256, 256)
# Encode
image = image.unsqueeze(0) # (1, 3, 256, 256)
encoder_out = encoder(image) # (1, enc_image_size, enc_image_size, encoder_dim)
enc_image_size = encoder_out.size(1)
encoder_dim = encoder_out.size(3)
# Flatten encoding
encoder_out = encoder_out.view(1, -1, encoder_dim) # (1, num_pixels, encoder_dim)
num_pixels = encoder_out.size(1)
# We'll treat the problem as having a batch size of k
encoder_out = encoder_out.expand(k, num_pixels, encoder_dim) # (k, num_pixels, encoder_dim)
# Tensor to store top k previous words at each step; now they're just <start>
k_prev_words = torch.LongTensor([[word_map['<start>']]] * k).to(device) # (k, 1)
# Tensor to store top k sequences; now they're just <start>
seqs = k_prev_words # (k, 1)
# Tensor to store top k sequences' scores; now they're just 0
top_k_scores = torch.zeros(k, 1).to(device) # (k, 1)
# Tensor to store top k sequences' alphas; now they're just 1s
seqs_alpha = torch.ones(k, 1, enc_image_size, enc_image_size).to(device) # (k, 1, enc_image_size, enc_image_size)
# Lists to store completed sequences, their alphas and scores
complete_seqs = list()
complete_seqs_alpha = list()
complete_seqs_scores = list()
# Start decoding
step = 1
h, c = decoder.init_hidden_state(encoder_out)
# s is a number less than or equal to k, because sequences are removed from this process once they hit <end>
while True:
embeddings = decoder.embedding(k_prev_words).squeeze(1) # (s, embed_dim)
awe, alpha = decoder.attention(encoder_out, h) # (s, encoder_dim), (s, num_pixels)
alpha = alpha.view(-1, enc_image_size, enc_image_size) # (s, enc_image_size, enc_image_size)
gate = decoder.sigmoid(decoder.f_beta(h)) # gating scalar, (s, encoder_dim)
awe = gate * awe
h, c = decoder.decode_step(torch.cat([embeddings, awe], dim=1), (h, c)) # (s, decoder_dim)
scores = decoder.fc(h) # (s, vocab_size)
scores = F.log_softmax(scores, dim=1)
# Add
scores = top_k_scores.expand_as(scores) + scores # (s, vocab_size)
# For the first step, all k points will have the same scores (since same k previous words, h, c)
if step == 1:
top_k_scores, top_k_words = scores[0].topk(k, 0, True, True) # (s)
else:
# Unroll and find top scores, and their unrolled indices
top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True) # (s)
# Convert unrolled indices to actual indices of scores
prev_word_inds = top_k_words / vocab_size # (s)
next_word_inds = top_k_words % vocab_size # (s)
# Add new words to sequences, alphas
seqs = torch.cat([seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1)
seqs_alpha = torch.cat([seqs_alpha[prev_word_inds].long(), alpha[prev_word_inds].unsqueeze(1)],
dim=1) # (s, step+1, enc_image_size, enc_image_size)
# Which sequences are incomplete (didn't reach <end>)?
incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if
next_word != word_map['<end>']]
complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds))
# Set aside complete sequences
if len(complete_inds) > 0:
complete_seqs.extend(seqs[complete_inds].tolist())
complete_seqs_alpha.extend(seqs_alpha[complete_inds].tolist())
complete_seqs_scores.extend(top_k_scores[complete_inds])
k -= len(complete_inds) # reduce beam length accordingly
# Proceed with incomplete sequences
if k == 0:
break
seqs = seqs[incomplete_inds]
seqs_alpha = seqs_alpha[incomplete_inds]
h = h[prev_word_inds[incomplete_inds]]
c = c[prev_word_inds[incomplete_inds]]
encoder_out = encoder_out[prev_word_inds[incomplete_inds]]
top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)
k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1)
# Break if things have been going on too long
if step > 50:
break
step += 1
i = complete_seqs_scores.index(max(complete_seqs_scores))
seq = complete_seqs[i]
alphas = complete_seqs_alpha[i]
return seq, alphas
您投错了 long 的部分：
seqs[prev_word_inds].long()
应该是：
seqs[prev_word_inds.long()]
也出现在计算 seqs_alpha 的下一行中。
虽然我不完全确定你如何将它们转换为 long (non-decimal) 就像你在上面的行中推导出 prev_word_inds as:
prev_word_inds = top_k_words / vocab_size
seqs = torch.cat([seqs[prev_word_inds.long()], \
next_word_inds.unsqueeze(1)], dim=1) # (s, step+1)
当 pytorch 已经给出 topk 的第二个输出时？为什么要除以词汇量？
我正在使用来自此 Repository 的预训练图像字幕模型，但我收到此错误，尽管我将类型更改为 long!!
错误：
File "caption.py", line 213, in seq, alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size) File "caption.py", line 111, in caption_image_beam_search seqs = torch.cat([seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) IndexError: tensors used as indices must be long, byte or bool tensors
代码：
def caption_image_beam_search(encoder, decoder, image_path, word_map, beam_size=3):
k = beam_size
vocab_size = len(word_map)
# Read image and process
img = imageio.imread(image_path)
if len(img.shape) == 2:
img = img[:, :, np.newaxis]
img = np.concatenate([img, img, img], axis=2)
img = skimage.transform.resize(img, (256, 256))
img = img.transpose(2, 0, 1)
img = img / 255.
img = torch.FloatTensor(img).to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([normalize])
image = transform(img) # (3, 256, 256)
# Encode
image = image.unsqueeze(0) # (1, 3, 256, 256)
encoder_out = encoder(image) # (1, enc_image_size, enc_image_size, encoder_dim)
enc_image_size = encoder_out.size(1)
encoder_dim = encoder_out.size(3)
# Flatten encoding
encoder_out = encoder_out.view(1, -1, encoder_dim) # (1, num_pixels, encoder_dim)
num_pixels = encoder_out.size(1)
# We'll treat the problem as having a batch size of k
encoder_out = encoder_out.expand(k, num_pixels, encoder_dim) # (k, num_pixels, encoder_dim)
# Tensor to store top k previous words at each step; now they're just <start>
k_prev_words = torch.LongTensor([[word_map['<start>']]] * k).to(device) # (k, 1)
# Tensor to store top k sequences; now they're just <start>
seqs = k_prev_words # (k, 1)
# Tensor to store top k sequences' scores; now they're just 0
top_k_scores = torch.zeros(k, 1).to(device) # (k, 1)
# Tensor to store top k sequences' alphas; now they're just 1s
seqs_alpha = torch.ones(k, 1, enc_image_size, enc_image_size).to(device) # (k, 1, enc_image_size, enc_image_size)
# Lists to store completed sequences, their alphas and scores
complete_seqs = list()
complete_seqs_alpha = list()
complete_seqs_scores = list()
# Start decoding
step = 1
h, c = decoder.init_hidden_state(encoder_out)
# s is a number less than or equal to k, because sequences are removed from this process once they hit <end>
while True:
embeddings = decoder.embedding(k_prev_words).squeeze(1) # (s, embed_dim)
awe, alpha = decoder.attention(encoder_out, h) # (s, encoder_dim), (s, num_pixels)
alpha = alpha.view(-1, enc_image_size, enc_image_size) # (s, enc_image_size, enc_image_size)
gate = decoder.sigmoid(decoder.f_beta(h)) # gating scalar, (s, encoder_dim)
awe = gate * awe
h, c = decoder.decode_step(torch.cat([embeddings, awe], dim=1), (h, c)) # (s, decoder_dim)
scores = decoder.fc(h) # (s, vocab_size)
scores = F.log_softmax(scores, dim=1)
# Add
scores = top_k_scores.expand_as(scores) + scores # (s, vocab_size)
# For the first step, all k points will have the same scores (since same k previous words, h, c)
if step == 1:
top_k_scores, top_k_words = scores[0].topk(k, 0, True, True) # (s)
else:
# Unroll and find top scores, and their unrolled indices
top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True) # (s)
# Convert unrolled indices to actual indices of scores
prev_word_inds = top_k_words / vocab_size # (s)
next_word_inds = top_k_words % vocab_size # (s)
# Add new words to sequences, alphas
seqs = torch.cat([seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1)
seqs_alpha = torch.cat([seqs_alpha[prev_word_inds].long(), alpha[prev_word_inds].unsqueeze(1)],
dim=1) # (s, step+1, enc_image_size, enc_image_size)
# Which sequences are incomplete (didn't reach <end>)?
incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if
next_word != word_map['<end>']]
complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds))
# Set aside complete sequences
if len(complete_inds) > 0:
complete_seqs.extend(seqs[complete_inds].tolist())
complete_seqs_alpha.extend(seqs_alpha[complete_inds].tolist())
complete_seqs_scores.extend(top_k_scores[complete_inds])
k -= len(complete_inds) # reduce beam length accordingly
# Proceed with incomplete sequences
if k == 0:
break
seqs = seqs[incomplete_inds]
seqs_alpha = seqs_alpha[incomplete_inds]
h = h[prev_word_inds[incomplete_inds]]
c = c[prev_word_inds[incomplete_inds]]
encoder_out = encoder_out[prev_word_inds[incomplete_inds]]
top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)
k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1)
# Break if things have been going on too long
if step > 50:
break
step += 1
i = complete_seqs_scores.index(max(complete_seqs_scores))
seq = complete_seqs[i]
alphas = complete_seqs_alpha[i]
return seq, alphas
您投错了 long 的部分：
seqs[prev_word_inds].long()
应该是：
seqs[prev_word_inds.long()]
也出现在计算 seqs_alpha 的下一行中。
虽然我不完全确定你如何将它们转换为 long (non-decimal) 就像你在上面的行中推导出 prev_word_inds as:
prev_word_inds = top_k_words / vocab_size
seqs = torch.cat([seqs[prev_word_inds.long()], \
next_word_inds.unsqueeze(1)], dim=1) # (s, step+1)
当 pytorch 已经给出 topk 的第二个输出时？为什么要除以词汇量？