RuntimeError: input.size(-1) must be equal to input_size. Expected 64, got 84544

francesco_patane: <p>hi there. i’m new in pytorch and i’m trying to predict membrane protein topology with a lstm but i have an issue with the embedding layer (i think).</p>
<p>I set embedding_dim = 64 but it seems that after every cycle, the dimension grows up. If I adapt the embedding dim empirically the RAM memory goes out max capacity.</p>
<p>I took data from Topcons website and I made a df with two columns (first for protein sequence and second for labelled one). Then I convert them to integers and padded them. I created tensors and batches (64)</p>
<p>Expected 64, got 84544. Max length of sequences is 1321, and 84544/64=1321. Made this discovery, how can I resolve the issue?</p>
<p>here the error and below the code.</p>
<p>Can you help me please?</p>
<pre><code class="lang-auto">RuntimeError                              Traceback (most recent call last)
&lt;ipython-input-20-c13029126c88&gt; in &lt;module&gt;
     15         # Step 3. Run our forward pass.
     16 
---&gt; 17         output = model(sentence_in)
     18 
     19         loss = model.neg_log_likelihood(output, targets)

6 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1192         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193                 or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1194             return forward_call(*input, **kwargs)
   1195         # Do not call functions when jit is used
   1196         full_backward_hooks, non_full_backward_hooks = [], []

&lt;ipython-input-4-68e616720828&gt; in forward(self, sentence)
    135         # Get the emission scores from the BiLSTM
    136 
--&gt; 137         lstm_feats = self._get_lstm_features(sentence)
    138 
    139         # Find the best path, given the features.

&lt;ipython-input-4-68e616720828&gt; in _get_lstm_features(self, sentence)
     67         #sentence = sentence.transpose(1, 0)#
     68         embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
---&gt; 69         lstm_out, self.hidden = self.lstm(embeds, self.hidden)
     70         lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
     71         lstm_feats = self.hidden2tag(lstm_out)

/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1192         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193                 or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1194             return forward_call(*input, **kwargs)
   1195         # Do not call functions when jit is used
   1196         full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.8/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
    770             hx = self.permute_hidden(hx, sorted_indices)
    771 
--&gt; 772         self.check_forward_args(input, hx, batch_sizes)
    773         if batch_sizes is None:
    774             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,

/usr/local/lib/python3.8/dist-packages/torch/nn/modules/rnn.py in check_forward_args(self, input, hidden, batch_sizes)
    695                            batch_sizes: Optional[Tensor],
    696                            ):
--&gt; 697         self.check_input(input, batch_sizes)
    698         self.check_hidden_size(hidden[0], self.get_expected_hidden_size(input, batch_sizes),
    699                                'Expected hidden[0] size {}, got {}')

/usr/local/lib/python3.8/dist-packages/torch/nn/modules/rnn.py in check_input(self, input, batch_sizes)
    208                     expected_input_dim, input.dim()))
    209         if self.input_size != input.size(-1):
--&gt; 210             raise RuntimeError(
    211                 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(
    212                     self.input_size, input.size(-1)))

RuntimeError: input.size(-1) must be equal to input_size. Expected 64, got 84544
</code></pre>
<p>code:</p>
<pre><code class="lang-auto">import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import random_split, DataLoader, TensorDataset 
from torch.nn.utils.rnn import pad_sequence

with open("TM.3line") as f:
    lines = f.readlines()

sequences = []
labels = []
current_sequence = ""
current_label = ""

for line in lines:
    if line.startswith("&gt;"):
        if current_sequence:
            sequences.append(current_sequence)
            labels.append(current_label)
        current_sequence = ""
        current_label = ""
    elif line.startswith("M"):
        current_sequence = line.strip()
    else:
        current_label += line.strip()

sequences.append(current_sequence)
labels.append(current_label)

with open("output_file.csv", "w") as f:
    f.write("sequence,label\n")
    for i in range(len(sequences)):
        f.write(f"{sequences[i]},{labels[i]}\n")
def argmax(vec):
    # return the argmax as a python int
    _, idx = torch.max(vec, 1)
    return idx.item()


def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    return torch.tensor(idxs, dtype=torch.long)


# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec):
    max_score = vec[0, argmax(vec)]
    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
    return max_score + \
        torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))

class BiLSTM_CRF(nn.Module):

    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        self.word_embeds = nn.Embedding(vocab_size, embedding_dim, padding_idx=20)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim,
                            num_layers=1, bidirectional=True, batch_first=True)

        # Maps the output of the LSTM into tag space.
        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        # Matrix of transition parameters.  Entry i,j is the score of
        # transitioning *to* i *from* j.
        self.transitions = nn.Parameter(
            torch.randn(self.tagset_size, self.tagset_size))

        # These two statements enforce the constraint that we never transfer
        # to the start tag and we never transfer from the stop tag
        self.transitions.data[tag_to_ix[START_TAG], :] = -10000
        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000

        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (torch.randn(2, 1, self.hidden_dim),
                torch.randn(2, 1, self.hidden_dim))

    def _forward_alg(self, feats):
        # Do the forward algorithm to compute the partition function
        init_alphas = torch.full((1, self.tagset_size), -10000)
        # START_TAG has all of the score.
        init_alphas[0][self.tag_to_ix[START_TAG]] = 0

        # Wrap in a variable so that we will get automatic backprop
        forward_var = init_alphas

        # Iterate through the sentence
        for feat in feats:
            alphas_t = []  # The forward tensors at this timestep
            for next_tag in range(self.tagset_size):
                # broadcast the emission score: it is the same regardless of
                # the previous tag
                emit_score = feat[next_tag].view(
                    1, -1).expand(1, self.tagset_size)
                # the ith entry of trans_score is the score of transitioning to
                # next_tag from i
                trans_score = self.transitions[next_tag].view(1, -1)
                # The ith entry of next_tag_var is the value for the
                # edge (i -&gt; next_tag) before we do log-sum-exp
                next_tag_var = forward_var + trans_score + emit_score
                # The forward variable for this tag is log-sum-exp of all the
                # scores.
                alphas_t.append(log_sum_exp(next_tag_var).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        alpha = log_sum_exp(terminal_var)
        return alpha

    def _get_lstm_features(self, sentence):
        self.hidden = self.init_hidden()
        #sentence = sentence.transpose(1, 0)#
        embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        lstm_feats = self.hidden2tag(lstm_out)
        return lstm_feats

    def _score_sentence(self, feats, tags):
        # Gives the score of a provided tag sequence
        score = torch.zeros(1)
        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
        for i, feat in enumerate(feats):
            score = score + \
                self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]
        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
        return score

    def _viterbi_decode(self, feats):
        backpointers = []

        # Initialize the viterbi variables in log space
        init_vvars = torch.full((1, self.tagset_size), -10000.)
        init_vvars[0][self.tag_to_ix[START_TAG]] = 0

        # forward_var at step i holds the viterbi variables for step i-1
        forward_var = init_vvars
        for feat in feats:
            bptrs_t = []  # holds the backpointers for this step
            viterbivars_t = []  # holds the viterbi variables for this step

            for next_tag in range(self.tagset_size):
                # next_tag_var[i] holds the viterbi variable for tag i at the
                # previous step, plus the score of transitioning
                # from tag i to next_tag.
                # We don't include the emission scores here because the max
                # does not depend on them (we add them in below)
                next_tag_var = forward_var + self.transitions[next_tag]
                best_tag_id = argmax(next_tag_var)
                bptrs_t.append(best_tag_id)
                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
            # Now add in the emission scores, and assign forward_var to the set
            # of viterbi variables we just computed
            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        # Transition to STOP_TAG
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        best_tag_id = argmax(terminal_var)
        path_score = terminal_var[0][best_tag_id]

        # Follow the back pointers to decode the best path.
        best_path = [best_tag_id]
        for bptrs_t in reversed(backpointers):
            best_tag_id = bptrs_t[best_tag_id]
            best_path.append(best_tag_id)
        # Pop off the start tag (we dont want to return that to the caller)
        start = best_path.pop()
        assert start == self.tag_to_ix[START_TAG]  # Sanity check
        best_path.reverse()
        return path_score, best_path

    def neg_log_likelihood(self, sentence, tags):
        feats = self._get_lstm_features(sentence)
        forward_score = self._forward_alg(feats)
        gold_score = self._score_sentence(feats, tags)
        return forward_score - gold_score

    def forward(self, sentence):  # dont confuse this with _forward_alg above.
        # Get the emission scores from the BiLSTM
        
        lstm_feats = self._get_lstm_features(sentence)

        # Find the best path, given the features.
        score, tag_seq = self._viterbi_decode(lstm_feats)
        return score, tag_seq

df = pd.read_csv('output_file.csv')
training_data = [(row.sequence, row.label) for row in df.itertuples()]

word_to_ix = {}
for sentence, tags in training_data:
    for word in sentence:
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)

START_TAG = "&lt;START&gt;"
STOP_TAG = "&lt;STOP&gt;"
PAD_TAG = '5'
EMBEDDING_DIM = 64
HIDDEN_DIM = 20
num_epochs = 15
batch_size = 16
vocab_size=21

tag_to_ix = {"O": 0, "I": 1, "M": 2, START_TAG: 3, STOP_TAG: 4, PAD_TAG: 5}

# Prepare input data
sentence_in = []
targets = []
for sentence, tags in training_data:
    sentence_in.append(prepare_sequence(sentence, word_to_ix))
    targets.append([tag_to_ix[t] for t in tags])

# Pad each sentence to the same length
sentence_in = pad_sequence(sentence_in, padding_value=20, batch_first=True)

# Pad entire batch to the same length
targets = pad_sequence([torch.tensor(t) for t in targets], padding_value=5, batch_first=True)

sentence_in = sentence_in.long()
targets = targets.long()
# Create dataset and dataloader
dataset = TensorDataset(sentence_in, targets)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

model = BiLSTM_CRF(vocab_size, tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.001, weight_decay=1e-4)

for epoch in range(
        num_epochs):  # again, normally you would NOT do 300 epochs, it is toy data
    for i,(sentence, tags) in enumerate(training_data):
        # Step 1. Remember that Pytorch accumulates gradients.
        # We need to clear them out before each instance
        model.zero_grad()

    
        for batch in dataloader:
           sentence_in, targets = batch

        # Step 3. Run our forward pass.
        
        output = model(sentence_in)
        
        loss = model.neg_log_likelihood(output, targets)

        # Step 4. Compute the loss, gradients, and update the parameters by
        # calling optimizer.step()
        loss.backward()
        optimizer.step()

        # Check predictions after training
        with torch.no_grad():
          SAVE_DIR = '/content/'
          #https://discuss.pytorch.org/t/how-to-save-a-model-from-a-previous-epoch/20252/6
          path = os.path.join(SAVE_DIR, 'model.pth')

          torch.save(model.cpu().state_dict(), path) # saving model
          #model.cuda() # moving model to GPU for further training
          print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{len(training_data) // batch_size}], Loss: {loss.item():.4f}')



</code></pre>

ptrblck: <p>The error is raised in:</p>
<pre data-code-wrap="python"><code class="lang-python">embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
lstm_out, self.hidden = self.lstm(embeds, self.hidden)
</code></pre>
<p>as it seems <code>embeds</code> has an invalid shape.<br>
I’m a bit confused about your explanation stating:</p>
<aside class="quote no-group" data-username="francesco_patane" data-post="1" data-topic="173711">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/francesco_patane/48/56947_2.png" class="avatar"> francesco_patane:</div>
<blockquote>
<p>Max length of sequences is 1321, and 84544/64=1321.</p>
</blockquote>
</aside>
<p>as it seems you are working with a sequence length of <code>1321</code> and <code>64</code> features.<br>
However, you are explicitly flattening the temporal dimension into the feature dimension in the <code>view</code> operation.<br>
Take a look at this small example:</p>
<pre data-code-wrap="python"><code class="lang-python">embedding_dim = 64
word_embeds = nn.Embedding(10, embedding_dim)
lstm = nn.LSTM(embedding_dim, 10, batch_first=True)

batch_size = 16
seq_len = 1321
x = torch.randint(0, 10, (batch_size, seq_len))

out = word_embeds(x)
print(out.shape)
# torch.Size([16, 1321, 64]) = [batch_size, seq_len, embedding_dim]

# works
out = lstm(out) 

# fails
out = out.view(len(out), 1, -1)
print(out.shape)
# torch.Size([16, 1, 84544]) = [batch_size, 1, seq_len*embedding_dim]

out = lstm(out)
# RuntimeError: input.size(-1) must be equal to input_size. Expected 64, got 84544
</code></pre>
<p>I would assume removing the <code>view</code> operation should fix the issue as seen in my code snippet.</p>

francesco_patane: <aside class="quote no-group" data-username="francesco_patane" data-post="1" data-topic="173711">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/francesco_patane/48/56947_2.png" class="avatar"> francesco_patane:</div>
<blockquote>
<pre><code class="lang-auto">    # Now add in the emission scores, and assign forward_var to the set
            # of viterbi variables we just computed
            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        # Transition to STOP_TAG
        terminal_var = forward_var + self.transitions[self.tag_to_ix[ST
</code></pre>
</blockquote>
</aside>
<p>thank you very much, now it works <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=12" title=":slight_smile:" class="emoji" alt=":slight_smile:" loading="lazy" width="20" height="20"></p>

walkii22: <p>I have a similar issue with a CRNN module <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=12" title=":slight_smile:" class="emoji" alt=":slight_smile:" loading="lazy" width="20" height="20"></p>
<blockquote>
<p>class CRNN(torch.nn.Module):<br>
def <strong>init</strong>(self, in_feat_shape, out_shape, params):<br>
super().<strong>init</strong>()<br>
self.nb_classes = params[‘unique_classes’]<br>
self.conv_block_list = torch.nn.ModuleList()</p>
<pre><code>    if params['nb_rnn_layers']:
        self.in_gru_size = params['nb_cnn2d_filt'] * int( np.floor(in_feat_shape[-1] / np.prod(params['f_pool_size'])))
        self.gru = torch.nn.GRU(input_size=self.in_gru_size, hidden_size=params['rnn_size'],
                                num_layers=params['nb_rnn_layers'], batch_first=True,
                                dropout=params['dropout_rate'], bidirectional=True)
    self.attn = None
    if params['self_attn']:
</code></pre>
<h1></h1>
<pre><code>        self.attn = MultiHeadAttentionLayer(params['rnn_size'], params['nb_heads'], params['dropout_rate'])

    self.fnn_list = torch.nn.ModuleList()
    if params['nb_rnn_layers'] and params['nb_fnn_layers']:
        for fc_cnt in range(params['nb_fnn_layers']):
            self.fnn_list.append(
                torch.nn.Linear(params['fnn_size'] if fc_cnt else params['rnn_size'] , params['fnn_size'], bias=True)
            )
    self.fnn_list.append(
        torch.nn.Linear(params['fnn_size'] if params['nb_fnn_layers'] else params['rnn_size'], out_shape[-1], bias=True)
    )
    self.basemodel = Cnn14(pretrained_weights_path="/mnt/raid/di/redl/EIN-SELD/Cnn14_DecisionLevelMax_mAP=0.385.pth")
    self.basemodel.conv_block1 = ConvBlock(in_channels= 7, out_channels= 64)
    self.basemodel.conv_block2.requires_grad = False
    self.basemodel.conv_block3.requires_grad = False
    self.basemodel.conv_block4.requires_grad = False
    self.basemodel.conv_block5.requires_grad = False
    self.basemodel.conv_block6.requires_grad = False
    self.basemodel = self.basemodel.cuda()


def forward(self, x):
    '''input: (batch_size, mic_channels, time_steps, mel_bins)'''

    # for conv_cnt in range(len(self.conv_block_list)):
    #     x = self.conv_block_list[conv_cnt](x)
    x = self.basemodel.forward(x)
    print(x.shape)

    '''(batch_size, feature_maps, time_steps, mel_bins)'''
    x = x.transpose(1, 2).contiguous()
    x = x.view(x.shape[0], x.shape[1], -1).contiguous()
    ''' (batch_size, time_steps, feature_maps):'''

    (x, _) = self.gru(x)
    x = torch.tanh(x)
    x = x[:, :, x.shape[-1]//2:] * x[:, :, :x.shape[-1]//2]
    '''(batch_size, time_steps, feature_maps)'''
    if self.attn is not None:
        x = self.attn.forward(x, x, x)
        # out - batch x hidden x seq
        x = torch.tanh(x)

    x_rnn = x
    for fnn_cnt in range(len(self.fnn_list)-1):
        x = self.fnn_list[fnn_cnt](x)
    doa = torch.tanh(self.fnn_list[-1](x))
    '''(batch_size, time_steps, label_dim)'''

    return doa
</code></pre>
</blockquote>
<p>Traceback error<br>
torch.Size([64, 7, 250, 64])<br>
torch.Size([64, 64, 125, 32])<br>
torch.Size([64, 128, 62, 16])<br>
torch.Size([64, 2048, 7, 2])<br>
torch.Size([64, 2048, 7, 2])<br>
Traceback (most recent call last):<br>
File “train_seldnet.py”, line 392, in <br>
sys.exit(main(sys.argv))<br>
File “train_seldnet.py”, line 315, in main<br>
train_loss = train_epoch(data_gen_train, optimizer, model, criterion, params, device)<br>
File “train_seldnet.py”, line 172, in train_epoch<br>
output = model(data)<br>
File “/mnt/angard_raid/home/flu/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 1130, in _call_impl<br>
return forward_call(*input, **kwargs)<br>
File “/mnt/raid/ni/SELD_SSL_2024/seld-dcase2022/seldnet_model.py”, line 258, in forward<br>
(x, _) = self.gru(x)<br>
File “/mnt/antares_raid/home/wale/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 1130, in _call_impl<br>
return forward_call(*input, **kwargs)<br>
File “/mnt/antares_raid/home/wale/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py”, line 948, in forward<br>
self.check_forward_args(input, hx, batch_sizes)<br>
File “/mnt/antares_raid/home/wale/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py”, line 229, in check_forward_args<br>
self.check_input(input, batch_sizes)<br>
File “/mnt/antares_raid/home/wale/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py”, line 207, in check_input<br>
self.input_size, input.size(-1)))<br>
RuntimeError: input.size(-1) must be equal to input_size. Expected 128, got 4096</p>

ptrblck: <p>Could you format your code by wrapping it into three backticks ``` and could you also add the code to initialize the model and reproduce the error?</p>

walkii22: <p>I am using a Pretrained CNN14</p>
<pre><code class="lang-auto">
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from IPython import embed
from transfercnn import Cnn14, ConvBlock
class MSELoss_ADPIT(object):
def __init__(self):
super().__init__()
self._each_loss = nn.MSELoss(reduction='none')

def _each_calc(self, output, target):
return self._each_loss(output, target).mean(dim=(2)) # class-wise frame-level

class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hid_dim, n_heads, dropout):
super().__init__()

assert hid_dim % n_heads == 0

self.hid_dim = hid_dim
self.n_heads = n_heads
self.head_dim = hid_dim // n_heads

self.fc_q = nn.Linear(hid_dim, hid_dim)
self.fc_k = nn.Linear(hid_dim, hid_dim)
self.fc_v = nn.Linear(hid_dim, hid_dim)

self.fc_o = nn.Linear(hid_dim, hid_dim)

self.dropout = nn.Dropout(dropout)

def forward(self, query, key, value, mask=None):

batch_size = query.shape[0]

#query = [batch size, query len, hid dim]
#key = [batch size, key len, hid dim]
#value = [batch size, value len, hid dim]

Q = self.fc_q(query)
K = self.fc_k(key)
V = self.fc_v(value)

#Q = [batch size, query len, hid dim]
#K = [batch size, key len, hid dim]
#V = [batch size, value len, hid dim]

Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)

#Q = [batch size, n heads, query len, head dim]
#K = [batch size, n heads, key len, head dim]
#V = [batch size, n heads, value len, head dim]

energy = torch.div(torch.matmul(Q, K.permute(0, 1, 3, 2)), np.sqrt(self.head_dim))

#energy = [batch size, n heads, query len, key len]

if mask is not None:
energy = energy.masked_fill(mask == 0, -1e10)

attention = torch.softmax(energy, dim = -1)

#attention = [batch size, n heads, query len, key len]

x = torch.matmul(self.dropout(attention), V)

#x = [batch size, n heads, query len, head dim]

x = x.permute(0, 2, 1, 3).contiguous()

#x = [batch size, query len, n heads, head dim]

x = x.view(batch_size, -1, self.hid_dim)

#x = [batch size, query len, hid dim]

x = self.fc_o(x)

#x = [batch size, query len, hid dim]

return x

class AttentionLayer(nn.Module):
def __init__(self, in_channels, out_channels, key_channels):
super(AttentionLayer, self).__init__()
self.conv_Q = nn.Conv1d(in_channels, key_channels, kernel_size=1, bias=False)
self.conv_K = nn.Conv1d(in_channels, key_channels, kernel_size=1, bias=False)
self.conv_V = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)

def forward(self, x):
Q = self.conv_Q(x)
K = self.conv_K(x)
V = self.conv_V(x)
A = Q.permute(0, 2, 1).matmul(K).softmax(2)
x = A.matmul(V.permute(0, 2, 1)).permute(0, 2, 1)
return x

def __repr__(self):
return self._get_name() + \
'(in_channels={}, out_channels={}, key_channels={})'.format(
self.conv_Q.in_channels,
self.conv_V.out_channels,
self.conv_K.out_channels
)

class CRNN(torch.nn.Module):
def __init__(self, in_feat_shape, out_shape, params):
super().__init__()
self.nb_classes = params['unique_classes']
self.conv_block_list = torch.nn.ModuleList()

self.basemodel = Cnn14(pretrained_weights_path="/mnt/raid/ni/WALE_SEdl/EIN-SELD/Cnn14_DecisionLevelMax_mAP=0.385.pth")
self.basemodel.conv_block1 = ConvBlock(in_channels= 7, out_channels= 64)
self.basemodel.conv_block2.requires_grad = False
self.basemodel.conv_block3.requires_grad = False
self.basemodel.conv_block4.requires_grad = False
self.basemodel.conv_block5.requires_grad = False
self.basemodel.conv_block6.requires_grad = False
self.basemodel = self.basemodel.cuda()

if params['nb_rnn_layers']:
self.in_gru_size = params['nb_cnn2d_filt'] * int( np.floor(in_feat_shape[-1] / np.prod(params['f_pool_size'])))
self.gru = torch.nn.GRU(input_size=self.in_gru_size, hidden_size=params['rnn_size'],
num_layers=params['nb_rnn_layers'], batch_first=True,
dropout=params['dropout_rate'], bidirectional=True)
self.attn = None
if params['self_attn']:
# self.attn = AttentionLayer(params['rnn_size'], params['rnn_size'], params['rnn_size'])
self.attn = MultiHeadAttentionLayer(params['rnn_size'], params['nb_heads'], params['dropout_rate'])

self.fnn_list = torch.nn.ModuleList()
if params['nb_rnn_layers'] and params['nb_fnn_layers']:
for fc_cnt in range(params['nb_fnn_layers']):
self.fnn_list.append(
torch.nn.Linear(params['fnn_size'] if fc_cnt else params['rnn_size'] , params['fnn_size'], bias=True)
)
self.fnn_list.append(
torch.nn.Linear(params['fnn_size'] if params['nb_fnn_layers'] else params['rnn_size'], out_shape[-1], bias=True)
)

def forward(self, x):
'''input: (batch_size, mic_channels, time_steps, mel_bins)'''

# for conv_cnt in range(len(self.conv_block_list)):
# x = self.conv_block_list[conv_cnt](x)
x = self.basemodel.forward(x)
print(x.shape)

'''(batch_size, feature_maps, time_steps, mel_bins)'''
x = x.transpose(1, 2).contiguous()
x = x.view(x.shape[0], x.shape[1], -1).contiguous() #view
''' (batch_size, time_steps, feature_maps):'''
print(x.shape)
(x, _) = self.gru(x)
x = torch.tanh(x)
x = x[:, :, x.shape[-1]//2:] * x[:, :, :x.shape[-1]//2]
'''(batch_size, time_steps, feature_maps)'''
if self.attn is not None:
x = self.attn.forward(x, x, x)
# out - batch x hidden x seq
x = torch.tanh(x)

x_rnn = x
for fnn_cnt in range(len(self.fnn_list)-1):
x = self.fnn_list[fnn_cnt](x)
doa = torch.tanh(self.fnn_list[-1](x))
'''(batch_size, time_steps, label_dim)'''

return doa

torch.Size([128, 7, 250, 64])
torch.Size([128, 64, 125, 32])
torch.Size([128, 128, 62, 16])
torch.Size([128, 2048, 7, 2])
torch.Size([128, 2048, 7, 2])
torch.Size([128, 7, 4096])
Traceback (most recent call last):
File "train_seldnet.py", line 392, in &lt;module&gt;
sys.exit(main(sys.argv))
File "train_seldnet.py", line 315, in main
train_loss = train_epoch(data_gen_train, optimizer, model, criterion, params, device)
File "train_seldnet.py", line 172, in train_epoch
output = model(data)
File "/mnt/jees_raid/home/wale/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/raid/do/SELD_SSL_2024/sod-model/seldnet_model.py", line 259, in forward
(x, _) = self.gru(x)
File "/mnt/jees_raid/home/jees/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/mnt/jees_raid/home/jees/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 948, in forward
self.check_forward_args(input, hx, batch_sizes)
File "/mnt/antares_raid/home/jees/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 229, in check_forward_args
self.check_input(input, batch_sizes)
File "/mnt/jees_raid/home/jees/anaconda3/envs/ein/lib/python3.7/site-packages/torch/nn/modules/rnn.py", line 207, in check_input
self.input_size, input.size(-1)))
RuntimeError: input.size(-1) must be equal to input_size. Expected 128, got 4096</code></pre>