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tmp.py
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tmp.py
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# import torch
# import torch.nn as nn
# from torch.autograd import Variable
# from torch.nn import utils as nn_utils
#
# batch_size = 2
# max_length = 3
# hidden_size = 4
# n_layers = 1
#
# tensor_in = torch.FloatTensor([[1, 2, 3], [1, 0, 0]]).resize_(2, 3, 1)
# print("in: ", tensor_in)
# tensor_in = Variable(tensor_in) # [batch, seq, feature], [2, 3, 1]
# seq_lengths = [3, 1] # list of integers holding information about the batch size at each sequence step
#
# # pack it
# pack = nn_utils.rnn.pack_padded_sequence(tensor_in, seq_lengths, batch_first=True)
#
# # initialize
# rnn = nn.RNN(1, hidden_size, n_layers, batch_first=True)
# h0 = Variable(torch.randn(n_layers, batch_size, hidden_size))
#
# # forward
# out, _ = rnn(pack, h0)
#
# # unpack
# unpacked = nn_utils.rnn.pad_packed_sequence(out)
# print('out', unpacked)
import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
# Step 1: Construct Vocabulary
# Step 2: Load indexed data (list of instances, where each instance is list of character indices)
# Step 3: Make Model
# * Step 4: Pad instances with 0s till max length sequence
# * Step 5: Sort instances by sequence length in descending order
# * Step 6: Embed the instances
# * Step 7: Call pack_padded_sequence with embeded instances and sequence lengths
# * Step 8: Forward with LSTM
# * Step 9: Call unpack_padded_sequences if required / or just pick last hidden vector
# * Summary of Shape Transformations
# We want to run LSTM on a batch following 3 character sequences
seqs = ['long_str', # len = 8
'tiny', # len = 4
'medium'] # len = 6
## Step 1: Construct Vocabulary ##
##------------------------------##
# make sure <pad> idx is 0
vocab = ['<pad>'] + sorted(set([char for seq in seqs for char in seq]))
# => ['<pad>', '_', 'd', 'e', 'g', 'i', 'l', 'm', 'n', 'o', 'r', 's', 't', 'u', 'y']
## Step 2: Load indexed data (list of instances, where each instance is list of character indices) ##
##-------------------------------------------------------------------------------------------------##
vectorized_seqs = [[vocab.index(tok) for tok in seq]for seq in seqs]
# vectorized_seqs => [[6, 9, 8, 4, 1, 11, 12, 10],
# [12, 5, 8, 14],
# [7, 3, 2, 5, 13, 7]]
## Step 3: Make Model ##
##--------------------##
embed = Embedding(len(vocab), 4) # embedding_dim = 4
lstm = LSTM(input_size=4, hidden_size=5, batch_first=True) # input_dim = 4, hidden_dim = 5
## Step 4: Pad instances with 0s till max length sequence ##
##--------------------------------------------------------##
# get the length of each seq in your batch
seq_lengths = LongTensor(list(map(len, vectorized_seqs)))
# seq_lengths => [ 8, 4, 6]
# batch_sum_seq_len: 8 + 4 + 6 = 18
# max_seq_len: 8
seq_tensor = Variable(torch.zeros((len(vectorized_seqs), seq_lengths.max()))).long()
# seq_tensor => [[0 0 0 0 0 0 0 0]
# [0 0 0 0 0 0 0 0]
# [0 0 0 0 0 0 0 0]]
for idx, (seq, seqlen) in enumerate(zip(vectorized_seqs, seq_lengths)):
seq_tensor[idx, :seqlen] = LongTensor(seq)
# seq_tensor => [[ 6 9 8 4 1 11 12 10] # long_str
# [12 5 8 14 0 0 0 0] # tiny
# [ 7 3 2 5 13 7 0 0]] # medium
# seq_tensor.shape : (batch_size X max_seq_len) = (3 X 8)
## Step 5: Sort instances by sequence length in descending order ##
##---------------------------------------------------------------##
seq_lengths, perm_idx = seq_lengths.sort(0, descending=True)
seq_tensor = seq_tensor[perm_idx]
# seq_tensor => [[ 6 9 8 4 1 11 12 10] # long_str
# [ 7 3 2 5 13 7 0 0] # medium
# [12 5 8 14 0 0 0 0]] # tiny
# seq_tensor.shape : (batch_size X max_seq_len) = (3 X 8)
## Step 6: Embed the instances ##
##-----------------------------##
embedded_seq_tensor = embed(seq_tensor)
# embedded_seq_tensor =>
# [[[-0.77578706 -1.8080667 -1.1168439 1.1059115 ] l
# [-0.23622951 2.0361056 0.15435742 -0.04513785] o
# [-0.6000342 1.1732816 0.19938554 -1.5976517 ] n
# [ 0.40524676 0.98665565 -0.08621677 -1.1728264 ] g
# [-1.6334635 -0.6100042 1.7509955 -1.931793 ] _
# [-0.6470658 -0.6266589 -1.7463604 1.2675372 ] s
# [ 0.64004815 0.45813003 0.3476034 -0.03451729] t
# [-0.22739866 -0.45782727 -0.6643252 0.25129375]] r
# [[ 0.16031227 -0.08209462 -0.16297023 0.48121014] m
# [-0.7303265 -0.857339 0.58913064 -1.1068314 ] e
# [ 0.48159844 -1.4886451 0.92639893 0.76906884] d
# [ 0.27616557 -1.224429 -1.342848 -0.7495876 ] i
# [ 0.01795524 -0.59048957 -0.53800726 -0.6611691 ] u
# [ 0.16031227 -0.08209462 -0.16297023 0.48121014] m
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ] <pad>
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ]] <pad>
# [[ 0.64004815 0.45813003 0.3476034 -0.03451729] t
# [ 0.27616557 -1.224429 -1.342848 -0.7495876 ] i
# [-0.6000342 1.1732816 0.19938554 -1.5976517 ] n
# [-1.284392 0.68294704 1.4064184 -0.42879772] y
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ] <pad>
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ] <pad>
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ] <pad>
# [ 0.2691206 -0.43435425 0.87935454 -2.2269666 ]]] <pad>
# embedded_seq_tensor.shape : (batch_size X max_seq_len X embedding_dim) = (3 X 8 X 4)
## Step 7: Call pack_padded_sequence with embeded instances and sequence lengths ##
##-------------------------------------------------------------------------------##
packed_input = pack_padded_sequence(embedded_seq_tensor, seq_lengths.cpu().numpy(), batch_first=True)
# packed_input (PackedSequence is NamedTuple with 2 attributes: data and batch_sizes
#
# packed_input.data =>
# [[-0.77578706 -1.8080667 -1.1168439 1.1059115 ] l
# [ 0.01795524 -0.59048957 -0.53800726 -0.6611691 ] m
# [-0.6470658 -0.6266589 -1.7463604 1.2675372 ] t
# [ 0.16031227 -0.08209462 -0.16297023 0.48121014] o
# [ 0.40524676 0.98665565 -0.08621677 -1.1728264 ] e
# [-1.284392 0.68294704 1.4064184 -0.42879772] i
# [ 0.64004815 0.45813003 0.3476034 -0.03451729] n
# [ 0.27616557 -1.224429 -1.342848 -0.7495876 ] d
# [ 0.64004815 0.45813003 0.3476034 -0.03451729] n
# [-0.23622951 2.0361056 0.15435742 -0.04513785] g
# [ 0.16031227 -0.08209462 -0.16297023 0.48121014] i
# [-0.22739866 -0.45782727 -0.6643252 0.25129375]] y
# [-0.7303265 -0.857339 0.58913064 -1.1068314 ] _
# [-1.6334635 -0.6100042 1.7509955 -1.931793 ] u
# [ 0.27616557 -1.224429 -1.342848 -0.7495876 ] s
# [-0.6000342 1.1732816 0.19938554 -1.5976517 ] m
# [-0.6000342 1.1732816 0.19938554 -1.5976517 ] t
# [ 0.48159844 -1.4886451 0.92639893 0.76906884] r
# packed_input.data.shape : (batch_sum_seq_len X embedding_dim) = (18 X 4)
#
# packed_input.batch_sizes => [ 3, 3, 3, 3, 2, 2, 1, 1]
# visualization :
# l o n g _ s t r #(long_str)
# m e d i u m #(medium)
# t i n y #(tiny)
# 3 3 3 3 2 2 1 1 (sum = 18 [batch_sum_seq_len])
## Step 8: Forward with LSTM ##
##---------------------------##
packed_output, (ht, ct) = lstm(packed_input)
# packed_output (PackedSequence is NamedTuple with 2 attributes: data and batch_sizes
#
# packed_output.data :
# [[-0.00947162 0.07743231 0.20343193 0.29611713 0.07992904] l
# [ 0.08596145 0.09205993 0.20892891 0.21788561 0.00624391] o
# [ 0.16861682 0.07807446 0.18812777 -0.01148055 -0.01091915] n
# [ 0.20994528 0.17932937 0.17748171 0.05025435 0.15717036] g
# [ 0.01364102 0.11060348 0.14704391 0.24145307 0.12879576] _
# [ 0.02610307 0.00965587 0.31438383 0.246354 0.08276576] s
# [ 0.09527554 0.14521319 0.1923058 -0.05925677 0.18633027] t
# [ 0.09872741 0.13324396 0.19446367 0.4307988 -0.05149471] r
# [ 0.03895474 0.08449443 0.18839942 0.02205326 0.23149511] m
# [ 0.14620507 0.07822411 0.2849248 -0.22616537 0.15480657] e
# [ 0.00884941 0.05762182 0.30557525 0.373712 0.08834908] d
# [ 0.12460691 0.21189159 0.04823487 0.06384943 0.28563985] i
# [ 0.01368293 0.15872964 0.03759198 -0.13403234 0.23890573] u
# [ 0.00377969 0.05943518 0.2961751 0.35107893 0.15148178] m
# [ 0.00737647 0.17101538 0.28344846 0.18878219 0.20339936] t
# [ 0.0864429 0.11173367 0.3158251 0.37537992 0.11876849] i
# [ 0.17885767 0.12713005 0.28287745 0.05562563 0.10871304] n
# [ 0.09486895 0.12772645 0.34048414 0.25930756 0.12044918]] y
# packed_output.data.shape : (batch_sum_seq_len X hidden_dim) = (18 X 5)
# packed_output.batch_sizes => [ 3, 3, 3, 3, 2, 2, 1, 1] (same as packed_input.batch_sizes)
# visualization :
# l o n g _ s t r #(long_str)
# m e d i u m #(medium)
# t i n y #(tiny)
# 3 3 3 3 2 2 1 1 (sum = 18 [batch_sum_seq_len])
## Step 9: Call unpack_padded_sequences if required / or just pick last hidden vector ##
##------------------------------------------------------------------------------------##
# unpack your output if required
output, input_sizes = pad_packed_sequence(packed_output, batch_first=True)
# output:
# output =>
# [[[-0.00947162 0.07743231 0.20343193 0.29611713 0.07992904] l
# [ 0.20994528 0.17932937 0.17748171 0.05025435 0.15717036] o
# [ 0.09527554 0.14521319 0.1923058 -0.05925677 0.18633027] n
# [ 0.14620507 0.07822411 0.2849248 -0.22616537 0.15480657] g
# [ 0.01368293 0.15872964 0.03759198 -0.13403234 0.23890573] _
# [ 0.00737647 0.17101538 0.28344846 0.18878219 0.20339936] s
# [ 0.17885767 0.12713005 0.28287745 0.05562563 0.10871304] t
# [ 0.09486895 0.12772645 0.34048414 0.25930756 0.12044918]] r
# [[ 0.08596145 0.09205993 0.20892891 0.21788561 0.00624391] m
# [ 0.01364102 0.11060348 0.14704391 0.24145307 0.12879576] e
# [ 0.09872741 0.13324396 0.19446367 0.4307988 -0.05149471] d
# [ 0.00884941 0.05762182 0.30557525 0.373712 0.08834908] i
# [ 0.00377969 0.05943518 0.2961751 0.35107893 0.15148178] u
# [ 0.0864429 0.11173367 0.3158251 0.37537992 0.11876849] m
# [ 0. 0. 0. 0. 0. ] <pad>
# [ 0. 0. 0. 0. 0. ]] <pad>
# [[ 0.16861682 0.07807446 0.18812777 -0.01148055 -0.01091915] t
# [ 0.02610307 0.00965587 0.31438383 0.246354 0.08276576] i
# [ 0.03895474 0.08449443 0.18839942 0.02205326 0.23149511] n
# [ 0.12460691 0.21189159 0.04823487 0.06384943 0.28563985] y
# [ 0. 0. 0. 0. 0. ] <pad>
# [ 0. 0. 0. 0. 0. ] <pad>
# [ 0. 0. 0. 0. 0. ] <pad>
# [ 0. 0. 0. 0. 0. ]]] <pad>
# output.shape : ( batch_size X max_seq_len X hidden_dim) = (3 X 8 X 5)
print(output)
# Or if you just want the final hidden state?
print(ht[-1])
## Summary of Shape Transformations ##
##----------------------------------##
# (batch_size X max_seq_len X embedding_dim) --> Sort by seqlen ---> (batch_size X max_seq_len X embedding_dim)
# (batch_size X max_seq_len X embedding_dim) ---> Pack ---> (batch_sum_seq_len X embedding_dim)
# (batch_sum_seq_len X embedding_dim) ---> LSTM ---> (batch_sum_seq_len X hidden_dim)
# (batch_sum_seq_len X hidden_dim) ---> UnPack ---> (batch_size X max_seq_len X hidden_dim)