I started working with Pytorch recently so my understanding of it isn't quite strong. I previously had a 1 layer CNN but wanted to extend it to 2 layers, but the input and output channels have been throwing errors I can seem to decipher. Why does it expect 192 channels? Can someone give me a pointer to help me understand this better? I have seen several related problems on here, but I don't understand those solutions either.
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from transformers import BertConfig, BertModel, BertTokenizer
import math
from transformers import AdamW, get_linear_schedule_with_warmup
def pad_sents(sents, pad_token): # Pad list of sentences according to the longest sentence in the batch.
sents_padded = []
max_len = max(len(s) for s in sents)
for s in sents:
padded = [pad_token] * max_len
padded[:len(s)] = s
sents_padded.append(padded)
return sents_padded
def sents_to_tensor(tokenizer, sents, device):
tokens_list = [tokenizer.tokenize(str(sent)) for sent in sents]
sents_lengths = [len(tokens) for tokens in tokens_list]
tokens_list_padded = pad_sents(tokens_list, '[PAD]')
sents_lengths = torch.tensor(sents_lengths, device=device)
masks = []
for tokens in tokens_list_padded:
mask = [0 if token == '[PAD]' else 1 for token in tokens]
masks.append(mask)
masks_tensor = torch.tensor(masks, dtype=torch.long, device=device)
tokens_id_list = [tokenizer.convert_tokens_to_ids(tokens) for tokens in tokens_list_padded]
sents_tensor = torch.tensor(tokens_id_list, dtype=torch.long, device=device)
return sents_tensor, masks_tensor, sents_lengths
class ConvModel(nn.Module):
def __init__(self, device, dropout_rate, n_class, out_channel=16):
super(ConvModel, self).__init__()
self.bert_config = BertConfig.from_pretrained('bert-base-uncased', output_hidden_states=True)
self.dropout_rate = dropout_rate
self.n_class = n_class
self.out_channel = out_channel
self.bert = BertModel.from_pretrained('bert-base-uncased', config=self.bert_config)
self.out_channels = self.bert.config.num_hidden_layers * self.out_channel
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', config=self.bert_config)
self.conv = nn.Conv2d(in_channels=self.bert.config.num_hidden_layers,
out_channels=self.out_channels,
kernel_size=(3, self.bert.config.hidden_size),
groups=self.bert.config.num_hidden_layers)
self.conv1 = nn.Conv2d(in_channels=self.out_channels,
out_channels=48,
kernel_size=(3, self.bert.config.hidden_size),
groups=self.bert.config.num_hidden_layers)
self.hidden_to_softmax = nn.Linear(self.out_channels, self.n_class, bias=True)
self.dropout = nn.Dropout(p=self.dropout_rate)
self.device = device
def forward(self, sents):
sents_tensor, masks_tensor, sents_lengths = sents_to_tensor(self.tokenizer, sents, self.device)
encoded_layers = self.bert(input_ids=sents_tensor, attention_mask=masks_tensor)
hidden_encoded_layer = encoded_layers[2]
hidden_encoded_layer = hidden_encoded_layer[0]
hidden_encoded_layer = torch.unsqueeze(hidden_encoded_layer, dim=1)
hidden_encoded_layer = hidden_encoded_layer.repeat(1, 12, 1, 1)
conv_out = self.conv(hidden_encoded_layer) # (batch_size, channel_out, some_length, 1)
conv_out = self.conv1(conv_out)
conv_out = torch.squeeze(conv_out, dim=3) # (batch_size, channel_out, some_length)
conv_out, _ = torch.max(conv_out, dim=2) # (batch_size, channel_out)
pre_softmax = self.hidden_to_softmax(conv_out)
return pre_softmax
def batch_iter(data, batch_size, shuffle=False, bert=None):
batch_num = math.ceil(data.shape[0] / batch_size)
index_array = list(range(data.shape[0]))
if shuffle:
data = data.sample(frac=1)
for i in range(batch_num):
indices = index_array[i * batch_size: (i + 1) * batch_size]
examples = data.iloc[indices]
sents = list(examples.train_BERT_tweet)
targets = list(examples.train_label.values)
yield sents, targets # list[list[str]] if not bert else list[str], list[int]
def train():
label_name = ['Yes', 'Maybe', 'No']
device = torch.device("cpu")
df_train = pd.read_csv('trainn.csv') # , index_col=0)
train_label = dict(df_train.train_label.value_counts())
label_max = float(max(train_label.values()))
train_label_weight = torch.tensor([label_max / train_label[i] for i in range(len(train_label))], device=device)
model = ConvModel(device=device, dropout_rate=0.2, n_class=len(label_name))
optimizer = AdamW(model.parameters(), lr=1e-3, correct_bias=False)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=1000) # changed the last 2 arguments to old ones
model = model.to(device)
model.train()
cn_loss = torch.nn.CrossEntropyLoss(weight=train_label_weight, reduction='mean')
train_batch_size = 16
for epoch in range(1):
for sents, targets in batch_iter(df_train, batch_size=train_batch_size, shuffle=True): # for each epoch
optimizer.zero_grad()
pre_softmax = model(sents)
loss = cn_loss(pre_softmax, torch.tensor(targets, dtype=torch.long, device=device))
loss.backward()
optimizer.step()
scheduler.step()
TrainingModel = train()
Here's a snippet of data https://github.com/Kosisochi/DataSnippet
It seems that the original version of the code you had in this question behaved differently. The final version of the code you have here gives me a different error from what you posted, more specifically - this:
RuntimeError: Calculated padded input size per channel: (20 x 1). Kernel size: (3 x 768). Kernel size can't be greater than actual input size
I apologize if I misunderstood the situation, but it seems to me that your understanding of what exactly nn.Conv2d layer does is not 100% clear and that is the main source of your struggle. I interpret the part "detailed explanation on 2 layer CNN in Pytorch" you requested as an ask to explain in detail on how that layer works and I hope that after this is done there will be no problem applying it 1 time, 2 times or more.
You can find all the documentation about the layer here, but let me give you a recap which hopefully will help to understand more the errors you're getting.
First of all nn.Conv2d inputs are 4-d tensors of the shape (BatchSize, ChannelsIn, Height, Width) and outputs are 4-d tensors of the shape (BatchSize, ChannelsOut, HeightOut, WidthOut). The simplest way to think about nn.Conv2d is of something applied to 2d images with pixel grid of size Height x Width and having ChannelsIn different colors or features per pixel. Even if your inputs have nothing to do with actual images the behavior of the layer is still the same. Simplest situation is when the nn.Conv2d is not using padding (as in your code). In that case the kernel_size=(kernel_height, kernel_width) argument specifies the rectangle which you can imagine sweeping through Height x Width rectangle of your inputs and producing one pixel for each valid position. Without padding the coordinate of the rectangle's point can be any pair of indicies (x, y) with x between 0 and Height - kernel_height and y between 0 and Width - kernel_width. Thus the output will look like a 2d image of size (Height - kernel_height + 1) x (Width - kernel_width + 1) and will have as many output channels as specified to nn.Conv2d constructor, so the output tensor will be of shape (BatchSize, ChannelsOut, Height - kernel_height + 1, Width - kernel_width + 1).
The parameter groups is not affecting how shapes are changed by the layer - it is only controlling which input channels are used as inputs for the output channels (groups=1 means that every input channel is used as input for every output channel, otherwise input and output channels are divided into corresponding number of groups and only input channels from group i are used as inputs for the output channels from group i).
Now in your current version of the code you have BatchSize = 16 and the output of pre-trained model is (BatchSize, DynamicSize, 768) with DynamicSize depending on the input, e.g. 22. You then introduce additional dimension as axis 1 with unsqueeze and repeat the values along that dimension transforming the tensor of shape (16, 22, 768) into (16, 12, 22, 768). Effectively you are using the output of the pre-trained model as 12-channel (with each channel having same values as others) 2-d images here of size (22, 768), where 22 is not fixed (depends on the batch). Then you apply a nn.Conv2d with kernel size (3, 768) - which means that there is no "wiggle room" for width and output 2-d images will be of size (20, 1) and since your layer has 192 channels final size of the output of first convolution layer has shape (16, 192, 20, 1). Then you try to apply second layer of convolution on top of that with kernel size (3, 768) again, but since your 2-d "image" is now just (20 x 1) there is no valid position to fit (3, 768) kernel rectangle inside a rectangle (20 x 1) which leads to the error message Kernel size can't be greater than actual input size.
Hope this explanation helps. Now to the choices you have to avoid the issue:
1).1 for the next layer(s) and that will work for you, but a more correct way would be to transpose pre-trained model's output tensor by switching the last dimensions - getting shape (16, 768, DynamicSize) from (16, DynamicSize, 768) and then apply nn.Conv1d layer with 768 input channels and arbitrary ChannelsOut as output channels and 1d kernel_size=3 (meaning you look at 3 consecutive elements of the sequence for convolution). If you do that than without padding input shape of (16, 768, DynamicSize) will become (16, ChannelsOut, DynamicSize-2), and after you apply second Conv1d with e.g. the same settings as first one you'll get a tensor of shape (16, ChannelsOut, DynamicSize-4), etc. (each time the 1d length will shrink by kernel_size-1). You can always change number of channels/kernel_size for each subsequent convolution layer too.