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import math
import time
import random
import torch
import torch.nn as nn
import dataset_helper
class AnimationTransformer(nn.Module):
def __init__(
self,
dim_model, # hidden_size; corresponds to embedding length
num_heads,
num_encoder_layers,
num_decoder_layers,
dropout_p,
use_positional_encoder=True
):
super().__init__()
self.model_type = "Transformer"
self.dim_model = dim_model
# TODO: Currently left out, as input sequence shuffled. Later check if use is beneficial.
self.use_positional_encoder = use_positional_encoder
self.positional_encoder = PositionalEncoding(
dim_model=dim_model,
dropout_p=dropout_p
)
self.transformer = nn.Transformer(
d_model=dim_model,
nhead=num_heads,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
dropout=dropout_p,
batch_first=True
)
def forward(self, src, tgt, tgt_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None):
# Src size must be (batch_size, src sequence length)
# Tgt size must be (batch_size, tgt sequence length)
if self.use_positional_encoder:
src = self.positional_encoder(src)
tgt = self.positional_encoder(tgt)
# Transformer blocks - Out size = (sequence length, batch_size, num_tokens)
out = self.transformer(src, tgt, tgt_mask=tgt_mask, src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask)
return out
def get_tgt_mask(size) -> torch.tensor:
# Generates a square matrix where each row allows one word more to be seen
mask = torch.tril(torch.ones(size, size) == 1) # Lower triangular matrix
mask = mask.float()
mask = mask.masked_fill(mask == 0, float('-inf')) # Convert zeros to -inf
mask = mask.masked_fill(mask == 1, float(0.0)) # Convert ones to 0
# EX for size=5:
# [[0., -inf, -inf, -inf, -inf],
# [0., 0., -inf, -inf, -inf],
# [0., 0., 0., -inf, -inf],
# [0., 0., 0., 0., -inf],
# [0., 0., 0., 0., 0.]]
return mask
def create_pad_mask(matrix: torch.tensor) -> torch.tensor:
pad_masks = []
# Iterate over each sequence in the batch.
for i in range(0, matrix.size(0)):
sequence = []
# Iterate over each element in the sequence and append True if padding value
for j in range(0, matrix.size(1)):
sequence.append(matrix[i, j, 0] == dataset_helper.PADDING_VALUE)
pad_masks.append(sequence)
#print("matrix", matrix, matrix.shape, "pad_mask", pad_masks)
return torch.tensor(pad_masks)
def _transformer_call_in_loops(model, batch, device, loss_function):
source, target = batch[0], batch[1]
source, target = source.to(device), target.to(device)
# First index is all batch entries, second is
target_input = target[:, :-1] # trg input is offset by one (SOS token and excluding EOS)
target_expected = target[:, 1:] # trg is offset by one (excluding SOS token)
# SOS - 1 - 2 - 3 - 4 - EOS - PAD - PAD // target_input
# 1 - 2 - 3 - 4 - EOS - PAD - PAD - PAD // target_expected
# Get mask to mask out the next words
tgt_mask = get_tgt_mask(target_input.size(1)).to(device)
# Standard training except we pass in y_input and tgt_mask
prediction = model(source, target_input,
tgt_mask=tgt_mask,
src_key_padding_mask=create_pad_mask(source).to(device),
# Mask with expected as EOS is no input (see above)
tgt_key_padding_mask=create_pad_mask(target_expected).to(device))
return loss_function(prediction, target_expected, create_pad_mask(target_expected).to(device))
#return loss_function(prediction, target_expected)
def train_loop(model, opt, loss_function, dataloader, device):
model.train()
total_loss = 0
t0 = time.time()
i = 1
for batch in dataloader:
loss = _transformer_call_in_loops(model, batch, device, loss_function)
opt.zero_grad()
loss.backward()
opt.step()
total_loss += loss.detach().item()
if i == 1 or i % 10 == 0:
elapsed_time = time.time() - t0
total_expected = elapsed_time / i * len(dataloader)
print(f">> {i}: Time per Batch {elapsed_time / i : .2f}s | "
f"Total expected {total_expected / 60 : .2f} min | "
f"Remaining {(total_expected - elapsed_time) / 60 : .2f} min ")
i += 1
print(f">> Epoch time: {(time.time() - t0)/60:.2f} min")
return total_loss / len(dataloader)
def validation_loop(model, loss_function, dataloader, device):
model.eval()
total_loss = 0
with torch.no_grad():
for batch in dataloader:
loss = _transformer_call_in_loops(model, batch, device, loss_function)
total_loss += loss.detach().item()
return total_loss / len(dataloader)
def fit(model, optimizer, loss_function, train_dataloader, val_dataloader, epochs, device):
train_loss_list, validation_loss_list = [], []
print("Training and validating model")
for epoch in range(epochs):
print("-" * 25, f"Epoch {epoch + 1}", "-" * 25)
train_loss = train_loop(model, optimizer, loss_function, train_dataloader, device)
train_loss_list += [train_loss]
validation_loss = validation_loop(model, loss_function, val_dataloader, device)
validation_loss_list += [validation_loss]
print(f"Training loss: {train_loss:.4f}")
print(f"Validation loss: {validation_loss:.4f}")
print()
return train_loss_list, validation_loss_list
def predict(model, source_sequence, sos_token: torch.Tensor, device, max_length=32, eos_scaling=1, backpropagate=False, showResult= True, temperature=1):
if backpropagate:
model.train()
else:
model.eval()
source_sequence = source_sequence.float().to(device)
y_input = torch.unsqueeze(sos_token, dim=0).float().to(device)
#print(source_sequence, source_sequence.unsqueeze(0))
i = 0
while i < max_length:
# Get source mask
#print(y_input, y_input.unsqueeze(0))
prediction = model(source_sequence.unsqueeze(0), y_input.unsqueeze(0), # un-squeeze for batch
# tgt_mask=get_tgt_mask(y_input.size(0)).to(device),
src_key_padding_mask=create_pad_mask(source_sequence.unsqueeze(0)).to(device))
next_embedding = prediction[0, -1, :] # prediction on last token
pred_deep_svg, pred_type, pred_parameters = dataset_helper.unpack_embedding(next_embedding, dim=0)
#print(pred_deep_svg, pred_type, pred_parameters)
pred_deep_svg, pred_type, pred_parameters = pred_deep_svg.to(device), pred_type.to(device), pred_parameters.to(
device)
pred_type = pred_type / temperature
# === TYPE ===
# Apply Softmax
type_softmax = torch.softmax(pred_type, dim=0)
type_softmax[0] = type_softmax[0] * eos_scaling # Reduce EOS
indices = torch.argsort(type_softmax, descending=True)
animation_type = random.choice(indices[:3])
#animation_type = torch.argmax(type_softmax, dim=0)
# Break if EOS is most likely
if animation_type == 0:
print("END OF ANIMATION")
y_input = torch.cat((y_input, sos_token.unsqueeze(0).to(device)), dim=0)
return y_input
pred_type = torch.zeros(11)
pred_type[animation_type] = 1
# === DEEP SVG ===
# Find the closest path
distances = [torch.norm(pred_deep_svg - embedding[:-26]) for embedding in source_sequence]
closest_index = distances.index(min(distances))
closest_token = source_sequence[closest_index]
# === PARAMETERS ===
# overwrite unused parameters
for j in range(len(pred_parameters)):
if j in dataset_helper.ANIMATION_PARAMETER_INDICES[int(animation_type)]:
continue
pred_parameters[j] = -1
# === SEQUENCE ===
y_new = torch.concat([closest_token[:-26], pred_type.to(device), pred_parameters], dim=0)
#y_new = torch.concat([pred_deep_svg, pred_type.to(device), pred_parameters], dim=0)
y_input = torch.cat((y_input, y_new.unsqueeze(0)), dim=0)
# === INFO PRINT ===
if showResult:
print(f"{int(y_input.size(0))}: Path {closest_index} ({round(float(distances[closest_index]), 3)}) "
f"got animation {animation_type} ({round(float(type_softmax[animation_type]), 3)}%) "
f"with parameters {[round(num, 2) for num in pred_parameters.tolist()]}")
i += 1
return y_input
class PositionalEncoding(nn.Module):
def __init__(self, dim_model, dropout_p, max_len=5000):
"""
Initializes the PositionalEncoding module which injects information about the relative or absolute position
of the tokens in the sequence. The positional encodings have the same dimension as the embeddings so that the
two can be summed. Uses a sinusoidal pattern for positional encoding.
Args:
dim_model (int): The dimension of the embeddings and the expected dimension of the positional encoding.
dropout_p (float): Dropout probability to be applied to the summed embeddings and positional encodings.
max_len (int): The max length of the sequences for which positional encodings are precomputed and stored.
"""
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout_p)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, dim_model, 2).float() * (-math.log(10000.0) / dim_model))
pos_encoding = torch.zeros(max_len, 1, dim_model)
pos_encoding[:, 0, 0::2] = torch.sin(position * div_term)
pos_encoding[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pos_encoding', pos_encoding)
def forward(self, embedding: torch.Tensor) -> torch.Tensor:
"""
Applies positional encoding to the input embeddings and applies dropout.
Args:
embedding (torch.Tensor): The input embeddings with shape [batch_size, seq_len, dim_model]
Returns:
torch.Tensor: The embeddings with positional encoding applied, and dropout, having the same shape as the
input token embeddings [seq_len, batch_size, dim_model].
"""
return self.dropout(embedding + self.pos_encoding[:embedding.size(0), :])
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