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import os
import numpy as np
import random
import pandas as pd
import math
from tqdm import tqdm
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import Stage3_source.preprocess as prep
import Stage3_source.cond_diff_transformer_layer as mod
import Stage3_source.transformer_training_helper as train_helper
# generate missing pixels with one shot
@torch.no_grad()
def cond_autocomplete_real_samples(
model: nn.Module,
args: any,
realization: torch.Tensor,
y_c: torch.Tensor,
idx: torch.Tensor
) -> (
any,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor
):
model.eval()
bs, channel, seq_length = realization.size()
# get a batch of random sampling paths
sampled_random_path = train_helper.sample_random_path(bs, seq_length, device=args.device)
# create a mask that masks the locations where we've already sampled
random_path_mask = train_helper.create_mask_at_random_path_index(sampled_random_path, idx, bs, seq_length)
# tokenize realizations
real_tokens, bs, seq_length= train_helper.create_token_labels(args, realization)
#real_tokens = realization.clone().squeeze(1)
# mask realizations
real_token_masked = train_helper.mask_realizations(real_tokens, random_path_mask)
# conditional probability
conditional_prob, probs = train_helper.cond_predict_conditional_prob(model, real_token_masked, y_c, idx, args)
# evaluate the value of the log probability for the given realization:
log_prob = train_helper.log_prob_of_realization(args, conditional_prob, real_tokens)
return (
conditional_prob,
probs.cpu(),
real_token_masked.cpu(),
real_tokens.cpu(),
log_prob.cpu(),
sampled_random_path.cpu(),
random_path_mask.cpu()
)
# get the label for the corresponding sequence in the dataloader
def extract_samples_with_labels(
dataloader: DataLoader,
target_labels: int,
total_num: int,
pad_included: bool=False
) -> dict:
extracted_sampled = {
'sample': [],
'label': []
}
for data, labels in dataloader:
for i, label in enumerate(labels):
if label.item() == target_labels:
if pad_included:
pass
else:
data[i] += 1 # account for the absorbing state (i.e. make room)
extracted_sampled['sample'].append(data[i]) # account for abosrbed state
extracted_sampled['label'].append(label)
if len(extracted_sampled['label']) == total_num:
return extracted_sampled
return extracted_sampled
# mask a given percentage of the sample
def corrupt_samples(
args: any,
realization: torch.Tensor,
perc: float
) -> torch.Tensor:
bs, channels, seq_length = realization.size()
# number of samples to corrupt (i.e. idx)
idx = (args.diffusion_steps * torch.Tensor([perc])).to(int).to(args.device)
# get a batch of random sampling paths
sampled_random_path = train_helper.sample_random_path(bs, seq_length, device=args.device)
# we create a mask that masks the locations where we've already sampled
random_path_mask = train_helper.create_mask_at_random_path_index(sampled_random_path, idx, bs, seq_length)
# tokenize realizations
real_tokens, bs, seq_length= train_helper.create_token_labels(args, realization)
# mask realizations
real_token_masked = train_helper.mask_realizations(real_tokens, random_path_mask)
return (
real_token_masked,
sampled_random_path,
idx
)
# inpaint missing regions by predicting the next position
@torch.no_grad()
def predict_next_index(
model: nn.Module,
args: any,
mask_realization: torch.Tensor,
y_c: torch.Tensor,
idx: torch.Tensor
) -> (
any,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor
):
model.eval()
bs, channel, seq_length = mask_realization.size()
# conditional prob
conditional_prob, probs = train_helper.cond_predict_conditional_prob(model, mask_realization.squeeze(1), y_c, idx, args)
return (
conditional_prob,
probs.cpu(),
)
def generate_denoised_sampled(
args: any,
model: nn.Module,
extract_digit_samples: torch.Tensor,
extract_time: torch.Tensor,
extract_digit_label: torch.Tensor,
sampling_path: torch.Tensor
) -> (
list,
list
):
mask_realization_list, time_idx_list = [], []
# prepare data
temp_y_c = extract_digit_label.to(args.device)
temp_mask_realization = extract_digit_samples.unsqueeze(1).long().to(args.device)
temp_idx = torch.Tensor([extract_time]).to(args.device).squeeze(0)
temp_sampling_path = sampling_path.to(args.device)
for ii in tqdm(range(int(temp_idx.item()), args.diffusion_steps)):
# where we need to sample next
current_location = temp_sampling_path == temp_idx
print(current_location.shape)
# make position prediction
conditional_prob, prob = predict_next_index(
model=model,
args=args,
mask_realization=temp_mask_realization,
y_c=temp_y_c,
idx=temp_idx
)
# get the label for the next token position
next_temp_realization = torch.argmax(
conditional_prob.sample(), dim=-1
)
temp_mask_realization[0, current_location] = next_temp_realization[current_location]
mask_realization_list.append(temp_mask_realization.cpu().numpy())
time_idx_list.append(temp_idx.cpu().numpy())
temp_idx+=1
return (
mask_realization_list,
time_idx_list
)
def batch_generate_denoised_sampled(
args: any,
model: nn.Module,
extract_digit_samples: torch.Tensor,
extract_time: torch.Tensor,
extract_digit_label: torch.Tensor,
sampling_path: torch.Tensor
) -> (list, list):
# Ensure batch dimension consistency across input tensors
assert extract_digit_samples.size(0) == extract_digit_label.size(0) == sampling_path.size(0) == extract_time.size(0), "Mismatched batch dimensions"
batch_size = extract_digit_samples.size(0)
mask_realization_list, time_idx_list = [], []
print('batch_size:', batch_size)
# Prepare data
temp_y_c = extract_digit_label.to(args.device)
temp_mask_realization = extract_digit_samples.unsqueeze(1).long().to(args.device)
temp_idx = extract_time.unsqueeze(-1).to(args.device) # Adding an extra dimension for batch processing
temp_sampling_path = sampling_path.to(args.device)
print(f"Starting temp_idx: {temp_idx[0].item()}")
start_time_index = temp_idx[0].item() # assume all temp_idx is the same values
max_diffusion_step = args.diffusion_steps # max number of timesteps
for ii in tqdm(range(start_time_index, max_diffusion_step), initial=start_time_index, total=max_diffusion_step):
# Check if any temp_idx has reached or exceeded diffusion_steps
if torch.any(temp_idx >= args.diffusion_steps):
break
# Broadcast ii to match the batch size
current_ii = torch.full((batch_size,), ii, dtype=torch.long, device=args.device)
# Make position prediction
conditional_prob, prob = predict_next_index(
model=model,
args=args,
mask_realization=temp_mask_realization,
y_c=temp_y_c,
idx=temp_idx
)
# Get the label for the next token position
next_temp_realization = torch.argmax(conditional_prob.sample(), dim=-1)
# Update temp_mask_realization for each item in the batch
current_location = temp_sampling_path == temp_idx # Adding an extra dimension for comparison
current_location = torch.argmax(current_location.detach().cpu()*1, dim=-1)
temp_mask_realization[:, 0, current_location] = next_temp_realization[:,current_location]
# Append results for each item in the batch
mask_realization_list.append(temp_mask_realization.cpu().numpy())
time_idx_list.append(temp_idx.cpu().numpy())
# Increment temp_idx for the next iteration
temp_idx += 1
return mask_realization_list, time_idx_list
# convert sequence with numerical variables into character letters
def convert_num_to_chars(
tokenizer: any,
num_seq: list
) -> list:
char_seq = [tokenizer[num] for num in num_seq]
return "".join(char_seq)
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