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import numpy as np
import pickle
from torch.utils.data import Dataset, DataLoader
import os
import torch
from copy import deepcopy
from blimpy import Waterfall
from tqdm import tqdm
from copy import deepcopy
from sigpyproc.readers import FilReader
from torch import nn
def load_pickled_data(file_path):
with open(file_path, 'rb') as f:
data = pickle.load(f)
return data
# Custom dataset class
class CustomDataset(Dataset):
def __init__(self, data_dir, bit8=False, transform=None):
self.data_dir = data_dir
self.transform = transform
self.images = []
self.labels = []
self.classes = os.listdir(data_dir)
self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
self.bit8 = bit8
# Load images and labels
for cls in self.classes:
class_dir = os.path.join(data_dir, cls)
for image_name in os.listdir(class_dir):
image_path = os.path.join(class_dir, image_name)
self.images.append(image_path)
self.labels.append(self.class_to_idx[cls])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image_path = self.images[idx]
label = self.labels[idx]
# Load image
image = load_pickled_data(image_path)
if self.transform is not None:
if self.bit8 == True:
new_image = self.transform(torch.from_numpy(image['8_data']).type(torch.float32))
else:
new_image = self.transform(torch.from_numpy(image['data']))
# new_image = self.transform(image['data'])
return new_image, label
# Custom dataset class
class CustomDataset_Masked(Dataset):
def __init__(self, data_dir, transform=None):
self.data_dir = data_dir
self.transform = transform
self.images = []
self.labels = []
self.classes = os.listdir(data_dir)
self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
# Load images and labels
for cls in self.classes:
class_dir = os.path.join(data_dir, cls)
for image_name in os.listdir(class_dir):
image_path = os.path.join(class_dir, image_name)
self.images.append(image_path)
self.labels.append(self.class_to_idx[cls])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image_path = self.images[idx]
label = self.labels[idx]
# Load image
image = load_pickled_data(image_path)
if self.transform is not None:
if image['burst'].max() ==0:
new_burst = torch.from_numpy(image['burst'])
else:
new_burst = torch.from_numpy(image['burst']/image['burst'].max())
ind = new_burst > 0.1
ind_not = new_burst <= 0.1
new_burst[ind] = 1
new_burst[ind_not] = 0
new_image = self.transform(torch.from_numpy(image['data'].data))
new_burst_arr = torch.zeros_like(new_image)
new_burst_arr[ 0, :,:] = new_burst
new_burst_arr[ 1, :,:] = new_burst
new_burst_arr[ 2, :,:] = new_burst
return new_image, label, new_burst_arr
# Custom dataset class
class TestingDataset(Dataset):
def __init__(self, data_dir, bit8=False, transform=None):
self.data_dir = data_dir
self.transform = transform
self.images = []
self.labels = []
self.classes = os.listdir(data_dir)
self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
self.bit8 = bit8
# Load images and labels
for cls in self.classes:
class_dir = os.path.join(data_dir, cls)
for image_name in os.listdir(class_dir):
image_path = os.path.join(class_dir, image_name)
self.images.append(image_path)
self.labels.append(self.class_to_idx[cls])
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image_path = self.images[idx]
label = self.labels[idx]
# Load image
image = load_pickled_data(image_path)
params = image['params']
if self.transform is not None:
params = image['params']
if self.bit8 == True:
new_image = self.transform(torch.from_numpy(image['8_data']).type(torch.float32))
else:
new_image = self.transform(torch.from_numpy(image['data']))
params['labels'] = label
return new_image, (label, params['dm'], params['freq_ref'], params['snr'], params['boxcard'])
# Custom dataset class
class SearchDataset(Dataset):
def __init__(self, data_dir, transform=None, pickle_data=False):
self.window_size = 2048
if pickle_data:
with open(data_dir, 'rb') as f:
self.d = pickle.load(f)
self.header = self.d['header']
self.images = self.crop(self.d['data'][:,0,:], self.window_size)
else:
self.obs = Waterfall(data_dir, max_load = 50)
self.header = self.obs.header
self.images = self.crop(self.obs.data[:,0,:], self.window_size)
self.transform = transform
self.SEC_PER_DAY = 86400
def crop(self, data, window_size = 2048):
n_samp = data.shape[0]//window_size
new_data = np.zeros((n_samp, window_size, 192 ))
for i in range(n_samp):
new_data[i, :,:] = data[ i*window_size : (i+1)*window_size, :]
return new_data
def __len__(self):
return self.images.shape[0]
def __getitem__(self, idx):
data = self.images[idx, :, :].T
tindex = idx * self.window_size
time = self.header['tsamp'] * tindex / self.SEC_PER_DAY + self.header['tstart']
if self.transform is not None:
new_image = self.transform(data)
return new_image, idx
# Custom dataset class
class SearchDataset_Sigproc(Dataset):
def __init__(self, data_dir, transform=None):
self.window_size = 2048
fil = FilReader(data_dir)
self.header = fil.header
# print("check shape ",fil.read_block(0, fil.header.nsamples).shape)
read_data = fil.read_block(0, fil.header.nsamples)[:,1024:-1024]
read_data = np.swapaxes(read_data, 0,-1)
self.images = self.crop(read_data, self.window_size)
self.transform = transform
self.SEC_PER_DAY = 86400
def crop(self, data, window_size = 2048):
n_samp = data.shape[0]//window_size
new_data = np.zeros((n_samp, window_size, 192 ))
for i in range(n_samp):
new_data[i, :,:] = data[ i*window_size : (i+1)*window_size, :]
return new_data
def __len__(self):
return self.images.shape[0]
def __getitem__(self, idx):
data = self.images[idx, :, :].T
tindex = idx * self.window_size
time = self.header.tsamp * tindex / self.SEC_PER_DAY + self.header.tstart
if self.transform is not None:
new_image = self.transform(torch.from_numpy(data))
return new_image, idx
# def renorm(data):
# shifted = data - data.min()
# shifted = shifted/shifted.max()
# return shifted
def renorm(data):
mean = torch.mean(data)
std = torch.std(data)
# Standardize the data
standardized_data = (data - mean) / std
return standardized_data
def transform(data):
copy_data = data.detach().clone()
rms = torch.std(data)
mean = torch.mean(data)
masks_rms = [-1, 5]
new_data = torch.zeros((len(masks_rms)+1, data.shape[0], data.shape[1]))
new_data[0,:,:] = renorm(torch.log10(copy_data+1e-10))
for i in range(1, len(masks_rms)+1):
scale = masks_rms[i-1]
copy_data = data.detach().clone() #deepcopy(data)
if scale < 0:
ind = copy_data < abs(scale) * rms + mean
copy_data[ind] = 0
else:
ind = copy_data > (scale) * rms + mean
copy_data[ind] = 0
new_data[i,:,:] = renorm(torch.log10(copy_data+1e-10))
new_data = new_data.type(torch.float32)
slices = torch.chunk(new_data, 8, dim=-1) # dim=1 is the height dimension
new_data = torch.stack(slices, dim=1) # New axis is inserted at dim=1
new_data = new_data.view(-1, new_data.size(2), new_data.size(3))
return new_data
# class preproc_debug(nn.Module):
# def forward(self, x):
# template = torch.zeros((32, 24, 192, 256))
# # for i in torch.arange(x.shape[0]): # Use a tensor-based range
# template[0,:,:,:] = transform_debug(torch.flip(x[0,:,:], dims = (0,)))
# template[1,:,:,:] = transform_debug(torch.flip(x[1,:,:], dims = (0,)))
# template[2,:,:,:] = transform_debug(torch.flip(x[2,:,:], dims = (0,)))
# template[3,:,:,:] = transform_debug(torch.flip(x[3,:,:], dims = (0,)))
# template[4,:,:,:] = transform_debug(torch.flip(x[4,:,:], dims = (0,)))
# template[5,:,:,:] = transform_debug(torch.flip(x[5,:,:], dims = (0,)))
# template[6,:,:,:] = transform_debug(torch.flip(x[6,:,:], dims = (0,)))
# template[7,:,:,:] = transform_debug(torch.flip(x[7,:,:], dims = (0,)))
# template[8,:,:,:] = transform_debug(torch.flip(x[8,:,:], dims = (0,)))
# template[9,:,:,:] = transform_debug(torch.flip(x[9,:,:], dims = (0,)))
# template[10,:,:,:] = transform_debug(torch.flip(x[10,:,:], dims = (0,)))
# template[11,:,:,:] = transform_debug(torch.flip(x[11,:,:], dims = (0,)))
# template[12,:,:,:] = transform_debug(torch.flip(x[12,:,:], dims = (0,)))
# template[13,:,:,:] = transform_debug(torch.flip(x[13,:,:], dims = (0,)))
# template[14,:,:,:] = transform_debug(torch.flip(x[14,:,:], dims = (0,)))
# template[15,:,:,:] = transform_debug(torch.flip(x[15,:,:], dims = (0,)))
# template[16,:,:,:] = transform_debug(torch.flip(x[16,:,:], dims = (0,)))
# template[17,:,:,:] = transform_debug(torch.flip(x[17,:,:], dims = (0,)))
# template[18,:,:,:] = transform_debug(torch.flip(x[18,:,:], dims = (0,)))
# template[19,:,:,:] = transform_debug(torch.flip(x[19,:,:], dims = (0,)))
# template[20,:,:,:] = transform_debug(torch.flip(x[20,:,:], dims = (0,)))
# template[21,:,:,:] = transform_debug(torch.flip(x[21,:,:], dims = (0,)))
# template[22,:,:,:] = transform_debug(torch.flip(x[22,:,:], dims = (0,)))
# template[23,:,:,:] = transform_debug(torch.flip(x[23,:,:], dims = (0,)))
# template[24,:,:,:] = transform_debug(torch.flip(x[24,:,:], dims = (0,)))
# template[25,:,:,:] = transform_debug(torch.flip(x[25,:,:], dims = (0,)))
# template[26,:,:,:] = transform_debug(torch.flip(x[26,:,:], dims = (0,)))
# template[27,:,:,:] = transform_debug(torch.flip(x[27,:,:], dims = (0,)))
# template[28,:,:,:] = transform_debug(torch.flip(x[28,:,:], dims = (0,)))
# template[29,:,:,:] = transform_debug(torch.flip(x[29,:,:], dims = (0,)))
# template[30,:,:,:] = transform_debug(torch.flip(x[30,:,:], dims = (0,)))
# template[31,:,:,:] = transform_debug(torch.flip(x[31,:,:], dims = (0,)))
# return template
# def transform_debug(data):
# copy_data = data.detach().clone()
# rms = torch.std(data)
# mean = torch.mean(data)
# masks_rms = [-1, 5]
# new_data = torch.zeros((len(masks_rms)+1, data.shape[0], data.shape[1]))
# new_data[0,:,:] = renorm(torch.log10(copy_data+1e-10))
# for i in range(1, len(masks_rms)+1):
# scale = masks_rms[i-1]
# copy_data = data.detach().clone()
# if scale < 0:
# ind = copy_data < abs(scale) * rms + mean
# copy_data[ind] = 0
# else:
# ind = copy_data > (scale) * rms + mean
# copy_data[ind] = 0
# new_data[i,:,:] = renorm(torch.log10(copy_data+1e-10))
# new_data = new_data.type(torch.float32)
# slices = torch.chunk(new_data, 8, dim=-1) # dim=1 is the height dimension
# new_data = torch.stack(slices, dim=1) # New axis is inserted at dim=1
# new_data = new_data.view(-1, new_data.size(2), new_data.size(3))
# return new_data
def renorm_batched(data):
mins = torch.amin(data, (-2, -1))
mins = mins.unsqueeze(1).unsqueeze(2)
mins = mins.expand(data.shape[0], 192, 2048)
shifted = data - mins
maxs = torch.amax(shifted, (-2, -1))
maxs = maxs.unsqueeze(1).unsqueeze(2)
maxs = maxs.expand(data.shape[0], 192, 2048)
shifted = shifted/maxs
return shifted
def transform_mask(data):
copy_data = deepcopy(data)
shift = copy_data - copy_data.min()
normalized_data = shift / shift.max()
new_data = np.zeros((3, data.shape[0], data.shape[1]))
for i in range(3):
new_data[i,:,:] = normalized_data
new_data = new_data.astype(np.float32)
return new_data
#Function to Convert to ONNX
def Convert_ONNX(model, saveloc, input_data_mock):
print("Saving to ONNX")
# set the model to inference mode
model.eval()
# Let's create a dummy input tensor
dummy_input = torch.autograd.Variable(input_data_mock)
# Export the model
torch.onnx.export(model, # model being run
dummy_input, # model input (or a tuple for multiple inputs)
saveloc, # where to save the model
input_names = ['modelInput'], # the model's input names
output_names = ['modelOutput'], # the model's output names
dynamic_axes={'modelInput' : {0 : 'batch_size'}, # variable length axes
'modelOutput' : {0 : 'batch_size'}} )
print(" ")
print('Model has been converted to ONNX')
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