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dataset.py
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from __future__ import print_function
from torch.utils.data import Dataset
import numpy as np
import h5py
from programs.label_config import num_params, max_param
from programs.loop_gen import translate, rotate, end
class PartPrimitive(Dataset):
"""
dataset for (part, block program) pairs
"""
def __init__(self, file_path):
f = h5py.File(file_path, 'r')
self.data = np.array(f['data'])
self.labels = np.array(f['label'])
assert self.data.shape[0] == self.labels.shape[0]
self.num = self.data.shape[0]
def __getitem__(self, index):
data = self.data[index]
label = self.labels[index, :, 0]
param = self.labels[index, :, 1:]
data = data.astype(np.int64)
label = label.astype(np.int64)
param = param.astype(np.float32)
return data, label, param
def __len__(self):
return self.num
class Synthesis3D(Dataset):
"""
dataset for (shape, program) pairs
"""
def __init__(self, file_path, n_block=6, n_step=3, w1=1, w2=1):
f = h5py.File(file_path, 'r')
self.data = np.array(f['data'])
self.labels = np.array(f['label'])
self.n_block = n_block
self.n_step = n_step
self.max_block = 0
self.pgm_weight = w1
self.param_weight = w2
assert self.data.shape[0] == self.labels.shape[0]
self.num = self.data.shape[0]
self.pgms = np.zeros((self.num, self.n_block, self.n_step), dtype=np.int32)
self.pgm_masks = np.zeros((self.num, self.n_block, self.n_step), dtype=np.float32)
self.params = np.zeros((self.num, self.n_block, self.n_step, max_param-1), dtype=np.float32)
self.param_masks = np.zeros((self.num, self.n_block, self.n_step, max_param-1), dtype=np.float32)
for i in range(self.num):
pgm, param, pgm_mask, param_mask = self.process_label(self.labels[i])
self.pgms[i] = pgm
self.pgm_masks[i] = pgm_mask
self.params[i] = param
self.param_masks[i] = param_mask
def __getitem__(self, index):
data = np.copy(self.data[index])
pgm = self.pgms[index]
pgm_mask = self.pgm_masks[index]
param = self.params[index]
param_mask = self.param_masks[index]
data = data.astype(np.float32)
pgm = pgm.astype(np.int64)
return data, pgm, pgm_mask, param, param_mask
def __len__(self):
return self.num
def process_label(self, label):
pgm = np.zeros((self.n_block, self.n_step), dtype=np.int32)
param = np.zeros((self.n_block, self.n_step, max_param - 1), dtype=np.float32)
pgm_mask = 0.1 * np.ones((self.n_block, self.n_step), dtype=np.float32)
param_mask = 0.1 * np.ones((self.n_block, self.n_step, max_param - 1), dtype=np.float32)
max_step = label.shape[0]
pgm_weight = self.pgm_weight
param_weight = self.param_weight
i = 0
j = 0
while j < max_step:
if label[j, 0] == translate:
if label[j+1, 0] == translate:
# pgm
pgm[i, 0] = translate
pgm[i, 1] = translate
pgm[i, 2] = label[j+2, 0]
pgm_mask[i, :3] = pgm_weight
# param
param[i, :3] = label[j:j+3, 1:]
param_mask[i, :2, :num_params[translate]] = param_weight
param_mask[i, 2, :num_params[pgm[i, 2]]] = param_weight
j = j + 5
i = i + 1
else:
# pgm
pgm[i, 0] = translate
pgm[i, 1] = label[j+1, 0]
pgm_mask[i, :2] = pgm_weight
# param
param[i, :2] = label[j:j+2, 1:]
param_mask[i, 0, :num_params[translate]] = param_weight
param_mask[i, 1, :num_params[pgm[i, 1]]] = param_weight
j = j + 3
i = i + 1
elif label[j, 0] == rotate:
# pgm
pgm[i, 0] = rotate
pgm[i, 1] = label[j+1, 0]
pgm_mask[i, :2] = pgm_weight
# param
param[i, :2] = label[j:j+2, 1:]
param_mask[i, 0, :num_params[rotate]] = param_weight
param_mask[i, 1, :num_params[pgm[i, 1]]] = param_weight
j = j + 3
i = i + 1
elif label[j, 0] == end:
j = j + 1
elif label[j, 0] > 0:
# pgm
pgm[i, 0] = label[j, 0]
pgm_mask[i, 0] = pgm_weight
# param
param[i, 0] = label[j, 1:]
param_mask[i, 0, :num_params[pgm[i, 0]]] = param_weight
j = j + 1
i = i + 1
else:
break
if i == self.n_block:
print(label)
if i > self.max_block:
self.max_block = i
return pgm, param, pgm_mask, param_mask
class ShapeNet3D(Dataset):
"""
dataset for ShapeNet
"""
def __init__(self, file_path):
super(ShapeNet3D, self).__init__()
f = h5py.File(file_path, "r")
self.data = np.array(f['data'])
self.num = self.data.shape[0]
def __getitem__(self, index):
data = np.copy(self.data[index, ...])
data = data.astype(np.float32)
return data
def __len__(self):
return self.num