from inversegraphics_generator.resnet50 import MultiResNet, ContrastiveLoss
EPOCHS = 40
BATCH = 64
LEARNING_RATE = 0.0001
SIZE = 1000
MARGIN = 2
exp = Experiment("[ig] cnn-siamese2")
exp.param("epoch", EPOCHS)
exp.param("size", SIZE)
exp.param("batch", BATCH)
exp.param("learning rate", LEARNING_RATE)
# ds = IqImgDataset("/data/lisa/data/iqtest/iqtest-dataset-ambient.h5", "train/labeled", max_size=SIZE)
ds = IqImgDataset(os.path.join(get_data_dir(), "test.h5"),
"train/labeled",
max_size=SIZE)
dl = DataLoader(ds, batch_size=BATCH, shuffle=True, num_workers=0)
model = MultiResNet(siamese=True) #.cuda()
# Loss and optimizer
criterion_contrast = ContrastiveLoss(MARGIN)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# Train the model
total_step = int(len(ds) / BATCH)
for epoch in range(EPOCHS):
for i, (questions, answers) in enumerate(dl):
# Forward pass
outputs, internals = model(questions) #.cuda()
import os
import pickle
import numpy as np
from tqdm import tqdm
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
OUT = os.path.join(get_data_dir(), "aggregated.pkl")
SNEK_LEN_1 = 11
GRID_SIZE_1 = 5
OUT_PATH_1 = os.path.join(
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_1, GRID_SIZE_1))
SNEK_LEN_2 = 13
GRID_SIZE_2 = 5
OUT_PATH_2 = os.path.join(
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_2, GRID_SIZE_2))
SNEK_LEN_3 = 14
GRID_SIZE_3 = 6
OUT_PATH_3 = os.path.join(
get_data_dir(),
"list-v2-snek_{}-grid_{}-final.pkl".format(SNEK_LEN_3, GRID_SIZE_3))
SNEK_LEN_4 = 15
GRID_SIZE_4 = 8
OUT_PATH_4 = os.path.join(
import os
import matplotlib.pyplot as plt
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
OUT_PATH = os.path.join(get_data_dir(), "object-{:06d}.obj")
# the size of the snake is randomly sampled
# from this interval
SNEK_LEN_MIN = 5
SNEK_LEN_MAX = 10
GRID_SIZE = 4 # the grid for the snake is N x N x N
CUBE_SIZE = 1.0 # cubes are unit-sized
### IMPORTANT
OBJECTS_TO_GENERATE = 1000
if __name__ == '__main__':
og = ObjGenerator(GRID_SIZE, CUBE_SIZE)
## test with one cube
# v, f = make_cube(CUBE_SIZE)
# plot_cube(v, f)
# test with one grid
g = og.walk_snek(SNEK_LEN_MIN, SNEK_LEN_MAX)
print(g)
v, f = og.grid_to_cubes(g)
og.write_obj(g, v, f, 0, OUT_PATH)
def __len__(self):
return len(self.data["input"][:self.max_size])
def __getitem__(self, idx):
# out = {"input": self.data["input"][idx], "output": None}
# if self.group in ["train/labeled", "test", "val"]:
# out["output"] = self.data["output"][idx]
# return out
answer = None
if self.group in ["train/labeled", "test", "val"]:
answer = np.zeros(3, dtype=np.float32)
answer[self.data["output"][idx]] = 1 # one-hot encoding
# print (self.data["output"][idx], answer)
question = np.swapaxes(np.swapaxes(self.data["input"][idx], 1, 3), 2,
3)
return (question, answer)
if __name__ == '__main__':
ds = IqImgDataset(os.path.join(get_data_dir(), "test.h5"), "train/labeled")
print(len(ds))
question, answer = ds[0]
print(question.shape)
print(answer)
import os
import matplotlib.pyplot as plt
from scipy.misc import imresize
from inversegraphics_generator.human_dataset import HumanDataset
from inversegraphics_generator.iqtest_objs import get_data_dir
ds = HumanDataset()
FACTOR = 400
OUT_PATH = os.path.join(get_data_dir(), "..", "3diqtt", "imgs")
corrects = []
for i in range(len(ds)):
(question, answers, correct) = ds[i]
q_scaled = imresize(question, FACTOR)
plt.imsave(os.path.join(OUT_PATH,"{}-q.png".format(i)), q_scaled)
for a in range(3):
a_scaled = imresize(answers[a], FACTOR)
plt.imsave(os.path.join(OUT_PATH,"{}-a{}.png".format(i,a)), a_scaled)
corrects.append(str(correct))
with open(os.path.join(OUT_PATH,"_correct.csv"),"w") as f:
f.writelines(corrects)
(question, answers, correct) = ds.get(1)
import os
import h5py
import numpy as np
from inversegraphics_generator.iqtest_objs import get_data_dir
with h5py.File(os.path.join(get_data_dir(), "test.h5"), "w") as f:
train = f.create_group("train")
test = f.create_group("test")
val = f.create_group("val")
labeled = train.create_group("labeled")
unlabeled = train.create_group("unlabeled")
labeled_q = labeled.create_dataset("input",
data=np.zeros((10, 4, 128, 128, 3),
dtype=np.float32))
labeled_a = labeled.create_dataset("output",
data=np.zeros(10),
dtype=np.uint8)
import pickle
import os
import matplotlib.pyplot as plt
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
SNEK_LEN = 9
GRID_SIZE = 4
OUT_PATH = os.path.join(
get_data_dir(), "tree-v2-snek_{}-grid_{}.pkl".format(SNEK_LEN, GRID_SIZE))
print("loading file:", OUT_PATH)
og = ObjGenerator(GRID_SIZE, 1.0)
def getLeafs(node):
if len(node.children) == 0:
return node
else:
out = [getLeafs(child) for child in node.children]
out_flat = []
for child in out: # each layer removes some mess
if type([]) == type(child):
for child2 in child:
out_flat.append(child2)
else:
out_flat.append(child)
return out_flat
root = pickle.load(open(OUT_PATH, "rb"))
import random
import time
import numpy as np
import h5py
from tqdm import tqdm
from inversegraphics_generator.iqtest_objs import get_data_dir
OUT_PATH = get_data_dir() + "/3diqtt-v2-x2.h5"
f = h5py.File(OUT_PATH, 'r')
print(list(f.keys()))
q = f["train/labeled/questions"]
print(q.shape)
print(f["train/labeled/answers"].shape)
print(f["train/unlabeled/questions"].shape)
print(f["test/questions"].shape)
print(f["val/questions"].shape)
print(f["val/answers"].shape)
import matplotlib.pyplot as plt
idx = 0
for i in range(3):
start = time.time()
idx = random.randrange(0,len(q))
import pickle
import os
from tqdm import tqdm
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
import numpy as np
SNEK_LEN = 9
GRID_SIZE = 4
OUT_PATH = os.path.join(get_data_dir(),
"list-v2-snek_{}-grid_{}".format(SNEK_LEN, GRID_SIZE))
og = ObjGenerator(GRID_SIZE, 1.0)
final = []
with open(OUT_PATH + "-unique.pkl", "rb") as output_file:
unique = pickle.load(output_file)
for u in tqdm(unique):
# print (u)
u_grid = og.str_to_grid(u)
clean = True
for i in range(3):
tmp = og.grid_to_str(
og.center_grid(np.rot90(u_grid, k=i + 1, axes=(0, 1))))
if tmp in final:
import numpy as np
from inversegraphics_generator.iqtest_objs import get_data_dir
ds = np.load(get_data_dir()+"/10000_baseline_data.npy")
print (ds.shape, ds.dtype, ds.max(), ds.min())
quit()
import matplotlib.pyplot as plt
for i in range(len(ds)):
f, axarr = plt.subplots(1, 4, sharex=True, sharey=True, figsize=(20, 5))
for img in range(4):
img_data = ds[i,img]/255
img_data = np.swapaxes(np.swapaxes(img_data,0,2),0,1)
print(img_data.min(), img_data.max(), img_data.mean())
axarr[img].imshow(img_data)
plt.tight_layout()
plt.show()
import os
import matplotlib.pyplot as plt
import h5py
from inversegraphics_generator.iqtest_objs import get_data_dir
import numpy as np
OUT_PATH = os.path.join(get_data_dir(), "..", "3diqtt", "human-test.h5")
questions = []
answers = []
correct = []
folder = os.path.join(get_data_dir(), "..", "3diqtt")
for i in range(100):
ref_file = os.path.join(folder, "sample-test-{}-ref.png".format(i))
if os.path.isfile(ref_file):
questions.append(plt.imread(ref_file)[:, :, :3])
answers_buf = []
for a in range(3):
ans_file = os.path.join(folder, "sample-test-{}-ans{}".format(i, a + 1))
if not os.path.isfile(ans_file + ".png"):
correct.append(a)
ans_file += "c"
answers_buf.append(plt.imread(ans_file + ".png")[:, :, :3])
answers.append(answers_buf)
from random import shuffle
import pickle
import os
from tqdm import tqdm
from inversegraphics_generator.obj_generator import ObjGenerator
from inversegraphics_generator.iqtest_objs import get_data_dir
SNEK_LEN = 9
GRID_SIZE = 4
IN_PATH = os.path.join(
get_data_dir(), "tree-v2-snek_{}-grid_{}.pkl".format(SNEK_LEN, GRID_SIZE))
print("loading file:", IN_PATH)
OUT_PATH = os.path.join(get_data_dir(),
"list-v2-snek_{}-grid_{}".format(SNEK_LEN, GRID_SIZE))
og = ObjGenerator(GRID_SIZE, 1.0)
def getLeafs(node):
if len(node.children) == 0:
return node
else:
out = [getLeafs(child) for child in node.children]
out_flat = []
for child in out: # each layer removes some mess
if type([]) == type(child):
for child2 in child:
out_flat.append(child2)
else:
out_flat.append(child)
return out_flat
import os
import matplotlib.pyplot as plt
import numpy as np
from inversegraphics_generator.img_dataset import IqImgDataset
from inversegraphics_generator.iqtest_objs import get_data_dir
ds_train = IqImgDataset("/Users/florian/data/ig/iqtest-dataset-ambient.h5",
"train/labeled")
ds_test = IqImgDataset("/Users/florian/data/ig/iqtest-dataset-ambient.h5",
"test")
OUT = os.path.join(get_data_dir(), "sample-{traintest}-{idx}-{refans}.png")
def reshape(img):
img = np.swapaxes(img, 0, 2)
img = np.swapaxes(img, 0, 1)
img *= 255
img = np.clip(img, 0, 255)
return img.astype(np.uint8)
def sample(ds, idx, traintest):
plt.imsave(OUT.format(traintest=traintest, refans="ref", idx=idx),
reshape(ds[idx][0][0]))
ans_idx = ds[idx][1].argmax()
for ans in range(3):
label = "ans" + str(ans + 1)
if ans == ans_idx:
label += "c"
import os
import numpy as np
import h5py
from torch.utils.data import Dataset
import matplotlib.pyplot as plt
from inversegraphics_generator.iqtest_objs import get_data_dir
default_path = os.path.join(get_data_dir(), "..", "3diqtt", "human-test.h5")
class HumanDataset(Dataset):
def __init__(self, h5_path=default_path):
file_handle = h5py.File(h5_path, 'r')
self.references = file_handle["references"]
self.answers = file_handle["answers"]
self.correct = file_handle["correct"]
self.tru_len = len(self.correct)
def __len__(self):
return 20
def __getitem__(self, idx):
if idx == 1: # we are not using sample 1 because it's used for the tutorial
idx = 20
return (self.references[idx, :, :, :3], self.answers[idx, :, :, :, :3], self.correct[idx])
def get(self, idx):
return (self.references[idx, :, :, :3], self.answers[idx, :, :, :, :3], self.correct[idx])
if __name__ == '__main__':
def get_training_questions_answers(self, n=1):
return self.sample_qa(self.train, 0, n)
def get_testing_questions_answers(self, n=1):
return self.sample_qa(self.test, 2, n)
def get_validation_questions_answers(self, n=1):
return self.sample_qa(self.val, 3, n)
def cleanup(self):
for tmp in self.paths:
tmp.cleanup()
if __name__ == '__main__':
# iq = IqDataset(os.path.expanduser("~/data/ig/iqtest-v1.npz"))
iq = IqObjDataset(os.path.join(get_data_dir(), "iqtest-v1.npz"))
print(iq.train.shape)
print(iq.test.shape)
print(iq.val.shape)
print(iq.get_training_samples_unordered(5))
print(iq.get_training_questions_answers(5))
print(iq.get_testing_questions_answers(5))
print(iq.get_validation_questions_answers(5))
time.sleep(
3000
) # use this time to do `ls` on the directories printed above or pull them into Blender
iq.cleanup()
