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test_beam_search.py
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test_beam_search.py
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"""
Testing fix len programs
"""
import numpy as np
import torch
import os
import json
from torch.autograd.variable import Variable
from src.Utils import read_config
from src.Generator.generator import Generator
from src.Models.models import CsgNet, ParseModelOutput
from src.Utils.train_utils import prepare_input_op, stack_from_expressions, beams_parser
import time
import sys
if len(sys.argv) > 1:
config = read_config.Config(sys.argv[1])
else:
config = read_config.Config("config.yml")
print(config.config)
data_labels_paths = {3: "data/one_op/expressions.txt",
5: "data/two_ops/expressions.txt",
7: "data/three_ops/expressions.txt"}
dataset_sizes = {3: [110000, 20000],
5: [220000, 40000],
7: [440000, 80000]}
test_gen_objs = {}
types_prog = len(dataset_sizes.keys())
max_len = max(data_labels_paths.keys())
beam_width = 10
stack_size = max_len // 2 + 1
generator = Generator(data_labels_paths=data_labels_paths,
batch_size=config.batch_size,
time_steps=max(data_labels_paths.keys()),
stack_size=max(data_labels_paths.keys()) // 2 + 1)
imitate_net = CsgNet(grid_shape=[64, 64, 64], dropout=config.dropout,
mode=config.mode, timesteps=max_len,
num_draws=len(generator.unique_draw),
in_sz=config.input_size,
hd_sz=config.hidden_size,
stack_len=config.top_k)
cuda_devices = torch.cuda.device_count()
if torch.cuda.device_count() > 1:
imitate_net.cuda_devices = torch.cuda.device_count()
print("using multi gpus", flush=True)
imitate_net = torch.nn.DataParallel(imitate_net, dim=0)
imitate_net.load_state_dict(torch.load(config.pretrain_modelpath))
else:
weights = torch.load(config.pretrain_modelpath)
new_weights = {}
for k in weights.keys():
if k.startswith("module"):
new_weights[k[7:]] = weights[k]
imitate_net.load_state_dict(new_weights)
imitate_net.cuda()
for param in imitate_net.parameters():
param.requires_grad = True
config.test_size = sum((dataset_sizes[k][1] // config.batch_size) * config.batch_size
for k in dataset_sizes.keys())
for k in data_labels_paths.keys():
# if using multi gpu training, train and test batch size should be multiple of
# number of GPU edvices.
test_batch_size = config.batch_size
test_gen_objs[k] = generator.get_test_data(test_batch_size,
k,
num_train_images=dataset_sizes[k][0],
num_test_images=dataset_sizes[k][1],
if_primitives=True,
if_jitter=False)
Target_expressions = []
Predicted_expressions = []
parser = ParseModelOutput(generator.unique_draw, max_len // 2 + 1, max_len, [64, 64, 64], primitives=generator.primitives)
imitate_net.eval()
Rs = 0
t1 = time.time()
IOU = {}
total_iou = 0
for k in data_labels_paths.keys():
Rs = 0.0
for batch_idx in range(dataset_sizes[k][1] // config.batch_size):
data_, labels = next(test_gen_objs[k])
data_ = data_[:, :, 0:config.top_k + 1, :, :, :]
one_hot_labels = prepare_input_op(labels, len(generator.unique_draw))
one_hot_labels = Variable(torch.from_numpy(one_hot_labels), volatile=True).cuda()
data = Variable(torch.from_numpy(data_)).cuda()
labels = Variable(torch.from_numpy(labels)).cuda()
# This is for data parallelism purpose
data = data.permute(1, 0, 2, 3, 4, 5)
all_beams, next_beams_prob, all_inputs = imitate_net.beam_search_mode_1(
[data, one_hot_labels], beam_width, max_len)
beam_labels = beams_parser(all_beams, batch_size=config.batch_size,
beam_width=beam_width)
beam_labels_numpy = np.zeros((config.batch_size * beam_width, max_len),
dtype=np.int32)
for i in range(data_.shape[1]):
beam_labels_numpy[i * beam_width: (i + 1) * beam_width, :] = beam_labels[i]
# find expression from these predicted beam labels
expressions = [""] * config.batch_size * beam_width
for i in range(config.batch_size * beam_width):
for j in range(max_len):
expressions[i] += generator.unique_draw[beam_labels_numpy[i, j]]
for index, prog in enumerate(expressions):
expressions[index] = prog.split("$")[0]
Predicted_expressions += expressions
target_images = data_[-1, :, 0, :, :].astype(dtype=bool)
target_images_new = np.repeat(target_images, axis=0,
repeats=beam_width)
predicted_stack = stack_from_expressions(parser, expressions)
beam_R = np.sum(np.logical_and(target_images_new, predicted_stack), (1, 2,
3)) / \
(np.sum(np.logical_or(target_images_new, predicted_stack), (1, 2,
3)) + 1)
# There are some expressions as input that result in the blank canvas. So we just
# set these input's reward to the 0 value of top_1 prediction.
where_are_NaNs = np.isnan(beam_R)
beam_R[where_are_NaNs] = 0.0
R = np.zeros(config.batch_size)
for r in range(config.batch_size):
R[r] = np.max(beam_R[r * beam_width: (r + 1) * beam_width])
Rs += np.sum(R)
total_iou += Rs
IOU[k] = Rs / ((dataset_sizes[k][1] // config.batch_size) * config.batch_size)
print("IOU for {} len program: ".format(k), IOU[k])
total_iou = total_iou / config.test_size
print ("total IOU score: ", total_iou)
results = {"total_iou": total_iou, "iou": IOU}
results_path = "trained_models/results/{}/".format(config.pretrain_modelpath)
os.makedirs(os.path.dirname(results_path), exist_ok=True)
with open(results_path + "beam_{}_pred.txt".format(beam_width), "w") as file:
for p in Predicted_expressions:
file.write(p + "\n")
with open(results_path + "beam_target.txt".format(beam_width), "w") as file:
for p in Target_expressions:
file.write(p + "\n")
with open(results_path + "beam_{}_results.org".format(beam_width), 'w') as outfile:
json.dump(results, outfile)