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get_test_stats.py
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import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
import numpy as np
from src.Models.loss import losses_joint
from src.Models.models import Encoder
from src.Models.models import ImitateJoint, ParseModelOutput
from src.utils import read_config
from src.utils.learn_utils import LearningRate
from src.utils.train_utils import prepare_input_op, cosine_similarity, chamfer, beams_parser, validity, image_from_expressions, stack_from_expressions
import matplotlib
import matplotlib.pyplot as plt
from src.utils.refine import optimize_expression
import os
import json
from src.utils.generators.shapenet_generater import Generator
from globals import device
import time
inference_train_size = 10000
inference_test_size = 3000
vocab_size = 400
max_len = 13
beam_width = 10
"""
Infer programs on cad dataset
"""
def infer_programs(imitate_net, self_training=False, ab=None):
config = read_config.Config("config_cad.yml")
# Load the terminals symbols of the grammar
with open("terminals.txt", "r") as file:
unique_draw = file.readlines()
for index, e in enumerate(unique_draw):
unique_draw[index] = e[0:-1]
config.train_size = 10000
config.test_size = 3000
imitate_net.eval()
imitate_net.epsilon = 0
parser = ParseModelOutput(unique_draw, max_len // 2 + 1, max_len,
config.canvas_shape)
generator = Generator()
test_gen = generator.test_gen(
batch_size=config.batch_size,
path="data/cad/cad.h5",
if_augment=False)
pred_expressions = []
Rs = 0
CDs = 0
Target_images = []
for batch_idx in range(config.test_size // config.batch_size):
with torch.no_grad():
print(f"Inferring test cad batch: {batch_idx}")
data_ = next(test_gen)
labels = np.zeros((config.batch_size, max_len), dtype=np.int32)
one_hot_labels = prepare_input_op(labels, len(unique_draw))
one_hot_labels = torch.from_numpy(one_hot_labels).to(device)
data = torch.from_numpy(data_).to(device)
all_beams, next_beams_prob, all_inputs = imitate_net.beam_search(
[data[-1, :, 0, :, :], one_hot_labels], beam_width, max_len)
beam_labels = beams_parser(
all_beams, data_.shape[1], beam_width=beam_width)
beam_labels_numpy = np.zeros(
(config.batch_size * beam_width, max_len), dtype=np.int32)
Target_images.append(data_[-1, :, 0, :, :])
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] += unique_draw[beam_labels_numpy[i, j]]
for index, prog in enumerate(expressions):
expressions[index] = prog.split("$")[0]
pred_expressions += expressions
predicted_images = image_from_expressions(parser, expressions)
target_images = data_[-1, :, 0, :, :].astype(dtype=bool)
target_images_new = np.repeat(
target_images, axis=0, repeats=beam_width)
beam_CD = chamfer(target_images_new, predicted_images)
CD = np.zeros((config.batch_size, 1))
for r in range(config.batch_size):
CD[r, 0] = min(beam_CD[r * beam_width:(r + 1) * beam_width])
CDs += np.mean(CD)
for j in range(0, config.batch_size):
f, a = plt.subplots(1, beam_width + 1, figsize=(30, 3))
a[0].imshow(data_[-1, j, 0, :, :], cmap="Greys_r")
a[0].axis("off")
a[0].set_title("target")
for i in range(1, beam_width + 1):
a[i].imshow(
predicted_images[j * beam_width + i - 1],
cmap="Greys_r")
a[i].set_title("{}".format(i))
a[i].axis("off")
plt.savefig(
"best_lest/" +
"{}.png".format(batch_idx * config.batch_size + j),
transparent=0)
plt.close("all")
# with open("best_st_expressions.txt", "w") as file:
# for e in pred_expressions:
# file.write(f"{e}\n")
# break
return CDs / (config.test_size // config.batch_size)
config = read_config.Config("config_synthetic.yml")
device = torch.device("cuda")
encoder_net = Encoder(config.encoder_drop)
encoder_net = encoder_net.to(device)
imitate_net = ImitateJoint(
hd_sz=config.hidden_size,
input_size=config.input_size,
encoder=encoder_net,
mode=config.mode,
num_draws=400,
canvas_shape=config.canvas_shape)
imitate_net = imitate_net.to(device)
try:
pretrained_dict = torch.load("imitate_27.pth", map_location=device)
except Exception as e:
print(e)
imitate_net_dict = imitate_net.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in imitate_net_dict
}
imitate_net_dict.update(pretrained_dict)
imitate_net.load_state_dict(imitate_net_dict)
print(infer_programs(imitate_net))
# cd_list = []
# for i in range(100):
# try:
# pretrained_dict = torch.load(f"trained_models/imitate_frozen_st_{i}.pth", map_location=device)
# except Exception as e:
# print(e)
# break
# imitate_net_dict = imitate_net.state_dict()
# pretrained_dict = {
# k: v
# for k, v in pretrained_dict.items() if k in imitate_net_dict
# }
# imitate_net_dict.update(pretrained_dict)
# imitate_net.load_state_dict(imitate_net_dict)
#
# cd = infer_programs(imitate_net)
# print(f"TEST CD: {cd}")
# cd_list.append(cd)
#
# with open("ws_frozen_st_test.txt", "w") as file:
# for c in cd_list:
# file.write(f"{c}\n")