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test.py
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import os
import glob
import argparse
import logging
import logging.config
import coloredlogs
import numpy as np
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from model import PoseNet
from dataset import H36M_Dataset
from evaluate import h36m_evaluate
config = {
"exp_root": "./log",
"cameras_path": "data/cameras.h5",
"bases_path": "data/bases.npy",
"bases_to_use": "dct",
"input_data": "det",
"n_bases": 8,
"n_frames": 50,
"window_slide": 5,
"n_joints": 17,
"num_workers": 0,
"gpus": [0],
"batch_size_per_gpu": 256,
"args": {},
}
def parse_command_line():
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--exp", required=True, type=str, help="experiment to load")
parser.add_argument("-g", "--gpu", type=int, help="gpu to use")
args, _ = parser.parse_known_args()
return args
def load_ckpt(model, exp_path):
load_path = os.path.join(exp_path, "ckpt.pth.tar")
ckpt = torch.load(load_path)
config["logger"].info("Load model from {}".format(load_path))
if isinstance(model, nn.DataParallel):
model.module.load_state_dict(ckpt["model_state_dict"], strict=False)
else:
model.load_state_dict(ckpt["model_state_dict"], strict=False)
def main():
cl_args = parse_command_line()
config["args"].update(vars(cl_args))
# override config with command line args
if config["args"]["gpu"] is not None:
config["gpus"] = [config["args"]["gpu"]]
# exp folder
dir_name = glob.glob(os.path.join(config["exp_root"], "{}*".format(config["args"]["exp"])))
assert len(dir_name) == 1, "Invalid exp folder to load: {}".format(config["args"]["exp"])
exp_tag = os.path.basename(dir_name[0])
exp_path = os.path.join(config["exp_root"], exp_tag)
config["exp_tag"] = exp_tag
config["exp_path"] = exp_path
# readout config from exp tag
_, _, exp_f, exp_k, exp_bases, exp_input = exp_tag.split("_")
exp_f = int(exp_f[1:])
exp_k = int(exp_k[1:])
config["bases_to_use"] = exp_bases
config["input_data"] = exp_input
config["n_bases"] = exp_k
config["n_frames"] = exp_f
# logger
logger = logging.getLogger()
coloredlogs.install(level="DEBUG", logger=logger)
config["logger"] = logger
# setup gpus
gpus = ','.join([str(x) for x in config["gpus"]])
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
logger.info("Set CUDA_VISIBLE_DEVICES to {}".format(gpus))
# model
model = PoseNet(config)
model = nn.DataParallel(model)
model = model.cuda()
# load bases
if config["bases_to_use"] == "svd":
fixed_bases = np.load(config["bases_path"])
assert config["n_bases"] <= fixed_bases.shape[0] and config["n_frames"] == fixed_bases.shape[1], fixed_bases.shape
fixed_bases = fixed_bases[:config["n_bases"]]
# scale svd bases to the same magnitude as dct bases
# the scaling factor here is for F=50
fixed_bases *= np.sqrt(25)
elif config["bases_to_use"] == "dct":
x = np.arange(config["n_frames"])
fixed_bases = [np.ones([config["n_frames"]]) * np.sqrt(0.5)]
for i in range(1, config["n_bases"]):
fixed_bases.append(np.cos(i * np.pi * ((x + 0.5) / config["n_frames"])))
fixed_bases = np.array(fixed_bases)
else:
assert False, config["bases_to_use"]
config["bases"] = fixed_bases
fixed_bases = torch.from_numpy(fixed_bases).float() # (K, F)
fixed_bases = fixed_bases.view(1, config["n_bases"], config["n_frames"]) # (1, K, F)
# dataset & dataloader
train_dataset = H36M_Dataset(config, "train") # training set must be loaded first to compute stats
test_dataset = H36M_Dataset(config, "test")
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=config["batch_size_per_gpu"]*len(config["gpus"]), shuffle=False, num_workers=config["num_workers"], pin_memory=True)
load_ckpt(model, exp_path)
model.eval()
logger.info("Inference on test set...")
gts_3d = []
preds_3d = []
indices = []
with torch.no_grad():
for step, batch in enumerate(test_loader):
# parse batch data
data_2d_gt = batch["data_2d_gt"] # (B, Jx2, F)
data_2d_cpn = batch["data_2d_cpn"] # (B, Jx2, F)
if config["input_data"] == "det":
data_2d = data_2d_cpn
elif config["input_data"] == "gt":
data_2d = data_2d_gt
else:
assert False, config["input_data"]
data_2d_gt_flip = batch["data_2d_gt_flip"] # (B, Jx2, F)
data_2d_cpn_flip = batch["data_2d_cpn_flip"] # (B, Jx2, F)
if config["input_data"] == "det":
data_2d_flip = data_2d_cpn_flip
elif config["input_data"] == "gt":
data_2d_flip = data_2d_gt_flip
else:
assert False, config["input_data"]
data_3d = batch["data_3d"] # (B, Jx3, F)
data_3d_flip = batch["data_3d_flip"] # (B, Jx3, F)
mean_3d = batch["mean_3d"] # (B, Jx3)
std_3d = batch["std_3d"] # (B, Jx3)
idx = batch["idx"] # (B,)
B = data_3d.shape[0]
batch_bases = fixed_bases.repeat(B, 1, 1) # (B, K, F)
data_2d = data_2d.cuda()
data_2d_flip = data_2d_flip.cuda()
data_3d = data_3d.cuda()
data_3d_flip = data_3d_flip.cuda()
batch_bases = batch_bases.cuda()
mean_3d = mean_3d.cuda()
std_3d = std_3d.cuda()
# forward pass
coeff = model(data_2d, batch_bases)
coeff_flip = model(data_2d_flip, batch_bases)
_, res = model.module.build_loss_test((coeff, coeff_flip), batch_bases, (data_3d, data_3d_flip), mean_3d, std_3d)
pred_3d, gt_3d = res
preds_3d.append(pred_3d)
gts_3d.append(gt_3d)
indices.append(idx.data.numpy())
preds_3d = np.concatenate(preds_3d, 0)
gts_3d = np.concatenate(gts_3d, 0)
indices = np.concatenate(indices, 0)
h36m_evaluate(preds_3d, gts_3d, indices, test_dataset, config)
if __name__ == "__main__":
main()