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infer.py
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import argparse
import torch
from torch import nn
from torchvision import transforms
from PIL import Image
import os
import sys
import pathlib
import numpy as np
import cv2
import importlib
import ssl
from datasets import utils as ds_utils
from runners import utils as rn_utils
from external.Graphonomy import wrapper
import face_alignment
class InferenceWrapper(nn.Module):
@staticmethod
def get_args(args_dict):
# Read and parse args of the module being loaded
args_path = pathlib.Path(args_dict['project_dir']) / 'runs' / args_dict['experiment_name'] / 'args.txt'
parser = argparse.ArgumentParser(conflict_handler='resolve')
parser.add = parser.add_argument
with open(args_path, 'rt') as args_file:
lines = args_file.readlines()
for line in lines:
k, v, v_type = rn_utils.parse_args_line(line)
parser.add('--%s' % k, type=v_type, default=v)
args, _ = parser.parse_known_args()
# Add args from args_dict that overwrite the default ones
for k, v in args_dict.items():
setattr(args, k, v)
args.world_size = args.num_gpus
return args
def __init__(self, args_dict):
super(InferenceWrapper, self).__init__()
# Get a config for the network
self.args = self.get_args(args_dict)
self.to_tensor = transforms.ToTensor()
# Load the model
self.runner = importlib.import_module(f'runners.{self.args.runner_name}').RunnerWrapper(self.args, training=False)
self.runner.eval()
# Load pretrained weights
checkpoints_dir = pathlib.Path(self.args.project_dir) / 'runs' / self.args.experiment_name / 'checkpoints'
# Load pre-trained weights
init_networks = rn_utils.parse_str_to_list(self.args.init_networks) if self.args.init_networks else {}
networks_to_train = self.runner.nets_names_to_train
if self.args.init_which_epoch != 'none' and self.args.init_experiment_dir:
for net_name in init_networks:
self.runner.nets[net_name].load_state_dict(torch.load(
pathlib.Path(self.args.init_experiment_dir)
/ 'checkpoints'
/ f'{self.args.init_which_epoch}_{net_name}.pth',
map_location='cpu'))
for net_name in networks_to_train:
if net_name not in init_networks and net_name in self.runner.nets.keys():
self.runner.nets[net_name].load_state_dict(torch.load(
checkpoints_dir
/ f'{self.args.which_epoch}_{net_name}.pth',
map_location='cpu'))
# Remove spectral norm to improve the performance
self.runner.apply(rn_utils.remove_spectral_norm)
# Stickman/facemasks drawer
self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True)
self.net_seg = wrapper.SegmentationWrapper(self.args)
if self.args.num_gpus > 0:
self.cuda()
def change_args(self, args_dict):
self.args = self.get_args(args_dict)
def preprocess_data(self, input_imgs, crop_data=True):
imgs = []
poses = []
stickmen = []
if len(input_imgs.shape) == 3:
input_imgs = input_imgs[None]
N = 1
else:
N = input_imgs.shape[0]
for i in range(N):
pose = self.fa.get_landmarks(input_imgs[i])[0]
center = ((pose.min(0) + pose.max(0)) / 2).round().astype(int)
size = int(max(pose[:, 0].max() - pose[:, 0].min(), pose[:, 1].max() - pose[:, 1].min()))
center[1] -= size // 6
if input_imgs is None:
# Crop poses
if crop_data:
s = size * 2
pose -= center - size
else:
# Crop images and poses
img = Image.fromarray(input_imgs[i])
if crop_data:
img = img.crop((center[0]-size, center[1]-size, center[0]+size, center[1]+size))
s = img.size[0]
pose -= center - size
img = img.resize((self.args.image_size, self.args.image_size), Image.BICUBIC)
imgs.append((self.to_tensor(img) - 0.5) * 2)
if crop_data:
pose = pose / float(s)
poses.append(torch.from_numpy((pose - 0.5) * 2).view(-1))
poses = torch.stack(poses, 0)[None]
if self.args.output_stickmen:
stickmen = ds_utils.draw_stickmen(self.args, poses[0])
stickmen = stickmen[None]
if input_imgs is not None:
imgs = torch.stack(imgs, 0)[None]
if self.args.num_gpus > 0:
poses = poses.cuda()
if input_imgs is not None:
imgs = imgs.cuda()
if self.args.output_stickmen:
stickmen = stickmen.cuda()
segs = None
if hasattr(self, 'net_seg') and not isinstance(imgs, list):
segs = self.net_seg(imgs)[None]
return poses, imgs, segs, stickmen
def forward(self, data_dict, crop_data=True, no_grad=True):
if 'target_imgs' not in data_dict.keys():
data_dict['target_imgs'] = None
# Inference without finetuning
(source_poses,
source_imgs,
source_segs,
source_stickmen) = self.preprocess_data(data_dict['source_imgs'], crop_data)
(target_poses,
target_imgs,
target_segs,
target_stickmen) = self.preprocess_data(data_dict['target_imgs'], crop_data)
data_dict = {
'source_imgs': source_imgs,
'source_poses': source_poses,
'target_poses': target_poses}
if len(target_imgs):
data_dict['target_imgs'] = target_imgs
if source_segs is not None:
data_dict['source_segs'] = source_segs
if target_segs is not None:
data_dict['target_segs'] = target_segs
if source_stickmen is not None:
data_dict['source_stickmen'] = source_stickmen
if target_stickmen is not None:
data_dict['target_stickmen'] = target_stickmen
if no_grad:
with torch.no_grad():
self.runner(data_dict)
else:
self.runner(data_dict)
return self.runner.data_dict