Source code for imaginaire.trainers.fs_vid2vid

# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out
import os

import imageio
import numpy as np
import torch
from tqdm import tqdm

from imaginaire.model_utils.fs_vid2vid import (concat_frames, get_fg_mask,
from imaginaire.model_utils.pix2pixHD import get_optimizer_with_params
from imaginaire.trainers.vid2vid import Trainer as vid2vidTrainer
from imaginaire.utils.distributed import is_master
from imaginaire.utils.distributed import master_only_print as print
from imaginaire.utils.misc import to_cuda
from imaginaire.utils.visualization import tensor2flow, tensor2im

[docs]class Trainer(vid2vidTrainer): r"""Initialize vid2vid trainer. Args: cfg (obj): Global configuration. net_G (obj): Generator network. net_D (obj): Discriminator network. opt_G (obj): Optimizer for the generator network. opt_D (obj): Optimizer for the discriminator network. sch_G (obj): Scheduler for the generator optimizer. sch_D (obj): Scheduler for the discriminator optimizer. train_data_loader (obj): Train data loader. val_data_loader (obj): Validation data loader. """ def __init__(self, cfg, net_G, net_D, opt_G, opt_D, sch_G, sch_D, train_data_loader, val_data_loader): super(Trainer, self).__init__(cfg, net_G, net_D, opt_G, opt_D, sch_G, sch_D, train_data_loader, val_data_loader) def _start_of_iteration(self, data, current_iteration): r"""Things to do before an iteration. Args: data (dict): Data used for the current iteration. current_iteration (int): Current number of iteration. """ data = self.pre_process(data) return to_cuda(data)
[docs] def pre_process(self, data): r"""Do any data pre-processing here. Args: data (dict): Data used for the current iteration. """ data_cfg = if hasattr(data_cfg, 'for_pose_dataset') and \ ('pose_maps-densepose' in data_cfg.input_labels): pose_cfg = data_cfg.for_pose_dataset data['label'] = pre_process_densepose(pose_cfg, data['label'], self.is_inference) data['few_shot_label'] = pre_process_densepose( pose_cfg, data['few_shot_label'], self.is_inference) return data
[docs] def get_test_output_images(self, data): r"""Get the visualization output of test function. Args: data (dict): Training data at the current iteration. """ vis_images = [ tensor2im(data['few_shot_images'][:, 0]), self.visualize_label(data['label'][:, -1]), tensor2im(data['images'][:, -1]), tensor2im(self.net_G_output['fake_images']), ] return vis_images
[docs] def get_data_t(self, data, net_G_output, data_prev, t): r"""Get data at current time frame given the sequence of data. Args: data (dict): Training data for current iteration. net_G_output (dict): Output of the generator (for previous frame). data_prev (dict): Data for previous frame. t (int): Current time. """ label = data['label'][:, t] if 'label' in data else None image = data['images'][:, t] if data_prev is not None: nG = prev_labels = concat_frames(data_prev['prev_labels'], data_prev['label'], nG - 1) prev_images = concat_frames( data_prev['prev_images'], net_G_output['fake_images'].detach(), nG - 1) else: prev_labels = prev_images = None data_t = dict() data_t['label'] = label data_t['image'] = image data_t['ref_labels'] = data['few_shot_label'] if 'few_shot_label' \ in data else None data_t['ref_images'] = data['few_shot_images'] data_t['prev_labels'] = prev_labels data_t['prev_images'] = prev_images data_t['real_prev_image'] = data['images'][:, t - 1] if t > 0 else None # if 'landmarks_xy' in data: # data_t['landmarks_xy'] = data['landmarks_xy'][:, t] # data_t['ref_landmarks_xy'] = data['few_shot_landmarks_xy'] return data_t
[docs] def post_process(self, data, net_G_output): r"""Do any postprocessing of the data / output here. Args: data (dict): Training data at the current iteration. net_G_output (dict): Output of the generator. """ if self.has_fg: fg_mask = get_fg_mask(data['label'], self.has_fg) if net_G_output['fake_raw_images'] is not None: net_G_output['fake_raw_images'] = \ net_G_output['fake_raw_images'] * fg_mask return data, net_G_output
[docs] def test(self, test_data_loader, root_output_dir, inference_args): r"""Run inference on the specified sequence. Args: test_data_loader (object): Test data loader. root_output_dir (str): Location to dump outputs. inference_args (optional): Optional args. """ self.reset() test_data_loader.dataset.set_sequence_length(0) # Set the inference sequences. test_data_loader.dataset.set_inference_sequence_idx( inference_args.driving_seq_index, inference_args.few_shot_seq_index, inference_args.few_shot_frame_index) video = [] for idx, data in enumerate(tqdm(test_data_loader)): key = data['key']['images'][0][0] filename = key.split('/')[-1] # Create output dir for this sequence. if idx == 0: seq_name = '%03d' % inference_args.driving_seq_index output_dir = os.path.join(root_output_dir, seq_name) os.makedirs(output_dir, exist_ok=True) video_path = output_dir # Get output and save images. data['img_name'] = filename data = self.start_of_iteration(data, current_iteration=-1) output = self.test_single(data, output_dir, inference_args) video.append(output) # Save output as mp4. imageio.mimsave(video_path + '.mp4', video, fps=15)
[docs] def save_image(self, path, data): r"""Save the output images to path. Note when the generate_raw_output is FALSE. Then, first_net_G_output['fake_raw_images'] is None and will not be displayed. In model average mode, we will plot the flow visualization twice. Args: path (str): Save path. data (dict): Training data for current iteration. """ self.net_G.eval() if self.cfg.trainer.model_average_config.enabled: self.net_G.module.averaged_model.eval() self.net_G_output = None with torch.no_grad(): first_net_G_output, last_net_G_output, _ = self.gen_frames(data) if self.cfg.trainer.model_average_config.enabled: first_net_G_output_avg, last_net_G_output_avg, _ = \ self.gen_frames(data, use_model_average=True) def get_images(data, net_G_output, return_first_frame=True, for_model_average=False): r"""Get the ourput images to save. Args: data (dict): Training data for current iteration. net_G_output (dict): Generator output. return_first_frame (bool): Return output for first frame in the sequence. for_model_average (bool): For model average output. Return: vis_images (list of numpy arrays): Visualization images. """ frame_idx = 0 if return_first_frame else -1 warped_idx = 0 if return_first_frame else 1 vis_images = [] if not for_model_average: vis_images += [ tensor2im(data['few_shot_images'][:, frame_idx]), self.visualize_label(data['label'][:, frame_idx]), tensor2im(data['images'][:, frame_idx]) ] vis_images += [ tensor2im(net_G_output['fake_images']), tensor2im(net_G_output['fake_raw_images'])] if not for_model_average: vis_images += [ tensor2im(net_G_output['warped_images'][warped_idx]), tensor2flow(net_G_output['fake_flow_maps'][warped_idx]), tensor2im(net_G_output['fake_occlusion_masks'][warped_idx], normalize=False) ] return vis_images if is_master(): vis_images_first = get_images(data, first_net_G_output) if self.cfg.trainer.model_average_config.enabled: vis_images_first += get_images(data, first_net_G_output_avg, for_model_average=True) if self.sequence_length > 1: vis_images_last = get_images(data, last_net_G_output, return_first_frame=False) if self.cfg.trainer.model_average_config.enabled: vis_images_last += get_images(data, last_net_G_output_avg, return_first_frame=False, for_model_average=True) # If generating a video, the first row of each batch will be # the first generated frame and the flow/mask for warping the # reference image, and the second row will be the last # generated frame and the flow/mask for warping the previous # frame. If using model average, the frames generated by model # average will be at the rightmost columns. vis_images = [[np.vstack((im_first, im_last)) for im_first, im_last in zip(imgs_first, imgs_last)] for imgs_first, imgs_last in zip(vis_images_first, vis_images_last) if imgs_first is not None] else: vis_images = vis_images_first image_grid = np.hstack([np.vstack(im) for im in vis_images if im is not None]) print('Save output images to {}'.format(path)) os.makedirs(os.path.dirname(path), exist_ok=True) imageio.imwrite(path, image_grid)
[docs] def finetune(self, data, inference_args): r"""Finetune the model for a few iterations on the inference data.""" # Get the list of params to finetune. self.net_G, self.net_D, self.opt_G, self.opt_D = \ get_optimizer_with_params(self.cfg, self.net_G, self.net_D, param_names_start_with=[ 'weight_generator.fc', 'conv_img', 'up']) data_finetune = {k: v for k, v in data.items()} ref_labels = data_finetune['few_shot_label'] ref_images = data_finetune['few_shot_images'] # Number of iterations to finetune. iterations = getattr(inference_args, 'finetune_iter', 100) for it in range(1, iterations + 1): # Randomly set one of the reference images as target. idx = np.random.randint(ref_labels.size(1)) tgt_label, tgt_image = ref_labels[:, idx], ref_images[:, idx] # Randomly shift and flip the target image. tgt_label, tgt_image = random_roll([tgt_label, tgt_image]) data_finetune['label'] = tgt_label.unsqueeze(1) data_finetune['images'] = tgt_image.unsqueeze(1) self.gen_update(data_finetune) self.dis_update(data_finetune) if (it % (iterations // 10)) == 0: print(it) self.has_finetuned = True