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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# some code from https://raw.githubusercontent.com/weigq/3d_pose_baseline_pytorch/master/src/model.py
from __future__ import absolute_import
from __future__ import print_function
import torch
import torch.nn as nn
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
# from priors.vae_pose_model.vae_model import VAEmodel
from priors.normalizing_flow_prior.normalizing_flow_prior import NormalizingFlowPrior
def weight_init_dangerous(m):
# this is dangerous as it may overwrite the normalizing flow weights
if isinstance(m, nn.Linear):
nn.init.kaiming_normal(m.weight)
class Linear(nn.Module):
def __init__(self, linear_size, p_dropout=0.5):
super(Linear, self).__init__()
self.l_size = linear_size
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(p_dropout)
self.w1 = nn.Linear(self.l_size, self.l_size)
self.batch_norm1 = nn.BatchNorm1d(self.l_size)
self.w2 = nn.Linear(self.l_size, self.l_size)
self.batch_norm2 = nn.BatchNorm1d(self.l_size)
def forward(self, x):
y = self.w1(x)
y = self.batch_norm1(y)
y = self.relu(y)
y = self.dropout(y)
y = self.w2(y)
y = self.batch_norm2(y)
y = self.relu(y)
y = self.dropout(y)
out = x + y
return out
class LinearModel(nn.Module):
def __init__(self,
linear_size=1024,
num_stage=2,
p_dropout=0.5,
input_size=16*2,
output_size=16*3):
super(LinearModel, self).__init__()
self.linear_size = linear_size
self.p_dropout = p_dropout
self.num_stage = num_stage
# input
self.input_size = input_size # 2d joints: 16 * 2
# output
self.output_size = output_size # 3d joints: 16 * 3
# process input to linear size
self.w1 = nn.Linear(self.input_size, self.linear_size)
self.batch_norm1 = nn.BatchNorm1d(self.linear_size)
self.linear_stages = []
for l in range(num_stage):
self.linear_stages.append(Linear(self.linear_size, self.p_dropout))
self.linear_stages = nn.ModuleList(self.linear_stages)
# post-processing
self.w2 = nn.Linear(self.linear_size, self.output_size)
# helpers (relu and dropout)
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(self.p_dropout)
def forward(self, x):
# pre-processing
y = self.w1(x)
y = self.batch_norm1(y)
y = self.relu(y)
y = self.dropout(y)
# linear layers
for i in range(self.num_stage):
y = self.linear_stages[i](y)
# post-processing
y = self.w2(y)
return y
class LinearModelComplete(nn.Module):
def __init__(self,
linear_size=1024,
num_stage_comb=2,
num_stage_heads=1,
num_stage_heads_pose=1,
trans_sep=False,
p_dropout=0.5,
input_size=16*2,
intermediate_size=1024,
output_info=None,
n_joints=25,
n_z=512,
add_z_to_3d_input=False,
n_segbps=64*2,
add_segbps_to_3d_input=False,
structure_pose_net='default',
fix_vae_weights=True,
nf_version=None): # 0): n_silh_enc
super(LinearModelComplete, self).__init__()
if add_z_to_3d_input:
self.n_z_to_add = n_z # 512
else:
self.n_z_to_add = 0
if add_segbps_to_3d_input:
self.n_segbps_to_add = n_segbps # 64
else:
self.n_segbps_to_add = 0
self.input_size = input_size
self.linear_size = linear_size
self.p_dropout = p_dropout
self.num_stage_comb = num_stage_comb
self.num_stage_heads = num_stage_heads
self.num_stage_heads_pose = num_stage_heads_pose
self.trans_sep = trans_sep
self.input_size = input_size
self.intermediate_size = intermediate_size
self.structure_pose_net = structure_pose_net
self.fix_vae_weights = fix_vae_weights # only relevant if structure_pose_net='vae'
self.nf_version = nf_version
if output_info is None:
pose = {'name': 'pose', 'n': n_joints*6, 'out_shape':[n_joints, 6]}
cam = {'name': 'flength', 'n': 1}
if self.trans_sep:
translation_xy = {'name': 'trans_xy', 'n': 2}
translation_z = {'name': 'trans_z', 'n': 1}
self.output_info = [pose, translation_xy, translation_z, cam]
else:
translation = {'name': 'trans', 'n': 3}
self.output_info = [pose, translation, cam]
if self.structure_pose_net == 'vae' or self.structure_pose_net == 'normflow':
global_pose = {'name': 'global_pose', 'n': 1*6, 'out_shape':[1, 6]}
self.output_info.append(global_pose)
else:
self.output_info = output_info
self.linear_combined = LinearModel(linear_size=self.linear_size,
num_stage=self.num_stage_comb,
p_dropout=p_dropout,
input_size=self.input_size + self.n_segbps_to_add + self.n_z_to_add, ######
output_size=self.intermediate_size)
self.output_info_linear_models = []
for ind_el, element in enumerate(self.output_info):
if element['name'] == 'pose':
num_stage = self.num_stage_heads_pose
if self.structure_pose_net == 'default':
output_size_pose_lin = element['n']
elif self.structure_pose_net == 'vae':
# load vae decoder
self.pose_vae_model = VAEmodel()
self.pose_vae_model.initialize_with_pretrained_weights()
# define the input size of the vae decoder
output_size_pose_lin = self.pose_vae_model.latent_size
elif self.structure_pose_net == 'normflow':
# the following will automatically be initialized
self.pose_normflow_model = NormalizingFlowPrior(nf_version=self.nf_version)
output_size_pose_lin = element['n'] - 6 # no global rotation
else:
raise NotImplementedError
self.output_info_linear_models.append(LinearModel(linear_size=self.linear_size,
num_stage=num_stage,
p_dropout=p_dropout,
input_size=self.intermediate_size,
output_size=output_size_pose_lin))
else:
if element['name'] == 'global_pose':
num_stage = self.num_stage_heads_pose
else:
num_stage = self.num_stage_heads
self.output_info_linear_models.append(LinearModel(linear_size=self.linear_size,
num_stage=num_stage,
p_dropout=p_dropout,
input_size=self.intermediate_size,
output_size=element['n']))
element['linear_model_index'] = ind_el
self.output_info_linear_models = nn.ModuleList(self.output_info_linear_models)
def forward(self, x):
device = x.device
# combined stage
if x.shape[1] == self.input_size + self.n_segbps_to_add + self.n_z_to_add:
y = self.linear_combined(x)
elif x.shape[1] == self.input_size + self.n_segbps_to_add:
x_mod = torch.cat((x, torch.normal(0, 1, size=(x.shape[0], self.n_z_to_add)).to(device)), dim=1)
y = self.linear_combined(x_mod)
else:
print(x.shape)
print(self.input_size)
print(self.n_segbps_to_add)
print(self.n_z_to_add)
raise ValueError
# heads
results = {}
results_trans = {}
for element in self.output_info:
linear_model = self.output_info_linear_models[element['linear_model_index']]
if element['name'] == 'pose':
if self.structure_pose_net == 'default':
results['pose'] = (linear_model(y)).reshape((-1, element['out_shape'][0], element['out_shape'][1]))
normflow_z = None
elif self.structure_pose_net == 'vae':
res_lin = linear_model(y)
if self.fix_vae_weights:
self.pose_vae_model.requires_grad_(False) # let gradients flow through but don't update the parameters
res_vae = self.pose_vae_model.inference(feat=res_lin)
self.pose_vae_model.requires_grad_(True)
else:
res_vae = self.pose_vae_model.inference(feat=res_lin)
res_pose_not_glob = res_vae.reshape((-1, element['out_shape'][0], element['out_shape'][1]))
normflow_z = None
elif self.structure_pose_net == 'normflow':
normflow_z = linear_model(y)*0.1
self.pose_normflow_model.requires_grad_(False) # let gradients flow though but don't update the parameters
res_pose_not_glob = self.pose_normflow_model.run_backwards(z=normflow_z).reshape((-1, element['out_shape'][0]-1, element['out_shape'][1]))
else:
raise NotImplementedError
elif element['name'] == 'global_pose':
res_pose_glob = (linear_model(y)).reshape((-1, element['out_shape'][0], element['out_shape'][1]))
elif element['name'] == 'trans_xy' or element['name'] == 'trans_z':
results_trans[element['name']] = linear_model(y)
else:
results[element['name']] = linear_model(y)
if self.trans_sep:
results['trans'] = torch.cat((results_trans['trans_xy'], results_trans['trans_z']), dim=1)
# prepare pose including global rotation
if self.structure_pose_net == 'vae':
# results['pose'] = torch.cat((res_pose_glob, res_pose_not_glob), dim=1)
results['pose'] = torch.cat((res_pose_glob, res_pose_not_glob[:, 1:, :]), dim=1)
elif self.structure_pose_net == 'normflow':
results['pose'] = torch.cat((res_pose_glob, res_pose_not_glob[:, :, :]), dim=1)
# return a dictionary which contains all results
results['normflow_z'] = normflow_z
return results # this is a dictionary
# ------------------------------------------
# for pretraining of the 3d model only:
# (see combined_model/model_shape_v2.py)
class Wrapper_LinearModelComplete(nn.Module):
def __init__(self,
linear_size=1024,
num_stage_comb=2,
num_stage_heads=1,
num_stage_heads_pose=1,
trans_sep=False,
p_dropout=0.5,
input_size=16*2,
intermediate_size=1024,
output_info=None,
n_joints=25,
n_z=512,
add_z_to_3d_input=False,
n_segbps=64*2,
add_segbps_to_3d_input=False,
structure_pose_net='default',
fix_vae_weights=True,
nf_version=None):
self.add_segbps_to_3d_input = add_segbps_to_3d_input
super(Wrapper_LinearModelComplete, self).__init__()
self.model_3d = LinearModelComplete(linear_size=linear_size,
num_stage_comb=num_stage_comb,
num_stage_heads=num_stage_heads,
num_stage_heads_pose=num_stage_heads_pose,
trans_sep=trans_sep,
p_dropout=p_dropout, # 0.5,
input_size=input_size,
intermediate_size=intermediate_size,
output_info=output_info,
n_joints=n_joints,
n_z=n_z,
add_z_to_3d_input=add_z_to_3d_input,
n_segbps=n_segbps,
add_segbps_to_3d_input=add_segbps_to_3d_input,
structure_pose_net=structure_pose_net,
fix_vae_weights=fix_vae_weights,
nf_version=nf_version)
def forward(self, input_vec):
# input_vec = torch.cat((keypoints_prepared.reshape((batch_size, -1)), bone_lengths_prepared), axis=1)
# predict 3d parameters (those are normalized, we need to correct mean and std in a next step)
output = self.model_3d(input_vec)
return output |