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interpretability.py
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from __future__ import print_function, absolute_import, division
import os
import time
import torch
import torch.nn as nn
import torch.optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
import numpy as np
from progress.bar import Bar
import pandas as pd
from utils import loss_funcs, utils as utils
from utils.opt import Options
from utils.h36motion import H36motion
from utils.cmu_motion import CMU_Motion
from utils.cmu_motion_3d import CMU_Motion3D
import utils.model as nnmodel
import utils.data_utils as data_utils
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='h3.6m', help='which dataset to use')
parser.add_argument('--model_path', type=str, default=None, help='path to checkpoint')
opt = parser.parse_args()
is_cuda = torch.cuda.is_available()
input_n = 10
output_n = 25
dct_n = 35
sample_rate = 2
cartesian = False
if opt.dataset == 'h3.6m':
node_n = 48
n_z = 384
actions = data_utils.define_actions('all', 'h3.6m', out_of_distribution=False)
elif opt.dataset == 'cmu_mocap':
node_n = 64
actions = data_utils.define_actions('all', 'cmu_mocap', out_of_distribution=False)
n_z = 512
model = nnmodel.GCN(input_feature=dct_n, hidden_feature=256, p_dropout=0.3,
num_stage=12, node_n=node_n, variational=True, n_z=8, num_decoder_stage=6)
if is_cuda:
model.cuda()
print(">>> total params: {:.2f}M".format(sum(p.numel() for p in model.parameters()) / 1000000.0))
# optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
if opt.model_path == None:
model_path_len = 'checkpoint/test/ckpt_main_cmu_mocap_in50_out25_dctn35_dropout_0.3_var_lambda_0.003_nz_8_lr_0.0005_n_layers_6_last.pth.tar'
else:
model_path_len = opt.model_path
print(">>> loading ckpt len from '{}'".format(model_path_len))
if is_cuda:
ckpt = torch.load(model_path_len)
else:
ckpt = torch.load(model_path_len, map_location='cpu')
batch_size = 512
train_data = dict()
val_data = dict()
test_data = dict()
for act in actions:
print("Loading action {} for train set".format(act))
if opt.dataset == 'h3.6m':
train_dataset = H36motion(path_to_data='h3.6m/', actions=[act], input_n=input_n, output_n=output_n,
sample_rate=sample_rate, split=0, dct_n=dct_n)
elif opt.dataset == 'cmu_mocap':
train_dataset = CMU_Motion(path_to_data='cmu_mocap/', actions=[act], input_n=input_n, output_n=output_n,
split=0, dct_n=dct_n)
# print(train_dataset.__len__())
data_std = train_dataset.data_std
data_mean = train_dataset.data_mean
dim_used = train_dataset.dim_used
train_data[act] = DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=10,
pin_memory=True)
print("Loading action {} for val and test set".format(act))
if opt.dataset == 'h3.6m':
val_dataset = H36motion(path_to_data='h3.6m/', actions=[act], input_n=input_n, output_n=output_n,
split=2, sample_rate=2, data_mean=data_mean, data_std=data_std, dct_n=dct_n)
val_data[act] = DataLoader(
dataset=val_dataset,
batch_size=128,
shuffle=False,
num_workers=10,
pin_memory=True)
test_dataset = H36motion(path_to_data='h3.6m/', actions=[act], input_n=input_n, output_n=output_n,
sample_rate=sample_rate, split=1, data_mean=data_mean, data_std=data_std, dct_n=dct_n)
elif opt.dataset == 'cmu_mocap':
test_dataset = CMU_Motion(path_to_data='cmu_mocap/', actions=[act], input_n=input_n, output_n=output_n,
split=1, data_mean=data_mean, data_std=data_std, dim_used=dim_used, dct_n=dct_n)
# print(test_dataset.__len__())
test_data[act] = DataLoader(
dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=10,
pin_memory=True)
first_instance_train = True
first_instance_val = True
first_instance_test = True
for act in actions:
for i, (inputs, targets, all_seq) in enumerate(train_data[act]):
if is_cuda:
inputs = Variable(inputs.cuda()).float()
all_seq = Variable(all_seq.cuda(non_blocking=True)).float()
outputs, reconstructions, log_var, z = model(inputs.float())
print("For action {} the train z is: {}".format(act, z.shape))
for i in range(z.shape[0]):
sample = z[i].reshape(n_z).cpu().detach().numpy()
sample_input = inputs[i].reshape(node_n * dct_n).cpu().detach().numpy()
df = pd.DataFrame(np.expand_dims(sample, axis=0))
df_input = pd.DataFrame(np.expand_dims(sample_input, axis=0))
df['label'] = act
df_input['label'] = act
if first_instance_train:
df.to_csv('latents/all_train_z.csv', header=False, index=False)
df_input.to_csv('inputs/all_train.csv', header=False, index=False)
first_instance_train = False
else:
with open('latents/all_train_z.csv', 'a') as f:
df.to_csv(f, header=False, index=False)
with open('inputs/all_train.csv', 'a') as f:
df_input.to_csv(f, header=False, index=False)
if opt.dataset == 'h3.6m':
for i, (inputs, targets, all_seq) in enumerate(val_data[act]):
if is_cuda:
inputs = Variable(inputs.cuda()).float()
all_seq = Variable(all_seq.cuda(non_blocking=True)).float()
outputs, reconstructions, log_var, z = model(inputs.float())
print("For action {} the val z is: {}".format(act, z.shape))
for i in range(z.shape[0]):
sample_z = z[i].reshape(n_z).cpu().detach().numpy()
sample_input = inputs[i].reshape(node_n * dct_n).cpu().detach().numpy()
df = pd.DataFrame(np.expand_dims(sample_z, axis=0))
df_input = pd.DataFrame(np.expand_dims(sample_input, axis=0))
df['label'] = act
df_input['label'] = act
if first_instance_val:
df.to_csv('latents/all_val_z.csv', header=False, index=False)
df_input.to_csv('inputs/all_val.csv', header=False, index=False)
first_instance_val = False
else:
with open('latents/all_val_z.csv', 'a') as f:
df.to_csv(f, header=False, index=False)
with open('inputs/all_val.csv', 'a') as f:
df_input.to_csv(f, header=False, index=False)
for i, (inputs, targets, all_seq) in enumerate(test_data[act]):
if is_cuda:
inputs = Variable(inputs.cuda()).float()
all_seq = Variable(all_seq.cuda(non_blocking=True)).float()
outputs, reconstructions, log_var, z = model(inputs.float())
print("For action {} the test z is: {}".format(act, z.shape))
for i in range(z.shape[0]):
sample = z[i].reshape(n_z).cpu().detach().numpy()
sample_input = inputs[i].reshape(node_n * dct_n).cpu().detach().numpy()
df = pd.DataFrame(np.expand_dims(sample, axis=0))
df_input = pd.DataFrame(np.expand_dims(sample_input, axis=0))
df['label'] = act
df_input['label'] = act
if first_instance_test:
df.to_csv('latents/all_test_z.csv', header=False, index=False)
df_input.to_csv('inputs/all_test.csv', header=False, index=False)
first_instance_test = False
else:
with open('latents/all_test_z.csv', 'a') as f:
df.to_csv(f, header=False, index=False)
with open('inputs/all_test.csv', 'a') as f:
df_input.to_csv(f, header=False, index=False)