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output_featuremap_distribution.py
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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import cv2
import os
import argparse
import json
from collections import namedtuple
from tqdm import tqdm
import torchgeometry as tgm
import config
import constants
from models import SMPL, densenet121,comp_dense, densenet_2stage, adaptive_densenet
from datasets import BaseDataset
from utils.imutils import uncrop
from utils.pose_utils import reconstruction_error
from utils.part_utils import PartRenderer
from utils import CheckpointDataLoader, CheckpointSaver
from utils import TrainOptions
import datetime
from datasets import MixedDataset
options = TrainOptions().parse_args()
# Define command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', default='/home/urp10/SPIN/models_trained/adaptive_local_3.pt', help='Path to network checkpoint')
parser.add_argument('--dataset', default='3dpw', choices=['h36m-p1', 'h36m-p2', 'lsp', '3dpw', 'mpi-inf-3dhp'], help='Choose evaluation dataset')
parser.add_argument('--log_freq', default=50, type=int, help='Frequency of printing intermediate results')
parser.add_argument('--batch_size', default=32, help='Batch size for testing')
parser.add_argument('--shuffle', default=False, action='store_true', help='Shuffle data')
parser.add_argument('--num_workers', default=8, type=int, help='Number of processes for data loading')
parser.add_argument('--result_file', default=None, help='If set, save detections to a .npz file')
parser.add_argument('--limit', type=int, default=3, help='The num of batch to get rank.')
parser.add_argument('--name', default=None, help='Name of the experiment')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
compress_rate_2 = [[0.203125], [0.28125, 0.4375, 0.34375, 0.25, 0.25, 0.3125], [0.375, 0.40625, 0.3125, 0.375, 0.28125, 0.375, 0.34375, 0.375, 0.46875, 0.4375, 0.5, 0.3125], [0.3125, 0.5, 0.4375, 0.4375, 0.375, 0.3125, 0.375, 0.5, 0.4375, 0.59375, 0.4375, 0.53125, 0.59375, 0.4375, 0.46875, 0.46875, 0.53125, 0.46875, 0.625, 0.4375, 0.34375, 0.53125, 0.5, 0.5], [0.5, 0.4375, 0.5625, 0.4375, 0.5, 0.5, 0.5, 0.34375, 0.5, 0.4375, 0.5, 0.5625, 0.53125, 0.53125, 0.5, 0.53125]]
compress_rate2_2 = [[0], [0.65625, 0.5859375, 0.4453125, 0.46875, 0.359375, 0.3515625], [0.46875, 0.3984375, 0.359375, 0.4140625, 0.328125, 0.3984375, 0.3203125, 0.3984375, 0.40625, 0.390625, 0.28125, 0.3828125], [0.453125, 0.40625, 0.3984375, 0.4609375, 0.421875, 0.4296875, 0.484375, 0.4296875, 0.4765625, 0.4609375, 0.453125, 0.4296875, 0.421875, 0.484375, 0.453125, 0.4375, 0.484375, 0.4765625, 0.4375, 0.4453125, 0.46875, 0.3984375, 0.421875, 0.3984375], [0.5390625, 0.546875, 0.53125, 0.5, 0.5078125, 0.5078125, 0.515625, 0.484375, 0.515625, 0.5390625, 0.53125, 0.453125, 0.546875, 0.5078125, 0.53125, 0.515625]]
model = adaptive_densenet(config.SMPL_MEAN_PARAMS, compress_rate_2, compress_rate2_2)
pre = 'rank_adaptive_local3'
checkpoint = torch.load(args.checkpoint)
state_dict = checkpoint['model']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
if 'module' not in k:
continue
# k = 'module.'+k
else:
k = k.replace('module.', '')
new_state_dict[k]=v
model.load_state_dict(new_state_dict)
model.to(device)
relu_test = nn.ReLU(inplace=True)
train_ds = MixedDataset(options, ignore_3d=options.ignore_3d, is_train=True)
train_data_loader = CheckpointDataLoader(train_ds,checkpoint=None,
batch_size=args.batch_size,
num_workers=options.num_workers,
pin_memory=options.pin_memory,
shuffle=options.shuffle_train)
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
def get_feature_hook(self, input, output):
global feature_result
global entropy
global total
# print(output)
output = relu_test(output)
# print(output)
# exit()
a = output.shape[0]
b = output.shape[1]
c = torch.tensor([torch.matrix_rank(output[i,j,:,:]).item() for i in range(a) for j in range(b)])
c = c.view(a, -1).float()
c = c.sum(0)
feature_result = feature_result * total + c
total = total + a
feature_result = feature_result / total
def inference():
model.eval()
limit = args.limit
with torch.no_grad():
for step, batch in enumerate(tqdm(train_data_loader, desc='Computing Rank',
total=len(train_ds) // args.batch_size,
initial=train_data_loader.checkpoint_batch_idx),
train_data_loader.checkpoint_batch_idx):
#use the first 5 batches to estimate the rank.
if step >= limit:
break
batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k,v in batch.items()}
images = batch['img']
outputs = model(images)
# densenet121 ranking generating
cov_layer = model.features.pool0
print(cov_layer)
handler = cov_layer.register_forward_hook(get_feature_hook)
inference()
handler.remove()
if not os.path.isdir(pre + '/densenet121_limit%d'%(args.limit)):
os.mkdir(pre + '/densenet121_limit%d'%(args.limit))
np.save(pre + '/densenet121_limit%d'%(args.limit) + '/rank_conv%d' % (1) + '.npy', feature_result.numpy())
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
# Densenet per Denselayer
cnt=1
model_block = [model.features.denseblock1, model.features.denseblock2, model.features.denseblock3, model.features.denseblock4]
trainsition_block = [model.features.transition1, model.features.transition2, model.features.transition3]
number_blocks = [6,12,24,16]
for i in range(4):
block = model_block[i]
for j in range(number_blocks[i]):
cov_layer = block[j].conv1
print(i, j, cov_layer)
handler = cov_layer.register_forward_hook(get_feature_hook)
inference()
handler.remove()
np.save(pre + '/densenet121_limit%d'%(args.limit) + '/rank_conv%d'%(cnt+1)+'.npy',
feature_result.numpy())
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
cnt+=1
cov_layer = block[j].conv2
print(i, j, cov_layer)
handler = cov_layer.register_forward_hook(get_feature_hook)
inference()
handler.remove()
np.save(pre + '/densenet121_limit%d' % (args.limit) + '/rank_conv%d' % (cnt + 1) + '.npy',
feature_result.numpy())
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
cnt += 1
if i != 3:
t_block = trainsition_block[i]
cov_layer = t_block.pool
print(i, cov_layer)
handler = cov_layer.register_forward_hook(get_feature_hook)
inference()
handler.remove()
np.save(pre + '/densenet121_limit%d' % (args.limit) + '/rank_conv%d' % (cnt + 1) + '.npy',
feature_result.numpy())
feature_result = torch.tensor(0.)
total = torch.tensor(0.)
cnt += 1