From af51d0ecb51ad4d6c8ed086855bd3c411ebc4aa0 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:29:51 +0800
Subject: [PATCH] Fixed stuff
---
code/test_preangles.py | 74 +++++++++++++++++++------------------
1 files changed, 38 insertions(+), 36 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index 08561fb..05f621a 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -1,4 +1,9 @@
+import sys, os, argparse
+
import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
import torch
import torch.nn as nn
from torch.autograd import Variable
@@ -8,15 +13,7 @@
import torchvision
import torch.nn.functional as F
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-import datasets
-import hopenet
-import utils
+import datasets, hopenet, utils
def parse_args():
"""Parse input arguments."""
@@ -39,6 +36,13 @@
return args
+def load_filtered_state_dict(model, snapshot):
+ # By user apaszke from discuss.pytorch.org
+ model_dict = model.state_dict()
+ snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+ model_dict.update(snapshot)
+ model.load_state_dict(model_dict)
+
if __name__ == '__main__':
args = parse_args()
@@ -46,17 +50,13 @@
gpu = args.gpu_id
snapshot_path = args.snapshot
- # ResNet101 with 3 outputs.
- # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
- # ResNet50
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, 0)
- # ResNet18
- # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
+ # ResNet50 structure
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
print 'Loading snapshot.'
# Load snapshot
saved_state_dict = torch.load(snapshot_path)
- model.load_state_dict(saved_state_dict)
+ load_filtered_state_dict(model, saved_state_dict)
print 'Loading data.'
@@ -64,15 +64,20 @@
transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- if args.dataset == 'AFLW2000':
- pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
- transformations)
+ if args.dataset == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Pose_300W_LP_random_ds':
+ pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW2000':
+ pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW2000_ds':
+ pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'Pose_300W_LP':
- pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW_aug':
+ pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
@@ -90,9 +95,6 @@
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
total = 0
- idx_tensor = [idx for idx in xrange(66)]
- idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
-
yaw_error = .0
pitch_error = .0
roll_error = .0
@@ -102,6 +104,7 @@
for i, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
total += cont_labels.size(0)
+
label_yaw = cont_labels[:,0].float()
label_pitch = cont_labels[:,1].float()
label_roll = cont_labels[:,2].float()
@@ -114,28 +117,27 @@
_, roll_bpred = torch.max(roll.data, 1)
# Continuous predictions
- yaw_predicted = utils.softmax_temperature(yaw.data, 1)
- pitch_predicted = utils.softmax_temperature(pitch.data, 1)
- roll_predicted = utils.softmax_temperature(roll.data, 1)
-
- yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99
- pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99
- roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99
+ yaw_predicted = angles[:,0].data.cpu()
+ pitch_predicted = angles[:,1].data.cpu()
+ roll_predicted = angles[:,2].data.cpu()
# Mean absolute error
yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))
pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
- # Save images with pose cube.
- # TODO: fix for larger batch size
+ # Save first image in batch with pose cube or axis.
if args.save_viz:
name = name[0]
- cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
+ if args.dataset == 'BIWI':
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
+ else:
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
if args.batch_size == 1:
error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
- cv2_img = cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1)
- utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0])
+ cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2)
+ # utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
+ utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
print('Test error in degrees of the model on the ' + str(total) +
--
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