From 134330d8a9c41d498078a78b87d06086d964d273 Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 13:26:15 +0800
Subject: [PATCH] multiplayer display
---
code/test_alexnet.py | 38 ++++++++++++++------------------------
1 files changed, 14 insertions(+), 24 deletions(-)
diff --git a/code/test_alexnet.py b/code/test_alexnet.py
index 7a3989a..45ad25f 100644
--- a/code/test_alexnet.py
+++ b/code/test_alexnet.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."""
@@ -39,13 +36,6 @@
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()
@@ -55,12 +45,12 @@
model = hopenet.AlexNet(66)
- print 'Loading snapshot.'
+ print('Loading snapshot.')
# Load snapshot
saved_state_dict = torch.load(snapshot_path)
- load_filtered_state_dict(model, saved_state_dict)
+ model.load_state_dict(saved_state_dict)
- print 'Loading data.'
+ print('Loading data.')
transformations = transforms.Compose([transforms.Scale(224),
transforms.CenterCrop(224), transforms.ToTensor(),
@@ -83,7 +73,7 @@
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
- print 'Error: not a valid dataset name'
+ print('Error: not a valid dataset name')
sys.exit()
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=args.batch_size,
@@ -91,7 +81,7 @@
model.cuda(gpu)
- print 'Ready to test network.'
+ print ('Ready to test network.')
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
@@ -134,8 +124,7 @@
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]
if args.dataset == 'BIWI':
@@ -145,7 +134,8 @@
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.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])
+ # 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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