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_alexnet.py | 25 ++++++++----
code/test_resnet50_regression.py | 46 +++++++++++++----------
code/train_alexnet.py | 22 +++++++----
code/test_preangles.py | 11 +++++
4 files changed, 67 insertions(+), 37 deletions(-)
diff --git a/code/test_alexnet.py b/code/test_alexnet.py
index d9cc0a3..7a3989a 100644
--- a/code/test_alexnet.py
+++ b/code/test_alexnet.py
@@ -39,6 +39,13 @@
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()
@@ -51,7 +58,7 @@
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.'
@@ -59,18 +66,20 @@
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)
+ 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:
diff --git a/code/test_preangles.py b/code/test_preangles.py
index be9bfda..05f621a 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -36,6 +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()
@@ -49,7 +56,7 @@
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.'
@@ -63,6 +70,8 @@
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':
diff --git a/code/test_resnet50_regression.py b/code/test_resnet50_regression.py
index 85207f8..6945269 100644
--- a/code/test_resnet50_regression.py
+++ b/code/test_resnet50_regression.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()
@@ -51,7 +55,7 @@
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.'
@@ -59,18 +63,20 @@
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)
+ 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:
@@ -111,8 +117,7 @@
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':
@@ -122,7 +127,8 @@
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) +
diff --git a/code/train_alexnet.py b/code/train_alexnet.py
index 9254ee7..51cf43b 100644
--- a/code/train_alexnet.py
+++ b/code/train_alexnet.py
@@ -129,6 +129,9 @@
# Regression loss coefficient
alpha = args.alpha
+ idx_tensor = [idx for idx in xrange(66)]
+ idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
+
optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
{'params': get_non_ignored_params(model), 'lr': args.lr},
{'params': get_fc_params(model), 'lr': args.lr * 5}],
@@ -150,17 +153,21 @@
label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
# Forward pass
- yaw, pitch, roll, angles = model(images)
+ pre_yaw, pre_pitch, pre_roll = model(images)
# Cross entropy loss
- loss_yaw = criterion(yaw, label_yaw)
- loss_pitch = criterion(pitch, label_pitch)
- loss_roll = criterion(roll, label_roll)
+ loss_yaw = criterion(pre_yaw, label_yaw)
+ loss_pitch = criterion(pre_pitch, label_pitch)
+ loss_roll = criterion(pre_roll, label_roll)
# MSE loss
- yaw_predicted = angles[:,0]
- pitch_predicted = angles[:,1]
- roll_predicted = angles[:,2]
+ yaw_predicted = softmax(pre_yaw)
+ pitch_predicted = softmax(pre_pitch)
+ roll_predicted = softmax(pre_roll)
+
+ yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99
+ pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99
+ roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99
loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
@@ -173,7 +180,6 @@
loss_seq = [loss_yaw, loss_pitch, loss_roll]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
- optimizer.zero_grad()
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
--
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