From 2f6778c2db9ce1a887f04fdc85ad0d5db4ba84b8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:15:30 +0800
Subject: [PATCH] Cleaned up a bit
---
code/train_resnet50_regression.py | 55 ++++++++++++++-----------------------------------------
1 files changed, 14 insertions(+), 41 deletions(-)
diff --git a/code/train_resnet50_regression.py b/code/train_resnet50_regression.py
index a67a6f2..04d27c7 100644
--- a/code/train_resnet50_regression.py
+++ b/code/train_resnet50_regression.py
@@ -1,4 +1,9 @@
+import sys, os, argparse, time
+
import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
import torch
import torch.nn as nn
from torch.autograd import Variable
@@ -8,25 +13,8 @@
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-import datasets
-import hopenet
+import datasets, hopenet
import torch.utils.model_zoo as model_zoo
-
-import time
-
-model_urls = {
- 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
- 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
- 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
- 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
- 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
-}
def parse_args():
"""Parse input arguments."""
@@ -51,9 +39,7 @@
def get_ignored_params(model):
# Generator function that yields ignored params.
- b = []
- b.append(model.conv1)
- b.append(model.bn1)
+ b = [model.conv1, model.bn1]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
if 'bn' in module_name:
@@ -63,11 +49,7 @@
def get_non_ignored_params(model):
# Generator function that yields params that will be optimized.
- b = []
- b.append(model.layer1)
- b.append(model.layer2)
- b.append(model.layer3)
- b.append(model.layer4)
+ b = [model.layer1, model.layer2, model.layer3, model.layer4]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
if 'bn' in module_name:
@@ -76,8 +58,8 @@
yield param
def get_fc_params(model):
- b = []
- b.append(model.fc_angles)
+ # Generator function that yields fc layer params.
+ b = [model.fc_angles]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
for name, param in module.named_parameters():
@@ -86,11 +68,8 @@
def load_filtered_state_dict(model, snapshot):
# By user apaszke from discuss.pytorch.org
model_dict = model.state_dict()
- # 1. filter out unnecessary keys
snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
- # 2. overwrite entries in the existing state dict
model_dict.update(snapshot)
- # 3. load the new state dict
model.load_state_dict(model_dict)
if __name__ == '__main__':
@@ -106,8 +85,7 @@
# ResNet50
model = hopenet.ResNet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 3)
-
- load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
+ load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
print 'Loading data.'
@@ -117,6 +95,8 @@
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 == 'BIWI':
@@ -150,23 +130,16 @@
images = Variable(images).cuda(gpu)
label_angles = Variable(cont_labels[:,:3]).cuda(gpu)
-
- optimizer.zero_grad()
- model.zero_grad()
-
angles = model(images)
loss = criterion(angles, label_angles)
-
+ optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss.data[0]))
- # if epoch == 0:
- # torch.save(model.state_dict(),
- # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl')
# Save models at numbered epochs.
if epoch % 1 == 0 and epoch < num_epochs:
--
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