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fixed utils. #20

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fixed utils. #20

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12 changes: 6 additions & 6 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from dataset_mask_val import Dataset as Dataset_val
import os
import torch
from network import Res_Deeplab
from one_shot_network import Res_Deeplab
import torch.nn as nn
import numpy as np

Expand Down Expand Up @@ -67,16 +67,17 @@
options = parser.parse_args()


data_dir = '/your/dataset/dir/VOCdevkit/VOC2012'
data_dir = 'data'




#set gpus
gpu_list = [int(x) for x in options.gpu.split(',')]
#print(gpu_list)
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = options.gpu

#print(os.environ['CUDA_VISIBLE_DEVICES'])
torch.backends.cudnn.benchmark = True


Expand All @@ -100,7 +101,7 @@
model = Res_Deeplab(num_classes=num_class)
#load resnet-50 preatrained parameter
model = load_resnet50_param(model, stop_layer='layer4')
model=nn.DataParallel(model,[0,1])
#model=nn.DataParallel(model,[0,1])

# disable the gradients of not optomized layers
turn_off(model)
Expand Down Expand Up @@ -136,8 +137,7 @@



optimizer = optim.SGD([{'params': get_10x_lr_params(model), 'lr': 10 * learning_rate}],
lr=learning_rate, momentum=momentum, weight_decay=weight_decay)
optimizer = optim.SGD([{'params': get_10x_lr_params(model), 'lr': 10 * learning_rate}], lr=learning_rate, momentum=momentum, weight_decay=weight_decay)



Expand Down
34 changes: 17 additions & 17 deletions utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,10 +40,10 @@ def optim_or_not(model, yes):


def turn_off(model):
optim_or_not(model.module.conv1, False)
optim_or_not(model.module.layer1, False)
optim_or_not(model.module.layer2, False)
optim_or_not(model.module.layer3, False)
optim_or_not(model.conv1, False)
optim_or_not(model.layer1, False)
optim_or_not(model.layer2, False)
optim_or_not(model.layer3, False)



Expand All @@ -54,18 +54,18 @@ def get_10x_lr_params(model):
"""

b = []
b.append(model.module.layer5.parameters())
b.append(model.module.layer55.parameters())
b.append(model.module.layer6_0.parameters())
b.append(model.module.layer6_1.parameters())
b.append(model.module.layer6_2.parameters())
b.append(model.module.layer6_3.parameters())
b.append(model.module.layer6_4.parameters())
b.append(model.module.layer7.parameters())
b.append(model.module.layer9.parameters())
b.append(model.module.residule1.parameters())
b.append(model.module.residule2.parameters())
b.append(model.module.residule3.parameters())
b.append(model.layer5.parameters())
b.append(model.layer55.parameters())
b.append(model.layer6_0.parameters())
b.append(model.layer6_1.parameters())
b.append(model.layer6_2.parameters())
b.append(model.layer6_3.parameters())
b.append(model.layer6_4.parameters())
b.append(model.layer7.parameters())
b.append(model.layer9.parameters())
b.append(model.residule1.parameters())
b.append(model.residule2.parameters())
b.append(model.residule3.parameters())

for j in range(len(b)):
for i in b[j]:
Expand Down Expand Up @@ -129,4 +129,4 @@ def get_iou_v1(query_mask,pred_label,mode='foreground'):#pytorch 1.0 version
union_list.append(union)
iou_list.append(0)
num_predict_list.append(num_predict)
return inter_list,union_list,iou_list,num_predict_list
return inter_list,union_list,iou_list,num_predict_list