def vismodel():
import sys
import torch
import tensorwatch as tw
from networks import ResnetConditionHR
netM = ResnetConditionHR(input_nc=(3, 3, 1, 4),
output_nc=4,
n_blocks1=7,
n_blocks2=3)
tw.draw_model(netM, [1, 3, 512, 512])
def test_build_ssd():
# 验证ssd模型构建成功
ssd = build_vgg_ssd(cfg)
ssd = ssd.eval()
inputs = torch.randn(1, 3, 300, 300)
conf, loc, _ = ssd(inputs)
print('conf size: {}, loc size: {}'.format(conf.size(), loc.size()))
g = tw.draw_model(ssd.eval(), [1, 3, 300, 300])
g.save('ssd_network.png')
def initialize_network_optimizer_and_scheduler(self):
"""
This is specific to the U-Net and must be adapted for other network architectures
:return:
"""
#self.print_to_log_file(self.net_num_pool_op_kernel_sizes)
#self.print_to_log_file(self.net_conv_kernel_sizes)
print('Has entered into the initialize_network_optimizer_and_scheduler！')
net_numpool = len(self.net_num_pool_op_kernel_sizes)
if self.threeD:
conv_op = nn.Conv3d
dropout_op = nn.Dropout3d
norm_op = nn.InstanceNorm3d
else:
conv_op = nn.Conv2d
dropout_op = nn.Dropout2d
norm_op = nn.InstanceNorm2d
norm_op_kwargs = {'eps': 1e-5, 'affine': True}
dropout_op_kwargs = {'p': 0, 'inplace': True}
net_nonlin = nn.LeakyReLU
net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
torch.cuda.set_device(0)
self.do_supervision=False
self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, net_numpool,
2, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, dropout_op_kwargs,
net_nonlin, net_nonlin_kwargs, self.do_supervision, False, lambda x: x, InitWeights_He(1e-2),
self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True)
# print('self.network is:',self.network)
# Here!!! Modify it to be parallel model
# self.network=nn.DataParallel(self.network,device_ids=[0,1,2,3])
g=tw.draw_model(self.network,[8,1,128,128,128])
g.save('/cache/WenshuaiZhao/ProjectFiles/NNUnet/nnunet/model_structure.png')
print('draw model has been done!')
self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, amsgrad=True)
# Here!!! optimizer is also need to be parallel
# self.optimizer=nn.DataParallel(self.optimizer,device_ids=[0,1,2,3])
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, patience=self.lr_scheduler_patience,
verbose=True, threshold=self.lr_scheduler_eps, threshold_mode="abs")
self.network.cuda()
self.network.inference_apply_nonlin = softmax_helper
def draw_visual_net(net, input, filename="", method="torchviz"):
if method == "torchviz":
output = net(input)
g = make_dot(output)
# g = make_dot(output, params=dict(backbone.named_parameters()))
# g = make_dot(y, params=dict(list(model.named_parameters()) + [('x', x)]))
# g.view() # 会生成一个 Digraph.gv.pdf 的PDF文件
g.render(filename + '_net', view=False
) # 会自动保存为一个 espnet.pdf，第二个参数为True,则会自动打开该PDF文件，为False则不打开
elif method == "tensorboard":
with SummaryWriter(comment='mnt') as w:
w.add_graph(net, input)
elif method == "tensorwatch":
img = tw.draw_model(net, input)
img.save(r'./alexnet.jpg')
def __init__(self,
params,
net,
datasets,
criterion,
optimizer,
scheduler,
sets=['train', 'val', 'test'],
verbose=100,
stat=False,
eval_func=compute_errors,
disp_func=display_figure):
self.time = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
self.params = params
self.verbose = verbose
self.eval_func = eval_func
self.disp_func = disp_func
# Init dir
if params.workdir is not None:
workdir = os.path.expanduser(params.workdir)
if params.logdir is None:
logdir = os.path.join(
workdir, 'log_{}_{}'.format(params.encoder + params.decoder,
params.dataset))
else:
logdir = os.path.join(workdir, params.logdir)
resdir = None
if self.params.mode == 'test':
if params.resdir is None:
resdir = os.path.join(logdir, 'res')
else:
resdir = os.path.join(logdir, params.resdir)
# Call the constructor of the parent class (Trainer)
super().__init__(net,
datasets,
optimizer,
scheduler,
criterion,
batch_size=params.batch,
batch_size_val=params.batch_val,
max_epochs=params.epochs,
threads=params.threads,
eval_freq=params.eval_freq,
use_gpu=params.gpu,
resume=params.resume,
mode=params.mode,
sets=sets,
workdir=workdir,
logdir=logdir,
resdir=resdir)
self.params.logdir = self.logdir
self.params.resdir = self.resdir
# params json
if self.params.mode == 'train':
with open(
os.path.join(self.logdir,
'params_{}.json'.format(self.time)),
'w') as f:
json.dump(vars(self.params), f)
# uncomment to display the model complexity
if stat:
from torchstat import stat
import tensorwatch as tw
#net_copy = deepcopy(self.net)
stat(self.net, (3, *self.datasets[sets[0]].input_size))
exit()
tw.draw_model(self.net, (1, 3, *self.datasets[sets[0]].input_size))
#del net_copy
self.print('###### Experiment Parameters ######')
for k, v in vars(self.params).items():
self.print('{0:<22s} : {1:}'.format(k, v))
def vis_model(model, input_size=[1, 3, 224, 224]):
import tensorwatch as tw
drawing = tw.draw_model(model, [1, 3, 224, 224])
drawing.save('model_distillation.pdf')
#!/usr/bin/python3 """ Copyright 2018-2019 Firmin.Sun ([email protected]) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ # ----------------------------------------------------- # @Time : 6/18/2019 11:36 AM # @Author : Firmin.Sun ([email protected]) # @Software: ZJ_AI # ----------------------------------------------------- # -*- coding: utf-8 -*- import sys import torch import tensorwatch as tw import torchvision.models alexnet_model = torchvision.models.alexnet() tw.draw_model(alexnet_model, [1, 3, 224, 224]) tw.model_stats(alexnet_model, [1, 3, 224, 224])
seg1 = self.classifier1(c2_b)
edge, edge_fea = self.edge(c2, c3)
merge = torch.cat([c2_b, edge_fea], dim=1)
seg2 = self.classifier2(merge)
return [[seg1, seg2], [edge]]
def FaceParseNet101(num_classes=19, url=None, pretrained=True):
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes)
if pretrained:
pretrained_dict = torch.load(url)
model_dict = model.state_dict().copy()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
if __name__ == '__main__':
from torchstat import stat
import tensorwatch as tw
net = FaceParseNet101(pretrained=False)
# stat(net, (3, 512, 512))
tw.draw_model(net, [1, 3, 512, 512])
from torchvision.models import AlexNet
from torchviz import make_dot
x=torch.rand(1,3,256,256)
model=AlexNet()
import hiddenlayer as h
vis_graph = h.build_graph(model, torch.zeros([1 ,3, 256, 256])) # 获取绘制图像的对象
vis_graph.theme = h.graph.THEMES["blue"].copy() # 指定主题颜色
vis_graph.save("./demo1.png") # 保存图像的路径
# y=model(x)
# # 这三种方式都可以
# g = make_dot(y)
# # g=make_dot(y, params=dict(model.named_parameters()))
# #g = make_dot(y, params=dict(list(model.named_parameters()) + [('x', x)]))
# # 这两种方法都可以
# # g.view() # 会生成一个 Digraph.gv.pdf 的PDF文件
# g.render('espnet_model', view=False) # 会自动保存为一个 espnet.pdf，第二个参数为True,则会自动打开该PDF文件，为False则不打开
import torch
import tensorwatch as tw
from torchvision.models import AlexNet
#from lanenet_model.blocks import ESPNet_Encoder # 这是我自己定义的一个网络
# 其实就两句话
model=AlexNet()
tw.draw_model(model, [1, 3, 512, 256])
import torch
import torchvision.models
import tensorwatch as tw
vgg16_model = torchvision.models.vgg16()
drawing = tw.draw_model(vgg16_model, [1, 3, 224, 224])
drawing.save('abc.png')
input("Press any key")
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
writer = SummaryWriter()
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layer = nn.Sequential(nn.Conv2d(3, 64, 7, padding=1, stride=1),
nn.ReLU(), nn.MaxPool2d(1, 2),
nn.BatchNorm2d(64), nn.AvgPool2d(1, 1),
nn.Dropout(0.5), nn.Linear(110, 30))
def forward(self, x):
return self.layer(x)
net = Net()
args = torch.ones([1, 3, 224, 224])
# writer.add_graph(net, args)
# writer.close()
#vgg16_model = torchvision.models.vgg16()
drawing = tw.draw_model(net, [1, 3, 224, 224])
drawing.save('abc2.png')
input("Press any key")
lists = glob(os.path.join(path, '*.tar'))
# 对获取的文件根据修改时间进行排序
lists.sort(key=lambda x: os.path.getmtime(x))
# 把目录和文件名合成一个路�?
file_new = lists[-1]
return file_new
model = pwc_dc_net()
model.cuda()
model.eval()
from torchviz import make_dot
# x = Variable(torch.randn(2,1, 3,256,256).cuda())
# vis_graph = make_dot(model(x), params=dict(model.named_parameters()))
# vis_graph.view()
tw.draw_model(model, [2,1, 3, 224, 224])
def main():
weight_path = find_NewFile('logname')
# weight_path = 'logname/finetune_60.tar'
model = pwc_dc_net(weight_path)
model.cuda()
model.eval()
x1 = Variable(torch.zeros((1, 3, 128, 128))).cuda()
x2 = Variable(torch.zeros((1, 3, 128, 128))).cuda()
for i in range(10):
t_s = time.time()
model((x1,x2))
t_d = time.time()
print((t_d-t_s)*1000)
# summary(model, input_size=(1, 1, 256, 256))
self.model4 = blocks['block4']
self.model5 = blocks['block5']
self.model6 = blocks['block6']
def forward(self, x):
out1_0 = self.model1_0(x)
out1_1 = self.model1_1(out1_0)
concat_stage2 = torch.cat([out1_1, out1_0], 1)
out_stage2 = self.model2(concat_stage2)
concat_stage3 = torch.cat([out_stage2, out1_0], 1)
out_stage3 = self.model3(concat_stage3)
concat_stage4 = torch.cat([out_stage3, out1_0], 1)
out_stage4 = self.model4(concat_stage4)
concat_stage5 = torch.cat([out_stage4, out1_0], 1)
out_stage5 = self.model5(concat_stage5)
concat_stage6 = torch.cat([out_stage5, out1_0], 1)
out_stage6 = self.model6(concat_stage6)
return out_stage6
if __name__ == "__main__":
import tensorwatch as tw
import sys
import torchvision.models
# model=torchvision.models.alexnet()
# tw.draw_model(model,[1,3,224,224])
bodypose_model__ = bodypose_model()
drawing = tw.draw_model(bodypose_model__, [1, 3, 224, 224])
drawing.save('bodypose_model.png')
input("Press any key")
