def get_body(self, resnet_layer_id):
'''
Create the feature extraction network based on resnet34.
The first layer of the res-net is converted into grayscale by averaging the weights of the 3 channels
of the original resnet.
Parameters
----------
resnet_layer_id: int
The resnet_layer_id specifies which layer to take from
the bottom of the network.
Returns
-------
network: gluon.nn.HybridSequential
The body network for feature extraction based on resnet
'''
pretrained = resnet50_v1(pretrained=True, ctx=self.ctx)
pretrained_2 = resnet50_v1(pretrained=True, ctx=mx.cpu(0))
first_weights = pretrained_2.features[0].weight.data().mean(axis=1).expand_dims(axis=1)
# First weights could be replaced with individual channels.
body = gluon.nn.HybridSequential()
with body.name_scope():
first_layer = gluon.nn.Conv2D(channels=64, kernel_size=(7, 7), padding=(3, 3), strides=(2, 2), in_channels=1, use_bias=False)
first_layer.initialize(mx.init.Xavier(), ctx=self.ctx)
first_layer.weight.set_data(first_weights)
body.add(first_layer)
body.add(*pretrained.features[1:-resnet_layer_id])
return body
def __init__(self, output_layers=7, pretrained=False, ctx=cpu(), **kwargs):
super(ResNet50, self).__init__(**kwargs)
if isinstance(output_layers, int):
output_layers = [
output_layers,
]
self.output_layers = output_layers
self.net = vision.resnet50_v1(pretrained=pretrained, ctx=ctx).features
def construct_baseline_model(self):
out_path = 'TrainedModels/default_name'
FC_len = 512
split_pattern = "[ <>]" # splits model internals for console output
# Obtain feed-forward data
self.train_iter.reset()
for b, batch in enumerate(self.train_iter):
data = batch.data[0].as_in_context(self.cpu_ctx)
break
# Transfer features from here
resnet50 = vision.resnet50_v1(pretrained=True, ctx=self.cpu_ctx) # Load pre-trained model
resnet50.hybridize()
# Transfer pre-trained features to a new net
net = vision.resnet50_v1(classes=self.N, prefix='resnetv10_')
with net.name_scope():
net.output = nn.Dense(FC_len, flatten=True)
net.output.collect_params().initialize(mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2))
net.features = resnet50.features
net.hybridize()
net(data)
net.export('UntrainedModels/Res50_pretrained_model')
# Load Resnet 50 Model
sym50 = mx.sym.load(self.sym_file_bbone) # Load symbol file
feature_layer = 'FC1_output' # Desired output layer
sym_layer = mx.symbol.FullyConnected(name='FC1', num_hidden=self.N) # Define dense layer
composed = sym_layer(FC1_data=sym50, name='FC1') # Combine with loaded symbol model
internals = composed.get_internals() # Fetch internals, for verification
# Bring together network and feed forward
net = gluon.nn.SymbolBlock(outputs=internals[feature_layer], inputs=mx.sym.var('data'))
net.collect_params('^FC').initialize(mx.init.Constant(0.1))
net.collect_params('^FC1_output').initialize(mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2))
net.collect_params('^(?!FC).*$').load(self.param_file_bbone, ignore_extra=True)
result = net(data)
# Export a modifed baseline model with a 256 FC layer & 13003 FC layer output
print("Exporting to: %s \n" % out_path)
net.export(out_path)
def __init__(self, pretrained, ctx=cpu(), **kwargs):
super(Resnet50Backbone, self).__init__(**kwargs)
self.output_layers = {
2: "c1",
4: "c2",
5: "c3",
6: "c4",
7: "c5",
}
self._net = vision.resnet50_v1(pretrained=pretrained, ctx=ctx).features
def __init__(self, ctx, config, lr_mult=None):
lr_mult = config['optimizer']['lr_mult']
lr_schedule = config['optimizer']['learning_rate']
lr_schedule = [(s, lr * lr_mult) for s, lr in lr_schedule]
optimizer_type = config['optimizer'].get('type', 'sgd')
optimizer_params = {'learning_rate': 1e-3, 'wd': 2e-4}
if optimizer_type == 'sgd':
optimizer_params['momentum'] = 0.9
optimizer_params.update(config['optimizer'].get('params', dict()))
model = vision.resnet50_v1(
root=
r'\\msralab\ProjectData\ScratchSSD\Users\v-dinliu\.mxnet\models',
pretrained=True,
ctx=ctx)
# HybridNetCoarse
Network = eval(config['network']['class'])
scale = get_param(config, 'network.scale', 20)
network = Network(model.features, config)
network.hybridize()
for k, v in network.collect_params().items():
if k.startswith(network.prefix):
v.initialize(ctx=ctx)
for k, v in config['optimizer'].get('lr_mult_layer', dict()).items():
for _, param in getattr(network, k).collect_params().items():
param.lr_mult = param.lr_mult * v
trainer = gluon.trainer.Trainer(network.collect_params(),
optimizer_type, optimizer_params)
super().__init__(network, trainer, lr_schedule, ctx)
self.epeloss = EpeLoss()
self.epeloss.hybridize()
self.color_mean = nd.reshape(nd.array([0.485, 0.456, 0.406]),
[1, 3, 1, 1])
self.color_std = nd.reshape(nd.array([0.229, 0.224, 0.225]),
[1, 3, 1, 1])
self.upsampler = Upsample(2, 32)
self.upsampler.collect_params().initialize(ctx=ctx)
self.scale = scale
loss_scales = get_param(config, 'loss.scales', [32])
loss_weights = get_param(config, 'loss.weights',
[1 for _ in loss_scales])
self.msloss = MultiscaleEpe(loss_scales,
loss_weights,
match=get_param(config, 'loss.match',
'downsampling'))
self.msloss.hybridize()
self.msloss.collect_params().initialize(ctx=self.ctx)
def __init__(self, pretrained, num_classes, ctx, **kwargs):
super(ResNet50, self).__init__(**kwargs)
with self.name_scope():
model = models.resnet50_v1(pretrained=pretrained, ctx=ctx)
self.num_classes = num_classes
self.features = model.features
# classification layer
self.classifier = nn.Dense(self.num_classes)
self.addition = nn.HybridSequential()
self.addition.add(self.classifier)
self.addition.initialize(ctx=ctx)
def __init__(self, num_bins, **kwargs):
ctx = kwargs.pop('ctx')
super(Gazenet_mxJiang_pitch_zoo, self).__init__(**kwargs)
self.net = mx.gluon.nn.HybridSequential(prefix='')
with self.net.name_scope():
mx.random.seed(int(time.time()))
self.model_resnet50 = vision.resnet50_v1(pretrained=True,
ctx=ctx,
root='./')
#self.model_resnet50.features[1]._kwargs['use_global_stats'] = True
freeze_bn(self.model_resnet50.features)
#print('net features:', self.model_resnet50.features[1])
self.net.add(self.model_resnet50)
self.model_pitch = mx.gluon.nn.Dense(num_bins)
self.model_pitch.collect_params().initialize(
mx.initializer.Uniform(1 / math.sqrt(2048)),
ctx=ctx,
force_reinit=True)
self.model_pitch.bias.set_data(
mx.nd.random.uniform(-0.2, 0.2, shape=(num_bins, ), ctx=ctx))
self.net.add(self.model_pitch)
def train():
# 初始化
ctx = try_gpu(2)
net = models.resnet50_v1(classes=4)
net.hybridize()
net.initialize(ctx=ctx)
# net.forward(nd.ones((1, 3, 227, 227)).as_in_context(ctx))
sw = SummaryWriter(
'/data1/lsp/lsp/pytorch_mnist/log/rotate/mxnet_resnet18')
# sw.add_graph(net)
print('initialize weights on', ctx)
# 获取数据
batch_size = 64
epochs = 10
train_data = custom_dataset(txt='/data2/dataset/image/train.txt',
data_shape=(224, 224),
channel=3)
test_data = custom_dataset(txt='/data2/dataset/image/val.txt',
data_shape=(224, 224),
channel=3)
transforms_train = transforms.ToTensor()
# transforms_train = transforms.Compose([transforms.Resize(227), transforms.ToTensor()])
train_data_loader = gluon.data.DataLoader(
train_data.transform_first(transforms_train),
batch_size=batch_size,
shuffle=True,
num_workers=12)
test_data_loader = gluon.data.DataLoader(
test_data.transform_first(transforms_train),
batch_size=batch_size,
shuffle=True,
num_workers=12)
# 训练
criterion = gluon.loss.SoftmaxCrossEntropyLoss()
steps = train_data.__len__() // batch_size
schedule = mx.lr_scheduler.FactorScheduler(step=3 * steps,
factor=0.1,
stop_factor_lr=1e-6)
sgd_optimizer = mx.optimizer.SGD(learning_rate=0.01, lr_scheduler=schedule)
trainer = gluon.Trainer(net.collect_params(), optimizer=sgd_optimizer)
for epoch in range(epochs):
# test_data.reset()
start = time.time()
train_loss = 0.0
train_acc = 0.0
cur_step = 0
n = train_data.__len__()
for i, (data, label) in enumerate(train_data_loader):
label = label.astype('float32').as_in_context(ctx)
data = data.as_in_context(ctx)
with autograd.record():
outputs = net(data)
loss = criterion(outputs, label)
loss.backward()
trainer.step(batch_size)
cur_loss = loss.sum().asscalar()
cur_acc = nd.sum(outputs.argmax(axis=1) == label).asscalar()
train_acc += cur_acc
train_loss += cur_loss
if i % 100 == 0:
batch_time = time.time() - start
print(
'epoch [%d/%d], Iter: [%d/%d]. Loss: %.4f. Accuracy: %.4f, time:%0.4f, lr:%s'
% (epoch, epochs, i, steps, cur_loss, cur_acc / batch_size,
batch_time, trainer.learning_rate))
start = time.time()
cur_step = epoch * steps + i
sw.add_scalar(tag='Train/loss',
value=cur_loss / label.shape[0],
global_step=cur_step)
sw.add_scalar(tag='Train/acc',
value=cur_acc / label.shape[0],
global_step=cur_step)
sw.add_scalar(tag='Train/lr',
value=trainer.learning_rate,
global_step=cur_step)
val_acc = evaluate_accuracy(test_data_loader, net, ctx)
sw.add_scalar(tag='Eval/acc', value=val_acc, global_step=cur_step)
net.save_parameters("models/resnet501/{}_{}.params".format(
epoch, val_acc))
print(
'epoch: %d, train_loss: %.4f, train_acc: %.4f, val_acc: %.4f, time: %.4f, lr=%s'
% (epoch, train_loss / n, train_acc / n, val_acc,
time.time() - start, str(trainer.learning_rate)))
sw.close()
batch_size = 64
learning_rate = .1
num_epochs = 500
randseed = 10
test_n = 'test15'
ctx = utils.try_all_gpus()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
train_data, test_data, data_num = get_data(
'C:/lagBear/S***N/finally_data/cnn_data.mat',
randseed,
batch_size=batch_size,
train_augs=train_augs)
### resnet network ###
pre_net = models.resnet50_v1(pretrained=True, prefix='sperm_3class_')
pre_net.output
pre_net.features[0].weight.data()[0][0]
net = models.resnet50_v1(classes=3, prefix='sperm_3class_')
net.features = pre_net.features
net.output.initialize(init.Xavier())
net.hybridize()
sw = SummaryWriter(logdir='./logs/resnet50/randseed%s/%s' % (randseed, test_n),
flush_secs=5)
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': learning_rate})
# utils.train(
# train_data, test_data, net, loss, trainer, ctx, num_epochs)
# #######
FC_len = opts.dense_size[0] # Size Of Dense Layer
N = opts.N[0]
batch_size = opts.batch_size[0]
cpu_ctx = mx.cpu() # CPU context
gpu_ctx = mx.gpu() # GPU context
train_iter, val_iter, _ = get_iters(batch_size) # Fetch Iterators
# Obtain feed-forward data
for b, batch in enumerate(train_iter):
data = batch.data[0].as_in_context(cpu_ctx)
break
# Transfer features from here
resnet50 = vision.resnet50_v1(pretrained=True, ctx=cpu_ctx) # Load pre-trained model
resnet50.hybridize()
# Transfer pre-trained features to a new net
net = vision.resnet50_v1(classes=N, prefix='resnetv10_')
with net.name_scope():
net.output = nn.Dense(FC_len, flatten=True)
net.output.collect_params().initialize(mx.init.Xavier(rnd_type='gaussian', factor_type='in', magnitude=2))
net.features = resnet50.features
net.hybridize()
net(data)
net.export('UntrainedModels/Res50_pretrained_model')
# Load Resnet 50 Model
sym50 = mx.sym.load(sym_file) # Load symbol file
feature_layer = 'FC1_output' # Desired output layer
def train(channel_input_dirs, hyperparameters, hosts, num_cpus, num_gpus,
output_data_dir, model_dir, **kwargs):
print(sys.version)
print(sys.executable)
print(sys.version_info)
print(mx.__version__)
'''
# Load preprocessed data #
Due to the memory limit of m4 instance, we only use a part of dataset to train the model.
'''
trn_im, test_im, trn_output, test_output = load_data(
os.path.join(channel_input_dirs['dataset']))
'''
# Additional Data Augmentation #
'''
# Mirror
trn_im_mirror = trn_im[:, :, :, ::-1]
trn_output_mirror = np.zeros(trn_output.shape)
trn_output_mirror[:, 0] = trn_output[:, 0]
trn_output_mirror[:, 1] = trn_output[:, 1] * -1
trn_im = np.concatenate((trn_im, trn_im_mirror), axis=0)
trn_output = np.concatenate((trn_output, trn_output_mirror), axis=0)
# Color Shift
for i0 in range(trn_im.shape[0]):
im_temp = trn_im[i0, :, :, :]
im_temp = np.transpose(
im_temp, (1, 2, 0)) * 255 #transposing and restoring the color
im_temp = shiftHSV(im_temp,
h_shift_lim=(-0.1, 0.1),
s_shift_lim=(-0.1, 0.1),
v_shift_lim=(-0.1, 0.1))
im_temp = np.transpose(
im_temp, (2, 0, 1)) / 255 #transposing and restoring the color
trn_im[i0, :, :, :] = im_temp
'''
# Head Pose Labeling #
'''
# angle class (3) i.e. headpose class ( 3 x 3)
n_grid = 3
angles_thrshld = [
np.arcsin(float(a) * 2 / n_grid - 1) / np.pi * 180 / 90
for a in range(1, n_grid)
]
# From (normalized) angle to angle class
trn_tilt_cls = []
trn_pan_cls = []
for i0 in range(trn_output.shape[0]):
trn_tilt_cls += [angles2Cat(angles_thrshld, trn_output[i0, 0])]
trn_pan_cls += [angles2Cat(angles_thrshld, trn_output[i0, 1])]
test_tilt_cls = []
test_pan_cls = []
for i0 in range(test_output.shape[0]):
test_tilt_cls += [angles2Cat(angles_thrshld, test_output[i0, 0])]
test_pan_cls += [angles2Cat(angles_thrshld, test_output[i0, 1])]
np_trn_tilt_cls = np.asarray(trn_tilt_cls)
np_test_tilt_cls = np.asarray(test_tilt_cls)
np_trn_pan_cls = np.asarray(trn_pan_cls)
np_test_pan_cls = np.asarray(test_pan_cls)
# From angle class to head pose class
np_trn_grid_cls = np_trn_pan_cls * n_grid + np_trn_tilt_cls
np_test_grid_cls = np_test_pan_cls * n_grid + np_test_tilt_cls
'''
Train the model
'''
if len(hosts) == 1:
kvstore = 'device' if num_gpus > 0 else 'local'
else:
kvstore = 'dist_device_sync'
ctx = mx.gpu() if num_gpus > 0 else mx.cpu()
batch_size = 64
train_iter = mx.gluon.data.DataLoader(mx.gluon.data.ArrayDataset(
(trn_im.astype(np.float32) - 0.5) * 2, np_trn_grid_cls),
batch_size=batch_size,
shuffle=True,
last_batch='discard')
test_iter = mx.gluon.data.DataLoader(mx.gluon.data.ArrayDataset(
(test_im.astype(np.float32) - 0.5) * 2, np_test_grid_cls),
batch_size=batch_size,
shuffle=True,
last_batch='discard')
# Modify the number of output classes
pretrained_net = resnet50_v1(pretrained=True, prefix='headpose_')
net = resnet50_v1(classes=9, prefix='headpose_')
net.collect_params().initialize()
net.features = pretrained_net.features
#net.output.initialize(init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2)) # MXNet 1.1.0
net.initialize(
init.Xavier(rnd_type='gaussian', factor_type="in",
magnitude=2)) # MXNet 0.12.1
net.collect_params().reset_ctx(ctx)
net.hybridize()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(
net.collect_params(), 'adam',
{'learning_rate': float(hyperparameters.get("learning_rate", 0.0005))})
# Fine-tune the model
logging.getLogger().setLevel(logging.DEBUG)
num_epoch = 5
print('training started')
net, net_with_softmax = train_util(output_data_dir, net, train_iter,
test_iter, loss, trainer, ctx,
num_epoch, batch_size)
print('training is done')
### Serial format v. Modular format
# "net" is a serial (i.e. Gluon) network.
# In order to save the network in the modular format, "net" needs to be passed to save function.
return net
return result
def img_rotate(degree, img):
height, width = img.shape[:2]
heightNew = int(width * fabs(sin(radians(degree))) +
height * fabs(cos(radians(degree))))
widthNew = int(height * fabs(sin(radians(degree))) +
width * fabs(cos(radians(degree))))
matRotation = cv2.getRotationMatrix2D((width / 2, height / 2), degree, 1)
matRotation[0, 2] += (widthNew - width) / 2
matRotation[1, 2] += (heightNew - height) / 2
imgRotation = cv2.warpAffine(img,
matRotation, (widthNew, heightNew),
borderValue=(255, 255, 255))
return imgRotation
if __name__ == '__main__':
img_path = '/data/datasets/mnist/train/0/0_1.png'
model_path = 'models/resnet50/2_1.0.params'
model1 = Gluon_Model(models.resnet50_v1(classes=4),
model_path,
gpu_id=0,
img_shape=[224, 224])
for img in os.listdir('/data2/zj/pingan/t_xz/input1'):
img_path = os.path.join('/data2/zj/pingan/t_xz/input1', img)
start = time.time()
result = model1.predict(img_path)
