def download_resnet50_weights():
if not os.path.exists(RESNET50_WEIGHTS_FILE):
download_file(
url=
"http://neupy.s3.amazonaws.com/tensorflow/imagenet-models/resnet50.hdf5",
filepath=RESNET50_WEIGHTS_FILE,
description='Downloading weights')
print("File with ResNet-50 weights: {}".format(RESNET50_WEIGHTS_FILE))
return RESNET50_WEIGHTS_FILE
def prepare_image(fname):
with open(IMAGENET_MEAN_FILE, 'rb') as f:
# Mean values is the average image accros all training dataset.
# if dataset (1000, 3, 224, 224) then mean image shape
# is (3, 224, 224) and computes as data.mean(axis=0)
mean_values = pickle.load(f)
image = read_image(fname, image_size=(256, 256), crop_size=(224, 224))
# Convert RGB to BGR
image[:, (0, 1, 2), :, :] = image[:, (2, 1, 0), :, :]
return asfloat(image - mean_values)
environment.speedup()
resnet50 = architectures.resnet50()
if not os.path.exists(RESNET50_WEIGHTS_FILE):
download_file(
url="http://neupy.s3.amazonaws.com/imagenet-models/resnet50.pickle",
filepath=RESNET50_WEIGHTS_FILE,
description='Downloading weights')
storage.load(resnet50, RESNET50_WEIGHTS_FILE)
predict = resnet50.compile()
dog_image = prepare_image(DOG_IMAGE_PATH)
output = predict(dog_image)
print_top_n(output, n=5)
import os
from neupy import storage, architectures
from imagenet_tools import (CURRENT_DIR, FILES_DIR, load_image, print_top_n,
download_file)
SQUEEZENET_WEIGHTS_FILE = os.path.join(FILES_DIR, 'squeezenet.hdf5')
# Networks weight ~4.8 Mb
squeezenet = architectures.squeezenet()
if not os.path.exists(SQUEEZENET_WEIGHTS_FILE):
download_file(
url=
"http://neupy.s3.amazonaws.com/tensorflow/imagenet-models/squeezenet.hdf5",
filepath=SQUEEZENET_WEIGHTS_FILE,
description='Downloading weights')
storage.load(squeezenet, SQUEEZENET_WEIGHTS_FILE)
monkey_image = load_image(os.path.join(CURRENT_DIR, 'images',
'titi-monkey.jpg'),
image_size=(227, 227),
crop_size=(227, 227),
use_bgr=True)
output = squeezenet.predict(monkey_image)
print_top_n(output, n=5)
[
[SliceChannels(0, 192), layers.Convolution((128, 3, 3), padding=1, name="conv_5_1"), layers.Relu()],
[SliceChannels(192, 384), layers.Convolution((128, 3, 3), padding=1, name="conv_5_2"), layers.Relu()],
],
layers.Concatenate(),
layers.MaxPooling((3, 3), stride=(2, 2)),
layers.Reshape(),
layers.Relu(4096, name="dense_1") > layers.Dropout(0.5),
layers.Relu(4096, name="dense_2") > layers.Dropout(0.5),
layers.Softmax(1000, name="dense_3"),
)
if not os.path.exists(ALEXNET_WEIGHTS_FILE):
download_file(
url=("http://srv70.putdrive.com/putstorage/DownloadFileHash/" "F497B1D43A5A4A5QQWE2295998EWQS/alexnet.pickle"),
filepath=ALEXNET_WEIGHTS_FILE,
description="Downloading weights",
)
storage.load(alexnet, ALEXNET_WEIGHTS_FILE)
dog_image = load_image(
os.path.join(CURRENT_DIR, "images", "dog.jpg"), image_size=(256, 256), crop_size=(227, 227), use_bgr=False
)
# Disables dropout layer
with alexnet.disable_training_state():
x = T.tensor4()
predict = theano.function([x], alexnet.output(x))
output = predict(dog_image)
import os
from neupy import storage, architectures
from imagenet_tools import (CURRENT_DIR, FILES_DIR, load_image, print_top_n,
download_file)
VGG16_WEIGHTS_FILE = os.path.join(FILES_DIR, 'vgg16.hdf5')
vgg16 = architectures.vgg16()
if not os.path.exists(VGG16_WEIGHTS_FILE):
download_file(
url=
"http://neupy.s3.amazonaws.com/tensorflow/imagenet-models/vgg16.hdf5",
filepath=VGG16_WEIGHTS_FILE,
description='Downloading weights')
storage.load(vgg16, VGG16_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
output = vgg16.predict(dog_image)
print_top_n(output, n=5)
layers.MaxPooling((2, 2)),
Fire(64, 256, 256, name='fire9'),
layers.Dropout(0.5),
layers.Convolution((1000, 1, 1), padding='valid', name='conv10'),
layers.GlobalPooling(function=T.mean),
layers.Reshape(),
layers.Softmax(),
)
if not os.path.exists(SQUEEZENET_WEIGHTS_FILE):
download_file(
url=(
"http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"6B0A15B43A5A4A5QQWE2304100EWQS/squeezenet.pickle"
),
filepath=SQUEEZENET_WEIGHTS_FILE,
description='Downloading weights'
)
storage.load(squeezenet, SQUEEZENET_WEIGHTS_FILE)
monkey_image = load_image(
os.path.join(CURRENT_DIR, 'images', 'titi-monkey.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
# Disables dropout layer
with squeezenet.disable_training_state():
x = T.tensor4()
predict = theano.function([x], squeezenet.output(x))
layers.Convolution((512, 3, 3), padding=1, name='conv4_1') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv4_2') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv4_3') > layers.Relu(),
layers.MaxPooling((2, 2)),
layers.Convolution((512, 3, 3), padding=1, name='conv5_1') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv5_2') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv5_3') > layers.Relu(),
layers.MaxPooling((2, 2)),
layers.Reshape(),
layers.Relu(4096, name='dense_1') > layers.Dropout(0.5),
layers.Relu(4096, name='dense_2') > layers.Dropout(0.5),
layers.Softmax(1000, name='dense_3'),
)
if not os.path.exists(VGG16_WEIGHTS_FILE):
download_file(url=("http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"5B7DCBF43A5A4A5QQWE2301430EWQS/vgg16.pickle"),
filepath=VGG16_WEIGHTS_FILE,
description='Downloading weights')
storage.load(vgg16, VGG16_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
predict = vgg16.compile()
output = predict(dog_image)
print_top_n(output[0], n=5)
import os
import theano
from neupy import layers, storage, architectures
from imagenet_tools import (CURRENT_DIR, FILES_DIR, load_image, print_top_n,
download_file)
theano.config.floatX = 'float32'
ALEXNET_WEIGHTS_FILE = os.path.join(FILES_DIR, 'alexnet.pickle')
alexnet = architectures.alexnet()
if not os.path.exists(ALEXNET_WEIGHTS_FILE):
download_file(
url="http://neupy.s3.amazonaws.com/imagenet-models/alexnet.pickle",
filepath=ALEXNET_WEIGHTS_FILE,
description='Downloading weights')
storage.load(alexnet, ALEXNET_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(227, 227),
use_bgr=False)
predict = alexnet.compile()
output = predict(dog_image)
print_top_n(output, n=5)
layers.Convolution((512, 3, 3), padding=1, name='conv5_3') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv5_4') > layers.Relu(),
layers.MaxPooling((2, 2)),
layers.Reshape(),
layers.Relu(4096, name='dense_1') > layers.Dropout(0.5),
layers.Relu(4096, name='dense_2') > layers.Dropout(0.5),
layers.Softmax(1000, name='dense_3'),
)
if not os.path.exists(VGG19_WEIGHTS_FILE):
download_file(
url=(
"http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"F9A70DEA3A5A4A5QQWE2301487EWQS/vgg19.pickle"
),
filepath=VGG19_WEIGHTS_FILE,
description='Downloading weights'
)
storage.load(vgg19, VGG19_WEIGHTS_FILE)
dog_image = load_image(
os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
predict = vgg19.compile()
output = predict(dog_image)
print_top_n(output[0], n=5)
import os
import theano
from neupy import layers, storage, architectures
from imagenet_tools import (CURRENT_DIR, FILES_DIR, load_image, print_top_n,
download_file)
VGG19_WEIGHTS_FILE = os.path.join(FILES_DIR, 'vgg19.pickle')
DOG_IMAGE_PATH = os.path.join(CURRENT_DIR, 'images', 'german-shepherd.jpg')
theano.config.floatX = 'float32'
vgg19 = architectures.vgg19()
if not os.path.exists(VGG19_WEIGHTS_FILE):
download_file(
url="http://neupy.s3.amazonaws.com/imagenet-models/vgg19.pickle",
filepath=VGG19_WEIGHTS_FILE,
description='Downloading weights')
storage.load(vgg19, VGG19_WEIGHTS_FILE)
dog_image = load_image(DOG_IMAGE_PATH,
image_size=(256, 256),
crop_size=(224, 224))
predict = vgg19.compile()
output = predict(dog_image)
print_top_n(output, n=5)
layers.MaxPooling((2, 2)),
layers.Convolution((512, 3, 3), padding=1, name="conv5_1") > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name="conv5_2") > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name="conv5_3") > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name="conv5_4") > layers.Relu(),
layers.MaxPooling((2, 2)),
layers.Reshape(),
layers.Relu(4096, name="dense_1") > layers.Dropout(0.5),
layers.Relu(4096, name="dense_2") > layers.Dropout(0.5),
layers.Softmax(1000, name="dense_3"),
)
if not os.path.exists(VGG19_WEIGHTS_FILE):
download_file(
url=("http://srv70.putdrive.com/putstorage/DownloadFileHash/" "F9A70DEA3A5A4A5QQWE2301487EWQS/vgg19.pickle"),
filepath=VGG19_WEIGHTS_FILE,
description="Downloading weights",
)
storage.load(vgg19, VGG19_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, "images", "dog.jpg"), image_size=(256, 256), crop_size=(224, 224))
# Disables dropout layer
with vgg19.disable_training_state():
x = T.tensor4()
predict = theano.function([x], vgg19.output(x))
output = predict(dog_image)
print_top_n(output[0], n=5)
layers.MaxPooling((2, 2)),
Fire(32, 128, 128, name='fire5'),
Fire(48, 192, 192, name='fire6'),
Fire(48, 192, 192, name='fire7'),
Fire(64, 256, 256, name='fire8'),
layers.MaxPooling((2, 2)),
Fire(64, 256, 256, name='fire9'),
layers.Dropout(0.5),
layers.Convolution((1000, 1, 1), padding='valid', name='conv10'),
layers.GlobalPooling(function=T.mean),
layers.Reshape(),
layers.Softmax(),
)
if not os.path.exists(SQUEEZENET_WEIGHTS_FILE):
download_file(url=("http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"6B0A15B43A5A4A5QQWE2304100EWQS/squeezenet.pickle"),
filepath=SQUEEZENET_WEIGHTS_FILE,
description='Downloading weights')
storage.load(squeezenet, SQUEEZENET_WEIGHTS_FILE)
monkey_image = load_image(os.path.join(CURRENT_DIR, 'images',
'titi-monkey.jpg'),
image_size=(224, 224))
predict = squeezenet.compile()
output = predict(monkey_image)
print_top_n(output[0], n=5)
layers.Convolution((512, 3, 3), padding=1, name='conv5_2') > layers.Relu(),
layers.Convolution((512, 3, 3), padding=1, name='conv5_3') > layers.Relu(),
layers.MaxPooling((2, 2)),
layers.Reshape(),
layers.Relu(4096, name='dense_1') > layers.Dropout(0.5),
layers.Relu(4096, name='dense_2') > layers.Dropout(0.5),
layers.Softmax(1000, name='dense_3'),
)
if not os.path.exists(VGG16_WEIGHTS_FILE):
download_file(
url=(
"http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"5B7DCBF43A5A4A5QQWE2301430EWQS/vgg16.pickle"
),
filepath=VGG16_WEIGHTS_FILE,
description='Downloading weights'
)
storage.load(vgg16, VGG16_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(224, 224))
# Disables dropout layer
with vgg16.disable_training_state():
x = T.tensor4()
predict = theano.function([x], vgg16.output(x))
[
SliceChannels(192, 384),
layers.Convolution((128, 3, 3), padding=1, name='conv_5_2'),
layers.Relu(),
]],
layers.Concatenate(),
layers.MaxPooling((3, 3), stride=(2, 2)),
layers.Reshape(),
layers.Relu(4096, name='dense_1') > layers.Dropout(0.5),
layers.Relu(4096, name='dense_2') > layers.Dropout(0.5),
layers.Softmax(1000, name='dense_3'),
)
if not os.path.exists(ALEXNET_WEIGHTS_FILE):
download_file(url=("http://srv70.putdrive.com/putstorage/DownloadFileHash/"
"F497B1D43A5A4A5QQWE2295998EWQS/alexnet.pickle"),
filepath=ALEXNET_WEIGHTS_FILE,
description='Downloading weights')
storage.load(alexnet, ALEXNET_WEIGHTS_FILE)
dog_image = load_image(os.path.join(CURRENT_DIR, 'images', 'dog.jpg'),
image_size=(256, 256),
crop_size=(227, 227),
use_bgr=False)
predict = alexnet.compile()
output = predict(dog_image)
print_top_n(output[0], n=5)