class UCF101Cropper(object):
def __init__(self, patch_shape, kernel, hyperparameters):
self.cropper1d = Cropper(patch_shape[:1], kernel, hyperparameters, name="cropper1d")
self.cropper3d = Cropper(patch_shape , kernel, hyperparameters, name="cropper3d")
self.patch_shape = patch_shape
self.n_spatial_dims = len(patch_shape)
# self.fc_conv = masonry.construct_cnn(
# name="fc_conv",
# layer_specs=[
# ],
# input_shape=(patch_shape[0], 1),
# n_channels=4096,
# batch_normalize=hyperparameters["batch_normalize_patch"])
self.conv_conv = masonry.construct_cnn(
name="fc_conv",
layer_specs=[
dict(size=(5, 1, 1), num_filters=512, pooling_size=(2, 1, 1), pooling_step=(2, 1, 1)),
dict(size=(5, 1, 1), num_filters=512, pooling_size=(2, 1, 1), pooling_step=(2, 1, 1)),
],
input_shape=patch_shape,
n_channels=512,
batch_normalize=hyperparameters["batch_normalize_patch"])
def initialize(self):
#self.fc_conv.initialize()
self.conv_conv.initialize()
def apply(self, image, image_shape, location, scale):
# image is secretly two variables; conv and fc features
fc, conv = image
fc_shape, conv_shape = image_shape
# (batch, 4096, 16, 1)
fc_patch = T.shape_padright(self.cropper1d.apply(
fc, fc_shape[:, 1:],
location[:, 0, np.newaxis],
scale[:, 0, np.newaxis],
)[0])
# (batch, 512, 16, 1, 1)
conv_patch = self.cropper3d.apply(
conv, conv_shape[:, 1:],
location, scale,
)[0]
fc_repr = fc_patch
#fc_repr = self.fc_conv.apply(fc_patch)
conv_repr = self.conv_conv.apply(conv_patch)
# global average pooling
fc_repr = fc_repr.mean(axis=range(2, fc_repr.ndim))
conv_repr = conv_repr.mean(axis=range(2, conv_repr.ndim))
patch = T.concatenate([fc_repr, conv_repr], axis=1)
return patch, 0.
@property
def output_shape(self):
return (4096 + 512,)
def __init__(self, patch_shape, kernel, hyperparameters):
self.cropper1d = Cropper(patch_shape[:1], kernel, hyperparameters, name="cropper1d")
self.cropper3d = Cropper(patch_shape , kernel, hyperparameters, name="cropper3d")
self.patch_shape = patch_shape
self.n_spatial_dims = len(patch_shape)
# self.fc_conv = masonry.construct_cnn(
# name="fc_conv",
# layer_specs=[
# ],
# input_shape=(patch_shape[0], 1),
# n_channels=4096,
# batch_normalize=hyperparameters["batch_normalize_patch"])
self.conv_conv = masonry.construct_cnn(
name="fc_conv",
layer_specs=[
dict(size=(5, 1, 1), num_filters=512, pooling_size=(2, 1, 1), pooling_step=(2, 1, 1)),
dict(size=(5, 1, 1), num_filters=512, pooling_size=(2, 1, 1), pooling_step=(2, 1, 1)),
],
input_shape=patch_shape,
n_channels=512,
batch_normalize=hyperparameters["batch_normalize_patch"])