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__init__.py
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# Adapted from https://github.com/ProGamerGov/Protobuf-Dreamer
from ovos_workshop.skills import OVOSSkill
from ovos_bus_client.message import Message
from ovos_bus_client.message import dig_for_message
from imgurpython import ImgurClient
import urllib.request, urllib.error, urllib.parse
from ovos_utils.log import LOG
import json
from bs4 import BeautifulSoup
from os.path import join
import tensorflow as tf
from pypexels import PyPexels
try:
import yagmail
except ImportError:
yagmail = None
__author__ = 'jarbas'
class DreamSkill(OVOSSkill):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# free keys for the people
if "use_pexels" not in self.settings:
self.settings["use_pexels"] = False
if "pexels" not in self.settings:
self.settings[
"pexels"] = "563492ad6f91700001000001c7b3c09baae746f648a8cdfceef1362c"
if "client_id" not in self.settings:
self.settings["client_id"] = "0357b39cbf22749"
if "client_secret" not in self.settings:
self.settings[
"client_secret"] = "15f679a7147c12407913aeb0ff893e6156fba16b"
client_id = self.settings.get("client_id")
client_secret = self.settings.get("client_secret")
self.client = ImgurClient(client_id, client_secret)
# TODO test all layers, not sure they all can be dreamed on
self.layers = [
'conv2d0_w', 'conv2d0_b', 'conv2d1_w', 'conv2d1_b', 'conv2d2_w',
'conv2d2_b', 'mixed3a_1x1_w', 'mixed3a_1x1_b',
'mixed3a_3x3_bottleneck_w', 'mixed3a_3x3_bottleneck_b',
'mixed3a_3x3_w', 'mixed3a_3x3_b', 'mixed3a_5x5_bottleneck_w',
'mixed3a_5x5_bottleneck_b', 'mixed3a_5x5_w', 'mixed3a_5x5_b',
'mixed3a_pool_reduce_w', 'mixed3a_pool_reduce_b', 'mixed3b_1x1_w',
'mixed3b_1x1_b', 'mixed3b_3x3_bottleneck_w',
'mixed3b_3x3_bottleneck_b', 'mixed3b_3x3_w', 'mixed3b_3x3_b',
'mixed3b_5x5_bottleneck_w', 'mixed3b_5x5_bottleneck_b',
'mixed3b_5x5_w', 'mixed3b_5x5_b', 'mixed3b_pool_reduce_w',
'mixed3b_pool_reduce_b', 'mixed4a_1x1_w', 'mixed4a_1x1_b',
'mixed4a_3x3_bottleneck_w', 'mixed4a_3x3_bottleneck_b',
'mixed4a_3x3_w', 'mixed4a_3x3_b', 'mixed4a_5x5_bottleneck_w',
'mixed4a_5x5_bottleneck_b', 'mixed4a_5x5_w', 'mixed4a_5x5_b',
'mixed4a_pool_reduce_w', 'mixed4a_pool_reduce_b', 'mixed4b_1x1_w',
'mixed4b_1x1_b', 'mixed4b_3x3_bottleneck_w',
'mixed4b_3x3_bottleneck_b', 'mixed4b_3x3_w', 'mixed4b_3x3_b',
'mixed4b_5x5_bottleneck_w', 'mixed4b_5x5_bottleneck_b',
'mixed4b_5x5_w', 'mixed4b_5x5_b', 'mixed4b_pool_reduce_w',
'mixed4b_pool_reduce_b', 'mixed4c_1x1_w', 'mixed4c_1x1_b',
'mixed4c_3x3_bottleneck_w', 'mixed4c_3x3_bottleneck_b',
'mixed4c_3x3_w', 'mixed4c_3x3_b', 'mixed4c_5x5_bottleneck_w',
'mixed4c_5x5_bottleneck_b', 'mixed4c_5x5_w', 'mixed4c_5x5_b',
'mixed4c_pool_reduce_w', 'mixed4c_pool_reduce_b', 'mixed4d_1x1_w',
'mixed4d_1x1_b', 'mixed4d_3x3_bottleneck_w',
'mixed4d_3x3_bottleneck_b', 'mixed4d_3x3_w', 'mixed4d_3x3_b',
'mixed4d_5x5_bottleneck_w', 'mixed4d_5x5_bottleneck_b',
'mixed4d_5x5_w', 'mixed4d_5x5_b', 'mixed4d_pool_reduce_w',
'mixed4d_pool_reduce_b', 'mixed4e_1x1_w', 'mixed4e_1x1_b',
'mixed4e_3x3_bottleneck_w', 'mixed4e_3x3_bottleneck_b',
'mixed4e_3x3_w', 'mixed4e_3x3_b', 'mixed4e_5x5_bottleneck_w',
'mixed4e_5x5_bottleneck_b', 'mixed4e_5x5_w', 'mixed4e_5x5_b',
'mixed4e_pool_reduce_w', 'mixed4e_pool_reduce_b', 'mixed5a_1x1_w',
'mixed5a_1x1_b', 'mixed5a_3x3_bottleneck_w',
'mixed5a_3x3_bottleneck_b', 'mixed5a_3x3_w', 'mixed5a_3x3_b',
'mixed5a_5x5_bottleneck_w', 'mixed5a_5x5_bottleneck_b',
'mixed5a_5x5_w', 'mixed5a_5x5_b', 'mixed5a_pool_reduce_w',
'mixed5a_pool_reduce_b', 'mixed5b_1x1_w', 'mixed5b_1x1_b',
'mixed5b_3x3_bottleneck_w', 'mixed5b_3x3_bottleneck_b',
'mixed5b_3x3_w', 'mixed5b_3x3_b', 'mixed5b_5x5_bottleneck_w',
'mixed5b_5x5_bottleneck_b', 'mixed5b_5x5_w', 'mixed5b_5x5_b',
'mixed5b_pool_reduce_w', 'mixed5b_pool_reduce_b',
'head0_bottleneck_w', 'head0_bottleneck_b', 'nn0_w', 'nn0_b',
'softmax0_w', 'softmax0_b', 'head1_bottleneck_w',
'head1_bottleneck_b', 'nn1_w', 'nn1_b', 'softmax1_w', 'softmax1_b',
'softmax2_w', 'softmax2_b', 'conv2d0_pre_relu/conv',
'conv2d0_pre_relu', 'conv2d0', 'maxpool0', 'localresponsenorm0',
'conv2d1_pre_relu/conv', 'conv2d1_pre_relu', 'conv2d1',
'conv2d2_pre_relu/conv', 'conv2d2_pre_relu', 'conv2d2',
'localresponsenorm1', 'maxpool1', 'mixed3a_1x1_pre_relu/conv',
'mixed3a_1x1_pre_relu', 'mixed3a_1x1',
'mixed3a_3x3_bottleneck_pre_relu/conv',
'mixed3a_3x3_bottleneck_pre_relu', 'mixed3a_3x3_bottleneck',
'mixed3a_3x3_pre_relu/conv', 'mixed3a_3x3_pre_relu', 'mixed3a_3x3',
'mixed3a_5x5_bottleneck_pre_relu/conv',
'mixed3a_5x5_bottleneck_pre_relu', 'mixed3a_5x5_bottleneck',
'mixed3a_5x5_pre_relu/conv', 'mixed3a_5x5_pre_relu', 'mixed3a_5x5',
'mixed3a_pool', 'mixed3a_pool_reduce_pre_relu/conv',
'mixed3a_pool_reduce_pre_relu', 'mixed3a_pool_reduce',
'mixed3a/concat_dim', 'mixed3a', 'mixed3b_1x1_pre_relu/conv',
'mixed3b_1x1_pre_relu', 'mixed3b_1x1',
'mixed3b_3x3_bottleneck_pre_relu/conv',
'mixed3b_3x3_bottleneck_pre_relu', 'mixed3b_3x3_bottleneck',
'mixed3b_3x3_pre_relu/conv', 'mixed3b_3x3_pre_relu', 'mixed3b_3x3',
'mixed3b_5x5_bottleneck_pre_relu/conv',
'mixed3b_5x5_bottleneck_pre_relu', 'mixed3b_5x5_bottleneck',
'mixed3b_5x5_pre_relu/conv', 'mixed3b_5x5_pre_relu', 'mixed3b_5x5',
'mixed3b_pool', 'mixed3b_pool_reduce_pre_relu/conv',
'mixed3b_pool_reduce_pre_relu', 'mixed3b_pool_reduce',
'mixed3b/concat_dim', 'mixed3b', 'maxpool4',
'mixed4a_1x1_pre_relu/conv', 'mixed4a_1x1_pre_relu', 'mixed4a_1x1',
'mixed4a_3x3_bottleneck_pre_relu/conv',
'mixed4a_3x3_bottleneck_pre_relu', 'mixed4a_3x3_bottleneck',
'mixed4a_3x3_pre_relu/conv', 'mixed4a_3x3_pre_relu', 'mixed4a_3x3',
'mixed4a_5x5_bottleneck_pre_relu/conv',
'mixed4a_5x5_bottleneck_pre_relu', 'mixed4a_5x5_bottleneck',
'mixed4a_5x5_pre_relu/conv', 'mixed4a_5x5_pre_relu', 'mixed4a_5x5',
'mixed4a_pool', 'mixed4a_pool_reduce_pre_relu/conv',
'mixed4a_pool_reduce_pre_relu', 'mixed4a_pool_reduce',
'mixed4a/concat_dim', 'mixed4a', 'mixed4b_1x1_pre_relu/conv',
'mixed4b_1x1_pre_relu', 'mixed4b_1x1',
'mixed4b_3x3_bottleneck_pre_relu/conv',
'mixed4b_3x3_bottleneck_pre_relu', 'mixed4b_3x3_bottleneck',
'mixed4b_3x3_pre_relu/conv', 'mixed4b_3x3_pre_relu', 'mixed4b_3x3',
'mixed4b_5x5_bottleneck_pre_relu/conv',
'mixed4b_5x5_bottleneck_pre_relu', 'mixed4b_5x5_bottleneck',
'mixed4b_5x5_pre_relu/conv', 'mixed4b_5x5_pre_relu', 'mixed4b_5x5',
'mixed4b_pool', 'mixed4b_pool_reduce_pre_relu/conv',
'mixed4b_pool_reduce_pre_relu', 'mixed4b_pool_reduce',
'mixed4b/concat_dim', 'mixed4b', 'mixed4c_1x1_pre_relu/conv',
'mixed4c_1x1_pre_relu', 'mixed4c_1x1',
'mixed4c_3x3_bottleneck_pre_relu/conv',
'mixed4c_3x3_bottleneck_pre_relu', 'mixed4c_3x3_bottleneck',
'mixed4c_3x3_pre_relu/conv', 'mixed4c_3x3_pre_relu', 'mixed4c_3x3',
'mixed4c_5x5_bottleneck_pre_relu/conv',
'mixed4c_5x5_bottleneck_pre_relu', 'mixed4c_5x5_bottleneck',
'mixed4c_5x5_pre_relu/conv', 'mixed4c_5x5_pre_relu', 'mixed4c_5x5',
'mixed4c_pool', 'mixed4c_pool_reduce_pre_relu/conv',
'mixed4c_pool_reduce_pre_relu', 'mixed4c_pool_reduce',
'mixed4c/concat_dim', 'mixed4c', 'mixed4d_1x1_pre_relu/conv',
'mixed4d_1x1_pre_relu', 'mixed4d_1x1',
'mixed4d_3x3_bottleneck_pre_relu/conv',
'mixed4d_3x3_bottleneck_pre_relu', 'mixed4d_3x3_bottleneck',
'mixed4d_3x3_pre_relu/conv', 'mixed4d_3x3_pre_relu', 'mixed4d_3x3',
'mixed4d_5x5_bottleneck_pre_relu/conv',
'mixed4d_5x5_bottleneck_pre_relu', 'mixed4d_5x5_bottleneck',
'mixed4d_5x5_pre_relu/conv', 'mixed4d_5x5_pre_relu', 'mixed4d_5x5',
'mixed4d_pool', 'mixed4d_pool_reduce_pre_relu/conv',
'mixed4d_pool_reduce_pre_relu', 'mixed4d_pool_reduce',
'mixed4d/concat_dim', 'mixed4d', 'mixed4e_1x1_pre_relu/conv',
'mixed4e_1x1_pre_relu', 'mixed4e_1x1',
'mixed4e_3x3_bottleneck_pre_relu/conv',
'mixed4e_3x3_bottleneck_pre_relu', 'mixed4e_3x3_bottleneck',
'mixed4e_3x3_pre_relu/conv', 'mixed4e_3x3_pre_relu', 'mixed4e_3x3',
'mixed4e_5x5_bottleneck_pre_relu/conv',
'mixed4e_5x5_bottleneck_pre_relu', 'mixed4e_5x5_bottleneck',
'mixed4e_5x5_pre_relu/conv', 'mixed4e_5x5_pre_relu', 'mixed4e_5x5',
'mixed4e_pool', 'mixed4e_pool_reduce_pre_relu/conv',
'mixed4e_pool_reduce', 'mixed4e/concat_dim', 'mixed4e',
'maxpool10', 'mixed5a_1x1_pre_relu/conv', 'mixed5a_1x1_pre_relu',
'mixed5a_1x1', 'mixed5a_3x3_bottleneck_pre_relu/conv',
'mixed5a_3x3_bottleneck_pre_relu', 'mixed5a_3x3_bottleneck',
'mixed5a_3x3_pre_relu/conv', 'mixed5a_3x3_pre_relu', 'mixed5a_3x3',
'mixed5a_5x5_bottleneck_pre_relu/conv',
'mixed5a_5x5_bottleneck_pre_relu', 'mixed5a_5x5_bottleneck',
'mixed5a_5x5_pre_relu/conv', 'mixed5a_5x5_pre_relu', 'mixed5a_5x5',
'mixed5a_pool', 'mixed5a_pool_reduce_pre_relu/conv',
'mixed5a_pool_reduce_pre_relu', 'mixed5a_pool_reduce',
'mixed5a/concat_dim', 'mixed5a', 'mixed5b_1x1_pre_relu/conv',
'mixed5b_1x1_pre_relu', 'mixed5b_1x1',
'mixed5b_3x3_bottleneck_pre_relu/conv',
'mixed5b_3x3_bottleneck_pre_relu', 'mixed5b_3x3_bottleneck',
'mixed5b_3x3_pre_relu/conv', 'mixed5b_3x3_pre_relu', 'mixed5b_3x3',
'mixed5b_5x5_bottleneck_pre_relu/conv',
'mixed5b_5x5_bottleneck_pre_relu', 'mixed5b_5x5_bottleneck',
'mixed5b_5x5_pre_relu/conv', 'mixed5b_5x5_pre_relu', 'mixed5b_5x5',
'mixed5b_pool', 'mixed5b_pool_reduce_pre_relu/conv',
'mixed5b_pool_reduce_pre_relu', 'mixed5b_pool_reduce',
'mixed5b/concat_dim', 'mixed5b', 'avgpool0', 'head0_pool',
'head0_bottleneck_pre_relu/conv', 'head0_bottleneck_pre_relu',
'head0_bottleneck', 'head0_bottleneck/reshape/shape',
'head0_bottleneck/reshape', 'nn0_pre_relu/matmul', 'nn0_pre_relu',
'nn0', 'nn0/reshape/shape', 'nn0/reshape',
'softmax0_pre_activation/matmul', 'softmax0_pre_activation',
'softmax0', 'head1_pool', 'head1_bottleneck_pre_relu/conv',
'head1_bottleneck_pre_relu', 'head1_bottleneck',
'head1_bottleneck/reshape/shape', 'head1_bottleneck/reshape',
'nn1_pre_relu/matmul', 'nn1_pre_relu', 'nn1', 'nn1/reshape/shape',
'nn1/reshape', 'softmax1_pre_activation/matmul',
'softmax1_pre_activation', 'softmax1', 'avgpool0/reshape/shape',
'avgpool0/reshape', 'softmax2_pre_activation/matmul',
'softmax2_pre_activation', 'softmax2'
]
self.layer_nicknames = {
"plants": ["mixed4a_3x3_bottleneck_pre_relu", 84],
"fractals": ["mixed4a_3x3_bottleneck_pre_relu", 83],
"snakes and lizards": ["mixed4c_pool_reduce", 7],
"feathers": ["mixed4c_pool_reduce", 14],
"rodents": ["mixed4c_pool_reduce", 23],
"spirals": ["mixed4c_pool_reduce", 53],
"3d": ["mixed4c_pool_reduce", 54],
"shiny": ["mixed4c_pool_reduce", 56],
"houses": ["mixed4c_pool_reduce", 61],
"fish": ["mixed5a_1x1", 158],
"balls": ["mixed5a_1x1", 9],
"bark": ["mixed5a_1x1", 107],
"clocks": ["mixed5a_1x1", 134],
"flowers on metal": ["mixed5a_1x1", 198],
"quadrilaterals": ["mixed4c", 56],
"letters": ["mixed4c", 87],
"squares": ["mixed4a_3x3_bottleneck_pre_relu", 51],
"wool": ["mixed4e", 62],
"arches": ["mixed4c", 477],
"fluffy dogs": ["mixed4c", 111],
"flowers": ["mixed4c_3x3_bottleneck", 30],
}
if "iter_value" not in self.settings:
self.settings["iter_value"] = 20
if "octave_value" not in self.settings:
self.settings["octave_value"] = 4
if "octave_scale_value" not in self.settings:
self.settings["octave_scale_value"] = 1.4
if "step_size" not in self.settings:
self.settings["step_size"] = 1.5
if "tile_size" not in self.settings:
self.settings["tile_size"] = 512
if "model_folder" not in self.settings:
self.settings["model_folder"] = \
expanduser("~/tensorflow_models/inception5h")
if "model" not in self.settings:
self.settings["model"] = join(self.settings["model_folder"],
"tensorflow_inception_graph.pb")
if "print_model" not in self.settings:
self.settings["print_model"] = True
if "verbose" not in self.settings:
self.settings["verbose"] = True
if "output_dir" not in self.settings:
self.settings["output_dir"] = expanduser("~/dreams")
if "layers" not in self.settings:
self.settings["layers"] = [
'conv2d2_pre_relu/conv', 'conv2d2_pre_relu', 'conv2d2',
'localresponsenorm1', 'maxpool1', 'mixed3a_pool', 'mixed3a',
'mixed3b_3x3_pre_relu/conv', 'mixed3b_3x3_pre_relu',
'mixed3b_3x3', 'mixed3b_5x5_bottleneck_pre_relu/conv',
'mixed3b_5x5_bottleneck_pre_relu', 'mixed3b_5x5_bottleneck',
'mixed3b_5x5_pre_relu/conv', 'mixed3b_5x5_pre_relu',
'mixed3b_5x5', 'mixed3b_pool',
'mixed3b_pool_reduce_pre_relu/conv',
'mixed3b_pool_reduce_pre_relu', 'mixed3b_pool_reduce',
'mixed3b/concat_dim', 'mixed3b', 'maxpool4',
'mixed4a_1x1_pre_relu/conv', 'mixed4a_1x1_pre_relu',
'mixed4a_1x1', 'mixed4a_3x3_bottleneck_pre_relu/conv',
'mixed4a_3x3_bottleneck_pre_relu', 'mixed4a_3x3_bottleneck',
'mixed4a_3x3_pre_relu/conv', 'mixed4a_3x3_pre_relu',
'mixed4a_3x3', 'mixed4a_5x5_bottleneck_pre_relu/conv',
'mixed4a_5x5_bottleneck_pre_relu', 'mixed4a_5x5_bottleneck',
'mixed4a_5x5_pre_relu/conv', 'mixed4a_5x5_pre_relu',
'mixed4a_5x5', 'mixed4a_pool',
'mixed4a_pool_reduce_pre_relu/conv',
'mixed4a_pool_reduce_pre_relu', 'mixed4a_pool_reduce',
'mixed4a/concat_dim', 'mixed4a', 'mixed4b_1x1_pre_relu/conv',
'mixed4b_1x1_pre_relu', 'mixed4b_1x1',
'mixed4b_3x3_bottleneck_pre_relu/conv',
'mixed4b_3x3_bottleneck_pre_relu', 'mixed4b_3x3_bottleneck',
'mixed4b_3x3_pre_relu/conv', 'mixed4b_3x3_pre_relu',
'mixed4b_3x3', 'mixed4b_5x5_bottleneck_pre_relu/conv',
'mixed4b_5x5_bottleneck_pre_relu', 'mixed4b_5x5_bottleneck',
'mixed4b_5x5_pre_relu/conv', 'mixed4b_5x5_pre_relu',
'mixed4b_5x5', 'mixed4b_pool',
'mixed4b_pool_reduce_pre_relu/conv',
'mixed4b_pool_reduce_pre_relu', 'mixed4b_pool_reduce',
'mixed4b/concat_dim', 'mixed4b', 'mixed4c_1x1_pre_relu/conv',
'mixed4c_1x1_pre_relu', 'mixed4c_1x1',
'mixed4c_3x3_bottleneck_pre_relu/conv',
'mixed4c_3x3_bottleneck_pre_relu', 'mixed4c_3x3_bottleneck',
'mixed4c_3x3_pre_relu/conv', 'mixed4c_3x3_pre_relu',
'mixed4c_3x3', 'mixed4c_5x5_bottleneck_pre_relu/conv',
'mixed4c_5x5_bottleneck_pre_relu', 'mixed4c_5x5_bottleneck',
'mixed4c_5x5_pre_relu/conv', 'mixed4c_5x5_pre_relu',
'mixed4c_5x5', 'mixed4c_pool',
'mixed4c_pool_reduce_pre_relu/conv',
'mixed4c_pool_reduce_pre_relu', 'mixed4c_pool_reduce',
'mixed4c/concat_dim', 'mixed4c', 'mixed4d_1x1_pre_relu/conv',
'mixed4d_1x1_pre_relu', 'mixed4d_1x1',
'mixed4d_3x3_bottleneck_pre_relu/conv',
'mixed4d_3x3_bottleneck_pre_relu', 'mixed4d_3x3_bottleneck',
'mixed4d_3x3_pre_relu/conv', 'mixed4d_3x3_pre_relu',
'mixed4d_3x3', 'mixed4d_5x5_bottleneck_pre_relu/conv',
'mixed4d_5x5_bottleneck_pre_relu', 'mixed4d_5x5_bottleneck',
'mixed4d_5x5_pre_relu/conv', 'mixed4d_5x5_pre_relu',
'mixed4d_5x5', 'mixed4d_pool',
'mixed4d_pool_reduce_pre_relu/conv',
'mixed4d_pool_reduce_pre_relu', 'mixed4d_pool_reduce',
'mixed4d/concat_dim', 'mixed4d', 'mixed4e_1x1_pre_relu/conv',
'mixed4e_1x1_pre_relu', 'mixed4e_1x1',
'mixed4e_3x3_bottleneck_pre_relu/conv',
'mixed4e_3x3_bottleneck_pre_relu', 'mixed4e_3x3_bottleneck',
'mixed4e_3x3_pre_relu/conv', 'mixed4e_3x3_pre_relu',
'mixed4e_3x3', 'mixed4e_5x5_bottleneck_pre_relu/conv',
'mixed4e_5x5_bottleneck_pre_relu', 'mixed4e_5x5_bottleneck',
'mixed4e_5x5_pre_relu/conv', 'mixed4e_5x5_pre_relu',
'mixed4e_5x5', 'mixed4e_pool',
'mixed4e_pool_reduce_pre_relu/conv', 'mixed4e_pool_reduce',
'mixed4e/concat_dim', 'mixed4e', 'maxpool10',
'mixed5a_1x1_pre_relu/conv', 'mixed5a_1x1_pre_relu',
'mixed5a_1x1', 'mixed5a_3x3_bottleneck_pre_relu/conv',
'mixed5a_3x3_bottleneck_pre_relu', 'mixed5a_3x3_bottleneck',
'mixed5a_3x3_pre_relu/conv', 'mixed5a_3x3_pre_relu',
'mixed5a_3x3', 'mixed5a_5x5_bottleneck_pre_relu/conv',
'mixed5a_5x5_bottleneck_pre_relu', 'mixed5a_5x5_bottleneck',
'mixed5a_5x5_pre_relu/conv', 'mixed5a_5x5_pre_relu',
'mixed5a_5x5', 'mixed5a_pool',
'mixed5a_pool_reduce_pre_relu/conv',
'mixed5a_pool_reduce_pre_relu', 'mixed5a_pool_reduce',
'mixed5a/concat_dim', 'mixed5a', 'mixed5b_1x1_pre_relu/conv',
'mixed5b_1x1_pre_relu', 'mixed5b_1x1',
'mixed5b_3x3_bottleneck_pre_relu/conv',
'mixed5b_3x3_bottleneck_pre_relu', 'mixed5b_3x3_bottleneck',
'mixed5b_3x3_pre_relu/conv', 'mixed5b_3x3_pre_relu',
'mixed5b_3x3', 'mixed5b_5x5_bottleneck_pre_relu/conv',
'mixed5b_5x5_bottleneck_pre_relu', 'mixed5b_5x5_bottleneck',
'mixed5b_5x5_pre_relu/conv', 'mixed5b_5x5_pre_relu',
'mixed5b_5x5', 'mixed5b_pool',
'mixed5b_pool_reduce_pre_relu/conv',
'mixed5b_pool_reduce_pre_relu', 'mixed5b_pool_reduce',
'mixed5b/concat_dim', 'mixed5b', 'avgpool0', 'head0_pool',
'head0_bottleneck_pre_relu/conv', 'head0_bottleneck_pre_relu',
'head0_bottleneck', 'head0_bottleneck/reshape/shape',
'head0_bottleneck/reshape', 'nn0_pre_relu/matmul',
'nn0_pre_relu', 'nn0', 'nn0/reshape/shape', 'nn0/reshape',
'softmax0_pre_activation/matmul', 'softmax0_pre_activation',
'softmax0', 'head1_pool', 'head1_bottleneck_pre_relu/conv',
'head1_bottleneck_pre_relu', 'head1_bottleneck',
'head1_bottleneck/reshape/shape', 'head1_bottleneck/reshape',
'nn1_pre_relu/matmul', 'nn1_pre_relu', 'nn1',
'nn1/reshape/shape', 'nn1/reshape',
'softmax1_pre_activation/matmul', 'softmax1_pre_activation',
'softmax1', 'avgpool0/reshape/shape', 'avgpool0/reshape',
'softmax2_pre_activation/matmul', 'softmax2_pre_activation',
'softmax2'
]
# check if folders
if not os.path.exists(self.settings["output_dir"]):
os.makedirs(self.settings["output_dir"])
# private email
mail_config = self.config_core.get("email", {})
self.email = mail_config.get("email")
self.password = mail_config.get("password")
self.target_mail = mail_config.get("destinatary", self.email)
def send(self, body):
title = "Mycroft DeepDream Skill"
# try private sending
if yagmail is not None and self.email and self.password:
with yagmail.SMTP(self.email, self.password) as yag:
yag.send(self.target_email, title, body)
else:
# else use mycroft home
self.send_email(title, body)
def speak(self, utterance, expect_response=False, metadata=None):
"""
Speak a sentence.
Args:
utterance: sentence mycroft should speak
expect_response: set to True if Mycroft should expect
a response from the user and start
listening for response.
metadata: Extra data to be transmitted
together with speech
"""
metadata = metadata or {}
# registers the skill as being active
self.enclosure.register(self.name)
data = {
'utterance': utterance,
'expect_response': expect_response,
"metadata": metadata
}
message = dig_for_message()
if message:
self.bus.emit(message.reply("speak", data))
else:
self.bus.emit(Message("speak", data))
def initialize(self):
# check if model exists, if not download!
DeepDreamer.maybe_download_and_extract(self.settings["model_folder"])
self.register_intent_file("dream.intent", self.handle_dream_intent)
self.add_event("dream.request", self.handle_dream_request)
def handle_dream_intent(self, message):
search = message.data.get("AboutKeyword")
data = {}
filepath = expanduser("~/dream_seed.jpg")
if search:
# collect dream entropy
self.speak("dreaming about " + search)
pics = self.search_pic(search)
url = random.choice(pics)
urllib.request.urlretrieve(url, filepath)
data["dream_name"] = search + "_" + time.asctime()
elif self.settings["use_pexels"]:
url = random.choice(self.popular_pic_urls())
req = urllib.request.Request(
url, None, {
'User-agent':
'Mozilla/5.0 (Windows; U; Windows NT 5.1; de; rv:1.9.1.5) Gecko/20091102 Firefox/3.5.5'
})
resp = urllib.request.urlopen(req)
content = resp.read()
with open(filepath, "wb") as f:
f.write(content)
else:
url = "https://unsplash.it/640/480/?random"
urllib.request.urlretrieve(url, filepath)
data["dream_source"] = filepath
self.bus.emit(message.reply("dream.request", data))
def handle_dream_request(self, message):
self.speak("dream started")
channel_value = message.data.get("channel_value")
seed = message.data.get("dream_source")
output_name = message.data.get("dream_name")
layer_name = message.data.get("layer_name")
iter_value = message.data.get("iter_value",
self.settings["iter_value"])
step_size = message.data.get("step_size", self.settings["step_size"])
octave_value = message.data.get("octave_value",
self.settings["octave_value"])
octave_scale_value = message.data.get(
"octave_scale_value", self.settings["octave_scale_value"])
DD = DeepDreamer(self.settings["model"], self.settings["print_model"],
self.settings["verbose"], self.settings["tile_size"])
filepath = DD.dream(output_name, seed, channel_value, layer_name,
iter_value, step_size, octave_value,
octave_scale_value)
if filepath:
data = self.client.upload_from_path(filepath)
metadata = message.data
metadata["url"] = data["link"]
self.speak("here is what i dreamed " + data["link"],
metadata=metadata)
mail = "url: " + data["link"] + "\nlayer: " + layer_name + \
"\nchannel: " + str(channel_value) + "\niter_num: " + str(iter_value)
self.send(mail)
self.bus.emit(message.reply("dream.response", metadata))
self.enclosure.mouth_text(data["link"])
else:
if layer_name in self.settings["layers"]:
self.settings["layers"].remove(layer_name)
self.speak("I could not dream this time")
## pic search
def popular_pic_urls(self, num_pages=1):
# instantiate PyPexels object
api_key = self.settings["pexels"]
urls = []
try:
py_pexels = PyPexels(api_key=api_key)
popular_photos = py_pexels.popular(per_page=10)
i = 0
while popular_photos.has_next and i <= num_pages:
for photo in popular_photos.entries:
urls.append(photo.src["original"])
# no need to specify per_page: will take from original object
popular_photos = popular_photos.get_next_page()
i += 1
except Exception as e:
LOG.error(e)
return urls
def get_soup(self, url, header):
return BeautifulSoup(
urllib.request.urlopen(urllib.request.Request(url,
headers=header)),
'html.parser')
def search_pic(self, searchkey, dlnum=5):
query = searchkey # raw_input("query image")# you can change the query for the image here
query = query.split()
query = '+'.join(query)
url = "https://www.google.co.in/search?q=" + query + "&source=lnms&tbm=isch"
header = {
'User-Agent':
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36"
}
soup = self.get_soup(url, header)
i = 0
ActualImages = [
] # contains the link for Large original images, type of image
for a in soup.find_all("div", {"class": "rg_meta"}):
link = json.loads(a.text)["ou"]
ActualImages.append(link)
i += 1
if i >= dlnum:
break
return ActualImages
def shutdown(self):
"""
Remove all registered handlers and perform skill shutdown.
"""
self.bus.remove("dream.request", self.handle_dream_request)
super(DreamSkill, self).shutdown()
from skimage.io import imread, imsave
import numpy as np
import os
import time
import urllib.request, urllib.parse, urllib.error
import random
from PIL import Image
from os.path import expanduser
class DeepDreamer(object):
def __init__(self,
model_path,
print_model=False,
verbose=True,
tile_size=512):
self.model_path = model_path
self.verbose = verbose
self.print_model = print_model
self.model_fn = os.path.join(
os.path.dirname(os.path.realpath(__file__)), model_path)
self.tile_size = tile_size
# creating TensorFlow session and loading the model
self.graph = tf.Graph()
self.sess = tf.InteractiveSession(graph=self.graph)
with tf.gfile.FastGFile(self.model_fn, 'rb') as f:
self.graph_def = tf.GraphDef()
self.graph_def.ParseFromString(f.read())
self.t_input = tf.placeholder(np.float32,
name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(self.t_input - imagenet_mean, 0)
tf.import_graph_def(self.graph_def, {'input': t_preprocessed})
# Optionally print the inputs and layers of the specified graph.
if self.print_model:
LOG.debug(self.graph.get_operations())
self.last_layer = None
self.last_grad = None
self.last_channel = None
# TODO test all layers, not sure they all can be dreamed on
self.layers = [
'conv2d2_pre_relu/conv', 'conv2d2_pre_relu', 'conv2d2',
'localresponsenorm1', 'maxpool1', 'mixed3a_pool', 'mixed3a',
'mixed3b_3x3_pre_relu/conv', 'mixed3b_3x3_pre_relu', 'mixed3b_3x3',
'mixed3b_5x5_bottleneck_pre_relu/conv',
'mixed3b_5x5_bottleneck_pre_relu', 'mixed3b_5x5_bottleneck',
'mixed3b_5x5_pre_relu/conv', 'mixed3b_5x5_pre_relu', 'mixed3b_5x5',
'mixed3b_pool', 'mixed3b_pool_reduce_pre_relu/conv',
'mixed3b_pool_reduce_pre_relu', 'mixed3b_pool_reduce',
'mixed3b/concat_dim', 'mixed3b', 'maxpool4',
'mixed4a_1x1_pre_relu/conv', 'mixed4a_1x1_pre_relu', 'mixed4a_1x1',
'mixed4a_3x3_bottleneck_pre_relu/conv',
'mixed4a_3x3_bottleneck_pre_relu', 'mixed4a_3x3_bottleneck',
'mixed4a_3x3_pre_relu/conv', 'mixed4a_3x3_pre_relu', 'mixed4a_3x3',
'mixed4a_5x5_bottleneck_pre_relu/conv',
'mixed4a_5x5_bottleneck_pre_relu', 'mixed4a_5x5_bottleneck',
'mixed4a_5x5_pre_relu/conv', 'mixed4a_5x5_pre_relu', 'mixed4a_5x5',
'mixed4a_pool', 'mixed4a_pool_reduce_pre_relu/conv',
'mixed4a_pool_reduce_pre_relu', 'mixed4a_pool_reduce',
'mixed4a/concat_dim', 'mixed4a', 'mixed4b_1x1_pre_relu/conv',
'mixed4b_1x1_pre_relu', 'mixed4b_1x1',
'mixed4b_3x3_bottleneck_pre_relu/conv',
'mixed4b_3x3_bottleneck_pre_relu', 'mixed4b_3x3_bottleneck',
'mixed4b_3x3_pre_relu/conv', 'mixed4b_3x3_pre_relu', 'mixed4b_3x3',
'mixed4b_5x5_bottleneck_pre_relu/conv',
'mixed4b_5x5_bottleneck_pre_relu', 'mixed4b_5x5_bottleneck',
'mixed4b_5x5_pre_relu/conv', 'mixed4b_5x5_pre_relu', 'mixed4b_5x5',
'mixed4b_pool', 'mixed4b_pool_reduce_pre_relu/conv',
'mixed4b_pool_reduce_pre_relu', 'mixed4b_pool_reduce',
'mixed4b/concat_dim', 'mixed4b', 'mixed4c_1x1_pre_relu/conv',
'mixed4c_1x1_pre_relu', 'mixed4c_1x1',
'mixed4c_3x3_bottleneck_pre_relu/conv',
'mixed4c_3x3_bottleneck_pre_relu', 'mixed4c_3x3_bottleneck',
'mixed4c_3x3_pre_relu/conv', 'mixed4c_3x3_pre_relu', 'mixed4c_3x3',
'mixed4c_5x5_bottleneck_pre_relu/conv',
'mixed4c_5x5_bottleneck_pre_relu', 'mixed4c_5x5_bottleneck',
'mixed4c_5x5_pre_relu/conv', 'mixed4c_5x5_pre_relu', 'mixed4c_5x5',
'mixed4c_pool', 'mixed4c_pool_reduce_pre_relu/conv',
'mixed4c_pool_reduce_pre_relu', 'mixed4c_pool_reduce',
'mixed4c/concat_dim', 'mixed4c', 'mixed4d_1x1_pre_relu/conv',
'mixed4d_1x1_pre_relu', 'mixed4d_1x1',
'mixed4d_3x3_bottleneck_pre_relu/conv',
'mixed4d_3x3_bottleneck_pre_relu', 'mixed4d_3x3_bottleneck',
'mixed4d_3x3_pre_relu/conv', 'mixed4d_3x3_pre_relu', 'mixed4d_3x3',
'mixed4d_5x5_bottleneck_pre_relu/conv',
'mixed4d_5x5_bottleneck_pre_relu', 'mixed4d_5x5_bottleneck',
'mixed4d_5x5_pre_relu/conv', 'mixed4d_5x5_pre_relu', 'mixed4d_5x5',
'mixed4d_pool', 'mixed4d_pool_reduce_pre_relu/conv',
'mixed4d_pool_reduce_pre_relu', 'mixed4d_pool_reduce',
'mixed4d/concat_dim', 'mixed4d', 'mixed4e_1x1_pre_relu/conv',
'mixed4e_1x1_pre_relu', 'mixed4e_1x1',
'mixed4e_3x3_bottleneck_pre_relu/conv',
'mixed4e_3x3_bottleneck_pre_relu', 'mixed4e_3x3_bottleneck',
'mixed4e_3x3_pre_relu/conv', 'mixed4e_3x3_pre_relu', 'mixed4e_3x3',
'mixed4e_5x5_bottleneck_pre_relu/conv',
'mixed4e_5x5_bottleneck_pre_relu', 'mixed4e_5x5_bottleneck',
'mixed4e_5x5_pre_relu/conv', 'mixed4e_5x5_pre_relu', 'mixed4e_5x5',
'mixed4e_pool', 'mixed4e_pool_reduce_pre_relu/conv',
'mixed4e_pool_reduce', 'mixed4e/concat_dim', 'mixed4e',
'maxpool10', 'mixed5a_1x1_pre_relu/conv', 'mixed5a_1x1_pre_relu',
'mixed5a_1x1', 'mixed5a_3x3_bottleneck_pre_relu/conv',
'mixed5a_3x3_bottleneck_pre_relu', 'mixed5a_3x3_bottleneck',
'mixed5a_3x3_pre_relu/conv', 'mixed5a_3x3_pre_relu', 'mixed5a_3x3',
'mixed5a_5x5_bottleneck_pre_relu/conv',
'mixed5a_5x5_bottleneck_pre_relu', 'mixed5a_5x5_bottleneck',
'mixed5a_5x5_pre_relu/conv', 'mixed5a_5x5_pre_relu', 'mixed5a_5x5',
'mixed5a_pool', 'mixed5a_pool_reduce_pre_relu/conv',
'mixed5a_pool_reduce_pre_relu', 'mixed5a_pool_reduce',
'mixed5a/concat_dim', 'mixed5a', 'mixed5b_1x1_pre_relu/conv',
'mixed5b_1x1_pre_relu', 'mixed5b_1x1',
'mixed5b_3x3_bottleneck_pre_relu/conv',
'mixed5b_3x3_bottleneck_pre_relu', 'mixed5b_3x3_bottleneck',
'mixed5b_3x3_pre_relu/conv', 'mixed5b_3x3_pre_relu', 'mixed5b_3x3',
'mixed5b_5x5_bottleneck_pre_relu/conv',
'mixed5b_5x5_bottleneck_pre_relu', 'mixed5b_5x5_bottleneck',
'mixed5b_5x5_pre_relu/conv', 'mixed5b_5x5_pre_relu', 'mixed5b_5x5',
'mixed5b_pool', 'mixed5b_pool_reduce_pre_relu/conv',
'mixed5b_pool_reduce_pre_relu', 'mixed5b_pool_reduce',
'mixed5b/concat_dim', 'mixed5b', 'avgpool0', 'head0_pool',
'head0_bottleneck_pre_relu/conv', 'head0_bottleneck_pre_relu',
'head0_bottleneck', 'head0_bottleneck/reshape/shape',
'head0_bottleneck/reshape', 'nn0_pre_relu/matmul', 'nn0_pre_relu',
'nn0', 'nn0/reshape/shape', 'nn0/reshape',
'softmax0_pre_activation/matmul', 'softmax0_pre_activation',
'softmax0', 'head1_pool', 'head1_bottleneck_pre_relu/conv',
'head1_bottleneck_pre_relu', 'head1_bottleneck',
'head1_bottleneck/reshape/shape', 'head1_bottleneck/reshape',
'nn1_pre_relu/matmul', 'nn1_pre_relu', 'nn1', 'nn1/reshape/shape',
'nn1/reshape', 'softmax1_pre_activation/matmul',
'softmax1_pre_activation', 'softmax1', 'avgpool0/reshape/shape',
'avgpool0/reshape', 'softmax2_pre_activation/matmul',
'softmax2_pre_activation', 'softmax2'
]
self.resize = self.tffunc(np.float32, np.int32)(self._resize)
# Helper function that uses TF to resize an image
@classmethod
def _resize(cls, img, size):
img = tf.expand_dims(img, 0)
return tf.image.resize_bilinear(img, size)[0, :, :, :]
def T(self, layer):
'''Helper for getting layer output tensor'''
return self.graph.get_tensor_by_name("import/%s:0" % layer)
def tffunc(self, *argtypes):
'''Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
'''
placeholders = list(map(tf.placeholder, argtypes))
def wrap(f):
out = f(*placeholders)
def wrapper(*args, **kw):
return out.eval(dict(list(zip(placeholders, args))),
session=self.sess)
return wrapper
return wrap
def get_random_pic(self, filepath=None):
filepath = filepath or expanduser("~/dream_seed.jpg")
url = "https://unsplash.it/640/480/?random"
urllib.request.urlretrieve(url, filepath)
return filepath
def calc_grad_tiled(self, img, t_grad, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
sz = tile_size
h, w = img.shape[:2]
sx, sy = np.random.randint(sz, size=2)
img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
grad = np.zeros_like(img)
for y in range(0, max(h - sz // 2, sz), sz):
for x in range(0, max(w - sz // 2, sz), sz):
sub = img_shift[y:y + sz, x:x + sz]
g = self.sess.run(t_grad, {self.t_input: sub})
grad[y:y + sz, x:x + sz] = g
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
def render_deepdream(self,
t_grad,
img0,
iter_n=10,
step=1.5,
octave_n=4,
octave_scale=1.4):
# split the image into a number of octaves
img = img0
octaves = []
for i in range(octave_n - 1):
hw = img.shape[:2]
lo = self.resize(img, np.int32(np.float32(hw) / octave_scale))
hi = img - self.resize(lo, hw)
img = lo
octaves.append(hi)
# generate details octave by octave
for octave in range(octave_n):
if octave > 0:
hi = octaves[-octave]
img = self.resize(img, hi.shape[:2]) + hi
for i in range(iter_n):
g = self.calc_grad_tiled(img, t_grad, self.tile_size)
img += g * (step / (np.abs(g).mean() + 1e-7))
if self.verbose:
LOG.info("Iteration Number: %d" % i)
if self.verbose:
LOG.info("Octave Number: %d" % octave)
return Image.fromarray(np.uint8(np.clip(img / 255.0, 0, 1) * 255))
def render(self,
img,
layer='mixed4d_3x3_bottleneck_pre_relu',
channel=139,
iter_n=10,
step=1.5,
octave_n=4,
octave_scale=1.4):
if self.last_layer == layer and self.last_channel == channel:
t_grad = self.last_grad
else:
if channel == 4242:
t_obj = tf.square(self.graph, self.T(layer))
else:
t_obj = self.T(layer)[:, :, :, channel]
t_score = tf.reduce_mean(
t_obj) # defining the optimization objective
t_grad = tf.gradients(t_score, self.t_input)[
0] # behold the power of automatic differentiation!
self.last_layer = layer
self.last_grad = t_grad
self.last_channel = channel
img0 = np.float32(img)
return self.render_deepdream(t_grad, img0, iter_n, step, octave_n,
octave_scale)
def dream(self,
output_name=None,
seed=None,
channel_value=None,
layer_name=None,
iter_value=10,
step_size=1.5,
octave_value=4,
octave_scale_value=1.5):
self.last_layer = None
self.last_grad = None
self.last_channel = None
seed = seed or self.get_random_pic()
input_img = imread(seed)
if layer_name not in self.layers:
layer_name = None
channel_value = channel_value or random.randint(0, 300)
layer_name = layer_name or random.choice(self.layers)
LOG.info(layer_name + "_" + str(channel_value))
try:
output_img = self.render(input_img,
layer=layer_name,
channel=channel_value,
iter_n=iter_value,
step=step_size,
octave_n=octave_value,
octave_scale=octave_scale_value)
output_name = output_name or time.asctime().strip(
) + "_" + layer_name.replace("/", "") + "_" + str(channel_value)
if ".jpg" not in output_name:
output_name += ".jpg"
LOG.debug(output_name)
imsave(output_name, output_img)
return output_name
except Exception as e:
LOG.error(e)
return False
def dream_all(self,
seed=None,
channel_value=None,
iter_value=10,
step_size=1.5,
octave_value=4,
octave_scale_value=1.5):
layers = list(self.layers)
random.shuffle(layers)
for layer_name in layers:
try:
self.dream(layer_name=layer_name,
seed=seed,
channel_value=channel_value,
iter_value=iter_value,
step_size=step_size,
octave_value=octave_value,
octave_scale_value=octave_scale_value)
except Exception as e:
LOG.error(e)
@classmethod
def maybe_download_and_extract(cls, model_folder):
# """Download and extract model zip file."""
# wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
# unzip -d model inception5h.zip
url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"
if not os.path.exists(model_folder):
os.makedirs(model_folder)
filename = url.split('/')[-1]
filepath = os.path.join(model_folder, filename)
if not os.path.exists(filepath):
LOG.info("Model is not in folder, downloading")
urllib.request.urlretrieve(url, filepath)
statinfo = os.stat(filepath)
LOG.info('Successfully downloaded ' + filename + "\n" +
str(statinfo.st_size) + ' bytes.')
# unzip
import zipfile
zip_ref = zipfile.ZipFile(filepath, 'r')
zip_ref.extractall(model_folder)
zip_ref.close()