def __init__(self):
super(DQNUnit, self).__init__()
if config().sim.env.state.type == "simple":
self.simple_fc = nn.Sequential(nn.Linear(8, 8), nn.ReLU(),
nn.Linear(8, 8), nn.ReLU(),
nn.Linear(8, 4))
else:
board_size = config().sim.env.size
n_actions = 4
conv_layers = [
nn.Conv2d(1, 16, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(16, 32, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(32, 32, 3, stride=2, padding=1),
nn.ReLU(),
]
out_dim = (board_size, board_size)
for layer in conv_layers:
if type(layer) != nn.ReLU:
out_dim = output_size_conv2d_layer(out_dim[0], out_dim[1],
layer)
self.conv = nn.Sequential(*conv_layers)
self.fc = nn.Sequential(
nn.Linear(out_dim, 32),
nn.ReLU(),
nn.Linear(32, n_actions),
)
def evaluate_cnn(dataset, model, criterion, get_semantic_label):
"""
Runs inference on an autoencoder model to evaluate the mse loss on the
validation sets. Reports per-class performance to terminal.
"""
num_classes = config('autoencoder.num_classes')
batch_size = config('autoencoder.batch_size')
performance = np.zeros(num_classes)
for c in range(num_classes):
with torch.no_grad():
y_true, y_pred = [], []
correct, total = 0, 0
X = dataset[c]
output = model(X)
predicted = predictions(output.data)
total = len(X)
correct += (predicted == c).sum().item()
val_acc = correct / total
performance[c] = val_acc
val_acc = 0.0
for c, p in enumerate(performance):
print('Class {}: {} accuracy'.format(get_semantic_label(c), p))
def __init__(self, partition, task="target", augment=False):
"""Read in the necessary data from disk.
For parts 2, 3 and data augmentation, `task` should be "target".
For source task of part 4, `task` should be "source".
For data augmentation, `augment` should be True.
"""
super().__init__()
if partition not in ["train", "val", "test", "challenge"]:
raise ValueError("Partition {} does not exist".format(partition))
np.random.seed(42)
torch.manual_seed(42)
random.seed(42)
self.partition = partition
self.task = task
self.augment = augment
# Load in all the data we need from disk
if task == "target" or task == "source":
self.metadata = pd.read_csv(config("csv_file"))
if self.augment:
print("Augmented")
self.metadata = pd.read_csv(config("augmented_csv_file"))
self.X, self.y = self._load_data()
self.semantic_labels = dict(
zip(
self.metadata[self.metadata.task ==
self.task]["numeric_label"],
self.metadata[self.metadata.task == self.task]
["semantic_label"],
))
def evaluate_autoencoder(dataset, get_semantic_label, model, criterion):
"""
Runs inference on an autoencoder model to evaluate the mse loss on the
validation sets. Reports per-class performance to terminal.
"""
num_classes = config('autoencoder.num_classes')
batch_size = config('autoencoder.batch_size')
performance = np.zeros(num_classes)
overall_performance = 0
overall_sample_num = 0
for c in range(num_classes):
len_ = 0
X = dataset[c]
_, recon = model(X) #todo: return this to recon instead of temp
#recon = torch.zeros(temp.shape)
batch_mse = criterion(recon, X).item()
performance[c] = batch_mse
overall_sample_num = overall_sample_num + X.shape[0]
overall_performance = overall_performance + batch_mse * X.shape[0]
overall_performance = overall_performance / overall_sample_num
print('Overall performance: {} mean squared error'.format(
overall_performance))
for c, p in enumerate(performance):
print('Class {}: {} mean squared error'.format(get_semantic_label(c),
p))
def default(self, *args, **kargs):
if len(args) > 1:
return self.parent.error_page("too many arguments to Keywords")
elif len(args) < 1:
return self.parent.error_page("Too few arguments to Keywords")
try:
offset = int(kargs.get('offset', 0))
except ValueError:
offset = 0
self.keyword = unquote_plus(args[0])
print "Key = %s" % self.keyword
#remember we're not sure if base_url has a trailing '/' or not...
if 'Atom' in config("plugins"):
self.atom_link = config('base_url').rstrip('/') + \
'/Atom/keywords/' + self.keyword
entries = get_entries_by_meta('keywords')
entries = [
e for e in entries
if self.keyword in keysplit(e.metadata['keywords'])
]
entries = entries[offset:offset + config("num_entries")]
return self.parent.render_page(entries, self.keyword, offset)
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, pathname, batch_size=config("unet.batch_size"))
# Model
model = Net()
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Accuracy:{acc}')
print(f'Test Loss:{loss}')
def __init__(self, partition, task="target", augment=False):
'''
Read in the data from the disk
'''
super().__init__()
if partition not in ["train", "val", "test"]:
raise ValueError("Partition {} does not exist".format(partition))
FILEPATH = config("csv_file")
self.PATH = config("image_path")
seed = 0
np.random.seed(seed) # set the seed for random
torch.manual_seed(seed)
random.seed = seed
self.task = task
self.partition = partition
self.metadata = pd.read_csv(
FILEPATH, converters={'numeric_label': from_np_array})
self.augment = augment
if self.augment == False:
self.metadata = pd.read_csv(FILEPATH)
print('loading data from csv file')
self.X, self.y = self._load_data()
self.semantic_labels = dict(
zip(
self.metadata["numeric_label"],
self.metadata["semantic_label"],
))
def main():
"""Print performance metrics for model at specified epoch."""
# Data loaders
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
task="target",
batch_size=config("cnn.batch_size"),
)
# Model
model = Target()
# define loss function
criterion = torch.nn.CrossEntropyLoss()
# Attempts to restore the latest checkpoint if exists
print("Loading cnn...")
model, start_epoch, stats = restore_checkpoint(model,
config("cnn.checkpoint"))
axes = utils.make_training_plot()
# Evaluate the model
evaluate_epoch(
axes,
tr_loader,
va_loader,
te_loader,
model,
criterion,
start_epoch,
stats,
include_test=True,
update_plot=False,
)
def wrapper_load_config(string, entities):
payload = dict()
payload["string"] = string
payload["entities"] = entities
api_key = utils.config("api_key")
pro = utils.config("pro")
payload["temperature_units"] = utils.config("temperature_units")
payload["wind_speed_units"] = utils.config("wind_speed_units")
if ((payload["temperature_units"] != "celsius")
and (payload["temperature_units"] != "fahrenheit")):
return utils.output("end", "invalid_temperature_units",
utils.translate("invalid_temperature_units"))
if payload["wind_speed_units"] == "meters per seconds":
payload["wind_speed_units_response"] = payload["wind_speed_units"]
payload["wind_speed_units"] = "meters_sec"
elif payload["wind_speed_units"] == "miles per hour":
payload["wind_speed_units_response"] = payload["wind_speed_units"]
payload["wind_speed_units"] = "miles_hour"
else:
return utils.output("end", "invalid_wind_speed_units",
utils.translate("invalid_wind_speed_units"))
if pro:
payload["owm"] = OWM(api_key, subscription_type="pro")
else:
payload["owm"] = OWM(api_key)
return func(payload)
def main():
"""Train transfer learning model and display training plots.
Train four different models with {0, 1, 2, 3} layers frozen.
"""
# data loaders
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
task="target",
batch_size=config("target.batch_size"),
)
freeze_none = Target()
print("Loading source...")
freeze_none, _, _ = restore_checkpoint(
freeze_none, config("source.checkpoint"), force=True, pretrain=True
)
freeze_one = copy.deepcopy(freeze_none)
freeze_two = copy.deepcopy(freeze_none)
freeze_three = copy.deepcopy(freeze_none)
freeze_layers(freeze_one, 1)
freeze_layers(freeze_two, 2)
freeze_layers(freeze_three, 3)
train(tr_loader, va_loader, te_loader, freeze_none, "./checkpoints/target0/", 0)
train(tr_loader, va_loader, te_loader, freeze_one, "./checkpoints/target1/", 1)
train(tr_loader, va_loader, te_loader, freeze_two, "./checkpoints/target2/", 2)
train(tr_loader, va_loader, te_loader, freeze_three, "./checkpoints/target3/", 3)
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, pathname, batch_size=config("unet.batch_size"))
# Model
#model = Net()
#model = Dense121()
model = Dense169()
model = model.to(device)
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Error:{acc}')
print(f'Test Loss:{loss}')
# Get Test Images
img_list = glob("examples/" + "*.png")
# Set model to eval mode
model.eval()
model = model.to(device)
# Begin testing loop
print("Begin Test Loop ...")
for idx, img_name in enumerate(img_list):
img = load_images([img_name])
img = torch.Tensor(img).float().to(device)
print("Processing {}, Tensor Shape: {}".format(img_name, img.shape))
with torch.no_grad():
preds = model(img).squeeze(0)
output = colorize(preds.data)
output = output.transpose((1, 2, 0))
cv2.imwrite(img_name.split(".")[0] + "_result.png", output)
print("Processing {} done.".format(img_name))
class Admin(object):
_cp_config = {
"tools.basic_auth.on": True,
"tools.basic_auth.realm": "localhost",
"tools.basic_auth.users": {
config("admin_user"): md5.new(config("admin_pw")).hexdigest()
},
"tools.expires.on": True
}
def __init__(self, parent):
self.parent = parent
@cpy.expose
def index(self):
#TODO: what's the right redirect to give here?
raise cpy.InternalRedirect("/Admin/ls")
def navbar(self, ns):
"""Run a callback so that any module can add an element to the Admin
navbar"""
### this callback gives an admin module the ability to add a link to
### the navigation bar of the Admin section. It should return a list of
### (string, string) tuples where the first string is the relative link
### to the function, and the second is the name to display on the menu
ns['modules'] = run_callback(
self.parent.plugins,
"cb_admin_navbar",
)
return ('admin_head', ns)
def cb_admin_navbar(self):
return [('ls', 'Edit Stories'), ('add', 'Add Story')]
@cpy.expose
def add(self):
#XXX: Should I move story editing to a plugin?
ns = {'title': "Adding New Entry"}
return [self.navbar(ns), ('admin_storyadd', ns)]
@cpy.expose
def add_story(self, story_title="", story_body="", filename=""):
if story_title == "" or story_body == "" or filename == "":
raise cpy.InternalRedirect("ls")
filename = os.path.join(config('datadir'), filename)
if not filename.endswith('.txt'): filename += '.txt'
if os.path.isfile(filename):
ns = {'title': 'File Already Exists', 'filename': filename}
return [self.navbar(ns), ('admin_story_already_exists', ns)]
try:
f = open(filename, 'w')
f.write(story_title + "\n")
f.write(htmlunescape(story_body))
except Exception, e:
cpy.log("unable to log: " + e.Message)
finally:
def connect(cls):
if cls._client is None:
cls._client = redis.Redis(host=config('REDIS_HOST'),
db=config('REDIS_DB'),
port=config('REDIS_PORT'),
password=config('REDIS_PASSWORD'),
socket_timeout=0.2,
socket_connect_timeout=0.2)
def __init__(self, Exchangeabi, ERC827abi, trade_token_add, rpc_url):
self.trade_token_add = trade_token_add
self.web3Instance = Web3(
Web3.HTTPProvider(rpc_url, request_kwargs={'timeout': 240}))
self.erc827 = self.web3Instance.eth.contract(
abi=ERC827abi, address=config('ETH_TOKEN_ADDRESS'))
self.exchange = self.web3Instance.eth.contract(
abi=Exchangeabi, address=config('EXCHANGE_ADDRESS'))
def get_exchange(cls):
clc_address, rpc_url = config("CLC_ADDRESS"), config("RPC_URL")
exchange, ERC827abi = config("Exchange_FILE"), config("ERC827abi_FILE")
with open(os.path.join(BASE_FILE_DIR, exchange), "r") as f:
Exchangeabi = json.load(f)
with open(os.path.join(BASE_FILE_DIR, ERC827abi), "r") as f:
ERC827abi = json.load(f)
return cls(Exchangeabi, ERC827abi, clc_address, rpc_url)
def main():
# Data loaders
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
num_classes=config('cnn.num_classes'))
# Model
model = CNN()
# TODO: define loss function, and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-4)
#
print('Number of float-valued parameters:', count_parameters(model))
# Attempts to restore the latest checkpoint if exists
print('Loading cnn...')
model, start_epoch, stats = restore_checkpoint(model,
config('cnn.checkpoint'))
axes = utils.make_cnn_training_plot()
# Evaluate the randomly initialized model
_evaluate_epoch(axes, tr_loader, va_loader, model, criterion, start_epoch,
stats)
# Loop over the entire dataset multiple times
for epoch in range(start_epoch, config('cnn.num_epochs')):
# Train model
_train_epoch(tr_loader, model, criterion, optimizer)
# Evaluate model
_evaluate_epoch(axes, tr_loader, va_loader, model, criterion,
epoch + 1, stats)
# Save model parameters
save_checkpoint(model, epoch + 1, config('cnn.checkpoint'), stats)
print('Finished Training')
y_true, y_pred = [], []
correct, total = 0, 0
running_loss = []
for X, y in va_loader:
with torch.no_grad():
output = model(X)
predicted = predictions(output.data)
y_true.extend(y)
y_pred.extend(predicted)
total += y.size(0)
correct += (predicted == y).sum().item()
running_loss.append(criterion(output, y).item())
print("Validation data accuracies:")
print(confusion_matrix(y_true, y_pred))
# Save figure and keep plot open
utils.save_cnn_training_plot()
utils.hold_training_plot()
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
# TODO:
####### Enter the model selection here! #####
modelSelection = input(
'Please input the type of model to be used(res50,dense121,dense169,mob_v2,mob):'
)
datasize = args.datasize
filename = "nyu_new.zip"
pathname = f"data/{filename}"
csv = "data/nyu_csv.zip"
te_loader = getTestingData(datasize,
csv,
pathname,
batch_size=config(modelSelection +
".batch_size"))
# Model
if modelSelection.lower() == 'res50':
model = Res50()
elif modelSelection.lower() == 'dense121':
model = Dense121()
elif modelSelection.lower() == 'mob_v2':
model = Mob_v2()
elif modelSelection.lower() == 'dense169':
model = Dense169()
elif modelSelection.lower() == 'mob':
model = Net()
elif modelSelection.lower() == 'squeeze':
model = Squeeze()
else:
assert False, 'Wrong type of model selection string!'
model = model.to(device)
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print(f"Loading {mdoelSelection}...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config(modelSelection + ".checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device, test=True)
# axes = util.make_training_plot()
print(f'Test Error:{acc}')
print(f'Test Loss:{loss}')
def get_order_book(limit=1):
terra_handler = TerraChain.get_exchange()
asks = terra_handler.exchange.functions.getAsks(
limit, terra_handler.trade_token_add,
config('ETH_TOKEN_ADDRESS')).call()
bids = terra_handler.exchange.functions.getBids(
limit, terra_handler.trade_token_add,
config('ETH_TOKEN_ADDRESS')).call()
return bids[2][0] / 10**18, asks[2][0] / 10**18
def forward(self, x):
if config().sim.env.state.type == "simple":
x = x.reshape(x.size(0), x.size(2))
return F.gumbel_softmax(self.simple_fc(x),
tau=config().learning.gumbel_softmax.tau)
out = self.conv(x)
out = out.view(x.size(0), -1)
return F.gumbel_softmax(self.fc(out),
tau=config().learning.gumbel_softmax.tau)
def main():
# Data loaders
tr_loader, va_loader, te_loader, get_semantic_labels = get_train_val_test_loaders(
num_classes=config('cnn.num_classes'))
# Model
model = CNN()
# TODO: define loss function, and optimizer
params = list(model.conv1.parameters()) + list(
model.conv2.parameters()) + list(model.conv3.parameters())
params = params + list(model.fc1.parameters()) + list(
model.fc2.parameters()) + list(model.fc3.parameters())
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params, lr=0.0001)
#
print('Number of float-valued parameters:', count_parameters(model))
# Attempts to restore the latest checkpoint if exists
print('Loading cnn...')
model, start_epoch, stats = restore_checkpoint(model,
config('cnn.checkpoint'))
fig, axes = utils.make_cnn_training_plot()
# Evaluate the randomly initialized model
_evaluate_epoch(axes, tr_loader, va_loader, model, criterion, start_epoch,
stats)
# Loop over the entire dataset multiple times
for epoch in range(start_epoch, config('cnn.num_epochs')):
# Train model
_train_epoch(tr_loader, model, criterion, optimizer)
# Evaluate model
_evaluate_epoch(axes, tr_loader, va_loader, model, criterion,
epoch + 1, stats)
# Save model parameters
save_checkpoint(model, epoch + 1, config('cnn.checkpoint'), stats)
print('Finished Training')
model, _, _ = restore_checkpoint(model, config('cnn.checkpoint'))
dataset = get_data_by_label(va_loader)
evaluate_cnn(dataset, model, criterion, get_semantic_labels)
# Save figure and keep plot open
utils.save_cnn_training_plot(fig)
utils.hold_training_plot()
def get_losses(self, state_batch, next_state_batch, action_batch,
reward_batch):
if config().learning.gumbel_softmax.use:
action_batch = F.gumbel_softmax(
action_batch, tau=config().learning.gumbel_softmax.tau)
predicted_next_actions = self.actor_target(next_state_batch)
y = reward_batch + self.gamma * self.critic_target(
next_state_batch, predicted_next_actions)
loss_critic = self.critic_criterion(
y, self.critic(state_batch, action_batch))
actor_loss = -self.critic(state_batch, self.actor(state_batch))
actor_loss = actor_loss.mean()
return loss_critic, actor_loss
def __init__(self):
self.board = None
self.scale = 16
self.episode = 0
self.iter = 0
self.size = config().sim.env.size
self.agent_position = None
self.coin_positions = None
self.board_memory = []
self.state_memory = []
self.mask_board = None
self.seen_positions = []
self.max_length = config().sim.env.max_length
def login(string):
config = {
'redirect_uri': utils.config('callback_uri'),
'response_type': 'code',
'client_id': utils.config('client_id'),
'scope': utils.config('scope')
}
url = 'https://accounts.spotify.com/authorize?' + urlencode(config)
webbrowser.open(url)
utils.output('end', 'success', utils.translate('login'))
def visualize_layer1_activations(i):
xi, yi = tr_loader.dataset[i]
xi = xi.view((1,3,config('image_dim'),config('image_dim')))
zi = F.relu(model.conv1(xi))
zi = zi.detach().numpy()[0]
sort_mask = np.argsort(model.conv1.weight.detach().numpy().mean(axis=(1,2,3)))
zi = zi[sort_mask]
fig, axes = plt.subplots(4, 4, figsize=(10,10))
for i, ax in enumerate(axes.ravel()):
ax.axis('off')
im = ax.imshow(zi[i], cmap='gray')
fig.suptitle('Layer 1 activations, y={}'.format(yi))
fig.savefig('CNN_viz1_{}.png'.format(yi), dpi=200, bbox_inches='tight')
def __init__(self, filename, datadir):
self.filename = filename
self.relpath, self.ext = os.path.splitext(filename.split(datadir)[-1])
self.relpath = self.relpath.strip('\/')
self.datefmt = config('date_fmt', '%b %d, %Y')
self._text = u'' #will store the text of the file
self._encoding = config('blog_encoding', 'utf-8')
#when this is set to 1, the entry will not reload from its source file
#this can be used for a cache mechanism, but is currently unused
self.reload_flag = 0
self.metadata = {}
self.parse_meta()
def main():
# data loaders
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
num_classes=config('autoencoder.classifier.num_classes'))
ae_classifier = AutoencoderClassifier(config('autoencoder.ae_repr_dim'),
config('autoencoder.classifier.num_classes'))
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(ae_classifier.parameters(),
lr=config('autoencoder.classifier.learning_rate'))
# freeze the weights of the encoder
for name, param in ae_classifier.named_parameters():
if 'fc1.' in name or 'fc2.' in name:
param.requires_grad = False
# Attempts to restore the latest checkpoint if exists
print('Loading autoencoder...')
ae_classifier, _, _ = restore_checkpoint(ae_classifier,
config('autoencoder.checkpoint'), force=True, pretrain=True)
print('Loading autoencoder classifier...')
ae_classifier, start_epoch, stats = restore_checkpoint(ae_classifier,
config('autoencoder.classifier.checkpoint'))
axes = utils.make_cnn_training_plot(name='Autoencoder Classifier')
# Evaluate the randomly initialized model
_evaluate_epoch(axes, tr_loader, va_loader, ae_classifier, criterion,
start_epoch, stats)
# Loop over the entire dataset multiple times
for epoch in range(start_epoch, config('autoencoder.classifier.num_epochs')):
# Train model
_train_epoch(tr_loader, ae_classifier, criterion, optimizer)
# Evaluate model
_evaluate_epoch(axes, tr_loader, va_loader, ae_classifier, criterion,
epoch + 1, stats)
# Save model parameters
save_checkpoint(ae_classifier, epoch + 1,
config('autoencoder.classifier.checkpoint'), stats)
print('Finished Training')
with torch.no_grad():
y_true, y_pred = [], []
correct, total = 0, 0
running_loss = []
for X, y in va_loader:
output = ae_classifier(X)
predicted = predictions(output.data)
y_true.extend(y)
y_pred.extend(predicted)
print("Validation data accuracies:")
print(confusion_matrix(y_true, y_pred))
# Keep plot open
utils.save_cnn_training_plot(name='ae_clf')
utils.hold_training_plot()
def _get_state_progressive(self, board, blank):
x, y = self.agent_position
state = np.zeros_like(board)
if blank: # Useful in the beginning when there are no frames in the memory
return state
state[x, y] = board[x, y]
depth_of_field = config().sim.env.state.depth_of_field
positions = [self.agent_position]
recursive_walk(positions, state, board, depth_of_field,
self.mask_board, self.seen_positions)
if config().sim.env.state.type == "memory":
# we add previously seen positions
state[self.mask_board] = board[self.mask_board]
return state
def main():
"""Create confusion matrix and save to file."""
tr_loader, va_loader, te_loader, semantic_labels = get_train_val_test_loaders(
task="source", batch_size=config("source.batch_size"))
model = Source()
print("Loading source...")
model, epoch, stats = restore_checkpoint(model,
config("source.checkpoint"))
sem_labels = "0 - Samoyed\n1 - Miniature Poodle\n2 - Saint Bernard\n3 - Great Dane\n4 - Dalmatian\n5 - Chihuahua\n6 - Siberian Husky\n7 - Yorkshire Terrier"
# Evaluate model
plot_conf(va_loader, model, sem_labels, "conf_matrix.png")
def authorize(url):
db = utils.db()['db']
code = url.split("?code=")[1].split("&")[0]
payload = {'redirect_uri': utils.config('callback_uri'),
'code': code,
'grant_type': 'authorization_code',
'scope': utils.config('scope')}
auth_header = base64.b64encode(six.text_type(utils.config('client_id') + ':' + utils.config('client_secret')).encode('ascii'))
headers = {'Authorization': 'Basic %s' % auth_header.decode('ascii')}
results = requests.post('https://accounts.spotify.com/api/token', data=payload, headers=headers)
token_info = results.json()
token_info['expires_at'] = int(time.time()) + token_info['expires_in']
token_info['client_id'] = utils.config('client_id')
token_info['client_secret'] = utils.config('client_secret')
token_info['prefix'] = utils.config('prefix')
token_info['scope'] = utils.config('scope')
db.insert(token_info)
utils.output('end', 'success', utils.translate('logged'))
def report_validation_performance(dataset, get_semantic_label, model, criterion):
cols = ['Orig', 'Autoencoder recon', 'Orig', 'Autoencoder recon', 'Orig', 'Autoencoder recon']
rows = []
fig, axes = plt.subplots(nrows=config('autoencoder.num_classes'),
ncols=6, figsize=(16,16))
for i in range(config('autoencoder.num_classes')):
X = dataset[i]
rows.append(get_semantic_label(i))
_, recon = model(X)
error = criterion(recon, X).item()
print('label {}, test error is {}'.format(i, error))
losses = ((recon - X) ** 2).mean(3).mean(2).mean(1).data.numpy()
best, worst = np.argmin(losses), np.argmax(losses)
typical = np.argsort(losses)[len(losses)//2]
print(' best case:', losses[best])
print(' worst case:', losses[worst])
print(' typical:', losses[typical])
axes[i,0].imshow(utils.denormalize_image(
np.transpose(X[best].numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
axes[i,1].imshow(utils.denormalize_image(
np.transpose(recon[best].data.numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
axes[i,2].imshow(utils.denormalize_image(
np.transpose(X[worst].numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
axes[i,3].imshow(utils.denormalize_image(
np.transpose(recon[worst].data.numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
axes[i,4].imshow(utils.denormalize_image(
np.transpose(X[typical].numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
axes[i,5].imshow(utils.denormalize_image(
np.transpose(recon[typical].data.numpy(), (1, 2, 0))),
plt.get_cmap('gray'), interpolation='bicubic', clim=(-1.0, +1.0))
for ax, row in zip(axes[:,0], rows):
ax.annotate(row, xy=(0, 0.5), xytext=(110, 0),
xycoords=ax.yaxis.label, textcoords='offset points',
size='large', ha='right', va='center')
for ax, col in zip(axes[0], cols):
ax.set_title(col)
for ax in axes.ravel():
ax.axis('off')
plt.suptitle('Autoencoder reconstruction\n Best, Worst, Typical', size=20)
plt.savefig("ae_per_class_perf.png", dpi=200, bbox_inches='tight')
def sendEmailNow (**ka):
ok = u.sendEmail(**ka)
if ok and u.config('recordEmails'):
try:
m.SentEmail.create (**ka)
except: # (apiproxy_errors.OverQuotaError, BadValueError):
logging.exception("Error saving SentEmail in datastore")
def prepare_atom_template(self, entries):
ns = cpy.config.get('/').copy()
entry_structs = []
last_updated = ''
for e in entries:
es = EntryStruct()
es.title = e.title
#this callback gives any interested plugins the chance to change
#the text of a story, as presented in a feed. It gives an Entry
#object, and ignores any return value
run_callback(self.parent.plugins, "cb_feed_story", e)
fulltext = escape(e.text)
#If you only want short descriptions:
#es.desc = escape(e.text[:255])
#for full text descriptions:
es.desc = fulltext
es.text = fulltext
es.time = time.strftime('%Y-%m-%dT%H:%M:%SZ', e.time_tuple)
if not last_updated: last_updated = es.time
es.link = urljoin(config('base_url'), e.relpath + '.html')
entry_structs.append(es)
ns['last_updated'] = last_updated
ns['entries'] = entry_structs
return ('atom', ns)
def replace_eluts(arr, scores, name='eluts'):
species = set(ut.config()['elut_species'].split('_'))
keep_cols = [n for n in arr.dtype.names[3:] if n[:2] not in species]
keep_cols = keep_cols + [name]
newarr = ppi.base_array(arr[['id1','id2','hit']], keep_cols, len(arr))
newarr[name] = scores
return newarr
def init_plugins(self, pluginlist):
"""
Initialize plugins. Assumes that each plugin contains a class of the
same name as the file. If it does, this function attaches an instance
of that class to self, and adds that instance to the plugins array.
"""
plugindir = config('plugin_dir', None)
if not plugindir or not os.path.isdir(plugindir):
cpy.log("Invalid Plugin Directory, no plugins loaded")
return
else:
sys.path.append(plugindir)
#XXX: Should we just scan for *.py files in the plugin dir? Should the
# mechanism to remove a plugin be renaming it or taking it out of
# conf?
for p in pluginlist:
try:
mod = __import__(p)
if not hasattr(self, p):
instance = getattr(mod, p)(self)
setattr(self, p, instance)
self.plugins.append(instance)
cpy.log("successfully imported plugin module %s" % p)
else:
raise ImportError
#bare except, because the modules could raise any number of errors
#on import, and we want them not to kill our server
except:
cpy.log("import failed on module %s, module not loaded" % p)
cpy.log("%s" % sys.exc_info()[0])
cpy.log("%s" % traceback.format_exc())
def getLocaleStrings (handler):
ctag = set_locale (handler) # current locale as a string in form: 'aa' or 'aa_AA' eg: 'en' or 'fr_CA'
ls = memcache.get (ctag) # requests from a user will generally have same locale so it makes sense to hold this in memcache @UndefinedVariable
if ls is None: # ... and even more so because also many different users will use same locale (memcache is global to the app)
locale_tags = u.config ('locales')
ls = LocaleStrings (ctag, locale_tags)
memcache.add (ctag, ls) # @UndefinedVariable
return ls
def ls(self, dir=""):
dirname = os.path.join(config('datadir'), dir)
l = [f for f in os.listdir(dirname) if f.endswith('.txt')]
title = "Listing dir %s" % dir
ns = locals()
del ns['self']
return [self.navbar(ns), ('admin_ls', ns)]
def __init__(self):
self.timeformats = [["%Y", "%d", "%m", "%b", "%B"],
["%Y %b", "%Y %m", "%Y %b", "%Y %B", "%m %d", "%b %d", "%B %d"],
["%Y %m %d", "%Y %b %d", "%Y %B %d"]]
self.plugins = [] #contains all loaded plugins
#set the output encoding
self._cp_config["cpy.tools.encode.encoding"] = "utf-8"
self.now = datetime.datetime.now
self.last_update = self.now()
self.num_entries = config('num_entries')
self.datadir = config('datadir')
self.ignore_directories = config('ignore_directories')
self.fp = '' #a cache of the front page content
self.index() #thus, we don't have to parse the metadata of the front
#page article when the second request comes in
self.init_plugins(config('plugins'))
FileCabinet.get_most_recent(self.datadir) #initialize entries
def __init__(self):
pynotify.uninit()
pynotify.init("sun")
self.pkg_count = fetch()[0]
self.message_added = ""
self.summary = "{0}Software Updates".format(" " * 14)
self.message = ("{0}{1} Software updates are available\n".format(
" " * 3, self.pkg_count))
self.icon = "{0}{1}.png".format(icon_path, __all__)
self.n = pynotify.Notification(self.summary, self.message, self.icon)
self.n.set_timeout(60000 * int(config()["STANDBY"]))
def cb_story(self, entry):
"""Add a $keywords variable to an entry which is a linkified,
comma-seperated string"""
kwstring = ''
base_url = config('base_url')
base_url = base_url.rstrip('/')
#list comp for 2.3 compatibility
kws = [k.strip() for k in keysplit(entry.metadata.get('keywords', "")) if k != '']
links = ['<a href=%s/Keywords/%s>%s</a>' % (base_url, quote_plus(kw), kw)
for kw in kws]
#add a comma seperated list of keywords to the entry
entry.keywords = ', '.join(links)
def main():
while True:
connection = True
time.sleep(1)
try:
urllib2.urlopen(mirror())
except urllib2.URLError:
connection = False
if connection:
Notify().show()
time.sleep(60 * int(config()["INTERVAL"]))
def update_story(self, story_title="", story_body="", filename=""):
if story_title == "" or story_body == "" or filename == "":
raise cpy.InternalRedirect("ls")
filename = os.path.join(config('datadir'), filename)
tmpfile = filename + ".bak"
try:
f = open(tmpfile, 'w')
f.write(story_title + "\n")
f.write(htmlunescape(story_body))
except Exception, e:
os.unlink(tmpfile)
cpy.log("unable to log: " + e.Message)
def pd_evidences(cluster,arr):
arr_clust = arr[[i for i,r in enumerate(arr)
if r[0] in cluster and r[1] in cluster]]
# doesn't seem right--if most the interactions are strong, shouldn't
# normalize them down--should still count. but does it matter since it's
# all comparative? messes with thinking about the clipping though in
# score_together.
#features, _ = ml.normalize(ml.arr_feats(arr_clust))
sps = ut.config()['elut_species'].split('_')
names = [n for n in arr.dtype.names[3:] if n[:2] in sps]
features = arr[names]
pd_ev = pd.PairDict([[r[0],r[1]] + list(features[i]) for i,r in
enumerate(arr_clust)])
return pd_ev, names
def highlight_code(self, textstr, font_tags=False):
for lang, code in CODE_RE.findall(textstr):
if not lang: lang = "python"
try:
lexer = get_lexer_by_name(lang.strip('"'))
except ClassNotFound:
return
formatter = HtmlFormatter(style=config("syntax_style", "default",
"SyntaxHighlight"), noclasses=font_tags)
code = pygments.highlight(code, lexer, formatter)
textstr = CODE_RE.sub(code, textstr, 1)
return textstr
def default(self, *args, **kargs):
if len(args) > 1:
return self.parent.error_page("too many arguments to Keywords")
elif len(args) < 1:
return self.parent.error_page("Too few arguments to Keywords")
try:
offset = int(kargs.get('offset', 0))
except ValueError:
offset = 0
self.keyword = unquote_plus(args[0])
print "Key = %s" % self.keyword
#remember we're not sure if base_url has a trailing '/' or not...
if 'Atom' in config("plugins"):
self.atom_link = config('base_url').rstrip('/') + \
'/Atom/keywords/' + self.keyword
entries = get_entries_by_meta('keywords')
entries = [e for e in entries if self.keyword in keysplit(e.metadata['keywords'])]
entries = entries[offset:offset + config("num_entries")]
return self.parent.render_page(entries, self.keyword, offset)
def _initDelay (minWait):
_s.delay = minWait # ds
for key, diff, cf in _s.monitors.itervalues():
nBad = _s._get (key, diff) [0]
if nBad:
#logging.debug('extra = %d for %d bad %s logins', cf.delayFn(nBad), nBad, cf.name)
_s.delay += cf.delayFn(nBad)
d = _s.delay*100.0 # Convert from int-deciseconds to float-milliseconds
mcka = u.config('MemCacheKeepAlive')# Divide d into a series of equal waits so each wait is the max that is less than MemCacheKeepAlive
n = -(-d//mcka) # number of waits. NB -(-a//b) rounds up and is equivalent to math.ceil (a/b)
_s.wait = int(-(-d//n)) # .. round up to int-millisecs
logging.debug('delay = %d ms, n = %d, wait = %d ms, total = %d', d, n, _s.wait, _s.wait*n)
assert _s.wait <= mcka
assert n * _s.wait >= d
assert (n-1) * _s.wait <= d
def _initMonitors (ema, ipa, hlr):
def _insert (name, key, diff):
assert name in lCfg
#diff is the distinct value
_s.monitors[name] = ('L:'+hlr+':'+key, diff, lCfg[name])
cfg = u.config(hlr)
lCfg = cfg.lockCfg
_s.monitors = {}
# name ,key ,diff
_insert ('ema_ipa',_s.ei,None)
_insert ('ema' ,ema ,ipa )
_insert ('ipa' ,ipa ,ema )
#logging.debug('monitors = %r',_s.monitors)
return cfg
def convert_dict_single(fromtype, totype):
"""
totype: must be Sp (eg 'Hs') or Sp_seqdb
Returns None if not necessary or not found.
"""
if len(totype.split('_')) > 1:
# Get rid of the 2nd half of totype if it's default for that species
tosp, toseqdb = totype.split('_')
if toseqdb == ut.config()[tosp+'_default']:
totype = tosp
if fromtype == totype:
return None
elif len(fromtype) == len(totype) == 2:
return odict(fromtype, totype)
else:
return custom_conversion(fromtype, totype)
def _taskqueue(h, *pa, **ka):
""" Check, if in Staging or Production, that h is being executed by Taskqueue
Otherwise, allow run in localhost calling the url
"""
if h.request.headers.get('X-AppEngine-TaskName'):
assert h.request.path.startswith('/tq')
elif u.config('Env') == 'Prod':
if not users.is_current_user_admin(): # we cant use this test in devServer or if logged-in as admin
logging.warning('Someone hacking a task url? pushQueueMethod does not have taskname header')
return h.error(403) #Forbidden
try:
return taskhandler(h, *pa, **ka)
except (TransientError, DeadlineExceededError):
raise # keep trying! (Exceptions in Push Queue Tasks are caught by the system and retried with exp backoff.)
except:
logging.exception("Task Failed:") #other exceptions - just give up!
def merge_by_species(arr, matches, func, remove=False):
"""
matches: like [apex] or [wcc, apex, ...]
Makes patterns with match for each species, like 'Hs.*apex
"""
assert not isinstance(matches,str), "matches is list, not string"
def merge_recurse(arr, patterns, func):
if patterns:
newarr = merge_features(arr, patterns[0], func, remove)
return merge_recurse(newarr, patterns[1:], func)
else:
return arr
# Won't match a merged feature since that will have a * in it.
patterns = [sp+'\w*'+match for match in matches for sp in
ut.config()['elut_species'].split('_')]
return merge_recurse(arr, patterns, func)
def score_arr_ext(arr, species, ext_key):
"""
Key_or_data: either a string matching one of the keys for ext data in
config.py, or a tuple of (name,data) where data is a sequence of (id1, id2,
score), and the sequence can be a generator.
fnet_cols: list of columns or first 2 letters to include, eg ['HS','CE']
"""
ext_file = ut.config()[ext_key]
conv_dict = convdict_from_fname(species, ext_file)
filename = ut.proj_path('fnet_path', ext_file)
stored_names = fnet_names(ext_file) # None if only one data column.
names = stored_names if stored_names else [ext_key]
data_dict = load_net(ut.load_tab_file(filename))
print 'External data file: %s; size: %s; cols: %s' % (ext_file,
len(data_dict), len(names))
score_arr(arr, species, names, data_dict, conv_dict)
def add_story(self, story_title="", story_body="", filename=""):
if story_title == "" or story_body == "" or filename == "":
raise cpy.InternalRedirect("ls")
filename = os.path.join(config('datadir'), filename)
if not filename.endswith('.txt'): filename += '.txt'
if os.path.isfile(filename):
ns = {'title': 'File Already Exists', 'filename': filename}
return [self.navbar(ns), ('admin_story_already_exists', ns)]
try:
f = open(filename, 'w')
f.write(story_title + "\n")
f.write(htmlunescape(story_body))
except Exception, e:
cpy.log("unable to log: " + e.Message)
def edit(self, filename):
try:
#XXX: are we sure that filename can't ref previous dirs? ../ didn't
# work in my basic test, but how should we better sanitize
# this?
fullname = os.path.join(config("datadir"), filename)
f = file(fullname)
except IOError:
cpy.log("Unable to open file %s for editing" % fullname)
return
title = "Editing file %s" % filename
story_title = f.readline()
body = htmlescape(f.read())
ns = locals()
del ns['self']
return [self.navbar(ns), ('admin_storyedit', ns)]
def essays(self, offset=0):
try:
offset = int(offset)
except ValueError:
offset = 0
ns = cpy.config.get('/').copy()
datadir = config('datadir')
essays = self.files(offset)
ns['offset'] = offset
if len(essays) == ns['num_entries']:
ns['offset_next'] = offset + ns['num_entries']
ns.update({'essays': essays,
'offset': offset,
'pagename': "essays"})
return self.render((('head', ns), ('essays', ns), ('foot', ns)))
def get_locale_from_accept_header(request, localeTags):
""" Detect a locale from request.header 'Accept-Language'
The locale with the highest quality factor (q) that most nearly matches our config.locales is returned.
rh: webapp2.RequestHandler
Note that in the future if all User Agents adopt the convention of sorting quality factors in descending order
then the first can be taken without needing to parse or sort the accept header leading to increased performance.
(see http://lists.w3.org/Archives/Public/ietf-http-wg/2012AprJun/0473.html)
"""
header = request.headers.get("Accept-Language", '')
parsed = parse_accept_language_header(header)
if parsed is None:
return None
pairs_sorted_by_q = sorted(parsed.items(), key=lambda (lang, q): q, reverse=True)
locale = Locale.negotiate( [lang for (lang, q) in pairs_sorted_by_q]
, u.config('locales')
, sep='_'
)
return u.utf8(locale)
def precalc_scores(scoref, dtype='f2'):
"""
Also zero out the diagonal to more efficiently remove all self-interactions
up-front.
"""
# NOTE to change dtype you must change it in loadtxt below!!
save_compact = ut.config()['save_compact_corrs']
compactf = '%s.%s.pyd' % (scoref, dtype)
if os.path.exists(compactf):
mat = ut.loadpy(compactf)
inds = range(mat.shape[0]) # always square score matrix
mat[inds, inds] = 0
return mat
else:
ascores = np.loadtxt(scoref, dtype='f2')
if save_compact:
print 'saving compact', compactf
ut.savepy(ascores, compactf)
return ascores
def default(self, *args, **kwargs):
#allow a plugin to handle a default url if it wants; it needs to return
#a tuple (pagename, [Entry objects]) if it does
call_result = run_callback(self.plugins, 'cb_default', args)
if call_result != []: return self.render_page(call_result[1:], call_result[0])
try:
offset = int(kwargs.get('offset', 0))
except ValueError:
offset = 0
z = args[0]
l = len(args)
if l <= len(self.timeformats):
#check to see if args represent a date
for fmt in self.timeformats[l-1]:
try:
t = time.strptime(' '.join(args), fmt)
if "%Y" in fmt:
year = t[0]
else:
year = self.now().year
if "%m" in fmt or "%b" in fmt or "%B" in fmt:
month = t[1]
else:
month = None
if "%d" in fmt:
day = t[2]
else:
day = None
entries = FileCabinet.get_entries_by_date(year, month, day)
if entries:
entries = entries[offset:offset + config('num_entries')]
return self.render_page(entries, ' '.join(args), offset)
except ValueError:
#not a date - move on
pass
z = os.path.join(*args)
fname = self.stripall(z, '.html', '.htm', '.txt')
e = FileCabinet.get_one(fname, self.datadir)
if e:
return self.render_page([e])
return self.error_page('Page Not Found', 404)
def getLocale(rh, tag):
##todo: review all this fn code
""" Retrieve the locale tag from a prioritized list of sources
NB We cannot return None because there has to be a locale - clearly UI text has to be in some language or other.
"""
localeTags = u.config('locales')
if localeTags:
# 1. use tag param
if tag in localeTags:
return tag
# 2. retrieve locale tag from url query string
tag = rh.request.get("hl", None)
if tag:
qs_items = rh.request.GET
del qs_items['hl'] # remove the hl item from the qs - it has now been processed
if tag in localeTags:
return tag
# 3. retrieve locale tag from cookie
tag = rh.request.cookies.get('hl', None)
if tag in localeTags:
return tag
# 4. retrieve locale tag from accept language header
tag = get_locale_from_accept_header(rh.request, localeTags)
if tag:
return tag
# 5. detect locale tag from IP address location
ctry = getRequestLocation(rh.request, 'Country')
if ctry:
tag = Locale.negotiate(ctry, localeTags)
if tag:
return tag
# 6. use the 1st member of localeTags
tag = localeTags[0]
if tag:
return tag
# 7. Use this locale if all attempts above have failed.
return 'en' # NB 'en' is chosen simply because the string literals and comments in this app happen to be in English. Its not because of a bias.
def prepare_rss_template(self, entries):
ns = cpy.config.get('/').copy()
entry_structs = []
for e in entries:
#XXX: what exactly is the <guid> element?
#XXX: what is the category tag? should keywords go here?
es = EntryStruct()
es.title = e.title
#this callback gives any interested plugins the chance to change
#the text of a story, as presented in a feed. It gives an Entry
#object, and ignores any return value
run_callback(self.parent.plugins, "cb_feed_story", e)
#because <style> messed me up, I'm going to stop stripping
#HTML out of my description. The RSS spec sucks.
es.desc = e.text
es.link = urljoin(config('base_url'), e.relpath + '.html')
es.relpath = e.relpath
es.time = time.strftime('%Y-%m-%dT%H:%M:%SZ', e.time_tuple)
es.text = e.text
entry_structs.append(es)
ns['entries'] = entry_structs
return ('rss', ns)
def keyword_rss(self, kw):
num_entries = config('num_entries', 10)
entries = get_entries_by_meta('keywords')
entries = [e for e in entries if kw in e.metadata['keywords']]
return self.prepare_rss_template(entries[:num_entries])
else:
print benchmark, 'price not retrieved, page not saved'
else:
# Timed out
print benchmark, "timed out"
pass
return None
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Pricebot -- update wiki page")
parser.add_argument('-q', '--quiet', action='store_true',
default=False, help='Quiet non-error output')
args = parser.parse_args()
config = config(quiet=args.quiet)
# Now do the work!
url = config.setting['pricebot']['wiki_url']
httpuser = config.setting['pricebot']['username']
httppass = config.setting['pricebot']['password']
site = setup(url, httpuser, httppass, config)
# Then check if the bot is disabled, and act accordingly
if bot_status(site) == 1:
set_exchange_rates(site)
set_crude_prices(site)