code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_settings_setters(self) -> None:
"""
Setters exist for well-known settings.
"""
s = h2.settings.Settings(client=True)
s.header_table_size = 0
s.enable_push = 1
s.initial_window_size = 2
s.max_frame_size = 16385
s.max_concurrent_streams = 4... |
Setters exist for well-known settings.
| test_settings_setters | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_cannot_set_invalid_vals_for_initial_window_size(self, val) -> None:
"""
SETTINGS_INITIAL_WINDOW_SIZE only allows values between 0 and 2**32 - 1
inclusive.
"""
s = h2.settings.Settings()
if 0 <= val <= 2**31 - 1:
s.initial_window_size = val
... |
SETTINGS_INITIAL_WINDOW_SIZE only allows values between 0 and 2**32 - 1
inclusive.
| test_cannot_set_invalid_vals_for_initial_window_size | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_equality_reflexive(self, settings) -> None:
"""
An object compares equal to itself using the == operator and the !=
operator.
"""
assert (settings == settings)
assert settings == settings |
An object compares equal to itself using the == operator and the !=
operator.
| test_equality_reflexive | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_equality_multiple(self, settings, o_settings) -> None:
"""
Two objects compare themselves using the == operator and the !=
operator.
"""
if settings == o_settings:
assert settings == o_settings
assert settings == o_settings
else:
... |
Two objects compare themselves using the == operator and the !=
operator.
| test_equality_multiple | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_another_type_equality(self, settings) -> None:
"""
The object does not compare equal to an object of an unrelated type
(which does not implement the comparison) using the == operator.
"""
obj = object()
assert (settings != obj)
assert settings != obj |
The object does not compare equal to an object of an unrelated type
(which does not implement the comparison) using the == operator.
| test_another_type_equality | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_delegated_eq(self, settings) -> None:
"""
The result of comparison is delegated to the right-hand operand if
it is of an unrelated type.
"""
class Delegate:
def __eq__(self, other):
return [self]
def __ne__(self, other):
... |
The result of comparison is delegated to the right-hand operand if
it is of an unrelated type.
| test_delegated_eq | python | python-hyper/h2 | tests/test_settings.py | https://github.com/python-hyper/h2/blob/master/tests/test_settings.py | MIT |
def test_state_machine_only_allows_connection_states(self) -> None:
"""
The Connection state machine only allows ConnectionState inputs.
"""
c = h2.connection.H2ConnectionStateMachine()
with pytest.raises(ValueError):
c.process_input(1) |
The Connection state machine only allows ConnectionState inputs.
| test_state_machine_only_allows_connection_states | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_priority_frames_allowed_in_all_states(self, state, input_) -> None:
"""
Priority frames can be sent/received in all connection states except
closed.
"""
c = h2.connection.H2ConnectionStateMachine()
c.state = state
c.process_input(input_) |
Priority frames can be sent/received in all connection states except
closed.
| test_priority_frames_allowed_in_all_states | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_state_machine_only_allows_stream_states(self) -> None:
"""
The Stream state machine only allows StreamState inputs.
"""
s = h2.stream.H2StreamStateMachine(stream_id=1)
with pytest.raises(ValueError):
s.process_input(1) |
The Stream state machine only allows StreamState inputs.
| test_state_machine_only_allows_stream_states | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_stream_state_machine_forbids_pushes_on_server_streams(self) -> None:
"""
Streams where this peer is a server do not allow receiving pushed
frames.
"""
s = h2.stream.H2StreamStateMachine(stream_id=1)
s.process_input(h2.stream.StreamInputs.RECV_HEADERS)
wi... |
Streams where this peer is a server do not allow receiving pushed
frames.
| test_stream_state_machine_forbids_pushes_on_server_streams | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_stream_state_machine_forbids_sending_pushes_from_clients(self) -> None:
"""
Streams where this peer is a client do not allow sending pushed frames.
"""
s = h2.stream.H2StreamStateMachine(stream_id=1)
s.process_input(h2.stream.StreamInputs.SEND_HEADERS)
with pyte... |
Streams where this peer is a client do not allow sending pushed frames.
| test_stream_state_machine_forbids_sending_pushes_from_clients | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_cannot_send_on_closed_streams(self, input_) -> None:
"""
Sending anything but a PRIORITY frame is forbidden on closed streams.
"""
c = h2.stream.H2StreamStateMachine(stream_id=1)
c.state = h2.stream.StreamState.CLOSED
expected_error = (
h2.exceptions... |
Sending anything but a PRIORITY frame is forbidden on closed streams.
| test_cannot_send_on_closed_streams | python | python-hyper/h2 | tests/test_state_machines.py | https://github.com/python-hyper/h2/blob/master/tests/test_state_machines.py | MIT |
def test_reset_stream_keeps_header_state_correct(self, frame_factory) -> None:
"""
A stream that has been reset still affects the header decoder.
"""
c = h2.connection.H2Connection()
c.initiate_connection()
c.send_headers(stream_id=1, headers=self.example_request_headers)... |
A stream that has been reset still affects the header decoder.
| test_reset_stream_keeps_header_state_correct | python | python-hyper/h2 | tests/test_stream_reset.py | https://github.com/python-hyper/h2/blob/master/tests/test_stream_reset.py | MIT |
def test_reset_stream_keeps_flow_control_correct(self,
close_id,
other_id,
frame_factory) -> None:
"""
A stream that has been reset does not affe... |
A stream that has been reset does not affect the connection flow
control window.
| test_reset_stream_keeps_flow_control_correct | python | python-hyper/h2 | tests/test_stream_reset.py | https://github.com/python-hyper/h2/blob/master/tests/test_stream_reset.py | MIT |
def test_returns_correct_sequence_for_clients(self, frame_factory) -> None:
"""
For a client connection, the correct sequence of stream IDs is
returned.
"""
# Running the exhaustive version of this test (all 1 billion available
# stream IDs) is too painful. For that reaso... |
For a client connection, the correct sequence of stream IDs is
returned.
| test_returns_correct_sequence_for_clients | python | python-hyper/h2 | tests/test_utility_functions.py | https://github.com/python-hyper/h2/blob/master/tests/test_utility_functions.py | MIT |
def test_returns_correct_sequence_for_servers(self, frame_factory) -> None:
"""
For a server connection, the correct sequence of stream IDs is
returned.
"""
# Running the exhaustive version of this test (all 1 billion available
# stream IDs) is too painful. For that reaso... |
For a server connection, the correct sequence of stream IDs is
returned.
| test_returns_correct_sequence_for_servers | python | python-hyper/h2 | tests/test_utility_functions.py | https://github.com/python-hyper/h2/blob/master/tests/test_utility_functions.py | MIT |
def test_does_not_increment_without_stream_send(self) -> None:
"""
If a new stream isn't actually created, the next stream ID doesn't
change.
"""
c = h2.connection.H2Connection()
c.initiate_connection()
first_stream_id = c.get_next_available_stream_id()
s... |
If a new stream isn't actually created, the next stream ID doesn't
change.
| test_does_not_increment_without_stream_send | python | python-hyper/h2 | tests/test_utility_functions.py | https://github.com/python-hyper/h2/blob/master/tests/test_utility_functions.py | MIT |
def element(name, *children, **attrs):
"""
Construct a string from the HTML element description.
"""
formatted_attributes = ' '.join(
'{}={}'.format(key, quote(str(value)))
for key, value in sorted(attrs.items())
)
formatted_children = ''.join(children)
return u'<{name} {attr... |
Construct a string from the HTML element description.
| element | python | python-hyper/h2 | visualizer/visualize.py | https://github.com/python-hyper/h2/blob/master/visualizer/visualize.py | MIT |
def row_for_output(event, side_effect):
"""
Given an output tuple (an event and its side effect), generates a table row
from it.
"""
point_size = {'point-size': '9'}
event_cell = element(
"td",
element("font", enum_member_name(event), **point_size)
)
side_effect_name = (
... |
Given an output tuple (an event and its side effect), generates a table row
from it.
| row_for_output | python | python-hyper/h2 | visualizer/visualize.py | https://github.com/python-hyper/h2/blob/master/visualizer/visualize.py | MIT |
def table_maker(initial_state, final_state, outputs, port):
"""
Construct an HTML table to label a state transition.
"""
header = "{} -> {}".format(
enum_member_name(initial_state), enum_member_name(final_state)
)
header_row = element(
"tr",
element(
"td",
... |
Construct an HTML table to label a state transition.
| table_maker | python | python-hyper/h2 | visualizer/visualize.py | https://github.com/python-hyper/h2/blob/master/visualizer/visualize.py | MIT |
def build_digraph(state_machine):
"""
Produce a L{graphviz.Digraph} object from a state machine.
"""
digraph = graphviz.Digraph(node_attr={'fontname': 'Menlo'},
edge_attr={'fontname': 'Menlo'},
graph_attr={'dpi': '200'})
# First, add the... |
Produce a L{graphviz.Digraph} object from a state machine.
| build_digraph | python | python-hyper/h2 | visualizer/visualize.py | https://github.com/python-hyper/h2/blob/master/visualizer/visualize.py | MIT |
def main():
"""
Renders all the state machines in h2 into images.
"""
program_name = sys.argv[0]
argv = sys.argv[1:]
description = """
Visualize h2 state machines as graphs.
"""
epilog = """
You must have the graphviz tool suite installed. Please visit
http://www.graphviz.o... |
Renders all the state machines in h2 into images.
| main | python | python-hyper/h2 | visualizer/visualize.py | https://github.com/python-hyper/h2/blob/master/visualizer/visualize.py | MIT |
def pid_exists(pid):
"""Check whether pid exists in the current process table."""
if pid == 0:
# According to "man 2 kill" PID 0 has a special meaning:
# it refers to <<every process in the process group of the
# calling process>> so we don't want to go any further.
# If we get h... | Check whether pid exists in the current process table. | pid_exists | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def run(self, block=True, binary=False, cwd=None, env=None):
"""Runs the given command, with or without pexpect functionality enabled."""
self.blocking = block
# Use subprocess.
if self.blocking:
popen_kwargs = self._default_popen_kwargs.copy()
del popen_kwargs["... | Runs the given command, with or without pexpect functionality enabled. | run | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def expect(self, pattern, timeout=-1):
"""Waits on the given pattern to appear in std_out"""
if self.blocking:
raise RuntimeError("expect can only be used on non-blocking commands.")
try:
self.subprocess.expect(pattern=pattern, timeout=timeout)
except pexpect.EO... | Waits on the given pattern to appear in std_out | expect | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def send(self, s, end=os.linesep, signal=False):
"""Sends the given string or signal to std_in."""
if self.blocking:
raise RuntimeError("send can only be used on non-blocking commands.")
if not signal:
if self._uses_subprocess:
return self.subprocess.com... | Sends the given string or signal to std_in. | send | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def pipe(self, command, timeout=None, cwd=None):
"""Runs the current command and passes its output to the next
given process.
"""
if not timeout:
timeout = self.timeout
if not self.was_run:
self.run(block=False, cwd=cwd)
data = self.out
... | Runs the current command and passes its output to the next
given process.
| pipe | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def _expand_args(command):
"""Parses command strings and returns a Popen-ready list."""
# Prepare arguments.
if isinstance(command, STR_TYPES):
if sys.version_info[0] == 2:
splitter = shlex.shlex(command.encode("utf-8"))
elif sys.version_info[0] == 3:
splitter = shle... | Parses command strings and returns a Popen-ready list. | _expand_args | python | amitt001/delegator.py | delegator.py | https://github.com/amitt001/delegator.py/blob/master/delegator.py | MIT |
def __init__(self, env):
"""Implements the models and training of
Policy Gradient Methods
Argument:
env (Object): OpenAI gym environment
"""
self.env = env
# entropy loss weight
self.beta = 0.0
# value loss for all policy gradients except... | Implements the models and training of
Policy Gradient Methods
Argument:
env (Object): OpenAI gym environment
| __init__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def action(self, args):
"""Given mean and stddev, sample an action, clip
and return
We assume Gaussian distribution of probability
of selecting an action given a state
Argument:
args (list) : mean, stddev list
Return:
action (tensor):... | Given mean and stddev, sample an action, clip
and return
We assume Gaussian distribution of probability
of selecting an action given a state
Argument:
args (list) : mean, stddev list
Return:
action (tensor): policy action
| action | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def logp(self, args):
"""Given mean, stddev, and action compute
the log probability of the Gaussian distribution
Argument:
args (list) : mean, stddev action, list
Return:
logp (tensor): log of action
"""
mean, stddev, action = args
dist... | Given mean, stddev, and action compute
the log probability of the Gaussian distribution
Argument:
args (list) : mean, stddev action, list
Return:
logp (tensor): log of action
| logp | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def entropy(self, args):
"""Given the mean and stddev compute
the Gaussian dist entropy
Argument:
args (list) : mean, stddev list
Return:
entropy (tensor): action entropy
"""
mean, stddev = args
dist = tfp.distributions.Normal(loc=mean... | Given the mean and stddev compute
the Gaussian dist entropy
Argument:
args (list) : mean, stddev list
Return:
entropy (tensor): action entropy
| entropy | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def build_autoencoder(self):
"""Autoencoder to convert states into features
"""
# first build the encoder model
inputs = Input(shape=(self.state_dim, ), name='state')
feature_size = 32
x = Dense(256, activation='relu')(inputs)
x = Dense(128, activation='relu')(x)
... | Autoencoder to convert states into features
| build_autoencoder | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def train_autoencoder(self, x_train, x_test):
"""Training the autoencoder using randomly sampled
states from the environment
Arguments:
x_train (tensor): autoencoder train dataset
x_test (tensor): autoencoder test dataset
"""
# train the autoencoder
... | Training the autoencoder using randomly sampled
states from the environment
Arguments:
x_train (tensor): autoencoder train dataset
x_test (tensor): autoencoder test dataset
| train_autoencoder | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def build_actor_critic(self):
"""4 models are built but 3 models share the
same parameters. hence training one, trains the rest.
The 3 models that share the same parameters
are action, logp, and entropy models.
Entropy model is used by A2C only.
... | 4 models are built but 3 models share the
same parameters. hence training one, trains the rest.
The 3 models that share the same parameters
are action, logp, and entropy models.
Entropy model is used by A2C only.
Each model has the same MLP structure:
... | build_actor_critic | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def logp_loss(self, entropy, beta=0.0):
"""logp loss, the 3rd and 4th variables
(entropy and beta) are needed by A2C
so we have a different loss function structure
Arguments:
entropy (tensor): Entropy loss
beta (float): Entropy loss weight
Return... | logp loss, the 3rd and 4th variables
(entropy and beta) are needed by A2C
so we have a different loss function structure
Arguments:
entropy (tensor): Entropy loss
beta (float): Entropy loss weight
Return:
loss (tensor): computed loss
| logp_loss | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def save_weights(self,
actor_weights,
encoder_weights,
value_weights=None):
"""Save the actor, critic and encoder weights
useful for restoring the trained models
Arguments:
actor_weights (tensor): actor net paramet... | Save the actor, critic and encoder weights
useful for restoring the trained models
Arguments:
actor_weights (tensor): actor net parameters
encoder_weights (tensor): encoder weights
value_weights (tensor): value net parameters
| save_weights | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def load_weights(self,
actor_weights,
value_weights=None):
"""Load the trained weights
useful if we are interested in using
the network right away
Arguments:
actor_weights (string): filename containing actor net
... | Load the trained weights
useful if we are interested in using
the network right away
Arguments:
actor_weights (string): filename containing actor net
weights
value_weights (string): filename containing value net
weights
| load_weights | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def train_by_episode(self):
"""Train by episode
Prepare the dataset before the step by step training
"""
# only REINFORCE and REINFORCE with baseline
# use the ff code
# convert the rewards to returns
rewards = []
gamma = 0.99
for item in self.m... | Train by episode
Prepare the dataset before the step by step training
| train_by_episode | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def train(self, item, gamma=1.0):
"""Main routine for training
Arguments:
item (list) : one experience unit
gamma (float) : discount factor [0,1]
"""
[step, state, next_state, reward, done] = item
# must save state for entropy computation
self.st... | Main routine for training
Arguments:
item (list) : one experience unit
gamma (float) : discount factor [0,1]
| train | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def __init__(self, env):
"""Implements the models and training of
A2C policy gradient method
Arguments:
env (Object): OpenAI gym environment
"""
super().__init__(env)
# beta of entropy used in A2C
self.beta = 0.9
# loss function of A2C val... | Implements the models and training of
A2C policy gradient method
Arguments:
env (Object): OpenAI gym environment
| __init__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def train_by_episode(self, last_value=0):
"""Train by episode
Prepare the dataset before the step by step training
Arguments:
last_value (float): previous prediction of value net
"""
# implements A2C training from the last state
# to the first state
... | Train by episode
Prepare the dataset before the step by step training
Arguments:
last_value (float): previous prediction of value net
| train_by_episode | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def train(self, item, gamma=1.0):
"""Main routine for training
Arguments:
item (list) : one experience unit
gamma (float) : discount factor [0,1]
"""
[step, state, next_state, reward, done] = item
# must save state for entropy computation
self.sta... | Main routine for training
Arguments:
item (list) : one experience unit
gamma (float) : discount factor [0,1]
| train | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def setup_files(args):
"""Housekeeping to keep the output logs in separate folders
Arguments:
args: user-defined arguments
"""
postfix = 'reinforce'
has_value_model = False
if args.baseline:
postfix = "reinforce-baseline"
has_value_model = True
elif args.actor_critic:... | Housekeeping to keep the output logs in separate folders
Arguments:
args: user-defined arguments
| setup_files | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def setup_agent(env, args):
"""Agent initialization
Arguments:
env (Object): OpenAI environment
args : user-defined arguments
"""
# instantiate agent
if args.baseline:
agent = REINFORCEBaselineAgent(env)
elif args.a2c:
agent = A2CAgent(env)
elif args.actor_cri... | Agent initialization
Arguments:
env (Object): OpenAI environment
args : user-defined arguments
| setup_agent | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def setup_writer(fileid, postfix):
"""Use to prepare file and writer for data logging
Arguments:
fileid (string): unique file identfier
postfix (string): path
"""
# we dump episode num, step, total reward, and
# number of episodes solved in a csv file for analysis
csvfilename = ... | Use to prepare file and writer for data logging
Arguments:
fileid (string): unique file identfier
postfix (string): path
| setup_writer | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter10-policy/policygradient-car-10.1.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py | MIT |
def nms(args, classes, offsets, anchors):
"""Perform NMS (Algorithm 11.12.1).
Arguments:
args : User-defined configurations
classes (tensor): Predicted classes
offsets (tensor): Predicted offsets
Returns:
objects (tensor): class predictions per anchor
indexe... | Perform NMS (Algorithm 11.12.1).
Arguments:
args : User-defined configurations
classes (tensor): Predicted classes
offsets (tensor): Predicted offsets
Returns:
objects (tensor): class predictions per anchor
indexes (tensor): indexes of detected objects
... | nms | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/boxes.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/boxes.py | MIT |
def show_boxes(args,
image,
classes,
offsets,
feature_shapes,
show=True):
"""Show detected objects on an image. Show bounding boxes
and class names.
Arguments:
image (tensor): Image to show detected objects (0.0 to 1.0)
... | Show detected objects on an image. Show bounding boxes
and class names.
Arguments:
image (tensor): Image to show detected objects (0.0 to 1.0)
classes (tensor): Predicted classes
offsets (tensor): Predicted offsets
feature_shapes (tensor): SSD head feature maps
show (boo... | show_boxes | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/boxes.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/boxes.py | MIT |
def show_anchors(image,
feature_shape,
anchors,
maxiou_indexes=None,
maxiou_per_gt=None,
labels=None,
show_grids=False):
"""Utility for showing anchor boxes for debugging purposes"""
image_height, image_width, ... | Utility for showing anchor boxes for debugging purposes | show_anchors | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/boxes.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/boxes.py | MIT |
def apply_random_noise(self, image, percent=30):
"""Apply random noise on an image (not used)"""
random = np.random.randint(0, 100)
if random < percent:
image = random_noise(image)
return image | Apply random noise on an image (not used) | apply_random_noise | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/data_generator.py | MIT |
def apply_random_intensity_rescale(self, image, percent=30):
"""Apply random intensity rescale on an image (not used)"""
random = np.random.randint(0, 100)
if random < percent:
v_min, v_max = np.percentile(image, (0.2, 99.8))
image = exposure.rescale_intensity(image, in_r... | Apply random intensity rescale on an image (not used) | apply_random_intensity_rescale | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/data_generator.py | MIT |
def apply_random_exposure_adjust(self, image, percent=30):
"""Apply random exposure adjustment on an image (not used)"""
random = np.random.randint(0, 100)
if random < percent:
image = exposure.adjust_gamma(image, gamma=0.4, gain=0.9)
# another exposure algo
#... | Apply random exposure adjustment on an image (not used) | apply_random_exposure_adjust | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/data_generator.py | MIT |
def __data_generation(self, keys):
"""Generate train data: images and
object detection ground truth labels
Arguments:
keys (array): Randomly sampled keys
(key is image filename)
Returns:
x (tensor): Batch images
y (tensor): Batch cl... | Generate train data: images and
object detection ground truth labels
Arguments:
keys (array): Randomly sampled keys
(key is image filename)
Returns:
x (tensor): Batch images
y (tensor): Batch classes, offsets, and masks
| __data_generation | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/data_generator.py | MIT |
def get_box_color(index=None):
"""Retrieve plt-compatible color string based on object index"""
colors = ['w', 'r', 'b', 'g', 'c', 'm', 'y', 'g', 'c', 'm', 'k']
if index is None:
return colors[randint(0, len(colors) - 1)]
return colors[index % len(colors)] | Retrieve plt-compatible color string based on object index | get_box_color | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def get_box_rgbcolor(index=None):
"""Retrieve rgb color based on object index"""
colors = [(0, 0, 0), (255, 0, 0), (0, 0, 255), (0, 255, 0), (128, 128, 0)]
if index is None:
return colors[randint(0, len(colors) - 1)]
return colors[index % len(colors)] | Retrieve rgb color based on object index | get_box_rgbcolor | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def load_csv(path):
"""Load a csv file into an np array"""
data = []
with open(path) as csv_file:
rows = csv.reader(csv_file, delimiter=',')
for row in rows:
data.append(row)
return np.array(data) | Load a csv file into an np array | load_csv | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def get_label_dictionary(labels, keys):
"""Associate key (filename) to value (box coords, class)"""
dictionary = {}
for key in keys:
dictionary[key] = [] # empty boxes
for label in labels:
if len(label) != 6:
print("Incomplete label:", label[0])
continue
... | Associate key (filename) to value (box coords, class) | get_label_dictionary | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def build_label_dictionary(path):
"""Build a dict with key=filename, value=[box coords, class]"""
labels = load_csv(path)
# skip the 1st line header
labels = labels[1:]
# keys are filenames
keys = np.unique(labels[:,0])
dictionary = get_label_dictionary(labels, keys)
classes = np.unique(... | Build a dict with key=filename, value=[box coords, class] | build_label_dictionary | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def show_labels(image, labels, ax=None):
"""Draw bounding box on an object given box coords (labels[1:5])"""
if ax is None:
fig, ax = plt.subplots(1)
ax.imshow(image)
for label in labels:
# default label format is xmin, xmax, ymin, ymax
w = label[1] - label[0]
h = lab... | Draw bounding box on an object given box coords (labels[1:5]) | show_labels | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/label_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/label_utils.py | MIT |
def anchor_sizes(n_layers=4):
"""Generate linear distribution of sizes depending on
the number of ssd top layers
Arguments:
n_layers (int): Number of ssd head layers
Returns:
sizes (list): A list of anchor sizes
"""
s = np.linspace(0.2, 0.9, n_layers + 1)
sizes = []
fo... | Generate linear distribution of sizes depending on
the number of ssd top layers
Arguments:
n_layers (int): Number of ssd head layers
Returns:
sizes (list): A list of anchor sizes
| anchor_sizes | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def anchor_boxes(feature_shape,
image_shape,
index=0,
n_layers=4,
aspect_ratios=(1, 2, 0.5)):
""" Compute the anchor boxes for a given feature map.
Anchor boxes are in minmax format
Arguments:
feature_shape (list): Feature map shap... | Compute the anchor boxes for a given feature map.
Anchor boxes are in minmax format
Arguments:
feature_shape (list): Feature map shape
image_shape (list): Image size shape
index (int): Indicates which of ssd head layers
are we referring to
n_layers (int): Number of ... | anchor_boxes | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def centroid2minmax(boxes):
"""Centroid to minmax format
(cx, cy, w, h) to (xmin, xmax, ymin, ymax)
Arguments:
boxes (tensor): Batch of boxes in centroid format
Returns:
minmax (tensor): Batch of boxes in minmax format
"""
minmax= np.copy(boxes).astype(np.float)
minmax[...... | Centroid to minmax format
(cx, cy, w, h) to (xmin, xmax, ymin, ymax)
Arguments:
boxes (tensor): Batch of boxes in centroid format
Returns:
minmax (tensor): Batch of boxes in minmax format
| centroid2minmax | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def minmax2centroid(boxes):
"""Minmax to centroid format
(xmin, xmax, ymin, ymax) to (cx, cy, w, h)
Arguments:
boxes (tensor): Batch of boxes in minmax format
Returns:
centroid (tensor): Batch of boxes in centroid format
"""
centroid = np.copy(boxes).astype(np.float)
centro... | Minmax to centroid format
(xmin, xmax, ymin, ymax) to (cx, cy, w, h)
Arguments:
boxes (tensor): Batch of boxes in minmax format
Returns:
centroid (tensor): Batch of boxes in centroid format
| minmax2centroid | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def intersection(boxes1, boxes2):
"""Compute intersection of batch of boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
intersection_areas (tensor): intersection of areas of
boxes1 and box... | Compute intersection of batch of boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
intersection_areas (tensor): intersection of areas of
boxes1 and boxes2
| intersection | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def union(boxes1, boxes2, intersection_areas):
"""Compute union of batch of boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
union_areas (tensor): union of areas of
boxes1 and boxes2
"""
... | Compute union of batch of boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
union_areas (tensor): union of areas of
boxes1 and boxes2
| union | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def iou(boxes1, boxes2):
"""Compute IoU of batch boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
iou (tensor): intersectiin of union of areas of
boxes1 and boxes2
"""
intersection_ar... | Compute IoU of batch boxes1 and boxes2
Arguments:
boxes1 (tensor): Boxes coordinates in pixels
boxes2 (tensor): Boxes coordinates in pixels
Returns:
iou (tensor): intersectiin of union of areas of
boxes1 and boxes2
| iou | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def get_gt_data(iou,
n_classes=4,
anchors=None,
labels=None,
normalize=False,
threshold=0.6):
"""Retrieve ground truth class, bbox offset, and mask
Arguments:
iou (tensor): IoU of each bounding box wrt each anchor box
... | Retrieve ground truth class, bbox offset, and mask
Arguments:
iou (tensor): IoU of each bounding box wrt each anchor box
n_classes (int): Number of object classes
anchors (tensor): Anchor boxes per feature layer
labels (list): Ground truth labels
normalize (bool): If nor... | get_gt_data | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/layer_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/layer_utils.py | MIT |
def focal_loss_ce(y_true, y_pred):
"""Alternative CE focal loss (not used)
"""
# only missing in this FL is y_pred clipping
weight = (1 - y_pred)
weight *= weight
# alpha = 0.25
weight *= 0.25
return K.categorical_crossentropy(weight*y_true, y_pred) | Alternative CE focal loss (not used)
| focal_loss_ce | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/loss.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/loss.py | MIT |
def mask_offset(y_true, y_pred):
"""Pre-process ground truth and prediction data"""
# 1st 4 are offsets
offset = y_true[..., 0:4]
# last 4 are mask
mask = y_true[..., 4:8]
# pred is actually duplicated for alignment
# either we get the 1st or last 4 offset pred
# and apply the mask
... | Pre-process ground truth and prediction data | mask_offset | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/loss.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/loss.py | MIT |
def smooth_l1_loss(y_true, y_pred):
"""Smooth L1 loss using tensorflow Huber loss
"""
offset, pred = mask_offset(y_true, y_pred)
# Huber loss as approx of smooth L1
return Huber()(offset, pred) | Smooth L1 loss using tensorflow Huber loss
| smooth_l1_loss | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/loss.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/loss.py | MIT |
def build_ssd(input_shape,
backbone,
n_layers=4,
n_classes=4,
aspect_ratios=(1, 2, 0.5)):
"""Build SSD model given a backbone
Arguments:
input_shape (list): input image shape
backbone (model): Keras backbone model
n_layers (int): N... | Build SSD model given a backbone
Arguments:
input_shape (list): input image shape
backbone (model): Keras backbone model
n_layers (int): Number of layers of ssd head
n_classes (int): Number of obj classes
aspect_ratios (list): annchor box aspect ratios
Returns:
... | build_ssd | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/model.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/model.py | MIT |
def lr_scheduler(epoch):
"""Learning rate scheduler - called every epoch"""
lr = 1e-3
epoch_offset = config.params['epoch_offset']
if epoch > (200 - epoch_offset):
lr *= 1e-4
elif epoch > (180 - epoch_offset):
lr *= 5e-4
elif epoch > (160 - epoch_offset):
lr *= 1e-3
e... | Learning rate scheduler - called every epoch | lr_scheduler | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/model_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/model_utils.py | MIT |
def ssd_parser():
"""Instatiate a command line parser for ssd network model
building, training, and testing
"""
parser = argparse.ArgumentParser(description='SSD for object detection')
# arguments for model building and training
help_ = "Number of feature extraction layers of SSD head after back... | Instatiate a command line parser for ssd network model
building, training, and testing
| ssd_parser | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/model_utils.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/model_utils.py | MIT |
def resnet_layer(inputs,
num_filters=16,
kernel_size=3,
strides=1,
activation='relu',
batch_normalization=True,
conv_first=True):
"""2D Convolution-Batch Normalization-Activation stack builder
Arguments:
... | 2D Convolution-Batch Normalization-Activation stack builder
Arguments:
inputs (tensor): Input tensor from input image or previous layer
num_filters (int): Conv2D number of filters
kernel_size (int): Conv2D square kernel dimensions
strides (int): Conv2D square stride dimensions
... | resnet_layer | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/resnet.py | MIT |
def resnet_v1(input_shape, depth, num_classes=10):
"""ResNet Version 1 Model builder [a]
Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
Last ReLU is after the shortcut connection.
At the beginning of each stage, the feature map size is halved (downsampled)
by a convolutional layer with strides=2, while the n... | ResNet Version 1 Model builder [a]
Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
Last ReLU is after the shortcut connection.
At the beginning of each stage, the feature map size is halved (downsampled)
by a convolutional layer with strides=2, while the number of filters is
doubled. Within each stage, the la... | resnet_v1 | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/resnet.py | MIT |
def resnet_v2(input_shape, depth, n_layers=4):
"""ResNet Version 2 Model builder [b]
Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
bottleneck layer
First shortcut connection per layer is 1 x 1 Conv2D.
Second and onwards shortcut connection is identity.
At the beginning of ea... | ResNet Version 2 Model builder [b]
Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
bottleneck layer
First shortcut connection per layer is 1 x 1 Conv2D.
Second and onwards shortcut connection is identity.
At the beginning of each stage, the feature map size is halved (downsampled)... | resnet_v2 | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/resnet.py | MIT |
def build_resnet(input_shape,
n_layers=4,
version=2,
n=6):
"""Build a resnet as backbone of SSD
# Arguments:
input_shape (list): Input image size and channels
n_layers (int): Number of feature layers for SSD
version (int): Supports ResN... | Build a resnet as backbone of SSD
# Arguments:
input_shape (list): Input image size and channels
n_layers (int): Number of feature layers for SSD
version (int): Supports ResNetv1 and v2 but v2 by default
n (int): Determines number of ResNet layers
(Default is ResNet... | build_resnet | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/resnet.py | MIT |
def __init__(self, args):
"""Copy user-defined configs.
Build backbone and ssd network models.
"""
self.args = args
self.ssd = None
self.train_generator = None
self.build_model() | Copy user-defined configs.
Build backbone and ssd network models.
| __init__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/ssd-11.6.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/ssd-11.6.1.py | MIT |
def build_dictionary(self):
"""Read input image filenames and obj detection labels
from a csv file and store in a dictionary.
"""
# train dataset path
path = os.path.join(self.args.data_path,
self.args.train_labels)
# build dictionary:
... | Read input image filenames and obj detection labels
from a csv file and store in a dictionary.
| build_dictionary | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/ssd-11.6.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/ssd-11.6.1.py | MIT |
def build_generator(self):
"""Build a multi-thread train data generator."""
self.train_generator = \
DataGenerator(args=self.args,
dictionary=self.dictionary,
n_classes=self.n_classes,
feature_shap... | Build a multi-thread train data generator. | build_generator | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/ssd-11.6.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/ssd-11.6.1.py | MIT |
def evaluate(self, image_file=None, image=None):
"""Evaluate image based on image (np tensor) or filename"""
show = False
if image is None:
image = skimage.img_as_float(imread(image_file))
show = True
image, classes, offsets = self.detect_objects(image)
c... | Evaluate image based on image (np tensor) or filename | evaluate | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/ssd-11.6.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/ssd-11.6.1.py | MIT |
def print_summary(self):
"""Print network summary for debugging purposes."""
from tensorflow.keras.utils import plot_model
if self.args.summary:
self.backbone.summary()
self.ssd.summary()
plot_model(self.backbone,
to_file="backbone.png",... | Print network summary for debugging purposes. | print_summary | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter11-detection/ssd-11.6.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter11-detection/ssd-11.6.1.py | MIT |
def get_dictionary(self):
"""Load ground truth dictionary of
image filename : segmentation masks
"""
path = os.path.join(self.args.data_path,
self.args.train_labels)
self.dictionary = np.load(path,
allow_pickle=Tr... | Load ground truth dictionary of
image filename : segmentation masks
| get_dictionary | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/data_generator.py | MIT |
def __data_generation(self, keys):
"""Generate train data: images and
segmentation ground truth labels
Arguments:
keys (array): Randomly sampled keys
(key is image filename)
Returns:
x (tensor): Batch of images
y (tensor): Batch of ... | Generate train data: images and
segmentation ground truth labels
Arguments:
keys (array): Randomly sampled keys
(key is image filename)
Returns:
x (tensor): Batch of images
y (tensor): Batch of pixel-wise categories
| __data_generation | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/data_generator.py | MIT |
def __init__(self, args):
"""Copy user-defined configs.
Build backbone and fcn network models.
"""
self.args = args
self.fcn = None
self.train_generator = DataGenerator(args)
self.build_model()
self.eval_init() | Copy user-defined configs.
Build backbone and fcn network models.
| __init__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def build_model(self):
"""Build a backbone network and use it to
create a semantic segmentation
network based on FCN.
"""
# input shape is (480, 640, 3) by default
self.input_shape = (self.args.height,
self.args.width,
... | Build a backbone network and use it to
create a semantic segmentation
network based on FCN.
| build_model | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def preload_test(self):
"""Pre-load test dataset to save time """
path = os.path.join(self.args.data_path,
self.args.test_labels)
# ground truth data is stored in an npy file
self.test_dictionary = np.load(path,
allow_pi... | Pre-load test dataset to save time | preload_test | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def segment_objects(self, image, normalized=True):
"""Run segmentation prediction for a given image
Arguments:
image (tensor): Image loaded in a numpy tensor.
RGB components range is [0.0, 1.0]
normalized (Bool): Use normalized=True for
pixel... | Run segmentation prediction for a given image
Arguments:
image (tensor): Image loaded in a numpy tensor.
RGB components range is [0.0, 1.0]
normalized (Bool): Use normalized=True for
pixel-wise categorical prediction. False if
segmen... | segment_objects | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def evaluate(self, imagefile=None, image=None):
"""Perform segmentation on a given image filename
and display the results.
"""
import matplotlib.pyplot as plt
save_dir = "prediction"
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if image is... | Perform segmentation on a given image filename
and display the results.
| evaluate | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def eval(self):
"""Evaluate a trained FCN model using mean IoU
metric.
"""
s_iou = 0
s_pla = 0
# evaluate iou per test image
eps = np.finfo(float).eps
for key in self.test_keys:
# load a test image
image_path = os.path.join(self... | Evaluate a trained FCN model using mean IoU
metric.
| eval | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/fcn-12.3.1.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/fcn-12.3.1.py | MIT |
def conv_layer(inputs,
filters=32,
kernel_size=3,
strides=1,
use_maxpool=True,
postfix=None,
activation=None):
"""Helper function to build Conv2D-BN-ReLU layer
with optional MaxPooling2D.
"""
x = Conv2D(filter... | Helper function to build Conv2D-BN-ReLU layer
with optional MaxPooling2D.
| conv_layer | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/model.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/model.py | MIT |
def tconv_layer(inputs,
filters=32,
kernel_size=3,
strides=2,
postfix=None):
"""Helper function to build Conv2DTranspose-BN-ReLU
layer
"""
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
... | Helper function to build Conv2DTranspose-BN-ReLU
layer
| tconv_layer | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/model.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/model.py | MIT |
def build_fcn(input_shape,
backbone,
n_classes=4):
"""Helper function to build an FCN model.
Arguments:
backbone (Model): A backbone network
such as ResNetv2 or v1
n_classes (int): Number of object classes
including background.
"""... | Helper function to build an FCN model.
Arguments:
backbone (Model): A backbone network
such as ResNetv2 or v1
n_classes (int): Number of object classes
including background.
| build_fcn | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/model.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/model.py | MIT |
def features_pyramid(x, n_layers):
"""Generate features pyramid from the output of the
last layer of a backbone network (e.g. ResNetv1 or v2)
Arguments:
x (tensor): Output feature maps of a backbone network
n_layers (int): Number of additional pyramid layers
Return:
outputs (l... | Generate features pyramid from the output of the
last layer of a backbone network (e.g. ResNetv1 or v2)
Arguments:
x (tensor): Output feature maps of a backbone network
n_layers (int): Number of additional pyramid layers
Return:
outputs (list): Features pyramid
| features_pyramid | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/resnet.py | MIT |
def build_resnet(input_shape,
n_layers=4,
version=2,
n=6):
"""Build a resnet as backbone
# Arguments:
input_shape (list): Input image size and channels
n_layers (int): Number of feature layers
version (int): Supports ResNetv1 and v2 bu... | Build a resnet as backbone
# Arguments:
input_shape (list): Input image size and channels
n_layers (int): Number of feature layers
version (int): Supports ResNetv1 and v2 but v2 by default
n (int): Determines number of ResNet layers
(Default is ResNet50)
# Ret... | build_resnet | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter12-segmentation/resnet.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter12-segmentation/resnet.py | MIT |
def __init__(self,
args,
shuffle=True,
siamese=False,
mine=False,
crop_size=4):
"""Multi-threaded data generator. Each thread reads
a batch of images and performs image transformation
such that the image... | Multi-threaded data generator. Each thread reads
a batch of images and performs image transformation
such that the image class is unaffected
Arguments:
args (argparse): User-defined options such as
batch_size, etc
shuffle (Bool): Whether to shuffl... | __init__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter13-mi-unsupervised/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter13-mi-unsupervised/data_generator.py | MIT |
def __getitem__(self, index):
"""Image sample Indexes for the current batch
"""
start_index = index * self.args.batch_size
end_index = (index+1) * self.args.batch_size
return self.__data_generation(start_index, end_index) | Image sample Indexes for the current batch
| __getitem__ | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter13-mi-unsupervised/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter13-mi-unsupervised/data_generator.py | MIT |
def random_crop(self, image, target_shape, crop_sizes):
"""Perform random crop, resize back to its target shape
Arguments:
image (tensor): Image to crop and resize
target_shape (tensor): Output shape
crop_sizes (list): A list of sizes the image
can b... | Perform random crop, resize back to its target shape
Arguments:
image (tensor): Image to crop and resize
target_shape (tensor): Output shape
crop_sizes (list): A list of sizes the image
can be cropped
| random_crop | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter13-mi-unsupervised/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter13-mi-unsupervised/data_generator.py | MIT |
def random_rotate(self,
image,
deg=20,
target_shape=(24, 24, 1)):
"""Random image rotation
Arguments:
image (tensor): Image to crop and resize
deg (int): Degrees of rotation
target_shape (tensor): Ou... | Random image rotation
Arguments:
image (tensor): Image to crop and resize
deg (int): Degrees of rotation
target_shape (tensor): Output shape
| random_rotate | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter13-mi-unsupervised/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter13-mi-unsupervised/data_generator.py | MIT |
def __data_generation(self, start_index, end_index):
"""Data generation algorithm. The method generates
a batch of pair of images (original image X and
transformed imaged Xbar). The batch of Siamese
images is used to trained MI-based algorithms:
1) IIC and 2) MINE... | Data generation algorithm. The method generates
a batch of pair of images (original image X and
transformed imaged Xbar). The batch of Siamese
images is used to trained MI-based algorithms:
1) IIC and 2) MINE (Section 7)
Arguments:
start_index (int): ... | __data_generation | python | PacktPublishing/Advanced-Deep-Learning-with-Keras | chapter13-mi-unsupervised/data_generator.py | https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter13-mi-unsupervised/data_generator.py | MIT |
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