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2025-01-04 20:06:33 -08:00
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util/action_matching.py
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'''
Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild
'''
import jax
import jax.numpy as jnp
import numpy as np
# import action_type as action_type_lib
import enum
class ActionType(enum.IntEnum):
# Placeholders for unused enum values
UNUSED_0 = 0
UNUSED_1 = 1
UNUSED_2 = 2
UNUSED_8 = 8
UNUSED_9 = 9
########### Agent actions ###########
# A type action that sends text to the emulator. Note that this simply sends
# text and does not perform any clicks for element focus or enter presses for
# submitting text.
TYPE = 3
# The dual point action used to represent all gestures.
DUAL_POINT = 4
# These actions differentiate pressing the home and back button from touches.
# They represent explicit presses of back and home performed using ADB.
PRESS_BACK = 5
PRESS_HOME = 6
# An action representing that ADB command for hitting enter was performed.
PRESS_ENTER = 7
########### Episode status actions ###########
# An action used to indicate the desired task has been completed and resets
# the environment. This action should also be used in the case that the task
# has already been completed and there is nothing to do.
# e.g. The task is to turn on the Wi-Fi when it is already on
STATUS_TASK_COMPLETE = 10
# An action used to indicate that desired task is impossible to complete and
# resets the environment. This can be a result of many different things
# including UI changes, Android version differences, etc.
STATUS_TASK_IMPOSSIBLE = 11
_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen
ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4
ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4
# Interval determining if an action is a tap or a swipe.
_SWIPE_DISTANCE_THRESHOLD = 0.04
def _yx_in_bounding_boxes(
yx, bounding_boxes
):
"""Check if the (y,x) point is contained in each bounding box.
Args:
yx: The (y, x) coordinate in pixels of the point.
bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row
represents a bounding box: (y_top_left, x_top_left, box_height,
box_width). Note: containment is inclusive of the bounding box edges.
Returns:
is_inside: A 1D bool array where each element specifies if the point is
contained within the respective box.
"""
y, x = yx
# `bounding_boxes` has shape (n_elements, 4); we extract each array along the
# last axis into shape (n_elements, 1), then squeeze unneeded dimension.
top, left, height, width = [
jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1)
]
# The y-axis is inverted for AndroidEnv, so bottom = top + height.
bottom, right = top + height, left + width
return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and(
x >= left, x <= right)
def _resize_annotation_bounding_boxes(
annotation_positions, annotation_width_augment_fraction,
annotation_height_augment_fraction):
"""Resize the bounding boxes by the given fractions.
Args:
annotation_positions: Array of shape (N, 4), where each row represents the
(y, x, height, width) of the bounding boxes.
annotation_width_augment_fraction: The fraction to augment the box widths,
E.g., 1.4 == 240% total increase.
annotation_height_augment_fraction: Same as described for width, but for box
height.
Returns:
Resized bounding box.
"""
height_change = (
annotation_height_augment_fraction * annotation_positions[:, 2])
width_change = (
annotation_width_augment_fraction * annotation_positions[:, 3])
# Limit bounding box positions to the screen.
resized_annotations = jnp.stack([
jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)),
jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)),
jnp.minimum(1, annotation_positions[:, 2] + height_change),
jnp.minimum(1, annotation_positions[:, 3] + width_change),
],
axis=1)
return resized_annotations
def is_tap_action(normalized_start_yx,
normalized_end_yx):
distance = jnp.linalg.norm(
jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx))
return distance <= _SWIPE_DISTANCE_THRESHOLD
def _is_non_dual_point_action(action_type):
return jnp.not_equal(action_type, ActionType.DUAL_POINT)
def _check_tap_actions_match(
tap_1_yx,
tap_2_yx,
annotation_positions,
matching_tap_distance_threshold_screen_percentage,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
):
"""Determines if two tap actions are the same."""
resized_annotation_positions = _resize_annotation_bounding_boxes(
annotation_positions,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
)
# Check if the ground truth tap action falls in an annotation's bounding box.
tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions)
tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions)
both_in_box = jnp.max(tap1_in_box & tap2_in_box)
# If the ground-truth tap action falls outside any of the annotation
# bounding boxes or one of the actions is inside a bounding box and the other
# is outside bounding box or vice versa, compare the points using Euclidean
# distance.
within_threshold = (
jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx))
<= matching_tap_distance_threshold_screen_percentage
)
return jnp.logical_or(both_in_box, within_threshold)
def _check_drag_actions_match(
drag_1_touch_yx,
drag_1_lift_yx,
drag_2_touch_yx,
drag_2_lift_yx,
):
"""Determines if two drag actions are the same."""
# Store drag deltas (the change in the y and x coordinates from touch to
# lift), magnitudes, and the index of the main axis, which is the axis with
# the greatest change in coordinate value (e.g. a drag starting at (0, 0) and
# ending at (0.3, 0.5) has a main axis index of 1).
drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx
drag_1_magnitudes = jnp.abs(drag_1_deltas)
drag_1_main_axis = np.argmax(drag_1_magnitudes)
drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx
drag_2_magnitudes = jnp.abs(drag_2_deltas)
drag_2_main_axis = np.argmax(drag_2_magnitudes)
return jnp.equal(drag_1_main_axis, drag_2_main_axis)
def check_actions_match(
action_1_touch_yx,
action_1_lift_yx,
action_1_action_type,
action_2_touch_yx,
action_2_lift_yx,
action_2_action_type,
annotation_positions,
tap_distance_threshold = _TAP_DISTANCE_THRESHOLD,
annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION,
annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION,
):
"""Determines if two actions are considered to be the same.
Two actions being "the same" is defined here as two actions that would result
in a similar screen state.
Args:
action_1_touch_yx: The (y, x) coordinates of the first action's touch.
action_1_lift_yx: The (y, x) coordinates of the first action's lift.
action_1_action_type: The action type of the first action.
action_2_touch_yx: The (y, x) coordinates of the second action's touch.
action_2_lift_yx: The (y, x) coordinates of the second action's lift.
action_2_action_type: The action type of the second action.
annotation_positions: The positions of the UI annotations for the screen. It
is A 2D int array of shape (num_bboxes, 4), where each row represents a
bounding box: (y_top_left, x_top_left, box_height, box_width). Note that
containment is inclusive of the bounding box edges.
tap_distance_threshold: The threshold that determines if two taps result in
a matching screen state if they don't fall the same bounding boxes.
annotation_width_augment_fraction: The fraction to increase the width of the
bounding box by.
annotation_height_augment_fraction: The fraction to increase the height of
of the bounding box by.
Returns:
A boolean representing whether the two given actions are the same or not.
"""
action_1_touch_yx = jnp.asarray(action_1_touch_yx)
action_1_lift_yx = jnp.asarray(action_1_lift_yx)
action_2_touch_yx = jnp.asarray(action_2_touch_yx)
action_2_lift_yx = jnp.asarray(action_2_lift_yx)
# Checks if at least one of the actions is global (i.e. not DUAL_POINT),
# because if that is the case, only the actions' types need to be compared.
has_non_dual_point_action = jnp.logical_or(
_is_non_dual_point_action(action_1_action_type),
_is_non_dual_point_action(action_2_action_type),
)
#print("non dual point: "+str(has_non_dual_point_action))
different_dual_point_types = jnp.logical_xor(
is_tap_action(action_1_touch_yx, action_1_lift_yx),
is_tap_action(action_2_touch_yx, action_2_lift_yx),
)
#print("different dual type: "+str(different_dual_point_types))
is_tap = jnp.logical_and(
is_tap_action(action_1_touch_yx, action_1_lift_yx),
is_tap_action(action_2_touch_yx, action_2_lift_yx),
)
#print("is tap: "+str(is_tap))
taps_match = _check_tap_actions_match(
action_1_touch_yx,
action_2_touch_yx,
annotation_positions,
tap_distance_threshold,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
)
#print("tap match: "+str(taps_match))
taps_match = jnp.logical_and(is_tap, taps_match)
#print("tap match: "+str(taps_match))
drags_match = _check_drag_actions_match(
action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx
)
drags_match = jnp.where(is_tap, False, drags_match)
#print("drag match: "+str(drags_match))
return jnp.where(
has_non_dual_point_action,
jnp.equal(action_1_action_type, action_2_action_type),
jnp.where(
different_dual_point_types,
False,
jnp.logical_or(taps_match, drags_match),
),
)
def action_2_format(step_data):
# 把test数据集中的动作格式转换为计算matching score的格式
action_type = step_data["action_type_id"]
if action_type == 4:
if step_data["action_type_text"] == 'click': # 点击
touch_point = step_data["touch"]
lift_point = step_data["lift"]
else: # 上下左右滑动
if step_data["action_type_text"] == 'scroll down':
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
elif step_data["action_type_text"] == 'scroll up':
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
elif step_data["action_type_text"] == 'scroll left':
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
elif step_data["action_type_text"] == 'scroll right':
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
else:
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
if action_type == 3:
typed_text = step_data["type_text"]
else:
typed_text = ""
action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
return action
def pred_2_format(step_data):
# 把模型输出的内容转换为计算action_matching的格式
action_type = step_data["action_type"]
if action_type == 4: # 点击
action_type_new = 4
touch_point = step_data["click_point"]
lift_point = step_data["click_point"]
typed_text = ""
elif action_type == 0:
action_type_new = 4
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
typed_text = ""
elif action_type == 1:
action_type_new = 4
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
typed_text = ""
elif action_type == 8:
action_type_new = 4
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
typed_text = ""
elif action_type == 9:
action_type_new = 4
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
typed_text = ""
else:
action_type_new = action_type
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
if action_type_new == 3:
typed_text = step_data["typed_text"]
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
return action
def pred_2_format_simplified(step_data):
# 把模型输出的内容转换为计算action_matching的格式
action_type = step_data["action_type"]
if action_type == 'click' : # 点击
action_type_new = 4
touch_point = step_data["click_point"]
lift_point = step_data["click_point"]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'down':
action_type_new = 4
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'up':
action_type_new = 4
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'left':
action_type_new = 4
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'right':
action_type_new = 4
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
typed_text = ""
elif action_type == 'type':
action_type_new = 3
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = step_data["text"]
elif action_type == 'navigate_back':
action_type_new = 5
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
elif action_type == 'navigate_home':
action_type_new = 6
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
else:
action_type_new = action_type
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
# if action_type_new == 'type':
# typed_text = step_data["text"]
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
'''
Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild
'''
import jax
import jax.numpy as jnp
import numpy as np
# import action_type as action_type_lib
import enum
class ActionType(enum.IntEnum):
# Placeholders for unused enum values
UNUSED_0 = 0
UNUSED_1 = 1
UNUSED_2 = 2
UNUSED_8 = 8
UNUSED_9 = 9
########### Agent actions ###########
# A type action that sends text to the emulator. Note that this simply sends
# text and does not perform any clicks for element focus or enter presses for
# submitting text.
TYPE = 3
# The dual point action used to represent all gestures.
DUAL_POINT = 4
# These actions differentiate pressing the home and back button from touches.
# They represent explicit presses of back and home performed using ADB.
PRESS_BACK = 5
PRESS_HOME = 6
# An action representing that ADB command for hitting enter was performed.
PRESS_ENTER = 7
########### Episode status actions ###########
# An action used to indicate the desired task has been completed and resets
# the environment. This action should also be used in the case that the task
# has already been completed and there is nothing to do.
# e.g. The task is to turn on the Wi-Fi when it is already on
STATUS_TASK_COMPLETE = 10
# An action used to indicate that desired task is impossible to complete and
# resets the environment. This can be a result of many different things
# including UI changes, Android version differences, etc.
STATUS_TASK_IMPOSSIBLE = 11
_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen
ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4
ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4
# Interval determining if an action is a tap or a swipe.
_SWIPE_DISTANCE_THRESHOLD = 0.04
def _yx_in_bounding_boxes(
yx, bounding_boxes
):
"""Check if the (y,x) point is contained in each bounding box.
Args:
yx: The (y, x) coordinate in pixels of the point.
bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row
represents a bounding box: (y_top_left, x_top_left, box_height,
box_width). Note: containment is inclusive of the bounding box edges.
Returns:
is_inside: A 1D bool array where each element specifies if the point is
contained within the respective box.
"""
y, x = yx
# `bounding_boxes` has shape (n_elements, 4); we extract each array along the
# last axis into shape (n_elements, 1), then squeeze unneeded dimension.
top, left, height, width = [
jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1)
]
# The y-axis is inverted for AndroidEnv, so bottom = top + height.
bottom, right = top + height, left + width
return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and(
x >= left, x <= right)
def _resize_annotation_bounding_boxes(
annotation_positions, annotation_width_augment_fraction,
annotation_height_augment_fraction):
"""Resize the bounding boxes by the given fractions.
Args:
annotation_positions: Array of shape (N, 4), where each row represents the
(y, x, height, width) of the bounding boxes.
annotation_width_augment_fraction: The fraction to augment the box widths,
E.g., 1.4 == 240% total increase.
annotation_height_augment_fraction: Same as described for width, but for box
height.
Returns:
Resized bounding box.
"""
height_change = (
annotation_height_augment_fraction * annotation_positions[:, 2])
width_change = (
annotation_width_augment_fraction * annotation_positions[:, 3])
# Limit bounding box positions to the screen.
resized_annotations = jnp.stack([
jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)),
jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)),
jnp.minimum(1, annotation_positions[:, 2] + height_change),
jnp.minimum(1, annotation_positions[:, 3] + width_change),
],
axis=1)
return resized_annotations
def is_tap_action(normalized_start_yx,
normalized_end_yx):
distance = jnp.linalg.norm(
jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx))
return distance <= _SWIPE_DISTANCE_THRESHOLD
def _is_non_dual_point_action(action_type):
return jnp.not_equal(action_type, ActionType.DUAL_POINT)
def _check_tap_actions_match(
tap_1_yx,
tap_2_yx,
annotation_positions,
matching_tap_distance_threshold_screen_percentage,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
):
"""Determines if two tap actions are the same."""
resized_annotation_positions = _resize_annotation_bounding_boxes(
annotation_positions,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
)
# Check if the ground truth tap action falls in an annotation's bounding box.
tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions)
tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions)
both_in_box = jnp.max(tap1_in_box & tap2_in_box)
# If the ground-truth tap action falls outside any of the annotation
# bounding boxes or one of the actions is inside a bounding box and the other
# is outside bounding box or vice versa, compare the points using Euclidean
# distance.
within_threshold = (
jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx))
<= matching_tap_distance_threshold_screen_percentage
)
return jnp.logical_or(both_in_box, within_threshold)
def _check_drag_actions_match(
drag_1_touch_yx,
drag_1_lift_yx,
drag_2_touch_yx,
drag_2_lift_yx,
):
"""Determines if two drag actions are the same."""
# Store drag deltas (the change in the y and x coordinates from touch to
# lift), magnitudes, and the index of the main axis, which is the axis with
# the greatest change in coordinate value (e.g. a drag starting at (0, 0) and
# ending at (0.3, 0.5) has a main axis index of 1).
drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx
drag_1_magnitudes = jnp.abs(drag_1_deltas)
drag_1_main_axis = np.argmax(drag_1_magnitudes)
drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx
drag_2_magnitudes = jnp.abs(drag_2_deltas)
drag_2_main_axis = np.argmax(drag_2_magnitudes)
return jnp.equal(drag_1_main_axis, drag_2_main_axis)
def check_actions_match(
action_1_touch_yx,
action_1_lift_yx,
action_1_action_type,
action_2_touch_yx,
action_2_lift_yx,
action_2_action_type,
annotation_positions,
tap_distance_threshold = _TAP_DISTANCE_THRESHOLD,
annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION,
annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION,
):
"""Determines if two actions are considered to be the same.
Two actions being "the same" is defined here as two actions that would result
in a similar screen state.
Args:
action_1_touch_yx: The (y, x) coordinates of the first action's touch.
action_1_lift_yx: The (y, x) coordinates of the first action's lift.
action_1_action_type: The action type of the first action.
action_2_touch_yx: The (y, x) coordinates of the second action's touch.
action_2_lift_yx: The (y, x) coordinates of the second action's lift.
action_2_action_type: The action type of the second action.
annotation_positions: The positions of the UI annotations for the screen. It
is A 2D int array of shape (num_bboxes, 4), where each row represents a
bounding box: (y_top_left, x_top_left, box_height, box_width). Note that
containment is inclusive of the bounding box edges.
tap_distance_threshold: The threshold that determines if two taps result in
a matching screen state if they don't fall the same bounding boxes.
annotation_width_augment_fraction: The fraction to increase the width of the
bounding box by.
annotation_height_augment_fraction: The fraction to increase the height of
of the bounding box by.
Returns:
A boolean representing whether the two given actions are the same or not.
"""
action_1_touch_yx = jnp.asarray(action_1_touch_yx)
action_1_lift_yx = jnp.asarray(action_1_lift_yx)
action_2_touch_yx = jnp.asarray(action_2_touch_yx)
action_2_lift_yx = jnp.asarray(action_2_lift_yx)
# Checks if at least one of the actions is global (i.e. not DUAL_POINT),
# because if that is the case, only the actions' types need to be compared.
has_non_dual_point_action = jnp.logical_or(
_is_non_dual_point_action(action_1_action_type),
_is_non_dual_point_action(action_2_action_type),
)
#print("non dual point: "+str(has_non_dual_point_action))
different_dual_point_types = jnp.logical_xor(
is_tap_action(action_1_touch_yx, action_1_lift_yx),
is_tap_action(action_2_touch_yx, action_2_lift_yx),
)
#print("different dual type: "+str(different_dual_point_types))
is_tap = jnp.logical_and(
is_tap_action(action_1_touch_yx, action_1_lift_yx),
is_tap_action(action_2_touch_yx, action_2_lift_yx),
)
#print("is tap: "+str(is_tap))
taps_match = _check_tap_actions_match(
action_1_touch_yx,
action_2_touch_yx,
annotation_positions,
tap_distance_threshold,
annotation_width_augment_fraction,
annotation_height_augment_fraction,
)
#print("tap match: "+str(taps_match))
taps_match = jnp.logical_and(is_tap, taps_match)
#print("tap match: "+str(taps_match))
drags_match = _check_drag_actions_match(
action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx
)
drags_match = jnp.where(is_tap, False, drags_match)
#print("drag match: "+str(drags_match))
return jnp.where(
has_non_dual_point_action,
jnp.equal(action_1_action_type, action_2_action_type),
jnp.where(
different_dual_point_types,
False,
jnp.logical_or(taps_match, drags_match),
),
)
def action_2_format(step_data):
# 把test数据集中的动作格式转换为计算matching score的格式
action_type = step_data["action_type_id"]
if action_type == 4:
if step_data["action_type_text"] == 'click': # 点击
touch_point = step_data["touch"]
lift_point = step_data["lift"]
else: # 上下左右滑动
if step_data["action_type_text"] == 'scroll down':
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
elif step_data["action_type_text"] == 'scroll up':
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
elif step_data["action_type_text"] == 'scroll left':
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
elif step_data["action_type_text"] == 'scroll right':
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
else:
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
if action_type == 3:
typed_text = step_data["type_text"]
else:
typed_text = ""
action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
return action
def pred_2_format(step_data):
# 把模型输出的内容转换为计算action_matching的格式
action_type = step_data["action_type"]
if action_type == 4: # 点击
action_type_new = 4
touch_point = step_data["click_point"]
lift_point = step_data["click_point"]
typed_text = ""
elif action_type == 0:
action_type_new = 4
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
typed_text = ""
elif action_type == 1:
action_type_new = 4
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
typed_text = ""
elif action_type == 8:
action_type_new = 4
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
typed_text = ""
elif action_type == 9:
action_type_new = 4
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
typed_text = ""
else:
action_type_new = action_type
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
if action_type_new == 3:
typed_text = step_data["typed_text"]
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
return action
def pred_2_format_simplified(step_data):
# 把模型输出的内容转换为计算action_matching的格式
action_type = step_data["action_type"]
if action_type == 'click' : # 点击
action_type_new = 4
touch_point = step_data["click_point"]
lift_point = step_data["click_point"]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'down':
action_type_new = 4
touch_point = [0.5, 0.8]
lift_point = [0.5, 0.2]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'up':
action_type_new = 4
touch_point = [0.5, 0.2]
lift_point = [0.5, 0.8]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'left':
action_type_new = 4
touch_point = [0.2, 0.5]
lift_point = [0.8, 0.5]
typed_text = ""
elif action_type == 'scroll' and step_data["direction"] == 'right':
action_type_new = 4
touch_point = [0.8, 0.5]
lift_point = [0.2, 0.5]
typed_text = ""
elif action_type == 'type':
action_type_new = 3
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = step_data["text"]
elif action_type == 'navigate_back':
action_type_new = 5
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
elif action_type == 'navigate_home':
action_type_new = 6
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
else:
action_type_new = action_type
touch_point = [-1.0, -1.0]
lift_point = [-1.0, -1.0]
typed_text = ""
# if action_type_new == 'type':
# typed_text = step_data["text"]
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
"typed_text": typed_text}
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
action["typed_text"] = action["typed_text"].lower()
return action