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