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https://github.com/iperov/DeepFaceLab.git
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Scale previews
This commit is contained in:
Jeremy Hummel
parent
f94c77b226
commit
d8c5e42a55
2 changed files with 76 additions and 36 deletions
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@ -3,6 +3,8 @@ import traceback
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import queue
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import threading
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import time
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from enum import Enum
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import numpy as np
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import itertools
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from pathlib import Path
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@ -181,48 +183,88 @@ def trainerThread (s2c, c2s, e, args, device_args):
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c2s.put ( {'op':'close'} )
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def scale_previews(previews, preview_min_height, preview_max_height):
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preview_height = max((h for h, w, c in (im.shape for name, im in previews)))
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class Zoom(Enum):
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ZOOM_25 = (1/4, '25%')
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ZOOM_33 = (1/3, '33%')
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ZOOM_50 = (1/2, '50%')
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ZOOM_67 = (2/3, '67%')
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ZOOM_75 = (3/4, '75%')
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ZOOM_80 = (4/5, '80%')
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ZOOM_90 = (9/10, '90%')
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ZOOM_100 = (1, '100%')
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ZOOM_110 = (11/10, '110%')
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ZOOM_125 = (5/4, '125%')
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ZOOM_150 = (3/2, '150%')
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ZOOM_175 = (7/4, '175%')
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ZOOM_200 = (2, '200%')
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ZOOM_250 = (5/2, '250%')
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ZOOM_300 = (3, '300%')
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ZOOM_400 = (4, '400%')
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ZOOM_500 = (5, '500%')
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if preview_height > preview_max_height:
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preview_height = preview_max_height
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elif preview_height < preview_min_height:
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preview_height = preview_min_height
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def __init__(self, scale, label):
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self.scale = scale
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self.label = label
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# make all previews size equal
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def prev(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) - 1
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if index < 0:
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return self
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return members[index]
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def next(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) + 1
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if index >= len(members):
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return self
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return members[index]
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def scale_previews(previews, zoom=Zoom.ZOOM_100):
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# Zoom previews
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for preview in previews[:]:
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(preview_name, preview_rgb) = preview
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(h, w, c) = preview_rgb.shape
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if h != preview_height:
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scale_factor = preview_height / float(h)
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preview_name, preview_rgb = preview
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h, w, c = preview_rgb.shape
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scale_factor = zoom.scale * float(h)
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if scale_factor < 1:
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previews.remove(preview)
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previews.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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elif scale_factor > 1:
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previews.remove(preview)
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previews.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_LANCZOS4)))
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return previews
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def create_preview_pane_image(previews, selected_preview, loss_history,
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show_last_history_iters_count, batch_size):
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show_last_history_iters_count, iteration, batch_size, zoom=Zoom.ZOOM_100):
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previews = scale_previews(previews, zoom)
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selected_preview_name = previews[selected_preview][0]
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selected_preview_rgb = previews[selected_preview][1]
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(h,w,c) = selected_preview_rgb.shape
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h, w, c = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit',
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'[s]:save [enter]:exit [-/+]:zoom: %s' % zoom.label,
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
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]
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head_line_height = 15
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head_line_height = int(15 * zoom.scale)
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head_height = len(head_lines) * head_line_height
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head = np.ones ( (head_height,w,c) ) * 0.1
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head = np.ones((head_height, w, c)) * 0.1
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for i in range(0, len(head_lines)):
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t = i*head_line_height
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b = (i+1)*head_line_height
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head[t:b, 0:w] += imagelib.get_text_image ( (head_line_height,w,c) , head_lines[i], color=[0.8]*c )
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t = i * head_line_height
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b = (i+1) * head_line_height
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head[t:b, 0:w] += imagelib.get_text_image((head_line_height, w, c), head_lines[i], color=[0.8]*c)
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final = head
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@ -231,11 +273,11 @@ def create_preview_pane_image(previews, selected_preview, loss_history,
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loss_history_to_show = loss_history
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else:
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loss_history_to_show = loss_history[-show_last_history_iters_count:]
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, batch_size, w, c)
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lh_height = int(100 * zoom.scale)
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iteration, batch_size, w, c, lh_height)
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final = np.concatenate ( [final, lh_img], axis=0 )
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final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
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final = np.concatenate([final, selected_preview_rgb], axis=0)
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final = np.clip(final, 0, 1)
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return (final*255).astype(np.uint8)
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@ -278,10 +320,9 @@ def main(args, device_args):
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is_showing = False
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is_waiting_preview = False
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show_last_history_iters_count = 0
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iter = 0
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iteration = 0
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batch_size = 1
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preview_min_height = 512
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preview_max_height = 1024
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zoom = Zoom.ZOOM_100
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while True:
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if not c2s.empty():
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@ -291,23 +332,24 @@ def main(args, device_args):
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is_waiting_preview = False
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loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
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previews = input['previews'] if 'previews' in input.keys() else None
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iter = input['iter'] if 'iter' in input.keys() else 0
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iteration = input['iter'] if 'iter' in input.keys() else 0
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#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
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if previews is not None:
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previews = scale_previews(previews, preview_min_height, preview_max_height)
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selected_preview = selected_preview % len(previews)
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update_preview = True
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elif op == 'close':
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break
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if update_preview:
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update_preview = False
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selected_preview = selected_preview % len(previews)
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preview_pane_image = create_preview_pane_image(previews,
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selected_preview,
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loss_history,
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show_last_history_iters_count,
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batch_size)
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io.show_image( wnd_name, preview_pane_image)
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iteration,
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batch_size,
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zoom)
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io.show_image(wnd_name, preview_pane_image)
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is_showing = True
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key_events = io.get_key_events(wnd_name)
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@ -337,12 +379,10 @@ def main(args, device_args):
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selected_preview = (selected_preview + 1) % len(previews)
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update_preview = True
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elif key == ord('-'):
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# Decrease zoom
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zoom = zoom.prev()
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pass
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elif key == ord('+'):
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# Increase zoom
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pass
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zoom = zoom.next()
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try:
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io.process_messages(0.1)
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except KeyboardInterrupt:
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@ -641,11 +641,11 @@ class ModelBase(object):
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self.batch_size = d[ keys[-1] ]
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@staticmethod
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def get_loss_history_preview(loss_history, iter,batch_size, w, c):
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def get_loss_history_preview(loss_history, iter, batch_size, w, c, lh_height=100):
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loss_history = np.array (loss_history.copy())
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lh_height = 100
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lh_img = np.ones ( (lh_height,w,c) ) * 0.1
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lh_img = np.ones((lh_height, w, c)) * 0.1
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if len(loss_history) != 0:
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loss_count = len(loss_history[0])
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