v0.01-v0.03

a bunch of updates, pushing v0.01-v0.03 code here, updated vjoy testing
(xbox360 emulation...etc)

1. collect_data.py

@@ -0,0 +1,116 @@
+import numpy as np
+from grabscreen import grab_screen
+import cv2
+import time
+from getkeys import key_check
+import os
+
+w = [1,0,0,0,0,0,0,0,0]
+s = [0,1,0,0,0,0,0,0,0]
+a = [0,0,1,0,0,0,0,0,0]
+d = [0,0,0,1,0,0,0,0,0]
+wa = [0,0,0,0,1,0,0,0,0]
+wd = [0,0,0,0,0,1,0,0,0]
+sa = [0,0,0,0,0,0,1,0,0]
+sd = [0,0,0,0,0,0,0,1,0]
+nk = [0,0,0,0,0,0,0,0,1]
+
+starting_value = 1058
+
+while True:
+    file_name = 'training_data-{}.npy'.format(starting_value)
+
+    if os.path.isfile(file_name):
+        print('File exists, moving along',starting_value)
+        starting_value += 1
+    else:
+        print('File does not exist, starting fresh!',starting_value)
+
+        break
+
+
+def keys_to_output(keys):
+    '''
+    Convert keys to a ...multi-hot... array
+     0  1  2  3  4   5   6   7    8
+    [W, S, A, D, WA, WD, SA, SD, NOKEY] boolean values.
+    '''
+    output = [0,0,0,0,0,0,0,0,0]
+
+    if 'W' in keys and 'A' in keys:
+        output = wa
+    elif 'W' in keys and 'D' in keys:
+        output = wd
+    elif 'S' in keys and 'A' in keys:
+        output = sa
+    elif 'S' in keys and 'D' in keys:
+        output = sd
+    elif 'W' in keys:
+        output = w
+    elif 'S' in keys:
+        output = s
+    elif 'A' in keys:
+        output = a
+    elif 'D' in keys:
+        output = d
+    else:
+        output = nk
+    return output
+
+
+def main(file_name, starting_value):
+    file_name = file_name
+    starting_value = starting_value
+    training_data = []
+    for i in list(range(4))[::-1]:
+        print(i+1)
+        time.sleep(1)
+
+    last_time = time.time()
+    paused = False
+    print('STARTING!!!')
+    while(True):
+
+        if not paused:
+            screen = grab_screen(region=(0,40,1920,1120))
+            last_time = time.time()
+            # resize to something a bit more acceptable for a CNN
+            screen = cv2.resize(screen, (480,270))
+            # run a color convert:
+            screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
+
+            keys = key_check()
+            output = keys_to_output(keys)
+            training_data.append([screen,output])
+
+            #print('loop took {} seconds'.format(time.time()-last_time))
+            last_time = time.time()
+##            cv2.imshow('window',cv2.resize(screen,(640,360)))
+##            if cv2.waitKey(25) & 0xFF == ord('q'):
+##                cv2.destroyAllWindows()
+##                break
+
+            if len(training_data) % 100 == 0:
+                print(len(training_data))
+
+                if len(training_data) == 500:
+                    np.save(file_name,training_data)
+                    print('SAVED')
+                    training_data = []
+                    starting_value += 1
+                    file_name = 'X:/pygta5/phase7-larger-color/training_data-{}.npy'.format(starting_value)
+
+
+        keys = key_check()
+        if 'T' in keys:
+            if paused:
+                paused = False
+                print('unpaused!')
+                time.sleep(1)
+            else:
+                print('Pausing!')
+                paused = True
+                time.sleep(1)
+
+
+main(file_name, starting_value)

2. train_model.py

@@ -0,0 +1,117 @@
+import numpy as np
+from grabscreen import grab_screen
+import cv2
+import time
+import os
+import pandas as pd
+from tqdm import tqdm
+from collections import deque
+from models import inception_v3 as googlenet
+from random import shuffle
+
+
+FILE_I_END = 1860
+
+WIDTH = 480
+HEIGHT = 270
+LR = 1e-3
+EPOCHS = 30
+
+MODEL_NAME = ''
+PREV_MODEL = ''
+
+LOAD_MODEL = True
+
+wl = 0
+sl = 0
+al = 0
+dl = 0
+
+wal = 0
+wdl = 0
+sal = 0
+sdl = 0
+nkl = 0
+
+w = [1,0,0,0,0,0,0,0,0]
+s = [0,1,0,0,0,0,0,0,0]
+a = [0,0,1,0,0,0,0,0,0]
+d = [0,0,0,1,0,0,0,0,0]
+wa = [0,0,0,0,1,0,0,0,0]
+wd = [0,0,0,0,0,1,0,0,0]
+sa = [0,0,0,0,0,0,1,0,0]
+sd = [0,0,0,0,0,0,0,1,0]
+nk = [0,0,0,0,0,0,0,0,1]
+
+model = googlenet(WIDTH, HEIGHT, 3, LR, output=9, model_name=MODEL_NAME)
+
+if LOAD_MODEL:
+    model.load(PREV_MODEL)
+    print('We have loaded a previous model!!!!')
+
+
+# iterates through the training files
+
+
+for e in range(EPOCHS):
+    #data_order = [i for i in range(1,FILE_I_END+1)]
+    data_order = [i for i in range(1,FILE_I_END+1)]
+    shuffle(data_order)
+    for count,i in enumerate(data_order):
+
+        try:
+            file_name = 'J:/phase10-random-padded/training_data-{}.npy'.format(i)
+            # full file info
+            train_data = np.load(file_name)
+            print('training_data-{}.npy'.format(i),len(train_data))
+
+##            # [   [    [FRAMES], CHOICE   ]    ] 
+##            train_data = []
+##            current_frames = deque(maxlen=HM_FRAMES)
+##            
+##            for ds in data:
+##                screen, choice = ds
+##                gray_screen = cv2.cvtColor(screen, cv2.COLOR_RGB2GRAY)
+##
+##
+##                current_frames.append(gray_screen)
+##                if len(current_frames) == HM_FRAMES:
+##                    train_data.append([list(current_frames),choice])
+
+
+            # #
+            # always validating unique data: 
+            #shuffle(train_data)
+            train = train_data[:-50]
+            test = train_data[-50:]
+
+            X = np.array([i[0] for i in train]).reshape(-1,WIDTH,HEIGHT,3)
+            Y = [i[1] for i in train]
+
+            test_x = np.array([i[0] for i in test]).reshape(-1,WIDTH,HEIGHT,3)
+            test_y = [i[1] for i in test]
+
+            model.fit({'input': X}, {'targets': Y}, n_epoch=1, validation_set=({'input': test_x}, {'targets': test_y}), 
+                snapshot_step=2500, show_metric=True, run_id=MODEL_NAME)
+
+
+            if count%10 == 0:
+                print('SAVING MODEL!')
+                model.save(MODEL_NAME)
+
+        except Exception as e:
+            print(str(e))
+
+
+
+
+
+
+
+
+
+
+#
+
+#tensorboard --logdir=foo:J:/phase10-code/log
+