def play_model(model_path, romfile):
player = AtariPlayer(AtariDriver(romfile, viz=0.01),
action_repeat=ACTION_REPEAT)
global NUM_ACTIONS
NUM_ACTIONS = player.driver.get_num_actions()
M = Model()
cfg = PredictConfig(model=M,
input_data_mapping=[0],
session_init=SaverRestore(model_path),
output_var_names=['fct/output:0'])
predfunc = get_predict_func(cfg)
tot_reward = 0
que = deque(maxlen=30)
while True:
s = player.current_state()
outputs = predfunc([[s]])
action_value = outputs[0][0]
act = action_value.argmax()
print action_value, act
if random.random() < 0.01:
act = random.choice(range(player.driver.get_num_actions()))
if len(que) == que.maxlen \
and que.count(que[0]) == que.maxlen:
act = 1
que.append(act)
print(act)
reward, isOver = player.action(act)
tot_reward += reward
if isOver:
print("Total:", tot_reward)
tot_reward = 0
def run_test(path, input):
param_dict = np.load(path).item()
pred_config = PredictConfig(
model=Model(),
input_data_mapping=[0],
session_init=ParamRestore(param_dict),
output_var_names=['output:0'] # output:0 is the probability distribution
)
predict_func = get_predict_func(pred_config)
import cv2
im = cv2.imread(input)
assert im is not None
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, (227, 227))
im = np.reshape(im, (1, 227, 227, 3)).astype('float32')
im = im - 110
outputs = predict_func([im])[0]
prob = outputs[0]
ret = prob.argsort()[-10:][::-1]
print ret
meta = ILSVRCMeta().get_synset_words_1000()
print [meta[k] for k in ret]
def play_model(model_path, romfile):
player = AtariPlayer(AtariDriver(romfile, viz=0.01),
action_repeat=ACTION_REPEAT)
global NUM_ACTIONS
NUM_ACTIONS = player.driver.get_num_actions()
M = Model()
cfg = PredictConfig(
model=M,
input_data_mapping=[0],
session_init=SaverRestore(model_path),
output_var_names=['fct/output:0'])
predfunc = get_predict_func(cfg)
tot_reward = 0
que = deque(maxlen=30)
while True:
s = player.current_state()
outputs = predfunc([[s]])
action_value = outputs[0][0]
act = action_value.argmax()
print action_value, act
if random.random() < 0.01:
act = random.choice(range(player.driver.get_num_actions()))
if len(que) == que.maxlen \
and que.count(que[0]) == que.maxlen:
act = 1
que.append(act)
print(act)
reward, isOver = player.action(act)
tot_reward += reward
if isOver:
print("Total:", tot_reward)
tot_reward = 0
def run_test(path, input):
param_dict = np.load(path).item()
pred_config = PredictConfig(
model=Model(),
input_data_mapping=[0],
session_init=ParamRestore(param_dict),
output_var_names=['output:0', 'pool5/MaxPool:0'])
predict_func = get_predict_func(pred_config)
im = cv2.imread(input)
assert im is not None
im = im.astype('float32')
im = cv2.resize(im, (224, 224)).reshape((1, 224, 224, 3))
im = im - 110
raw_out = predict_func([im])
tfout = raw_out[1][0]
from tensorio import read_value
dumpf = 'dump.tensortxt'
with open(dumpf) as f:
name, arr = read_value(f)
os.unlink(dumpf)
hout = arr[:, :, :, 0]
diff = hout - tfout
maxdiff = np.abs(diff).max()
print "Diff:", maxdiff
assert maxdiff < 1e-3
return
prob = raw_out[0][0]
ret = prob.argsort()[-10:][::-1]
print ret
meta = ILSVRCMeta().get_synset_words_1000()
print[meta[k] for k in ret]
def run_test(path, input):
param_dict = np.load(path).item()
pred_config = PredictConfig(
model=Model(),
input_var_names=['input'],
session_init=ParamRestore(param_dict),
session_config=get_default_sess_config(0.9),
output_var_names=['output'] # output:0 is the probability distribution
)
predict_func = get_predict_func(pred_config)
import cv2
im = cv2.imread(input)
assert im is not None
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, (224, 224))
im = np.reshape(im, (1, 224, 224, 3)).astype('float32')
im = im - 110
outputs = predict_func([im])[0]
prob = outputs[0]
ret = prob.argsort()[-10:][::-1]
print(ret)
meta = ILSVRCMeta().get_synset_words_1000()
print([meta[k] for k in ret])
def eval_model_singlethread(cfg, nr_eval):
func = get_predict_func(cfg)
player = get_player(train=False)
scores = []
for _ in tqdm(range(nr_eval)):
score = play_one_episode(player, func, cfg.model.predict_value)
scores.append(score)
logger.info("Average Score: {}; Max Score: {}".format(
sum(scores) / float(len(scores), max(scores))))
def assemble_func(config_module, checkpoint_path):
model = config_module.Model()
pred_config = PredictConfig(
model=model,
input_data_mapping=[0, 1],
session_init=SaverRestore(checkpoint_path),
output_var_names=['pred'],
)
predict_func = get_predict_func(pred_config)
return predict_func
def play_model(model_path):
player = get_player(viz=0.01)
cfg = PredictConfig(
model=Model(),
input_data_mapping=[0],
session_init=SaverRestore(model_path),
output_var_names=['fct/output:0'])
predfunc = get_predict_func(cfg)
while True:
score = play_one_episode(player, predfunc)
print("Total:", score)
def eval_model_multithread(model_path):
cfg = PredictConfig(
model=Model(),
input_data_mapping=[0],
session_init=SaverRestore(model_path),
output_var_names=['fct/output:0'])
p = get_player(); del p # set NUM_ACTIONS
func = get_predict_func(cfg)
NR_PROC = min(multiprocessing.cpu_count() // 2, 8)
mean, max = eval_with_funcs([func] * NR_PROC)
logger.info("Average Score: {}; Max Score: {}".format(mean, max))
def assemble_func(config_module, checkpoint_path):
model = config_module.Model()
pred_config = PredictConfig(
model=model,
input_var_names=['input'],
session_init=SaverRestore(checkpoint_path),
session_config=get_default_sess_config(0.5),
output_var_names=['score', 'boxes'],
)
predict_func = get_predict_func(pred_config)
return predict_func
def run_test(path, input):
param_dict = np.load(path).item()
pred_config = PredictConfig(
model=Model(),
input_data_mapping=[0],
session_init=ParamRestore(param_dict),
output_var_names=['output:0', 'pool5/MaxPool:0']
)
predict_func = get_predict_func(pred_config)
im = cv2.imread(input)
assert im is not None
im = im.astype('float32')
im = cv2.resize(im, (224, 224)).reshape((1,224,224,3))
im = im - 110
raw_out = predict_func([im])
tfout = raw_out[1][0]
from tensorio import read_value
dumpf = 'dump.tensortxt'
with open(dumpf) as f:
name, arr = read_value(f)
os.unlink(dumpf)
hout = arr[:,:,:,0]
diff = hout - tfout
maxdiff = np.abs(diff).max()
print "Diff:", maxdiff
assert maxdiff < 1e-3
return
prob = raw_out[0][0]
ret = prob.argsort()[-10:][::-1]
print ret
meta = ILSVRCMeta().get_synset_words_1000()
print [meta[k] for k in ret]
def eval_model_multithread(cfg, nr_eval):
func = get_predict_func(cfg)
NR_PROC = min(multiprocessing.cpu_count() // 2, 8)
mean, max = eval_with_funcs([func] * NR_PROC, nr_eval)
logger.info("Average Score: {}; Max Score: {}".format(mean, max))
config.nr_tower = len(args.gpu.split(','))
QueueInputTrainer(config).train()
with tf.Graph().as_default():
with tf.device('/cpu:0'):
pred_config = PredictConfig(
model=Model(n=5),
input_data_mapping=[0],
session_init=SaverRestore(
"/home/ubuntu/tensorpack/examples/ResNet/train2_log{iteration}/Kaggle-Resnet-Strat/model-{mod}"
.format(iteration=iteration, mod=str(680))),
output_var_names=[
'output:0'
] # output:0 is the probability distribution
)
if args.gpu:
config.nr_tower = len(args.gpu.split(','))
predict_func = get_predict_func(pred_config)
preds = []
for i in xrange(0, len(cm.ds.X_test), 1000):
x = cm.ds.X_test[i:i + 1000]
preds.append(predict_func([x])[0])
preds = np.vstack(preds)
results.append(preds)
results = np.mean(results, axis=0)
write_submission(results, cm.ds.X_test_ids, "sub2.csv")
def eval_model_multithread(cfg, nr_eval):
func = get_predict_func(cfg)
NR_PROC = min(multiprocessing.cpu_count() // 2, 8)
mean, max = eval_with_funcs([func] * NR_PROC, nr_eval)
logger.info("Average Score: {}; Max Score: {}".format(mean, max))
def play_model(cfg):
player = get_player(viz=0.01)
predfunc = get_predict_func(cfg)
while True:
score = play_one_episode(player, predfunc)
print("Total:", score)
def play_model(cfg):
player = get_player(viz=0.01)
predfunc = get_predict_func(cfg)
while True:
score = play_one_episode(player, predfunc)
print("Total:", score)
if args.load:
config.session_init = SaverRestore(args.load)
if args.gpu:
config.nr_tower = len(args.gpu.split(','))
QueueInputTrainer(config).train()
with tf.Graph().as_default():
with tf.device('/cpu:0'):
pred_config = PredictConfig(
model=Model(n=5),
input_data_mapping=[0],
session_init=SaverRestore("/home/ubuntu/tensorpack/examples/ResNet/train2_log{iteration}/Kaggle-Resnet-Strat/model-{mod}".format(iteration=iteration,mod=str(680))),
output_var_names=['output:0'] # output:0 is the probability distribution
)
if args.gpu:
config.nr_tower = len(args.gpu.split(','))
predict_func = get_predict_func(pred_config)
preds = []
for i in xrange(0, len(cm.ds.X_test), 1000):
x = cm.ds.X_test[i:i+1000]
preds.append(predict_func([x])[0]
)
preds = np.vstack(preds)
results.append(preds)
results = np.mean(results, axis=0 )
write_submission(results, cm.ds.X_test_ids, "sub2.csv")
