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seminar-breakout
Commit a1a957b4 authored by Yuxin Wu's avatar Yuxin Wu
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linearwrap for several examples

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examples/Atari2600/DQN.py
View file @ a1a957b4
......@@ -71,21 +71,22 @@ class Model(ModelDesc):
""" image: [0,255]"""
image = image / 255.0
with argscope(Conv2D, nl=PReLU.f, use_bias=True):
l = Conv2D('conv0', image, out_channel=32, kernel_shape=5, stride=1)
l = MaxPooling('pool0', l, 2)
l = Conv2D('conv1', l, out_channel=32, kernel_shape=5, stride=1)
l = MaxPooling('pool1', l, 2)
l = Conv2D('conv2', l, out_channel=64, kernel_shape=4)
l = MaxPooling('pool2', l, 2)
l = Conv2D('conv3', l, out_channel=64, kernel_shape=3)
# the original arch
#l = Conv2D('conv0', image, out_channel=32, kernel_shape=8, stride=4)
#l = Conv2D('conv1', l, out_channel=64, kernel_shape=4, stride=2)
#l = Conv2D('conv2', l, out_channel=64, kernel_shape=3)
l = FullyConnected('fc0', l, 512, nl=lambda x, name: LeakyReLU.f(x, 0.01, name))
l = FullyConnected('fct', l, out_dim=NUM_ACTIONS, nl=tf.identity)
l = (LinearWrap(image)
.Conv2D('conv0', out_channel=32, kernel_shape=5)
.MaxPooling('pool0', 2)
.Conv2D('conv1', out_channel=32, kernel_shape=5)
.MaxPooling('pool1', 2)
.Conv2D('conv2', out_channel=64, kernel_shape=4)
.MaxPooling('pool2', 2)
.Conv2D('conv3', out_channel=64, kernel_shape=3)
# the original arch
#.Conv2D('conv0', image, out_channel=32, kernel_shape=8, stride=4)
#.Conv2D('conv1', out_channel=64, kernel_shape=4, stride=2)
#.Conv2D('conv2', out_channel=64, kernel_shape=3)
.FullyConnected('fc0', 512, nl=lambda x, name: LeakyReLU.f(x, 0.01, name))
.FullyConnected('fct', NUM_ACTIONS, nl=tf.identity)())
return l
def _build_graph(self, inputs, is_training):
......
examples/DoReFa-Net/svhn-digit-dorefa.py
View file @ a1a957b4
......@@ -101,40 +101,43 @@ class Model(ModelDesc):
def activate(x):
return fa(cabs(x))
l = image / 256.0
image = image / 256.0
with argscope(BatchNorm, decay=0.9, epsilon=1e-4, use_local_stat=is_training), \
argscope(Conv2D, use_bias=False, nl=tf.identity):
l = Conv2D('conv0', l, 48, 5, padding='VALID', use_bias=True)
l = MaxPooling('pool0', l, 2, padding='SAME')
l = activate(l)
# 18
l = Conv2D('conv1', l, 64, 3, padding='SAME')
l = activate(BatchNorm('bn1', fg(l)))
l = Conv2D('conv2', l, 64, 3, padding='SAME')
l = BatchNorm('bn2', fg(l))
l = MaxPooling('pool1', l, 2, padding='SAME')
l = activate(l)
# 9
l = Conv2D('conv3', l, 128, 3, padding='VALID')
l = activate(BatchNorm('bn3', fg(l)))
# 7
l = Conv2D('conv4', l, 128, 3, padding='SAME')
l = activate(BatchNorm('bn4', fg(l)))
l = Conv2D('conv5', l, 128, 3, padding='VALID')
l = activate(BatchNorm('bn5', fg(l)))
# 5
l = tf.nn.dropout(l, 0.5 if is_training else 1.0)
l = Conv2D('conv6', l, 512, 5, padding='VALID')
l = BatchNorm('bn6', fg(l))
l = cabs(l)
logits = FullyConnected('fc1', l, 10, nl=tf.identity)
logits = (LinearWrap(image)
.Conv2D('conv0', 48, 5, padding='VALID', use_bias=True)
.MaxPooling('pool0', 2, padding='SAME')
.apply(activate)
# 18
.Conv2D('conv1', 64, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn1').apply(activate)
.Conv2D('conv2', 64, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn2')
.MaxPooling('pool1', 2, padding='SAME')
.apply(activate)
# 9
.Conv2D('conv3', 128, 3, padding='VALID')
.apply(fg)
.BatchNorm('bn3').apply(activate)
# 7
.Conv2D('conv4', 128, 3, padding='SAME')
.apply(fg)
.BatchNorm('bn4').apply(activate)
.Conv2D('conv5', 128, 3, padding='VALID')
.apply(fg)
.BatchNorm('bn5').apply(activate)
# 5
.tf.nn.dropout(0.5 if is_training else 1.0)
.Conv2D('conv6', 512, 5, padding='VALID')
.apply(fg).BatchNorm('bn6')
.apply(cabs)
.FullyConnected('fc1', 10, nl=tf.identity)())
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
......
examples/load-alexnet.py
View file @ a1a957b4
......@@ -3,22 +3,14 @@
# File: load-alexnet.py
# Author: Yuxin Wu <ppwwyyxx@gmail.com>
import cv2 # tf bug
import tensorflow as tf
import numpy as np
import os
import argparse
import cPickle as pkl
from tensorpack.train import TrainConfig
from tensorpack.predict import PredictConfig, get_predict_func
from tensorpack.models import *
from tensorpack.utils import *
from tensorpack.tfutils import *
from tensorpack import *
from tensorpack.tfutils.symbolic_functions import *
from tensorpack.tfutils.summary import *
from tensorpack.callbacks import *
from tensorpack.dataflow import *
from tensorpack.dataflow.dataset import ILSVRCMeta
"""
......
examples/load-vgg16.py
View file @ a1a957b4
......@@ -40,39 +40,35 @@ class Model(ModelDesc):
with argscope(Conv2D, kernel_shape=3):
# 224
l = Conv2D('conv1_1', image, 64)
l = Conv2D('conv1_2', l, 64)
l = MaxPooling('pool1', l, 2)
# 112
l = Conv2D('conv2_1', l, 128)
l = Conv2D('conv2_2', l, 128)
l = MaxPooling('pool2', l, 2)
# 56
l = Conv2D('conv3_1', l, 256)
l = Conv2D('conv3_2', l, 256)
l = Conv2D('conv3_3', l, 256)
l = MaxPooling('pool3', l, 2)
# 28
l = Conv2D('conv4_1', l, 512)
l = Conv2D('conv4_2', l, 512)
l = Conv2D('conv4_3', l, 512)
l = MaxPooling('pool4', l, 2)
# 14
l = Conv2D('conv5_1', l, 512)
l = Conv2D('conv5_2', l, 512)
l = Conv2D('conv5_3', l, 512)
l = MaxPooling('pool5', l, 2)
# 7
l = FullyConnected('fc6', l, 4096)
l = tf.nn.dropout(l, keep_prob)
l = FullyConnected('fc7', l, 4096)
l = tf.nn.dropout(l, keep_prob)
logits = FullyConnected('fc8', l, out_dim=1000, nl=tf.identity)
logits = (LinearWrap(image)
.Conv2D('conv1_1', 64)
.Conv2D('conv1_2', 64)
.MaxPooling('pool1', 2)
# 112
.Conv2D('conv2_1', 128)
.Conv2D('conv2_2', 128)
.MaxPooling('pool2', 2)
# 56
.Conv2D('conv3_1', 256)
.Conv2D('conv3_2', 256)
.Conv2D('conv3_3', 256)
.MaxPooling('pool3', 2)
# 28
.Conv2D('conv4_1', 512)
.Conv2D('conv4_2', 512)
.Conv2D('conv4_3', 512)
.MaxPooling('pool4', 2)
# 14
.Conv2D('conv5_1', 512)
.Conv2D('conv5_2', 512)
.Conv2D('conv5_3', 512)
.MaxPooling('pool5', 2)
# 7
.FullyConnected('fc6', 4096)
.tf.nn.dropout(keep_prob)
.FullyConnected('fc7', 4096)
.tf.nn.dropout(keep_prob)
.FullyConnected('fc8', out_dim=1000, nl=tf.identity)())
prob = tf.nn.softmax(logits, name='output')
def run_test(path, input):
......@@ -104,8 +100,8 @@ def run_test(path, input):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default='0',
help='comma separated list of GPU(s) to use.') # nargs='*' in multi mode
parser.add_argument('--gpu',
help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', required=True,
help='.npy model file generated by tensorpack.utils.loadcaffe')
parser.add_argument('--input', help='an input image', required=True)
......
examples/mnist-convnet.py
View file @ a1a957b4
......@@ -34,16 +34,16 @@ class Model(ModelDesc):
nl = PReLU.f
image = image * 2 - 1
with argscope(Conv2D, kernel_shape=3, nl=nl, out_channel=32):
logits = LinearWrap(image) \
.Conv2D('conv0', padding='VALID') \
.MaxPooling('pool0', 2) \
.Conv2D('conv1', padding='SAME') \
.Conv2D('conv2', padding='VALID') \
.MaxPooling('pool1', 2) \
.Conv2D('conv3', padding='VALID') \
.FullyConnected('fc0', 512) \
.tf.nn.dropout(keep_prob) \
.FullyConnected('fc1', out_dim=10, nl=tf.identity)()
logits = (LinearWrap(image) # the starting brace is only for line-breaking
.Conv2D('conv0', padding='VALID')
.MaxPooling('pool0', 2)
.Conv2D('conv1', padding='SAME')
.Conv2D('conv2', padding='VALID')
.MaxPooling('pool1', 2)
.Conv2D('conv3', padding='VALID')
.FullyConnected('fc0', 512)
.tf.nn.dropout(keep_prob)
.FullyConnected('fc1', out_dim=10, nl=tf.identity)())
prob = tf.nn.softmax(logits, name='prob')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
......
examples/svhn-digit-convnet.py
View file @ a1a957b4
......@@ -28,17 +28,17 @@ class Model(ModelDesc):
image = image / 128.0 - 1
l = Conv2D('conv1', image, 24, 5, padding='VALID')
l = MaxPooling('pool1', l, 2, padding='SAME')
l = Conv2D('conv2', l, 32, 3, padding='VALID')
l = Conv2D('conv3', l, 32, 3, padding='VALID')
l = MaxPooling('pool2', l, 2, padding='SAME')
l = Conv2D('conv4', l, 64, 3, padding='VALID')
l = tf.nn.dropout(l, keep_prob)
l = FullyConnected('fc0', l, 512,
b_init=tf.constant_initializer(0.1))
logits = FullyConnected('linear', l, out_dim=10, nl=tf.identity)
logits = LinearWrap(image) \
.Conv2D('conv1', 24, 5, padding='VALID') \
.MaxPooling('pool1', 2, padding='SAME') \
.Conv2D('conv2', 32, 3, padding='VALID') \
.Conv2D('conv3', 32, 3, padding='VALID') \
.MaxPooling('pool2', 2, padding='SAME') \
.Conv2D('conv4', 64, 3, padding='VALID') \
.tf.nn.dropout(keep_prob) \
.FullyConnected('fc0', 512,
b_init=tf.constant_initializer(0.1)) \
.FullyConnected('linear', out_dim=10, nl=tf.identity)()
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
......
tensorpack/models/__init__.py
View file @ a1a957b4
......@@ -54,10 +54,15 @@ class LinearWrap(object):
return f
else:
if layer_name != 'tf':
logger.warn("You're calling LinearWrap with something neither a layer nor 'tf'. Not officially supported yet!")
logger.warn("You're calling LinearWrap.__getattr__ with something neither a layer nor 'tf'. Not officially supported yet!")
assert isinstance(layer, ModuleType)
return LinearWrap.TFModuleFunc(layer, self._t)
def apply(self, func, *args, **kwargs):
""" send tensor to the first argument of a simple func"""
ret = func(self._t, *args, **kwargs)
return LinearWrap(ret)
def __call__(self):
return self._t
......
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