TensorFlow 2.0 above support added

README.md

@@ -17,19 +17,22 @@ Python3, tensorflow 1.0, numpy, opencv 3.
 
 ### Getting started
 
-You can choose _one_ of the following three ways to get started with darkflow.
+- Install tf_slim module since contrib is deprecated from tf2+
+ ```pip install git+https://github.com/ShanuDey/tf-slim.git```
 
-1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use `./flow` in the cloned darkflow directory instead of `flow` as darkflow is not installed globally.
+- You can choose _one_ of the following three ways to get started with darkflow.
+
+	1. Just build the Cython extensions in place. NOTE: If installing this way you will have to use `./flow` in the cloned darkflow directory instead of `flow` as darkflow is not installed globally.
     ```
     python3 setup.py build_ext --inplace
     ```
 
-2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
+	2. Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
     ```
     pip install -e .
     ```
 
-3. Install with pip globally
+	3. Install with pip globally
     ```
     pip install .
     ```

darkflow/net/build.py

@@ -12,14 +12,14 @@
 class TFNet(object):
 
 	_TRAINER = dict({
-		'rmsprop': tf.train.RMSPropOptimizer,
-		'adadelta': tf.train.AdadeltaOptimizer,
-		'adagrad': tf.train.AdagradOptimizer,
-		'adagradDA': tf.train.AdagradDAOptimizer,
-		'momentum': tf.train.MomentumOptimizer,
-		'adam': tf.train.AdamOptimizer,
-		'ftrl': tf.train.FtrlOptimizer,
-		'sgd': tf.train.GradientDescentOptimizer
+		'rmsprop': tf.compat.v1.train.RMSPropOptimizer,
+		'adadelta': tf.compat.v1.train.AdadeltaOptimizer,
+		'adagrad': tf.compat.v1.train.AdagradOptimizer,
+		'adagradDA': tf.compat.v1.train.AdagradDAOptimizer,
+		'momentum': tf.compat.v1.train.MomentumOptimizer,
+		'adam': tf.compat.v1.train.AdamOptimizer,
+		'ftrl': tf.compat.v1.train.FtrlOptimizer,
+		'sgd': tf.compat.v1.train.GradientDescentOptimizer
 	})
 
 	# imported methods
@@ -78,8 +78,8 @@ def __init__(self, FLAGS, darknet = None):
 			time.time() - start))
 
 	def build_from_pb(self):
-		with tf.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f:
-			graph_def = tf.GraphDef()
+		with tf.compat.v1.gfile.FastGFile(self.FLAGS.pbLoad, "rb") as f:
+			graph_def = tf.compat.v1.GraphDef()
 			graph_def.ParseFromString(f.read())
 
 		tf.import_graph_def(
@@ -91,9 +91,9 @@ def build_from_pb(self):
 		self.framework = create_framework(self.meta, self.FLAGS)
 
 		# Placeholders
-		self.inp = tf.get_default_graph().get_tensor_by_name('input:0')
+		self.inp = tf.compat.v1.get_default_graph().get_tensor_by_name('input:0')
 		self.feed = dict() # other placeholders
-		self.out = tf.get_default_graph().get_tensor_by_name('output:0')
+		self.out = tf.compat.v1.get_default_graph().get_tensor_by_name('output:0')
 
 		self.setup_meta_ops()
 
@@ -102,7 +102,7 @@ def build_forward(self):
 
 		# Placeholders
 		inp_size = [None] + self.meta['inp_size']
-		self.inp = tf.placeholder(tf.float32, inp_size, 'input')
+		self.inp = tf.compat.v1.placeholder(tf.float32, inp_size, 'input')
 		self.feed = dict() # other placeholders
 
 		# Build the forward pass
@@ -129,7 +129,7 @@ def setup_meta_ops(self):
 		utility = min(self.FLAGS.gpu, 1.)
 		if utility > 0.0:
 			self.say('GPU mode with {} usage'.format(utility))
-			cfg['gpu_options'] = tf.GPUOptions(
+			cfg['gpu_options'] = tf.compat.v1.GPUOptions(
 				per_process_gpu_memory_fraction = utility)
 			cfg['allow_soft_placement'] = True
 		else: 
@@ -139,14 +139,14 @@ def setup_meta_ops(self):
 		if self.FLAGS.train: self.build_train_op()
 
 		if self.FLAGS.summary:
-			self.summary_op = tf.summary.merge_all()
-			self.writer = tf.summary.FileWriter(self.FLAGS.summary + 'train')
+			self.summary_op = tf.compat.v1.summary.merge_all()
+			self.writer = tf.compat.v1.summary.FileWriter(self.FLAGS.summary + 'train')
 
-		self.sess = tf.Session(config = tf.ConfigProto(**cfg))
-		self.sess.run(tf.global_variables_initializer())
+		self.sess = tf.compat.v1.Session(config = tf.compat.v1.ConfigProto(**cfg))
+		self.sess.run(tf.compat.v1.global_variables_initializer())
 
 		if not self.ntrain: return
-		self.saver = tf.train.Saver(tf.global_variables(), 
+		self.saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(), 
 			max_to_keep = self.FLAGS.keep)
 		if self.FLAGS.load != 0: self.load_from_ckpt()
 
@@ -165,7 +165,7 @@ def savepb(self):
 		flags_pb.train = False
 		# rebuild another tfnet. all const.
 		tfnet_pb = TFNet(flags_pb, darknet_pb)		
-		tfnet_pb.sess = tf.Session(graph = tfnet_pb.graph)
+		tfnet_pb.sess = tf.compat.v1.Session(graph = tfnet_pb.graph)
 		# tfnet_pb.predict() # uncomment for unit testing
 		name = 'built_graph/{}.pb'.format(self.meta['name'])
 		os.makedirs(os.path.dirname(name), exist_ok=True)
@@ -174,4 +174,4 @@ def savepb(self):
 			json.dump(self.meta, fp)
 		self.say('Saving const graph def to {}'.format(name))
 		graph_def = tfnet_pb.sess.graph_def
-		tf.train.write_graph(graph_def,'./', name, False)
+		tf.io.write_graph(graph_def,'./', name, False)

darkflow/net/help.py

@@ -45,15 +45,15 @@ def load_old_graph(self, ckpt):
     ckpt_loader = create_loader(ckpt)
     self.say(old_graph_msg.format(ckpt))
 
-    for var in tf.global_variables():
+    for var in tf.compat.v1.global_variables():
         name = var.name.split(':')[0]
         args = [name, var.get_shape()]
         val = ckpt_loader(args)
         assert val is not None, \
         'Cannot find and load {}'.format(var.name)
         shp = val.shape
-        plh = tf.placeholder(tf.float32, shp)
-        op = tf.assign(var, plh)
+        plh = tf.compat.v1.placeholder(tf.float32, shp)
+        op = tf.compat.v1.assign(var, plh)
         self.sess.run(op, {plh: val})
 
 def _get_fps(self, frame):
@@ -156,7 +156,7 @@ def to_darknet(self):
     darknet_ckpt = self.darknet
 
     with self.graph.as_default() as g:
-        for var in tf.global_variables():
+        for var in tf.compat.v1.global_variables():
             name = var.name.split(':')[0]
             var_name = name.split('-')
             l_idx = int(var_name[0])

darkflow/net/ops/baseop.py

@@ -65,10 +65,10 @@ def wrap_variable(self, var):
         if not self.var: return
 
         val = self.lay.w[var]
-        self.lay.w[var] = tf.constant_initializer(val)
+        self.lay.w[var] = tf.compat.v1.constant_initializer(val)
         if var in self._SLIM: return
-        with tf.variable_scope(self.scope):
-            self.lay.w[var] = tf.get_variable(var,
+        with tf.compat.v1.variable_scope(self.scope):
+            self.lay.w[var] = tf.compat.v1.get_variable(var,
                 shape = self.lay.wshape[var],
                 dtype = tf.float32,
                 initializer = self.lay.w[var])
@@ -81,7 +81,7 @@ def wrap_pholder(self, ph, feed):
         sig = '{}/{}'.format(self.scope, ph)
         val = self.lay.h[ph]
 
-        self.lay.h[ph] = tf.placeholder_with_default(
+        self.lay.h[ph] = tf.compat.v1.placeholder_with_default(
             val['dfault'], val['shape'], name = sig)
         feed[self.lay.h[ph]] = val['feed']
 

darkflow/net/ops/convolution.py

@@ -1,4 +1,4 @@
-import tensorflow.contrib.slim as slim
+import tf_slim as slim
 from .baseop import BaseOp
 import tensorflow as tf
 import numpy as np
@@ -24,7 +24,7 @@ def _forward(self):
     def forward(self):
         inp = self.inp.out
         s = self.lay.stride
-        self.out = tf.extract_image_patches(
+        self.out = tf.image.extract_patches(
             inp, [1,s,s,1], [1,s,s,1], [1,1,1,1], 'VALID')
 
     def speak(self):
@@ -36,7 +36,7 @@ def speak(self):
 class local(BaseOp):
     def forward(self):
         pad = [[self.lay.pad, self.lay.pad]] * 2;
-        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
+        temp = tf.pad(tensor=self.inp.out, paddings=[[0, 0]] + pad + [[0, 0]])
 
         k = self.lay.w['kernels']
         ksz = self.lay.ksize
@@ -49,7 +49,7 @@ def forward(self):
                 i_, j_ = i + 1 - half, j + 1 - half
                 tij = temp[:, i_ : i_ + ksz, j_ : j_ + ksz,:]
                 row_i.append(
-                    tf.nn.conv2d(tij, kij, 
+                    tf.nn.conv2d(input=tij, filters=kij, 
                         padding = 'VALID', 
                         strides = [1] * 4))
             out += [tf.concat(row_i, 2)]
@@ -66,8 +66,8 @@ def speak(self):
 class convolutional(BaseOp):
     def forward(self):
         pad = [[self.lay.pad, self.lay.pad]] * 2;
-        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
-        temp = tf.nn.conv2d(temp, self.lay.w['kernel'], padding = 'VALID', 
+        temp = tf.pad(tensor=self.inp.out, paddings=[[0, 0]] + pad + [[0, 0]])
+        temp = tf.nn.conv2d(input=temp, filters=self.lay.w['kernel'], padding = 'VALID', 
             name = self.scope, strides = [1] + [self.lay.stride] * 2 + [1])
         if self.lay.batch_norm: 
             temp = self.batchnorm(self.lay, temp)
@@ -113,4 +113,4 @@ def speak(self):
         args += [l.batch_norm * '+bnorm']
         args += [l.activation]
         msg = 'extr {}x{}p{}_{}  {}  {}'.format(*args)
-        return msg
+        return msg

darkflow/net/ops/simple.py

@@ -1,4 +1,4 @@
-import tensorflow.contrib.slim as slim
+import tf_slim as slim
 from .baseop import BaseOp
 import tensorflow as tf
 from distutils.version import StrictVersion
@@ -22,7 +22,7 @@ def speak(self):
 
 class connected(BaseOp):
 	def forward(self):
-		self.out = tf.nn.xw_plus_b(
+		self.out = tf.compat.v1.nn.xw_plus_b(
 			self.inp.out,
 			self.lay.w['weights'], 
 			self.lay.w['biases'], 
@@ -56,7 +56,7 @@ def speak(self):
 class flatten(BaseOp):
 	def forward(self):
 		temp = tf.transpose(
-			self.inp.out, [0,3,1,2])
+			a=self.inp.out, perm=[0,3,1,2])
 		self.out = slim.flatten(
 			temp, scope = self.scope)
 
@@ -73,7 +73,7 @@ def speak(self): return 'softmax()'
 class avgpool(BaseOp):
 	def forward(self):
 		self.out = tf.reduce_mean(
-			self.inp.out, [1, 2], 
+			input_tensor=self.inp.out, axis=[1, 2], 
 			name = self.scope
 		)
 
@@ -86,7 +86,7 @@ def forward(self):
 			self.lay.h['pdrop'] = 1.0
 		self.out = tf.nn.dropout(
 			self.inp.out, 
-			self.lay.h['pdrop'], 
+			1 - (self.lay.h['pdrop']), 
 			name = self.scope
 		)
 
@@ -103,8 +103,8 @@ def speak(self):
 
 class maxpool(BaseOp):
 	def forward(self):
-		self.out = tf.nn.max_pool(
-			self.inp.out, padding = 'SAME',
+		self.out = tf.nn.max_pool2d(
+			input=self.inp.out, padding = 'SAME',
 	        ksize = [1] + [self.lay.ksize]*2 + [1], 
 	        strides = [1] + [self.lay.stride]*2 + [1],
 	        name = self.scope

darkflow/net/vanilla/train.py

@@ -34,8 +34,8 @@ def loss(self, net_out):
 		loss = l1_loss(diff)
 
 	elif loss_type == 'softmax':
-		loss = tf.nn.softmax_cross_entropy_with_logits(logits, y)
-		loss = tf.reduce_mean(loss)
+		loss = tf.nn.softmax_cross_entropy_with_logits(labels=tf.stop_gradient(y))
+		loss = tf.reduce_mean(input_tensor=loss)
 
 	elif loss_type == 'svm':
 		assert 'train_size' in m, \

darkflow/net/yolo/train.py

@@ -1,4 +1,4 @@
-import tensorflow.contrib.slim as slim
+import tf_slim as slim
 import pickle
 import tensorflow as tf
 from .misc import show
@@ -30,15 +30,15 @@ def loss(self, net_out):
     size2 = [None, SS, B]
 
     # return the below placeholders
-    _probs = tf.placeholder(tf.float32, size1)
-    _confs = tf.placeholder(tf.float32, size2)
-    _coord = tf.placeholder(tf.float32, size2 + [4])
+    _probs = tf.compat.v1.placeholder(tf.float32, size1)
+    _confs = tf.compat.v1.placeholder(tf.float32, size2)
+    _coord = tf.compat.v1.placeholder(tf.float32, size2 + [4])
     # weights term for L2 loss
-    _proid = tf.placeholder(tf.float32, size1)
+    _proid = tf.compat.v1.placeholder(tf.float32, size1)
     # material calculating IOU
-    _areas = tf.placeholder(tf.float32, size2)
-    _upleft = tf.placeholder(tf.float32, size2 + [2])
-    _botright = tf.placeholder(tf.float32, size2 + [2])
+    _areas = tf.compat.v1.placeholder(tf.float32, size2)
+    _upleft = tf.compat.v1.placeholder(tf.float32, size2 + [2])
+    _botright = tf.compat.v1.placeholder(tf.float32, size2 + [2])
 
     self.placeholders = {
         'probs':_probs, 'confs':_confs, 'coord':_coord, 'proid':_proid,
@@ -63,8 +63,8 @@ def loss(self, net_out):
 
     # calculate the best IOU, set 0.0 confidence for worse boxes
     iou = tf.truediv(intersect, _areas + area_pred - intersect)
-    best_box = tf.equal(iou, tf.reduce_max(iou, [2], True))
-    best_box = tf.to_float(best_box)
+    best_box = tf.equal(iou, tf.reduce_max(input_tensor=iou, axis=[2], keepdims=True))
+    best_box = tf.cast(best_box, dtype=tf.float32)
     confs = tf.multiply(best_box, _confs)
 
     # take care of the weight terms
@@ -87,6 +87,6 @@ def loss(self, net_out):
     print('Building {} loss'.format(m['model']))
     loss = tf.pow(net_out - true, 2)
     loss = tf.multiply(loss, wght)
-    loss = tf.reduce_sum(loss, 1)
-    self.loss = .5 * tf.reduce_mean(loss)
-    tf.summary.scalar('{} loss'.format(m['model']), self.loss)
+    loss = tf.reduce_sum(input_tensor=loss, axis=1)
+    self.loss = .5 * tf.reduce_mean(input_tensor=loss)
+    tf.compat.v1.summary.scalar('{} loss'.format(m['model']), self.loss)

darkflow/net/yolov2/train.py

@@ -1,4 +1,4 @@
-import tensorflow.contrib.slim as slim
+import tf_slim as slim
 import pickle
 import tensorflow as tf
 from ..yolo.misc import show
@@ -37,15 +37,15 @@ def loss(self, net_out):
     size2 = [None, HW, B]
 
     # return the below placeholders
-    _probs = tf.placeholder(tf.float32, size1)
-    _confs = tf.placeholder(tf.float32, size2)
-    _coord = tf.placeholder(tf.float32, size2 + [4])
+    _probs = tf.compat.v1.placeholder(tf.float32, size1)
+    _confs = tf.compat.v1.placeholder(tf.float32, size2)
+    _coord = tf.compat.v1.placeholder(tf.float32, size2 + [4])
     # weights term for L2 loss
-    _proid = tf.placeholder(tf.float32, size1)
+    _proid = tf.compat.v1.placeholder(tf.float32, size1)
     # material calculating IOU
-    _areas = tf.placeholder(tf.float32, size2)
-    _upleft = tf.placeholder(tf.float32, size2 + [2])
-    _botright = tf.placeholder(tf.float32, size2 + [2])
+    _areas = tf.compat.v1.placeholder(tf.float32, size2)
+    _upleft = tf.compat.v1.placeholder(tf.float32, size2 + [2])
+    _botright = tf.compat.v1.placeholder(tf.float32, size2 + [2])
 
     self.placeholders = {
         'probs':_probs, 'confs':_confs, 'coord':_coord, 'proid':_proid,
@@ -83,8 +83,8 @@ def loss(self, net_out):
 
     # calculate the best IOU, set 0.0 confidence for worse boxes
     iou = tf.truediv(intersect, _areas + area_pred - intersect)
-    best_box = tf.equal(iou, tf.reduce_max(iou, [2], True))
-    best_box = tf.to_float(best_box)
+    best_box = tf.equal(iou, tf.reduce_max(input_tensor=iou, axis=[2], keepdims=True))
+    best_box = tf.cast(best_box, dtype=tf.float32)
     confs = tf.multiply(best_box, _confs)
 
     # take care of the weight terms
@@ -102,6 +102,6 @@ def loss(self, net_out):
     loss = tf.pow(adjusted_net_out - true, 2)
     loss = tf.multiply(loss, wght)
     loss = tf.reshape(loss, [-1, H*W*B*(4 + 1 + C)])
-    loss = tf.reduce_sum(loss, 1)
-    self.loss = .5 * tf.reduce_mean(loss)
-    tf.summary.scalar('{} loss'.format(m['model']), self.loss)
+    loss = tf.reduce_sum(input_tensor=loss, axis=1)
+    self.loss = .5 * tf.reduce_mean(input_tensor=loss)
+    tf.compat.v1.summary.scalar('{} loss'.format(m['model']), self.loss)