Gan support and implementation for Tensorflow by Greg (#6)

* bAbI data plug-in

Add utils

Add inference form to bAbI dataset

Allow inference without answer

Allow unknown words in BaBI data plug-in

Fix bAbI plugin Lint errors

* Tensorflow integration updates

Use TFRecords for TF inference

TF: Don't rescale inputs

Fix some TF classification tests

Remove unnecessary print

Fix TF imports when uninstalled

Fix mean image scale

Fix generic model tests

Fix Torch single image inference

Fix inference

TMP TF Lint

Revert changes in digits-lint script

Lint: ignore tensorflow standard examples

More Lint fixes

* Add gradient hook

* Add memn2n model

* Update memn2n with gradient hooks

* GAN example

* Make batch size variable

* Training/inference paths

* Small update to TF 0.12

* Snapshot names, float inference, restore all vars

* Do not restore global_step or optimizer variables

* Add TB link

* Update GAN network

* Dynamically select inference form

* TF inference: convert images to float

* Update GAN z-gen network

* Small Update model view layout

* Add GAN plug-ins

* Update GAN plug-in to create CelebA dataset

* Add ability to show input in ImageOutput extension

* Add all data to raw data view extension

* Add model for CelebA dataset

* Update GAN data plug-in

* Update all losses in one session

* Remove conversion to .png in GAN data plug-in

* TF Slim Lenet example

Divide input by 255

* Update GAN data plug-in

* Fix TF model snapshot

* Reduce scheduler delays to speed up inference

* Update GAN plugins

* Fix TF tests

* Add API to LmdbReader (used by gan_features.py)

* Save animated gif

* Add GAN walk-through

* Update GAN walkthrough with embeddings video

* Fix GAN view for list encoding

* Add animation task to GAN plugins

* Add view task to see image attributes

* Add comments to GAN models

* Update README

* Fix GAN features script

* GAN app

* Fix DIGITS inference

* Adjust GAN window size automatically

* Add attributes to GAN app

* Move gandisplay.py

* Remove wxpython 3.0 selection

* Fix call to model

* Adding disclaimer

digits/config/__init__.py

@@ -21,4 +21,3 @@ def config_value(option):
     Return the current configuration value for the given option
     """
     return option_list[option]
-

digits/config/tensorflow.py

@@ -3,26 +3,27 @@
 
 import os
 import platform
-from subprocess import Popen,PIPE
+from subprocess import Popen, PIPE
 
 from . import option_list
 
 VARNAME_ENV_TFPY = 'TENSORFLOW_PYTHON'
-DEFAULT_PYTHON_EXE = 'python2' # @TODO(tzaman) - use the python executable that was used to launch digits?
+DEFAULT_PYTHON_EXE = 'python2'  # @TODO(tzaman) - use the python executable that was used to launch digits?
 
 if platform.system() == 'Darwin':
     # DYLD_LIBRARY_PATH and LD_LIBRARY_PATH is sometimes stripped, and the cuda libraries might need it
-    if not "DYLD_LIBRARY_PATH" in os.environ:
+    if "DYLD_LIBRARY_PATH" not in os.environ:
         if "CUDA_HOME" in os.environ:
             os.environ["DYLD_LIBRARY_PATH"] = str(os.environ["CUDA_HOME"] + '/lib')
 
+
 def test_tf_import(python_exe):
     """
     Tests if tensorflow can be imported, returns if it went okay and optional error.
     """
     p = Popen([python_exe, "-c", "import tensorflow"], stdout=PIPE, stderr=PIPE)
     (out, err) = p.communicate()
-    return p.returncode==0, str(err)
+    return p.returncode == 0, str(err)
 
 if VARNAME_ENV_TFPY in os.environ:
     tf_python_exe = os.environ[VARNAME_ENV_TFPY]

digits/dataset/views.py

@@ -6,6 +6,7 @@
 
 from . import images as dataset_images
 from . import generic
+from digits import extensions
 from digits.utils.routing import job_from_request, request_wants_json
 from digits.webapp import scheduler
 
@@ -54,3 +55,31 @@ def summary():
         return generic.views.summary(job)
     else:
         raise werkzeug.exceptions.BadRequest('Invalid job type')
+
+
+@blueprint.route('/inference-form/<extension_id>/<job_id>', methods=['GET'])
+def inference_form(extension_id, job_id):
+    """
+    Returns a rendering of an inference form
+    """
+    inference_form_html = ""
+
+    if extension_id != "all-default":
+        extension_class = extensions.data.get_extension(extension_id)
+        if not extension_class:
+            raise RuntimeError("Unable to find data extension with ID=%s"
+                               % job_id.dataset.extension_id)
+        job = scheduler.get_job(job_id)
+        if hasattr(job, 'extension_userdata'):
+            extension_userdata = job.extension_userdata
+        else:
+            extension_userdata = {}
+        extension_userdata.update({'is_inference_db': True})
+        extension = extension_class(**extension_userdata)
+
+        form = extension.get_inference_form()
+        if form:
+            template, context = extension.get_inference_template(form)
+            inference_form_html = flask.render_template_string(template, **context)
+
+    return inference_form_html

digits/extensions/view/imageOutput/config_template.html

@@ -23,3 +23,9 @@
     {{ form.pixel_conversion.tooltip }}
     {{ form.pixel_conversion(class='form-control') }}
 </div>
+
+<div class="form-group{{mark_errors([form.show_input])}}">
+    {{ form.show_input.label }}
+    {{ form.show_input.tooltip }}
+    {{ form.show_input(class='form-control') }}
+</div>

digits/extensions/view/imageOutput/forms.py

@@ -45,3 +45,13 @@ class ConfigForm(Form):
         tooltip='Select method to convert pixel values to the target bit '
                 'range'
     )
+
+    show_input = utils.forms.SelectField(
+        'Show input as image',
+        choices=[
+            ('yes', 'Yes'),
+            ('no', 'No'),
+        ],
+        default='no',
+        tooltip='Show input as image'
+    )

digits/extensions/view/imageOutput/view.py

@@ -31,6 +31,7 @@ def __init__(self, dataset, **kwargs):
         self.channel_order = kwargs['channel_order'].upper()
         self.data_order = kwargs['data_order'].upper()
         self.normalize = (kwargs['pixel_conversion'] == 'normalize')
+        self.show_input = (kwargs['show_input'] == 'yes')
 
     @staticmethod
     def get_config_form():
@@ -70,17 +71,28 @@ def get_view_template(self, data):
           - context is a dictionary of context variables to use for rendering
           the form
         """
-        return self.view_template, {'image': digits.utils.image.embed_image_html(data)}
+        return self.view_template, {'image_input': digits.utils.image.embed_image_html(data[0]),
+                                    'image_output': digits.utils.image.embed_image_html(data[1])}
 
     @override
     def process_data(self, input_id, input_data, output_data):
         """
         Process one inference and return data to visualize
         """
 
-        data = output_data[output_data.keys()[0]].astype('float32')
+        if self.show_input:
+            data_input = input_data.astype('float32')
+            image_input = self.process_image(self.data_order, data_input)
+        else:
+            image_input = None
+
+        data_output = output_data[output_data.keys()[0]].astype('float32')
+        image_output = self.process_image(self.data_order, data_output)
+
+        return [image_input, image_output]
 
-        if self.data_order == 'HWC':
+    def process_image(self, data_order, data):
+        if data_order == 'HWC':
             data = (data.transpose((2, 0, 1)))
 
         # assume CHW at this point

digits/extensions/view/imageOutput/view_template.html

@@ -1,3 +1,6 @@
 {# Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved. #}
 
-<img src="{{image}}" style="max-width:100%;" />
+{% if image_input %}
+    <img src="{{image_input}}" style="max-width:100%;" />
+{% endif %}
+<img src="{{image_output}}" style="max-width:100%;" />

digits/frameworks/__init__.py

@@ -3,7 +3,6 @@
 
 from .caffe_framework import CaffeFramework
 from .framework import Framework
-from .tensorflow_framework import TensorflowFramework
 from .torch_framework import TorchFramework
 from digits.config import config_value
 
@@ -13,6 +12,10 @@
     'TorchFramework',
 ]
 
+if config_value('tensorflow')['enabled']:
+    from .tensorflow_framework import TensorflowFramework
+    __all__.append('TensorflowFramework')
+
 #
 #  create framework instances
 #

digits/frameworks/caffe_framework.py

@@ -153,7 +153,6 @@ def can_accumulate_gradients(self):
         if config_value('caffe')['flavor'] == 'BVLC':
             return True
         elif config_value('caffe')['flavor'] == 'NVIDIA':
-            return (parse_version(config_value('caffe')['version'])
-                    > parse_version('0.14.0-alpha'))
+            return (parse_version(config_value('caffe')['version']) > parse_version('0.14.0-alpha'))
         else:
             raise ValueError('Unknown flavor.  Support NVIDIA and BVLC flavors only.')

digits/frameworks/tensorflow_framework.py

@@ -1,23 +1,20 @@
 # Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
 from __future__ import absolute_import
 
-import numpy as np
 import os
 import re
 import subprocess
-import time
 import tempfile
 
-import flask
-
-from .errors import Error, NetworkVisualizationError, BadNetworkError
+from .errors import NetworkVisualizationError
 from .framework import Framework
 import digits
 from digits import utils
 from digits.config import config_value
 from digits.model.tasks import TensorflowTrainTask
 from digits.utils import subclass, override, constants
 
+
 @subclass
 class TensorflowFramework(Framework):
     """
@@ -35,7 +32,7 @@ class TensorflowFramework(Framework):
     SUPPORTS_PYTHON_LAYERS_FILE = False
     SUPPORTS_TIMELINE_TRACING = True
 
-    SUPPORTED_SOLVER_TYPES = ['SGD','ADADELTA','ADAGRAD','ADAGRADDA','MOMENTUM','ADAM','FTRL','RMSPROP']
+    SUPPORTED_SOLVER_TYPES = ['SGD', 'ADADELTA', 'ADAGRAD', 'ADAGRADDA', 'MOMENTUM', 'ADAM', 'FTRL', 'RMSPROP']
 
     SUPPORTED_DATA_TRANSFORMATION_TYPES = ['MEAN_SUBTRACTION', 'CROPPING']
     SUPPORTED_DATA_AUGMENTATION_TYPES = ['FLIPPING', 'NOISE', 'CONTRAST', 'WHITENING', 'HSV_SHIFTING']
@@ -50,7 +47,7 @@ def create_train_task(self, **kwargs):
         """
         create train task
         """
-        return TensorflowTrainTask(framework_id = self.framework_id, **kwargs)
+        return TensorflowTrainTask(framework_id=self.framework_id, **kwargs)
 
     @override
     def get_standard_network_desc(self, network):
@@ -126,10 +123,10 @@ def get_network_visualization(self, **kwargs):
         # Another for the HTML
         _, temp_html_path = tempfile.mkstemp(suffix='.html')
 
-        try: # do this in a try..finally clause to make sure we delete the temp file
+        try:  # do this in a try..finally clause to make sure we delete the temp file
             # build command line
             args = [config_value('tensorflow')['executable'],
-                    os.path.join(os.path.dirname(digits.__file__),'tools','tensorflow','main.py'),
+                    os.path.join(os.path.dirname(digits.__file__), 'tools', 'tensorflow', 'main.py'),
                     '--network=%s' % os.path.basename(temp_network_path),
                     '--networkDirectory=%s' % os.path.dirname(temp_network_path),
                     '--visualizeModelPath=%s' % temp_graphdef_path,
@@ -141,7 +138,7 @@ def get_network_visualization(self, **kwargs):
 
             if use_mean and use_mean != 'none':
                 mean_file = dataset.get_mean_file()
-                assert mean_file != None, 'Failed to retrieve mean file.'
+                assert mean_file is not None, 'Failed to retrieve mean file.'
                 args.append('--subtractMean=%s' % use_mean)
                 args.append('--mean=%s' % dataset.path(mean_file))
 
@@ -163,15 +160,14 @@ def get_network_visualization(self, **kwargs):
 
             env = os.environ.copy()
             # make only a selected number of GPUs visible. The ID is not important for just the vis
-            env['CUDA_VISIBLE_DEVICES'] = ",".join([str(i) for i in range(0,int(num_gpus))])
+            env['CUDA_VISIBLE_DEVICES'] = ",".join([str(i) for i in range(0, int(num_gpus))])
 
             # execute command
             p = subprocess.Popen(args,
-                        stdout=subprocess.PIPE,
-                        stderr=subprocess.STDOUT,
-                        close_fds=True,
-                        env=env
-                        )
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.STDOUT,
+                                 close_fds=True,
+                                 env=env)
 
             stdout_log = ''
             while p.poll() is None:
@@ -181,7 +177,7 @@ def get_network_visualization(self, **kwargs):
                         stdout_log += line
             if p.returncode:
                 raise NetworkVisualizationError(stdout_log)
-            else: # Success!
+            else:  # Success!
                 return repr(str(open(temp_graphdef_path).read()))
         finally:
             os.remove(temp_network_path)

digits/model/forms.py

@@ -121,13 +121,14 @@ def validate_py_ext(form, field):
         tooltip="How many epochs of training between running through one pass of the validation data?"
     )
 
-    traces_interval = utils.forms.IntegerField('Tracing Interval (in steps)',
-            validators=[
-                validators.NumberRange(min=0)
-            ],
-            default=0,
-            tooltip="Generation of a timeline trace every few steps"
-            )
+    traces_interval = utils.forms.IntegerField(
+        'Tracing Interval (in steps)',
+        validators=[
+            validators.NumberRange(min=0)
+        ],
+        default=0,
+        tooltip="Generation of a timeline trace every few steps"
+    )
 
     random_seed = utils.forms.IntegerField(
         'Random seed',
@@ -311,10 +312,11 @@ def validate_lr_multistep_values(form, field):
     )
 
     def validate_custom_network_snapshot(form, field):
-        if form.method.data == 'custom':
-            for filename in field.data.strip().split(os.path.pathsep):
-                if filename and not os.path.exists(filename):
-                    raise validators.ValidationError('File "%s" does not exist' % filename)
+        pass
+        #if form.method.data == 'custom':
+        #    for filename in field.data.strip().split(os.path.pathsep):
+        #        if filename and not os.path.exists(filename):
+        #            raise validators.ValidationError('File "%s" does not exist' % filename)
 
     # Select one of several GPUs
     select_gpu = wtforms.RadioField(

digits/model/images/classification/job.py

@@ -28,7 +28,7 @@ def job_type(self):
     def download_files(self, epoch=-1):
         task = self.train_task()
 
-        snapshot_filename = task.get_snapshot(epoch)
+        snapshot_filename = task.get_snapshot(epoch, download=True)
 
         # get model files
         model_files = task.get_model_files()