def run_model_on_corpus_image(checkpoint, imagenum, output_blobs):
# This is based on decaf's "imagenet" script:
corpus = get_image_corpus()
image = corpus.get_all_images_data()[imagenum] - corpus.get_mean()
model = get_models()[checkpoint]
arr = image.astype(np.float32)
return model.predict(data=arr, output_blobs=output_blobs)
def run_model_on_corpus_image(checkpoint, imagenum, output_blobs):
# This is based on decaf's "imagenet" script:
corpus = get_image_corpus()
image = corpus.get_all_images_data()[imagenum] - corpus.get_mean()
model = get_models()[checkpoint]
arr = image.astype(np.float32)
return model.predict(data=arr, output_blobs=output_blobs)
def layer_overview_png(checkpoint, layername):
model = get_models()[checkpoint]
layer = model.layers[layername]
(num_filters, ksize, num_channels) = get_layer_dimensions(layer)
reshaped = reshape_layer_for_visualization(layer, combine_channels=(num_channels == 3))
ncols = 6 if num_channels in (1, 3) else num_channels
return show_multiple(normalize(reshaped), ncols=ncols)
def layer_overview_png(checkpoint, layername):
model = get_models()[checkpoint]
layer = model.layers[layername]
(num_filters, ksize, num_channels) = get_layer_dimensions(layer)
reshaped = reshape_layer_for_visualization(
layer, combine_channels=(num_channels == 3))
ncols = 6 if num_channels in (1, 3) else num_channels
return show_multiple(normalize(reshaped), ncols=ncols)
def layer_filters_channels_overview_json(checkpoints, layernames, filters,
channels):
region = select_region_query(get_models(),
times=checkpoints,
layers=layernames,
filters=filters,
channels=channels)
#images = mapterminals(show_multiple, region)
images = region
return images
def layer_filters_channels_image_json(checkpoints, layernames, filters, channels, imagenum):
corpus = get_image_corpus()
image = corpus.get_image(imagenum)
arr = np.array(image.getdata()).reshape(1, 32, 32, 3).astype(np.float32)
out = select_region_query(
get_models(), times=checkpoints, layers=layernames, filters=filters, channels=channels, image=arr
)
images = out
# images = mapterminals(show_multiple, out)
return images
def layer_overview_svg_container(layername):
"""
Generates transparent SVGs that are overlaid on filter views
to enable mouse interactions.
"""
model = get_models()[0]
layer = model.layers[layername]
(num_filters, ksize, num_channels) = get_layer_dimensions(layer)
ncols = 6 if num_channels in (1, 3) else num_channels
nrows = int(math.ceil(float(num_filters) / 6)) if num_channels in (1, 3) else num_filters
scale = int(request.args.get("scale", 1))
svg = generate_svg_filter_map(nrows * ncols, ksize, ncols, scale)
return Response(svg, mimetype="image/svg+xml")
def layer_overview_svg_container(layername):
"""
Generates transparent SVGs that are overlaid on filter views
to enable mouse interactions.
"""
model = get_models()[0]
layer = model.layers[layername]
(num_filters, ksize, num_channels) = get_layer_dimensions(layer)
ncols = 6 if num_channels in (1, 3) else num_channels
nrows = int(math.ceil(float(num_filters) /
6)) if num_channels in (1, 3) else num_filters
scale = int(request.args.get('scale', 1))
svg = generate_svg_filter_map(nrows * ncols, ksize, ncols, scale)
return Response(svg, mimetype="image/svg+xml")
def layer_filters_channels_image_json(checkpoints, layernames, filters,
channels, imagenum):
corpus = get_image_corpus()
image = corpus.get_image(imagenum)
arr = np.array(image.getdata()).reshape(1, 32, 32, 3).astype(np.float32)
out = select_region_query(get_models(),
times=checkpoints,
layers=layernames,
filters=filters,
channels=channels,
image=arr)
images = out
#images = mapterminals(show_multiple, out)
return images
def index():
context = {
'num_timesteps': len(get_models()),
'model': get_models()[0],
}
return render_template('index.html', **context)
def index():
context = {"num_timesteps": len(get_models()), "model": get_models()[0]}
return render_template("index.html", **context)
def layer_filters_channels_overview_json(checkpoints, layernames, filters, channels):
region = select_region_query(get_models(), times=checkpoints, layers=layernames, filters=filters, channels=channels)
# images = mapterminals(show_multiple, region)
images = region
return images
