def evaluate_graph(graph_file_name):
with load_graph(graph_file_name).as_default() as graph:
ground_truth_input = tf.placeholder(tf.float32, [None, 5],
name='GroundTruthInput')
image_buffer_input = graph.get_tensor_by_name('input:0')
final_tensor = graph.get_tensor_by_name('final_result:0')
accuracy, _ = retrain.add_evaluation_step(final_tensor,
ground_truth_input)
logits = graph.get_tensor_by_name("final_training_ops/Wx_plus_b/add:0")
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=ground_truth_input,
logits=logits))
# image_dir = 'tf_files/flower_photos'
image_dir = 'tf_files/landing_photos'
testing_percentage = 10
validation_percentage = 10
validation_batch_size = 100
category = 'testing'
image_lists = retrain.create_image_lists(image_dir, testing_percentage,
validation_percentage)
class_count = len(image_lists.keys())
ground_truths = []
filenames = []
for label_index, label_name in enumerate(image_lists.keys()):
for image_index, image_name in enumerate(
image_lists[label_name][category]):
image_name = retrain.get_image_path(image_lists, label_name,
image_index, image_dir,
category)
ground_truth = np.zeros([1, class_count], dtype=np.float32)
ground_truth[0, label_index] = 1.0
ground_truths.append(ground_truth)
filenames.append(image_name)
accuracies = []
xents = []
with tf.Session(graph=graph) as sess:
for filename, ground_truth in zip(filenames, ground_truths):
image = Image.open(filename).resize((224, 224), Image.ANTIALIAS)
image = np.array(image, dtype=np.float32)[None, ...]
image = (image - 128) / 128.0
feed_dict = {
image_buffer_input: image,
ground_truth_input: ground_truth
}
eval_accuracy, eval_xent = sess.run([accuracy, xent], feed_dict)
accuracies.append(eval_accuracy)
xents.append(eval_xent)
return np.mean(accuracies), np.mean(xents)
def evaluate_graph(graph_file_name):
with load_graph(graph_file_name).as_default() as graph:
ground_truth_input = tf.placeholder(
tf.float32, [None, 5], name='GroundTruthInput')
image_buffer_input = graph.get_tensor_by_name('input:0')
final_tensor = graph.get_tensor_by_name('final_result:0')
accuracy, _ = retrain.add_evaluation_step(final_tensor, ground_truth_input)
logits = graph.get_tensor_by_name("final_training_ops/Wx_plus_b/add:0")
xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
labels = ground_truth_input,
logits = logits))
image_dir = 'tf_files/flower_photos'
testing_percentage = 10
validation_percentage = 10
validation_batch_size = 100
category='testing'
image_lists = retrain.create_image_lists(
image_dir, testing_percentage,
validation_percentage)
class_count = len(image_lists.keys())
ground_truths = []
filenames = []
for label_index, label_name in enumerate(image_lists.keys()):
for image_index, image_name in enumerate(image_lists[label_name][category]):
image_name = retrain.get_image_path(
image_lists, label_name, image_index, image_dir, category)
ground_truth = np.zeros([1, class_count], dtype=np.float32)
ground_truth[0, label_index] = 1.0
ground_truths.append(ground_truth)
filenames.append(image_name)
accuracies = []
xents = []
with tf.Session(graph=graph) as sess:
for filename, ground_truth in zip(filenames, ground_truths):
image = Image.open(filename).resize((224,224),Image.ANTIALIAS)
image = np.array(image, dtype=np.float32)[None,...]
image = (image-128)/128.0
feed_dict={
image_buffer_input: image,
ground_truth_input: ground_truth}
eval_accuracy, eval_xent = sess.run([accuracy, xent], feed_dict)
accuracies.append(eval_accuracy)
xents.append(eval_xent)
return np.mean(accuracies), np.mean(xents)
def labelImages(img_dir, model_file, label_file):
input_height = 224
input_width = 224
input_mean = 128
input_std = 128
input_layer = "input"
output_layer = "final_result"
graph = load_graph(model_file)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
results_dict = defaultdict(list)
for dirname, subdirs, files in os.walk(img_dir):
for file in files:
if not file.endswith('jpg'):
continue
file_name = os.path.join(dirname, file)
results_dict['file_name'].append(file_name)
results_dict['true_label'].append(dirname.split('/')[-1])
t = read_tensor_from_image_file(file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
start = time.time()
results = sess.run(output_operation.outputs[0],
{input_operation.outputs[0]: t})
end = time.time()
results_dict['eval_time'].append(end - start)
print(
'\nEvaluation time (1-image): {:.3f}s\n'.format(end - start))
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
template = "{} (score={:0.5f})"
for i in top_k:
results_dict[labels[i]].append(results[i])
print(template.format(labels[i], results[i]))
return results_dict
def evaluate_graph(graph_file_name):
with load_graph(graph_file_name).as_default() as graph:
ground_truth_input = tf.placeholder(tf.float32, [None, 2],
name='GroundTruthInput')
image_buffer_input = graph.get_tensor_by_name('input:0')
final_tensor = graph.get_tensor_by_name('final_result:0')
accuracy, prediction = retrain.add_evaluation_step(
final_tensor, ground_truth_input)
logits = graph.get_tensor_by_name(
"final_training_ops/W2_plus_b2/add:0")
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=ground_truth_input,
logits=logits))
image_dir = 'tf_files/images/validation'
testing_percentage = 100
validation_percentage = 0
category = 'testing'
image_lists = retrain.create_image_lists(image_dir, testing_percentage,
validation_percentage)
class_count = len(image_lists.keys())
ground_truths = []
filenames = []
y_true = []
print(image_lists.keys())
data = {}
data['label_outros'] = int(image_lists.keys()[0] == 'melanoma')
data['label_melanoma'] = int(image_lists.keys()[1] == 'melanoma')
for label_index, label_name in enumerate(image_lists.keys()):
for image_index, image_name in enumerate(
image_lists[label_name][category]):
image_name = retrain.get_image_path(image_lists, label_name,
image_index, image_dir,
category)
ground_truth = np.zeros([1, class_count], dtype=np.float32)
ground_truth[0, label_index] = 1.0
ground_truths.append(ground_truth)
y_true.append(label_index)
filenames.append(image_name)
accuracies = []
xents = []
y = []
y_scores = []
with tf.Session(graph=graph) as sess:
#print('=== MISCLASSIFIED TEST IMAGES ===')
for filename, ground_truth in zip(filenames, ground_truths):
image = Image.open(filename).resize((224, 224), Image.ANTIALIAS)
image = np.array(image, dtype=np.float32)[None, ...]
image = (image - 127) / 127.0
feed_dict = {
image_buffer_input: image,
ground_truth_input: ground_truth
}
eval_accuracy, eval_xent = sess.run([accuracy, xent], feed_dict)
eval_prediction = sess.run([prediction], feed_dict)
eval_softmax = sess.run([final_tensor], feed_dict)
#if eval_prediction != ground_truth.argmax():
# print('%s' %
# (filename))
accuracies.append(eval_accuracy)
xents.append(eval_xent)
y.append(eval_prediction[0][0])
y_scores.append(eval_softmax[0][0][1])
data['y_true'] = y_true
data['y_pred'] = y
print(data)
return data
