def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
random_input = tf.random_uniform(shape=(batch_size, height, width,
3))
new_input = tf.placeholder(dtype=tf.float32,
shape=[batch_size, height, width, 3],
name='input_holder')
logits, predictions = mobilenet.mobilenet(new_input)
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('./graphs', sess.graph)
val = sess.run(random_input)
output = sess.run(logits, feed_dict={new_input: val})
self.assertTrue(output.any())
saver = tf.train.Saver()
save_path = saver.save(sess, "./graphs/model.ckpt")
print(save_path)
#tf.train.write_graph(sess.graph_def, '.','model.pbtxt')
print("Writing complete...")
ops_list = sess.graph.get_operations()
tensor_list = np.array([ops.values() for ops in ops_list])
for t in tensor_list:
print(t)
tf.saved_model.simple_save(sess,
'./saved',
inputs={"model_input": new_input},
outputs={"model_output": predictions})
writer.close()
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = mobilenet.mobilenet(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(),
[batch_size])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet.mobilenet(inputs, num_classes)
self.assertEquals(
end_points['MobileNet/conv_ds_2/depthwise_conv'].get_shape().
as_list(), [5, 112, 112, 32])
self.assertEquals(
end_points['MobileNet/conv_ds_3/depthwise_conv'].get_shape().
as_list(), [5, 56, 56, 64])
self.assertEquals(
end_points['MobileNet/conv_ds_4/depthwise_conv'].get_shape().
as_list(), [5, 56, 56, 128])
self.assertEquals(
end_points['MobileNet/conv_ds_5/depthwise_conv'].get_shape().
as_list(), [5, 28, 28, 128])
self.assertEquals(
end_points['MobileNet/conv_ds_6/depthwise_conv'].get_shape().
as_list(), [5, 28, 28, 256])
self.assertEquals(
end_points['MobileNet/conv_ds_7/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 256])
self.assertEquals(
end_points['MobileNet/conv_ds_8/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_9/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_10/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_11/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_12/depthwise_conv'].get_shape().
as_list(), [5, 14, 14, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_13/depthwise_conv'].get_shape().
as_list(), [5, 7, 7, 512])
self.assertEquals(
end_points['MobileNet/conv_ds_14/depthwise_conv'].get_shape().
as_list(), [5, 7, 7, 1024])
self.assertEquals(end_points['squeeze'].get_shape().as_list(),
[5, 1024])
self.assertEquals(logits.op.name, 'MobileNet/fc_16/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
if __name__ == '__main__':
""" python grad_cam.py <path_to_image>
1. Loads an image with opencv.
2. Preprocesses it for VGG19 and converts to a pytorch variable.
3. Makes a forward pass to find the category index with the highest score,
and computes intermediate activations.
Makes the visualization. """
args = get_args()
device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')
# Can work with any model, but it assumes that the model has a
# feature method, and a classifier method,
# as in the VGG models in torchvision.
model = mobilenet()
model.load(args.pth)
grad_cam = GradCam(model=model, feature_module=model.features, target_layer_names=["features"],
use_cuda=args.use_cuda)
image = Image.open(args.image_path).convert('RGB')
tf = transforms.Compose([
transforms.Resize((112, 224)),
transforms.ToTensor(),
])
image = tf(image)
input = image.to(device)
input = input.unsqueeze(0)
# If None, returns the map for the highest scoring category.