add Docker Container example

examples/docker_submission/README.md

@@ -4,24 +4,30 @@ TODO: Add a description of the submission process here.
 
 
 ## Launching the submission container
-TODO: Create a docker-compose file
+
+First we have to build the container
 ```bash
-cd ./http_submission
-docker build -t sample_pysaliency .
+docker build -t sample_pysaliency docker
 ```
 
+Then we can start it
+```bash
+docker run --rm -it -p 4000:4000 sample_pysaliency
+```
+The above command will launch the image as interactive container in the foregroun
+and expose the port `4000` to the host machine.
+If you prefer to run it in the background, use 
 ```bash
 docker run --name sample_pysaliency -dp 4000:4000 sample_pysaliency
 ```
-The above command will launch a container named `sample_pysaliency` and expose the port `4000` to the host machine. The container will be running in the background. 
+which will launch a container named `sample_pysaliency`. The container will be running in the background. 
 
 To test the model server, run the sample_evaluation script (Make sure to have the `pysaliency` package installed):
 ```bash
-python ./http_evaluation/sample_evaluation.py
+python ./sample_evaluation.py
 ```
 
-
-To delete the container, run the following command:
+To delete the background container, run the following command:
 ```bash
 docker stop sample_pysaliency && docker rm sample_pysaliency
 ```

examples/docker_submission/Dockerfile → examples/docker_submission/docker/Dockerfile

@@ -8,16 +8,19 @@ WORKDIR /app
 ENV HTTP_PORT=4000
 
 RUN apt-get update \
-    && apt-get -y install gcc
+    && apt-get -y install gcc \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/* /var/cache/apt/*
 
 COPY ./requirements.txt ./
-RUN python -m pip install -U pip \
-    && python -m pip install -r requirements.txt 
+RUN python -m pip install --no-cache -U pip \
+    && python -m pip install --no-cache -r requirements.txt
 
-COPY . ./
+COPY ./model_server.py ./
+COPY ./sample_submission.py ./
 
 # This is needed for Singularity builds.
 EXPOSE $HTTP_PORT
 
 # The entrypoint for a container,
-CMD ["gunicorn", "-w", "1", "-b", "0.0.0.0:4000", "--pythonpath", ".", "model_server:app"]
+CMD ["gunicorn", "-w", "1", "-b", "0.0.0.0:4000", "--pythonpath", ".", "--access-logfile", "-", "model_server:app"]

examples/docker_submission/model_server.py → examples/docker_submission/docker/model_server.py

@@ -1,20 +1,22 @@
 from flask import Flask, request, jsonify
+from flask_orjson import OrjsonProvider
 import numpy as np
 import json
 from PIL import Image
 from io import BytesIO
-# import pickle
+import orjson
+
 
 # Import your model here
 from sample_submission import MySimpleScanpathModel
 
 app = Flask("saliency-model-server")
+app.json_provider = OrjsonProvider(app)
 app.logger.setLevel("DEBUG")
 
 # # TODO - replace this with your model
 model = MySimpleScanpathModel()
 
-
 @app.route('/conditional_log_density', methods=['POST'])
 def conditional_log_density():
     data = json.loads(request.form['json_data'])
@@ -28,14 +30,16 @@ def conditional_log_density():
     stimulus = np.array(image)
 
     log_density = model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes)
-    return jsonify({'log_density': log_density.tolist()})
+    log_density_list = log_density.tolist()
+    response = orjson.dumps({'log_density': log_density_list})
+    return response
 
 
 @app.route('/type', methods=['GET'])
 def type():
     type = "ScanpathModel"
     version = "v1.0.0"
-    return jsonify({'type': type, 'version': version})
+    return orjson.dumps({'type': type, 'version': version})
 
 
 def main():

examples/docker_submission/docker/requirements.txt

@@ -0,0 +1,10 @@
+cython
+flask
+gunicorn
+numpy
+
+# Add additional dependencies here
+pysaliency
+scipy
+torch
+flask_orjson

examples/docker_submission/docker/sample_submission.py

@@ -0,0 +1,79 @@
+import numpy as np
+import sys
+from typing import Union
+from scipy.ndimage import gaussian_filter
+import pysaliency
+
+
+class LocalContrastModel(pysaliency.Model):
+    def __init__(self, bandwidth=0.05, **kwargs):
+        super().__init__(**kwargs)
+        self.bandwidth = bandwidth
+
+    def _log_density(self, stimulus: Union[pysaliency.datasets.Stimulus, np.ndarray]):
+
+        # _log_density can either take pysaliency Stimulus objects, or, for convenience, simply numpy arrays
+        # `as_stimulus` ensures that we have a Stimulus object
+        stimulus_object = pysaliency.datasets.as_stimulus(stimulus)
+
+        # grayscale image
+        gray_stimulus = np.mean(stimulus_object.stimulus_data, axis=2)
+
+        # size contains the height and width of the image, but not potential color channels
+        height, width = stimulus_object.size
+
+        # define kernel size based on image size
+        kernel_size = np.round(self.bandwidth * max(width, height)).astype(int)
+        sigma = (kernel_size - 1) / 6
+
+        # apply Gausian blur and calculate squared difference between blurred and original image
+        blurred_stimulus = gaussian_filter(gray_stimulus, sigma)
+
+        prediction = gaussian_filter((gray_stimulus - blurred_stimulus)**2, sigma)
+
+        # normalize to [1, 255]
+        prediction = (254 * (prediction / prediction.max())).astype(int) + 1
+
+        density = prediction / prediction.sum()
+
+        return np.log(density)
+
+class MySimpleScanpathModel(pysaliency.ScanpathModel):
+    def __init__(self, spatial_model_bandwidth: float=0.05, saccade_width: float=0.1):
+        self.spatial_model_bandwidth = spatial_model_bandwidth
+        self.saccade_width = saccade_width
+        self.spatial_model = LocalContrastModel(spatial_model_bandwidth)
+        # self.spatial_model = pysaliency.UniformModel()
+
+
+    def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None,):
+        stimulus_object = pysaliency.datasets.as_stimulus(stimulus)
+
+        # size contains the height and width of the image, but not potential color channels
+        height, width = stimulus_object.size
+
+        spatial_prior_log_density = self.spatial_model.log_density(stimulus)
+        spatial_prior_density = np.exp(spatial_prior_log_density)
+
+        # compute saccade bias
+        last_x = x_hist[-1]
+        last_y = y_hist[-1]
+
+        xs = np.arange(width, dtype=float)
+        ys = np.arange(height, dtype=float)
+        XS, YS = np.meshgrid(xs, ys)
+
+        XS -= last_x
+        YS -= last_y
+
+        # compute prior
+        max_size = max(width, height)
+        actual_kernel_size = self.saccade_width * max_size
+
+        saccade_bias = np.exp(-0.5 * (XS ** 2 + YS ** 2) / actual_kernel_size ** 2)
+
+        prediction = spatial_prior_density * saccade_bias
+
+        density = prediction / prediction.sum()
+        return np.log(density)
+

examples/docker_submission/sample_evaluation.py

@@ -6,6 +6,9 @@
 import pysaliency
 
 
+from tqdm import tqdm
+
+
 if __name__ == "__main__":
     http_model = HTTPScanpathModel("http://localhost:4000")
     http_model.check_type()
@@ -16,9 +19,16 @@
     # get MIT1003 dataset
     stimuli, fixations = pysaliency.get_mit1003(location='pysaliency_datasets')
 
+    for stimulus in tqdm(stimuli):
+        stimulus.stimulus_data
+
     eval_fixations = fixations[fixations.scanpath_history_length > 0]
+    eval_fixations = eval_fixations[:10]
+
+    information_gain = http_model.information_gain(stimuli, eval_fixations, average="image", verbose=True)
+    print("IG:", information_gain)
 
-    for fixation_index in range(10):
+    for fixation_index in tqdm(range(10)):
         # get server response for one stimulus
         server_density = http_model.conditional_log_density(
             stimulus=stimuli.stimuli[eval_fixations.n[fixation_index]], 

examples/docker_submission/sample_submission.py

@@ -2,7 +2,7 @@
 import sys
 from typing import Union
 from scipy.ndimage import gaussian_filter
-sys.path.insert(0, '..')
+sys.path.insert(0, '../..')
 import pysaliency
 
 

pysaliency/http_models.py

@@ -4,8 +4,20 @@
 import requests
 import json
 import numpy as np
+import orjson
+
+from .datasets import as_stimulus
 
 class HTTPScanpathModel(ScanpathModel):
+    """
+    A scanpath model that uses a HTTP server to make predictions.
+
+    The model is provided with an URL where it expects a server with the following API:
+
+    /conditional_log_density: expects a POST request with a file attachtment `stimulus` 
+    containing the stimulus and a json body containing x_hist, y_hist, t_hist and a dictionary with other attributes
+    /type: returns the model type and version
+    """
     def __init__(self, url):
         self.url = url
         self.check_type()
@@ -20,30 +32,38 @@ def type_url(self):
 
     def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
         # build request
-        pil_image = Image.fromarray(stimulus)
+        stimulus_object = as_stimulus(stimulus)
+
+        # TODO: check for file stimuli, in this case use original file to save encoding time
+        pil_image = Image.fromarray(stimulus_object.stimulus_data)
         image_bytes = BytesIO()
         pil_image.save(image_bytes, format='png')
 
         def _convert_attribute(attribute):
             if isinstance(attribute, np.ndarray):
                 return attribute.tolist()
+            if isinstance(attribute, (np.int64, np.int32)):
+                return int(attribute)
+            if isinstance(attribute, (np.float64, np.float32)):
+                return float(attribute)
             return attribute
 
         json_data = {
-            "x_hist": list(x_hist),
-            "y_hist": list(y_hist),
-            "t_hist": list(t_hist),
+            "x_hist": list(x_hist.tolist()),
+            "y_hist": list(y_hist.tolist()),
+            "t_hist": list(t_hist.tolist()),
             "attributes": {key: _convert_attribute(value) for key, value in (attributes or {}).items()}
         }
-
         # send request
-        response = requests.post(f"{self.log_density_url}", data={'json_data': json.dumps(json_data)}, files={'stimulus': image_bytes.getvalue()})
+        response = requests.post(f"{self.log_density_url}", data={'json_data': orjson.dumps(json_data)}, files={'stimulus': image_bytes.getvalue()})
 
         # parse response
         if response.status_code != 200:
             raise ValueError(f"Server returned status code {response.status_code}")
 
-        return np.array(response.json()['log_density'])
+        json_data = orjson.loads(response.text)
+        prediction = np.array(json_data['log_density'])
+        return prediction
 
     def check_type(self):
         response = requests.get(f"{self.type_url}").json()