fixing the errors during build

Dockerfile

@@ -60,7 +60,8 @@ RUN export uid=${HOST_UID} gid=${HOST_GID} && \
 
 USER ${USER_NAME}
 WORKDIR ${HOME_DIR}
-
+COPY ./requirements.txt .
+RUN pip install -r requirements.txt --user
 COPY . .
 
 RUN sudo chown ${HOST_UID}:${HOST_GID} -R *

aicrowd_helpers.py

+#!/usr/bin/env python
+import aicrowd_api
+import os
+
+########################################################################
+# Instatiate Event Notifier
+########################################################################
+aicrowd_events = aicrowd_api.events.AIcrowdEvents()
+
+
+def execution_start():
+    ########################################################################
+    # Register Evaluation Start event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_INFO,
+                message="execution_started",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_started"
+                    }
+                )
+
+
+def execution_progress(progress_payload):
+    image_ids = progress_payload["image_ids"]
+    ########################################################################
+    # Register Evaluation Progress event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_INFO,
+                message="execution_progress",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_progress",
+                    "image_ids" : image_ids
+                    }
+                )
+
+def execution_success(payload):
+    predictions_output_path = payload["predictions_output_path"]
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    expected_output_path = os.getenv("AICROWD_PREDICTIONS_OUTPUT_PATH", False)
+    if expected_output_path != predictions_output_path:
+        raise Exception("Please write the output to the path specified in the environment variable : AICROWD_PREDICTIONS_OUTPUT_PATH instead of {}".format(predictions_output_path))
+
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_SUCCESS,
+                message="execution_success",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_success",
+                    "predictions_output_path" : predictions_output_path
+                    },
+                blocking=True
+                )
+
+def execution_error(error):
+    ########################################################################
+    # Register Evaluation Complete event
+    ########################################################################
+    aicrowd_events.register_event(
+                event_type=aicrowd_events.AICROWD_EVENT_ERROR,
+                message="execution_error",
+                payload={ #Arbitrary Payload
+                    "event_type": "food_recognition_challenge:execution_error",
+                    "error" : error
+                    },
+                blocking=True
+                )

build.sh

+#!/bin/bash
+
+
+docker build -t $IMAGE_NAME .
\ No newline at end of file

debug.sh

+docker run -it \
+  --net=host \
+  -v ${TEST_IMAGES_PATH}:/test_images \
+  -v /tmp:/tmp_host \
+  -e AICROWD_IS_GRADING=True \
+  -e AICROWD_TEST_IMAGES_PATH="/test_images" \
+  -e AICROWD_PREDICTIONS_OUTPUT_PATH="/tmp/output.json" \
+  $IMAGE_NAME \
+  /home/aicrowd/run.sh

eval.py

@@ -43,7 +43,7 @@ from mrcnn import model as modellib, utils
 PRETRAINED_MODEL_PATH = os.path.join(ROOT_DIR,"final_model.h5")
 LOGS_DIRECTORY = os.path.join(ROOT_DIR, "logs")
 MODEL_DIR = os.path.join(ROOT_DIR, "logs")
-IMAGE_DIR = os.path.join(ROOT_DIR, "data", "test_images")
+# IMAGE_DIR = os.path.join(ROOT_DIR, "data", "test")
 
 
 class InferenceConfig(coco.CocoConfig):
@@ -72,13 +72,8 @@ model_path = PRETRAINED_MODEL_PATH
 model.load_weights(model_path, by_name=True)
 
 class_names = ['BG', 'bread-wholemeal', ' potatoes-steamed', ' broccoli', ' butter', ' hard-cheese', ' water', ' banana', ' wine-white', ' bread-white', ' apple', ' pizza-margherita-baked', ' salad-leaf-salad-green', ' zucchini', ' water-mineral', ' coffee-with-caffeine', ' avocado', ' tomato', ' dark-chocolate', ' white-coffee-with-caffeine', ' egg', ' mixed-salad-chopped-without-sauce', ' sweet-pepper', ' mixed-vegetables', ' mayonnaise', ' rice', ' chips-french-fries', ' carrot', ' tomato-sauce', ' cucumber', ' wine-red', ' cheese', ' strawberries', ' espresso-with-caffeine', ' tea', ' chicken', ' jam', ' leaf-spinach', ' pasta-spaghetti', ' french-beans', ' bread-whole-wheat'] # In our case, we have 1 class for the background, and 1 class for building
-file_names = next(os.walk(IMAGE_DIR))[2]
 
-TRAIN_ANNOTATIONS_PATH = "data/train/annotation.json"
-coco = COCO(TRAIN_ANNOTATIONS_PATH)
-category_ids = coco.loadCats(coco.getCatIds())
-id_category = [_["id"] for _ in category_ids]
-id_category = [0] + id_category
+id_category = [0, 1565, 1010, 1085, 2053, 1310, 2578, 1154, 2620, 1566, 1151, 2939, 1040, 1070, 2580, 2512, 1056, 1069, 2131, 2521, 2022, 1026, 1068, 1022, 2750, 1468, 1013, 1078, 2738, 1061, 2618, 1311, 1163, 2504, 2498, 1788, 2099, 1032, 1505, 1058, 1554]
 
 
 

requirements.txt

@@ -4,7 +4,8 @@ Pillow
 cython
 matplotlib
 scikit-image
-tensorflow>=1.3.0
+tensorflow===1.15.0
+tensorflow-gpu===1.15.0
 keras===2.1.6
 opencv-python
 h5py

run.sh

 #!/bin/bash
-pip install -r requirements.txt
-python3 run.py
\ No newline at end of file
+python3 run.py