Added CC3M data downloader script

data/CC3M_downloader.py

@@ -0,0 +1,156 @@
+# It expects you to have the train and validation `.tsv` file downloaded in the current directory
+# Head around to this link to download the `.tsv` files
+# https://ai.google.com/research/ConceptualCaptions/download
+
+'''
+This script was adapted from https://github.com/igorbrigadir/DownloadConceptualCaptions
+Few changes were made post that (excluding the post processing of data). We'll have
+only csv file with image url and captions written in different languages but not images
+as we do not own any of the images in the dataset and hence cannot legally provide them to you.
+'''
+import pandas as pd
+import numpy as np
+import requests
+import zlib
+import os
+import shelve
+import magic
+from multiprocessing import Pool
+from tqdm import tqdm
+
+headers = {
+    'User-Agent':'Googlebot-Image/1.0', # Pretend to be googlebot
+    'X-Forwarded-For': '64.18.15.200'
+}
+
+def _df_split_apply(tup_arg):
+    split_ind, subset, func = tup_arg
+    r = subset.apply(func, axis=1)
+    return (split_ind, r)
+
+def df_multiprocess(df, processes, chunk_size, func, dataset_name):
+    print("Generating parts...")
+    with shelve.open('%s_%s_%s_results.tmp' % (dataset_name, func.__name__, chunk_size)) as results:
+
+        pbar = tqdm(total=len(df), position=0)
+        # Resume:
+        finished_chunks = set([int(k) for k in results.keys()])
+        pbar.desc = "Resuming"
+        for k in results.keys():
+            pbar.update(len(results[str(k)][1]))
+
+        pool_data = ((index, df[i:i + chunk_size], func) for index, i in enumerate(range(0, len(df), chunk_size)) if index not in finished_chunks)
+        print(int(len(df) / chunk_size), "parts.", chunk_size, "per part.", "Using", processes, "processes")
+
+        pbar.desc = "Downloading"
+        with Pool(processes) as pool:
+            for i, result in enumerate(pool.imap_unordered(_df_split_apply, pool_data, 2)):
+                results[str(result[0])] = result
+                pbar.update(len(result[1]))
+        pbar.close()
+
+    print("Finished Downloading.")
+    return
+
+# Unique name based on url
+def _file_name(row):
+    return "%s/%s_%s" % (row['folder'], row.name, (zlib.crc32(row['url'].encode('utf-8')) & 0xffffffff))
+
+# For checking mimetypes separately without download
+def check_mimetype(row):
+    if os.path.isfile(str(row['file'])):
+        row['mimetype'] = magic.from_file(row['file'], mime=True)
+        row['size'] = os.stat(row['file']).st_size
+    return row
+
+# Don't download image, just check with a HEAD request, can't resume.
+# Can use this instead of download_image to get HTTP status codes.
+def check_download(row):
+    fname = _file_name(row)
+    try:
+        # not all sites will support HEAD
+        response = requests.head(row['url'], stream=False, timeout=5, allow_redirects=True, headers=headers)
+        row['status'] = response.status_code
+        row['headers'] = dict(response.headers)
+    except:
+        # log errors later, set error as 408 timeout
+        row['status'] = 408
+        return row
+    if response.ok:
+        row['file'] = fname
+    return row
+
+def download_image(row):
+    fname = _file_name(row)
+    # Skip Already downloaded, retry others later
+    if os.path.isfile(fname):
+        row['status'] = 200
+        row['file'] = fname
+        row['mimetype'] = magic.from_file(row['file'], mime=True)
+        row['size'] = os.stat(row['file']).st_size
+        return row
+
+    try:
+        # use smaller timeout to skip errors, but can result in failed downloads
+        response = requests.get(row['url'], stream=False, timeout=10, allow_redirects=True, headers=headers)
+        row['status'] = response.status_code
+        #row['headers'] = dict(response.headers)
+    except Exception as e:
+        # log errors later, set error as 408 timeout
+        row['status'] = 408
+        return row
+
+    if response.ok:
+        try:
+            with open(fname, 'wb') as out_file:
+                # some sites respond with gzip transport encoding
+                response.raw.decode_content = True
+                out_file.write(response.content)
+            row['mimetype'] = magic.from_file(fname, mime=True)
+            row['size'] = os.stat(fname).st_size
+        except:
+            # This is if it times out during a download or decode
+            row['status'] = 408
+            return row
+        row['file'] = fname
+    return row
+
+def open_tsv(fname, folder):
+    print("Opening %s Data File..." % fname)
+    df = pd.read_csv(fname, sep='\t', names=["caption","url"], usecols=range(1,2))
+    df['folder'] = folder
+    print("Processing", len(df), " Images:")
+    return df
+
+def df_from_shelve(chunk_size, func, dataset_name):
+    print("Generating Dataframe from results...")
+    with shelve.open('%s_%s_%s_results.tmp' % (dataset_name, func.__name__, chunk_size)) as results:
+        keylist = sorted([int(k) for k in results.keys()])
+        df = pd.concat([results[str(k)][1] for k in keylist], sort=True)
+    return df
+
+# number of processes in the pool can be larger than cores
+num_processes = 256
+# chunk_size is how many images per chunk per process - changing this resets progress when restarting.
+images_per_part = 200
+
+'''
+A bunch of them will fail to download, and return web pages instead. These will
+need to be cleaned up later. See downloaded_validation_report.tsv after it downloads
+for HTTP errors. Around 10-11% of images are gone, based on validation set results. Setting
+the user agent could fix some errors too maybe - not sure if any requests are rejected by
+sites based on this.
+'''
+data_name = "validation"
+df = open_tsv("Validation_GCC-1.1.0-Validation.tsv", data_name)
+df_multiprocess(df=df, processes=num_processes, chunk_size=images_per_part, func=download_image, dataset_name=data_name)
+df = df_from_shelve(chunk_size=images_per_part, func=download_image, dataset_name=data_name)
+df.to_csv("downloaded_%s_report.tsv.gz" % data_name, compression='gzip', sep='\t', header=False, index=False)
+print("Saved.")
+
+data_name = "training"
+df = open_tsv("Train-GCC-training.tsv",data_name)
+df_multiprocess(df=df, processes=num_processes, chunk_size=images_per_part, func=download_image, dataset_name=data_name)
+df = df_from_shelve(chunk_size=images_per_part, func=download_image, dataset_name=data_name)
+df.to_csv("downloaded_%s_report.tsv.gz" % data_name, compression='gzip', sep='\t', header=False, index=False)
+print("Saved.")